Device, method and system of stochastic investigation of formation at oil-field operations

FIELD: oil and gas industry.

SUBSTANCE: stages of the proposed method involve acquisition of database of oil deposit, which are related to oil-field objects. A self-organising map (SOM) is formed by means of the following: assignment of each of multiple data fields to one of multiple SOM maps. Each of multiple oil-field objects is assigned to one of multiple SOM positions based on the pre-determined SOM algorithm for presentation of statistical patterns in a variety of databases of oil deposit. Stochastic database is formed of databases of oil deposit based on artificial neuron network for databases of oil deposit. Screening of databases of oil deposit is performed to identify candidates from oil-field objects. Besides, screening is based on stochastic database. Detail assessment of each of the candidates and selection of oil-field object of candidates based on detail assessment is performed. Oil-field operations for the chosen oil-field object are performed.

EFFECT: improving assessment accuracy of oil-field objects.

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The technical field to which the invention relates.

The present invention relates to technology implementation oilfield operations related to underground formations that have reservoirs. More specifically, the invention relates to technology implementation oilfield operations, including analysis operations on the reservoir, and its impact on such oilfield operations.

The level of technology

Oilfield operations, such as geophysical surveys, drilling, studies on the cable, well completion, modeling, planning and production analysis, typically performed to determine the location and production of industrially valuable downhole fluid. Various aspects of oilfield operations and related operations shown in figa-1D. As shown in figa, geophysical research is often carried out with use of methods of data collection, such as seismic scanners to generate maps of underground structures. These structures are often analyzed to determine the presence of underground assets such as significant industrial fluids or minerals. This information is used to estimate the underground structures and determine the location of formations, containing the desired underground assets. Data collected using meth the dick of data collection, can be evaluated and analyzed to determine whether such commercially important substances, and whether they are reasonably available.

As shown in Fig-1B-1D, one or more wells can be placed along the underground structures for the extraction of commercial hydrocarbon fluid from an underground reservoir. Well supplied with tools that enable localization and extraction of hydrocarbons from underground reservoirs. As shown in figv, drilling tools, as a rule, the lower of the drilling rigs in the thickness of the rocks on the specified path to localize the location of commercial downhole fluid. During drilling operations the drilling tool can perform downhole measurements to study the conditions in the wellbore. In some cases, as shown. figs, the drilling tool is removed and the tool on the cable is placed in the wellbore for additional downhole measurements.

After completion of drilling the well may then be prepared for simulation. As shown in fig.1D, equipment completions is placed in the wellbore for completion in preparation for the simulation of fluid passing through it. Then fluid CPE is and is derived from an underground reservoir into the wellbore and comes to the surface. Device modeling are on the surface of the well to collect hydrocarbons from wells (wells). Fluid from an underground reservoir (reservoir) oil is directed to a device for simulating transport through mechanisms such as pipelines. Various equipment may be located near the oil fields for monitoring parameters of the oil and/or management of oilfield operations.

During oilfield operations, as a rule, data is collected for analysis and/or management of oilfield operations. Such data may include, for example, data on subsurface formations, equipment, historical, and/or other data. Data relating to a subterranean formation, collected from various sources. Such information about the formations may be a static or dynamic data. Static data include, for example, to the structure formation and geological stratigraphy, which determines the geological structure of the subsurface formation. Dynamic data includes, for example, to the fluid media flowing through the geological structure of the subsurface formations over time. Such a static and/or dynamic data can be collected in order to learn more about the formations and p is umyshlenno significant reserves, contained in them.

The sources used for collection of static data that can represent a seismic instruments, such as mobile seismic station, which sends waves of compression in the thickness of the rocks, as shown in figa. These waves are measured in order to characterize the density changes of the geological structure at different depths. This information can be used to generate the initial structural maps of the underground formation. Other static measurements can be collected using the technology selection cores and wireline logs. Core samples can be used to obtain physical samples of formations with different depths, as shown in figv. Wireline logging usually involves placing the downhole tool in the wellbore to collect various downhole measurements, such as density, resistivity, and the like, at various depths. Such wireline logging can be performed using, for example, the drilling tool on figv and/or tool on the cable on figs. After well built and finished, fluid flows to the surface with the use of the pipeline for simulation, as shown in fig.1D. When fluid reaches the surface, can osushestvljali the various dynamic measurement, for example, flow rate, pressure and composition of the fluid. These parameters can be used to determine various characteristics of the underground formation.

The sensors can be placed around the oil fields to collect data on various oilfield operations. For example, sensors in the drilling equipment can monitor the drilling conditions, the sensors in the wellbore can monitor the composition of the fluid sensors along the flow path, can monitor the flow rate, and the sensors in the process equipment can monitor the collected fluid. Other sensors can be provided for monitoring wells, surface, equipment, or other conditions. Monitoring data are often used for decision making in different locations of the oil fields at different times. The data collected by these sensors can also be analyzed and processed. Data may be collected and used for current or future operations. When used for future operations in the same or other provisions, such data are sometimes referred to as historical data.

The processed data can be used to predict conditions in the wellbore and decision making relative activities is but oilfield operations. Such decisions may include the planning of wells, wires wells, well completion, work levels, performance modeling of software development processes and other operations and/or conditions. Often this information is used to determine when to drill new wells, re-complete the well or to change the simulation development process well.

Data from one or more boreholes can be analyzed to plan or predict different results in the wellbore. In some cases, data from nearby wells or shafts shafts wells with similar conditions or equipment can be used to predict how will be used well. Typically, when analyzing the oilfield operations have a large number of variables and a large amount of data. Therefore, it is often useful to model the mode of oilfield operations to determine the required activities. During the continuation of the operation modes may need adjustment as conditions change and get new information.

The technology developed for modeling the behavior of various aspects of oilfield operations, such as geological structures, underground reservoirs, trunks wells, ground with the pursued and also other parts of the oilfield operations. Examples of such modelling techniques shown in patent/publications/orders No. US 5992519, WO 2004/049216, WO 1999/064896, WO 2005/122001, US 6313837, US 2003/0216897, US 2003/0132934, US 2005/0149307, US 2006/0197759, US 6980940, US 2004/0220846 and US 10/586283. Developed technology to perform operations simulation of the reservoir. See, for example, patent/publication/application No. US 6230101, US 6018497, US 6078869, GB 2336008, US 6106561, US 2006/0184329, US 7164990.

Examples of oilfield operations include ways of implementation of methods of enhanced oil recovery (EOR) to increase the lifespan of the deposits and the increase in the total oil recovery from naturally exhaustive reservoirs. Enhanced oil recovery can begin at any time during the term cost-effective performance of the reservoir. His goal is to not only restore reservoir pressure, but the increase in oil displacement or flow of the fluid in the reservoir. Three main types of methods of operations EOR represent the injection into the reservoir of chemicals (alkaline flooding or micellar-polymer flooding), miscible injection (injection of carbon dioxide or the discharge of hydrocarbons) and thermal development (injection of water vapor into the well, pumping water into the well and the and implementation of combustion in the reservoir by partial combustion of oil). Optimal application of each type depends on reservoir temperature, pressure, depth, the effective thickness of the reservoir, permeability, residual oil saturation and water saturation, porosity and on the characteristics of the fluid, such as oil density in degrees ANI and viscosity.

The injection of water vapor is a method of thermal recovery in which water vapor obtained at the surface and injected into the reservoir through distributed in a special way injection wells.

When water vapor enters the reservoir, it heats the crude oil and reduces its viscosity. Heat also "distill" lighter components of crude oil, which condenses in the oil the shaft ahead of the front paranacidade, also lowering the viscosity of the oil. Hot water that condenses from the vapor, and water vapor by itself create an artificial displacement of oil, which moves the oil towards the producing wells. Another contributing factor that increases the recovery of oil during the injection of water vapor, refers to stalk the cleanout. In this case, water vapor lowers the surface tension, which binds paraffins and asphaltenes with the surface of the breed, with the steam distillation of light fractions of crude oil creates a small fringe dissolve the I, which, mingling, to remove trapped oil in it.

Flooding is among the oldest, and perhaps the most economical way EOR. Hot water flooding is a method of thermal development, in which hot water is pumped into the reservoir through distributed in a special way injection wells. Hot water reduces the viscosity of crude oil, making it easier to move towards producing wells. Hot water flooding, also known as pumping hot water is generally less efficient than the method of injection of water vapor, due to the fact that water has a lower heat capacity than water vapor. However, under certain conditions, such as the sensitivity of the formation, fresh water is preferred.

Current high oil prices provide additional opportunities for companies to deeper consideration of their portfolios reservoirs for the use of additional features EOR (e.g., flooding). Time and information constraints may limit the depth and severity of such assessment screening. Time affects the result of screening a huge number of reservoirs to allow for the possibility EOR (for example the EP, flooding), at the same time affects the availability of data (consistency of measured and simulated data), with which it is necessary to obtain meaningful information to make informed decisions regarding development.

Examples of oilfield operations also include the installation of intelligent completion systems to improve the economic feasibility of oil field operations. These wells do not only give access to the marginal layers, for which specialized production may not be economically feasible, but also speed up production. Monitoring devices, flow control, and other devices can be used to control the production from the reservoir, giving a mixture of products from several horizons, and to optimize production.

Regulatory agencies usually require that the operator can distribute the production in specific reservoirs for accounting purposes of the deductions for depletion of mineral resources. In this case, if for every completed wells do not install the flow meters, reverse redistribution from the mouth of the well to complete the well elusive. Traditional methods, which could provide separation of products in real time, may not provide accurate results when the characteristics of the inflow of one completed the wells varies. Numerical modeling, which takes into account the change of mobility and the resulting redistribution of pressure in an open system the finished hole is long and cannot be used for reverse distribution in real time.

Despite the development of technologies in reservoir simulation for oilfield operations, there remains a need in the examination of the effects of a large number of reservoirs and uncertainty regarding the accuracy of numerical models of wells in oil field operations. It would be desirable to create a technology screening large number of candidates for selection, planning and/or implementation of various oilfield operations based on the static and dynamic aspects of the oil field. It would also be desirable to provide a reverse distribution of wells in mixed reservoirs in real time. It is also desirable that such techniques be selectively considered necessary parameters, such as measured data or simulated data with uncertainty as to the accuracy or compatibility. Such desired technologies can provide one or more actions selected inter alia from the following: the realization of the possibility of screening to reduce the number is and candidates from reservoirs (i.e. candidates from reservoirs, that estimate in verbose mode to choose, for various oilfield operations) to one or more orders of magnitude, allow modeling to assess the sensitivity and uncertainties affecting parameters and enabling the simulation to accelerate the screening process without compromising the quality of results.

The invention

In General, in one aspect the present invention relates to a method of performing oilfield operations. The steps of the method include receiving data arrays oil field related to oilfield objects, the formation of a stochastic database from data arrays oil field on the basis of artificial neural network for data arrays oil field screening data arrays oil field to identify candidates from oil production facilities, where the screening is based on the stochastic database, perform a detailed assessment of each candidate, the selection of oilfield object of the candidates, on the basis of detailed assessments, and performing oilfield operations for the selected field object.

In General, in one aspect the present invention relates to a method for performing oilfield operations. The steps of the method include the try to obtain multiple arrays of data about oil field, associated with multiple field objects, each of the multiple arrays of data on oil field contains many data fields, at least one data field from a variety of data fields, at least one array of data on oil field from multiple arrays of data on oil field is a blank data field, the formation of the first artificial neural network from a set of data arrays oil field, the first artificial neural network contains one or more correlations between multiple data fields, filling in the blank data fields, at least one array of data on oil field assessments based on one or more ratios for forming filled with additional data array of data on oil field, and performing the oilfield operation based on at least filled with additional data array of data on oil field.

In General, in one aspect the invention relates to a method for performing oilfield operations, contains many datasets on oil field associated with multiple field objects, each of the multiple arrays of data on oil field contains a number field the data the formation of an artificial neural network for multiple arrays of data about oil field, an artificial neural network is associated with one or more key performance indicators (KPIs) oilfield operations identified from a variety of data fields, the identification of multiple clusters from a variety of oil field facilities based on artificial neural network, each of the multiple cluster contains one or more field objects, the formation of many proxy models, corresponding to different clusters, each of the many proxy models models oilfield operation of one or more oilfield objects of the corresponding cluster, and performing the oilfield operation based on multiple proxy models.

In General, in one aspect the invention relates to a ground installation comprising a memory and a processor that executes instructions stored in memory and executable by the processor to perform steps of a method for performing oilfield operations, the instructions contain functions to return multiple arrays of data on oil field associated with many oil field facilities, the formation of the stochastic database from multiple data arrays oil field on the basis of artificial neural network DL the multiple arrays of data about oil field, screening of multiple arrays of data on oil field to determine the set of candidates from a variety of oil field facilities, where the screening is based on the stochastic database, perform a detailed evaluation of each of the set of candidates, selection of field object from the set of candidates, based on a detailed evaluation, and implementation of oilfield operations for an oilfield object.

In General, in one aspect the invention relates to a ground installation comprising a memory and a processor that executes instructions stored in memory and executable by the processor to implement the steps of the method of performing oilfield operations, the instructions provide the possibility of obtaining multiple arrays of data on oil field associated with multiple field objects, each of the multiple arrays of data on oil field contains many data fields, at least one data field from a variety of data fields, at least one array of data on oil field from multiple arrays of data on oil field is a blank data field, the formation of the first artificial neural network for multiple arrays of data about oil field, the first artificial neural network contains one or Bo is her correlations between multiple data fields, fill in blank data fields, at least one array of data on oil field with estimated data based on one or more ratios for forming filled with additional data array of data on oil field and the implementation of the oilfield operation based on at least filled with additional data array of data on oil field.

In General, in one aspect the invention relates to a ground installation comprising a memory and a processor that executes instructions stored in memory and executable by the processor to implement the steps of the method. the implementation of oilfield operations, instructions, provide the possibility of obtaining multiple arrays of data on oil field associated with multiple field objects, each of the multiple arrays of data on oil field contains many data fields of artificial neural network for multiple arrays of data about oil field, an artificial neural network is associated with one or more key performance indicators (KPIs) oilfield operations defined from a set of data fields that define a set of clusters from a variety of oil field facilities based on artificial neural network, each of the multiple cluster contains one or more field objects from a variety of oil field facilities, multiple proxy models. corresponds to the set of clusters, each of the many proxy models models oilfield operation of one or more oilfield objects of the corresponding cluster, and the implementation of the oilfield operation based on multiple proxy models.

Other aspects and advantages of the present invention will become clear through the following description and the accompanying claims.

Brief description of drawings

To the above-mentioned characteristics and advantages of the present invention can be understood in detail, a more detailed description of the invention briefly generalized above, are presented below with reference to embodiments of which are illustrated on the accompanying drawings. It should be noted, however, that the appended drawings illustrate only typical embodiments of this invention and therefore should not be construed as limiting its scope, the invention can include other equally effective ways of implementation.

In the drawings:

figa-1D depict exemplary views in the context of oil fields with underground structures containing reservoirs, and various oil field operations in the oil field. Figa shows the approximate geophysical research the Oia in wells, implemented mobile seismic station. Figv depicts an exemplary operation of the drilling carried out by the drilling tool suspended from the rig and put in an underground formation. Figs depicts an exemplary operation in the well, carried out by the tool, spokeman on the cable performed by the tool on the cable suspended from the rig and in the wellbore Figv. 3-D depicts an exemplary operation of an implementation of the simulation performed by the tool for simulation, deployed from the rig and put in the finished borehole for extracting fluid from an underground reservoir in the surface structures;

figa-2B are exemplary graphical representations of data collected by instruments on the Fig-1A-1D, respectively. Figa depicts an exemplary track seismograms underground formations figa. Figure 2 depicts In a sample core sample of the formation shown in Figv. Figs depicts an exemplary diagram of geophysical research borehole of an underground formation figs. Fig depicts an exemplary curve of depletion of the reservoir for the fluid flowing through the underground formation, fig.1D obtained through simulation;

figure 3 shows an exemplary schematic view of the section, partially in cross-section, oil field, which has many tools for data collection, located at various positions around the oil field for collecting data from the subterranean formation;

figure 4 shows an exemplary schematic view in section of the oil field, with many wells for production of oil from underground formations;

figure 5 shows an exemplary block diagram of part of the oil field in figure 4, depicting in detail the operation simulation;

figa and. 6B show exemplary data oil and statistical chart according to one or more variants of implementation of the present invention;

figa and 7B show a block diagram and a sample image of a way to populate additional data stochastic database according to one or more variants of implementation of the present invention;

figa and 8B show a flowchart of a method of screening to identify candidates oilfield objects according to one or more variants of implementation of the present invention;

figa and 9B show an exemplary self-organizing maps (SOM) according to one or more variants of implementation of the present invention;

figure 10 (depicted as figa-10C for the purposes of illustration) shows a sample Bayesian network is according to one or more variants of implementation of the present invention.

A detailed description of the preferred embodiment variants of the invention

Preferred currently embodiments of the present invention shown in the above drawings and are described in detail below. Some features and some of the drawings shows in a larger scale or in schematic in the interest of clarity and brevity.

Fig 1A-D show the field (100) of oil, which has in itself the geological structure and/or the subterranean formation. As shown in these figures, the different dimensions of the underground formation are different tools in the same location. These measurements can be used to generate information about the formation and/or geological structures, and/or fluids contained in them.

Figa-1D depict the species in the context of the field (100) oil having subterranean formations (102)containing a reservoir (104), and depict various oilfield operations performed on the oilfield (100). Figa depicts geophysical investigations in boreholes using a mobile seismic stations (a) for measuring characteristics of an underground formation. Geophysical investigations in boreholes represent seismic geophysical investigations in boreholes to create sound vibrations. Figure 1 is, this sound fluctuation (112) is generated by a source (110) and is reflected from a number of horizons (114) underground formation (116). Sound fluctuation (vibration) (112) is received by the sensor (S), such as geophones (118), placed on the earth's surface, and the geophones (118) create output electrical signals, referred to as received data (120) figure 1.

In response to received acoustic oscillation (112) obtain representative samples of the various parameters (such as amplitude and/or frequency) of sound vibrations (112). The data obtained (120) is passed as input to the computer (a) self-propelled seismic stations (a), and in response to the source data, the computer self-propelled station (a) creates an output record seismic data (124). Seismic data can be further processed as desired, for example, through the reduction of data.

Figv depicts a drilling operation carried out by the drilling tool (106b), suspended on the rig (128) and introduced into the underground formation (102) with the formation of the barrel bore hole (136). Capacity for drilling fluid (130) is used for supplying drilling fluid to the drilling tool (106b) through the discharge pipe (132) for circulation of drilling mud through the drilling tool (106b) and to return back to the surface. Drilling tools (106b) BBO is seeking in formation before reaching the reservoir (104) oil, drilling tools (106b) is preferably made with the possibility of measuring the characteristics of downhole conditions. Drilling tools (106b) is also made with the possibility of collecting core samples (133), as shown, or may be removed so that a core sample (133) can be removed using another tool.

Above ground installation (134) is used to communicate with the drilling tool (106b) and external operations. Above ground installation (134) are able to interact with the drilling tool (106b) for communication with the drilling tool (106b) and receive data from it. Above ground installation (134) is preferably supplied computer equipment for receiving, storing, processing and analysis of data from the field (100) oil. Above ground installation (134) collects output data (135), generated during the drilling operations. Computer equipment, such as ground unit (134)may be located in various locations around the field (100) of oil and/or in remote locations.

Sensors (S), such as sensors, may be located throughout the oilfield, oil rig, field equipment (such as a downhole tool) or in other parts of the oil field to collect information about the various parameters, such as parameters on the surface, downhole parameters and/or the tunes. These sensors (S) are preferably measured characteristics of the oil field, such as the axial load on bit, torque on bit, pressures, temperatures, flow rate, composition and other parameters of the oilfield operations.

The information gathered by the sensors (S)may be collected by surface unit (134) and/or other sources of data collection sources for analysis or other processing. Data collected by the sensors (S)may be used alone or in combination with other data. Data can be collected in a database, and all data or selected parts of the data can selectively be used for analysis and/or forecasting oilfield operations for this and/or other boreholes.

The output from the various sensors (S)located around the oil fields, can be processed for further use. The data may represent an historical data, real-time data or combinations thereof. Real-time data can be used in real time or stored for later use. Data can also be combined with historical data or other input data for subsequent analysis. Data can be stored in separate databases or be combined into one database.

The collected data and what to use for analysis, such as surgery simulation. For example, the output of the seismic data can be used for carrying out geological, geophysical modeling, simulation technology, enhanced oil recovery, and/or simulation development processes. Data on the reservoir, wellbore, surface and/or technologies can be used to implement the simulation of the reservoir, wellbore, or other production simulation. The output from oilfield operations may be generated by the sensor (S) directly or after some processing or simulation. This output may serve as input data for further analysis.

Data is collected and stored in ground installation (134). One or more ground units (134) are located on the oilfield (100) or connect to it with remote access. Above ground installation (134) may be an independent device or a complex network of devices used to implement the required functions of the data management for the entire field (100) oil. Above ground installation (134) may be a manually-operated or automated system. Above ground installation (134) can be controlled and/or adjusted by the user.

Ground condition is the time (134) may be provided with a transceiver unit (137) in order to make possible the communication between the ground unit (134) and the different parts or other locations within a field (100) oil. Above ground installation (134) may also be provided by the controller to actuate mechanisms for oil field or functionally to contact him. Above ground installation (134) may then send control signals to the field (100) of oil in response to the received data. Above ground installation (134) can receive commands through the receiver-transmitter or can execute commands for the controller. May include a processor for analyzing the data (locally or remotely) and making decisions about the actuation of the controller. Thus, the field (100) oil can be selectively adjusted based on the collected data to optimize the speed of the extraction fluid or for maximizing the lifespan of oil and its total output. These adjustments can be made automatically on the basis of computer Protocol or by the operator in manual mode. In some cases, the drilling plans can be adjusted to select the optimal modes of operation or to avoid problems.

Figs depicts the operation in the well implemented tool (s) on the cable hanging on orovoi rig (128), introduced into the wellbore (136), Figv. Tool (s) on the cable is preferably made with the possibility of placement in a borehole (136) with the aim of obtaining charts GIW with the implementation of well testing and/or sampling. Tool (s) on the cable can be used to provide another method and apparatus for carrying out seismic geophysical surveys in wells. Tool (s) on the cable on figs can have explosive or acoustic source (143) energy, which delivers electric signals to the surrounding subterranean formations (102).

Tool (106 C) on the cable can quickly be contacted, for example, geophones (118), which is in the computer (a) self-propelled seismic stations (a) figa. Tool (106 C) on the cable can also provide data ground installation (134). As shown, the output (135) formed by the tool (106 C) on the cable and collected on the surface. Tool (106 C) on the cable can be positioned at various depths in the wellbore (136) to provide research on the underground formation.

Fig.1D depicts a production operation is carried out commercial instrument (106d), placed on the rig (128) and put inside the completed wellbore (136) on figs, for pumping fluid is th environment from underground reservoirs in the terrestrial facilities (142). Fluid flows from the field (104) of oil in the wellbore (136) and in ground-based facilities (142) through the cumulative network (144). The sensor (S)located around the field (100) oil to collect data from it, operatively connected with a ground unit (142). During the manufacturing process output data (135) may be collected by various sensors (S) and transmitted to a ground unit (134) and/or technological equipment. These data may represent, for example, data reservoir data, borehole data from the surface and/or process data.

Fig.1D depicts a production operation is carried out commercial instrument (106d), deployed from the tower, or the gushing wellhead equipment (129) completed wellbore (136) PIGS for pumping fluid from an underground reservoir in ground-based facilities (142). Fluid flows from the field (104) of oil through the perforations in the casing (not shown) and field device (106d) in the wellbore (136) and in ground-based facilities (142) through the cumulative network (146). Sensors (S), such as sensors, can be placed around the oil fields to collect data on various oilfield operations, as described above. As shown, the sensor (S) can be placed in the field device (106d) is whether in the accompanying equipment, such as the gushing wellhead equipment (129), cumulative network (146), surface structure (142), and/or production equipment for measuring parameters of a fluid medium, such as the composition of the fluid, flow rate, pressure, temperature, and/or other parameters of the production operation.

Although shown only the simplified configuration of the well location, it is clear that the oil can take part of the land, sea and/or water of the provisions of its location, and contains one or more wells. Production may also include injection wells (not shown) for additional production. One or more collection systems, oil can be operatively connected with one or more wells for selective collection of the downhole fluid from the location of the wells (wells).

Although the Fig-1B-1D depict the instruments used for measuring characteristics of the field (100) of oil, it will be clear that these tools can be used in connection with oil field operations, such as mines, aquifers, basins, tanks or other underground objects. Also, although depicted the usual tools for data collection, it will be understood that there may be different measuring instruments capable of determining parameters such as seismic bilateral lying is propaganja, density, specific resistance, oil recovery, and the like, subterranean formations (102) and/or its geological formations. Various sensors (S) may be located at different positions along the wellbore and/or monitoring tools for collecting and/or monitoring the desired data. Other data sources can also be provided in the outer positions.

The configuration of the oil fields in Fig-1A-1D is designed to provide a brief description of an example oil, suitable for use with the present invention. Part of the oil field, or all of it (100) may be located on land and/or sea. Also, although depicted a single oil field, measured at a single location, the present invention can be used with any combination of one or more fields (100) of oil, of one or more types of equipment and one or more wells.

Figa-2B are graphical representations of the data collected by the tool of Fig-1A-D, respectively. Figa depicts the track (202) seismograms underground formations on Figo received a research tool (a). Track seismograms measures bilateral response within a certain period of time. Figure 2 depicts In the core sample (133)selected drilling tool (106b). Study the geological structure of the core, as a rule, provides a chart of density, specific resistance, or other physical characteristics of core sample (133) along the core length. Measurement of density and viscosity is often performed on fluids in the core at various pressures and temperatures. Figs depicts a wireline logs (204) underground formations on figs received by the instrument (s) on the cable. Wireline logs, generally, provides a measurement of the resistivity of the formation at various depths. Fig curve depicts the depletion layer (206) for the fluid flowing through the underground formation on fig.1D received commercial device (106d). The curve of the depletion layer (206), as a rule, shows the oil production rate Q as a function of time t.

The corresponding chart on figa-2C contain static measurements that describe the physical characteristics of the formation. These measurements can be compared to determine the accuracy of measurements and/or to check for errors. Thus, the graphs of each of the respective dimensions can be built and scaled for comparison and confirmation of characteristics.

Fig provides dynamic measurement properties of the fluid in the wellbore. When fluid flows through the wellbore, are measuring properties of a fluid medium, such excorist thread pressure, composition and the like. As described below, the static and dynamic measurements can be used to generate models underground formation to determine its characteristics.

Figure 3 is a schematic view in partial cross-section of the Deposit (300) oil, having the tools (a), (302b), (C) and (302d) to collect data located in various positions in the oil field for data collection subterranean formation (304). Tools (302a-302d) for data collection can be the same as the tools (a-106d) to collect data in figure 1, respectively. As shown, the tools (302a-302d) data collection form graphics (a-308d) data or measurements, respectively.

Graphics (a-C) data are examples of static graphs of data that can be generated by tools (302a-302d) for data collection, respectively. Schedule (a) static data represents a seismic bilateral response time and can be the same as path (202) seismograms at figa. The static schedule (308b) is a data core samples measured for core samples from the formation (304), such a core sample (133) on figv. The static schedule (s) data represents the logging curve that is similar to the logging chart (204) figs. Schedule (308d) data represent the possessing a graph with dynamic data rate of the fluid flow as a function of time, a similar chart (206) figw. Can also collect other data, such as historical data, information entered by the user, economic information, other data dimensions and other parameters of interest.

The subterranean formation (304) has a variety of geological structures (306a-306d). As shown, the formation has a formation (a) Sandstone reservoir (306b) limestone layer (s) and clay layer (306d) sand. Prirazlomnoye line (307) passes through the formation. Tools for collection of static data, preferably with the ability to measure the formation and detection characteristics of the geological formation.

Although depicted a particular subterranean formation (304) with specific geological structures, it will be clear that the formation may contain various geological structures. Fluid also may be present in different parts of the formation. Each of the measuring devices can be used for measuring the characteristics of the formation and/or its deep structures. Although each instrument for data collection is shown placed in a particular location along the formation, it will be understood that one or more types of measurements can be performed in one or more locations on one or more oil or other provisions for the comparison is/or analysis.

Data collected from various sources, such as tools for data collection, figure 3, can then be evaluated. Typically, seismic data displayed in the graph (a) static data tools (a) data collection used by the engineer-geophysicist to determine characteristics of the subterranean formation (304). Core data is shown in a static graph (308b), and/or logging data from the logs (308), typically used by a geologist to determine various characteristics of the geological structures of the subterranean formation (304). Field data from commercial graphics (308d), typically used in commercial engineer to determine the characteristics of the flow of fluid through the reservoir.

Figure 4 shows oilfield (400) for performing a simulation of the operations of the development process. As shown, the oil field has many wells (402)directly connected to the Central processing equipment (454). The configuration of the oil fields in figure 4 is not intended to limit the scope of the invention. Part of the oil or it can be located on the land and/or sea. Also, although depicted individual oil field with some technological equipment of many wells, may p the be any combination of one or more oil fields, one or more sets of processing equipment and one or more wells.

Each well (402) has equipment that forms in the deep rocks of the barrel bore hole (436). Trunks wells pass through underground formations (406)containing reservoirs (404). These reservoirs (404) contain fluids, such as hydrocarbons. Trunks wells draining the fluid from the oil fields and guide them in the process equipment through the cumulative network (444) Cumulative network (444) are the piping and control mechanisms for control of flow of fluid from the well to processing equipment (454).

Figure 5 shows a schematic view in section of part of the oilfield (400) figure 4, depicting in detail the borehole (402) and cumulative network (444). Well (402) figure 5 has the wellbore (436), passing in the strata of rock. As shown, the wellbore (436) have already been drilled, completed and prepared for simulation of reservoir (504).

Equipment for simulation modeling of the wellbore (564) passes from the mouth of the borehole (566) wells (402) and the reservoir (404) for pumping the fluid to the surface. Well (402) is functionally connected with the storage network (444) through the transport pipeline (561). Fluid flows from p the Acta-collector (404) through the borehole (436) and cumulative network (444). Then the fluid flows out of the storage network (444) process equipment (454).

As additionally shown in figure 5, the sensor (S) located around the oilfield (400) to monitor various parameters during oilfield operations. Sensors (S) may be measured, for example, pressure, temperature, flow rate, composition and other parameters of the field, borehole, the cumulative network, process equipment and/or other parts of the oilfield operations. These sensors (S) operatively connected with the ground unit (534) to collect data from them. Above ground installation may, for example, be similar to ground unit 134 in Fig-1A-D

As shown in figure 5, above ground installation (534) has computer hardware such as memory (520), the controller (522), the processor (524) and the node (526) to display, for data management. Data is collected in memory (520) and processed by the processor (524) for analysis. Data can be collected from oil field sensor (S) and/or other sources. For example, data on the oil field can be updated with historical data collected for other operations, or data entered by the user.

Then, the analyzed data can be used for acceptance. solutions. Transmitting and receiving unit (not shown) may be pre is provided for to enable traffic between ground unit (534) and field (400) of oil. Controller (522) may be used to actuate mechanisms for oil field (400) consequence of the acceptance of the transmitter and on the basis of these decisions. Thus, the field (400) oil can be selectively adjusted based on the collected data. These refinements can be made automatically on the basis of computer Protocol and/or manually by the operator. In some cases, the drilling plans are specified for selection of optimal operating conditions or in order to avoid problems.

Node (526) for display may be provided on the bore (402) and/or in a remote location to view data about the oil field (not shown). Data about the oil field, the display unit (526) to display, can represent raw data, processed data and/or output data generated from different data. Node (526) to display preferably made with flexible image data that shows the screens could be adjusted as described. The user can determine the desired course of actions during the simulation based on consideration of the displayed data on the oil field. Operation - simulation can select what UPE be configured under the influence of the host (526) to display. Node (526) for display may include a display for displaying data on the oil field or to denote events oil. For example, the display may correspond to the output from the printer, graphics, monitor, or other device.

To facilitate data processing and analysis can be used simulators for data processing. Specific simulators are often used in connection with a specific oilfield operations, such as simulation of the reservoir or wellbore. Data entered into the simulator (simulators)can represent the historical data, real-time data or combinations thereof. Simulation one or more simulators can be repeated or to be adjusted on the basis of the received data.

As shown, oilfield operations are performed downhole and nesquehonite imitators. Well imitators may contain simulator reservoir (340), the simulator wellbore (342) and the network simulator on the surface (344). Simulator reservoir (340) defines the flow of hydrocarbons through the collector of the rock and into the well bore. Simulator wellbore (342) and the network simulator on the surface (344) determine the flow of hydrocarbons through the borehole and cumulative network (444) pipelines n the surface. As shown, some simulators can be separate or combined, depending on the available systems.

Different simulators reservoirs may provide for the display of various levels of approximation in the mathematical model of the reservoir. For example, the simulator (340) of the reservoir can be a complete model of reservoir simulation with high accuracy, but at a reduced speed. Simulator (340) of the reservoir may be a proxy model simulator reservoir, which typically provides a simplified representation of the model simulation of the reservoir. This type of simulator reservoir, as a rule, is less accurate, but faster when exercising. Simulator (340) of the reservoir may also be a search proxy table simulator reservoir, which, typically, is even more simplified and faster when exercising. The proxy model of the reservoir and proxy lookup table are examples of proxy models.

Not associated with wells imitators may include process simulation and economic simulators. Processing device has a process simulator

(346). Process simulator (346) simulates pererabatyvayushchego (for example, the training of production wells for transportation (454)), where the hydrocarbon is separated into its components (e.g., methane, ethane, propane and the like) and is being prepared for sale. Field (400) oil provides economic simulator (348), economic simulator (348) models the cost of part of the oil field or the entire field over the full life of the gas or its part. May provide various combinations of these and other imitators oil fields.

In General, the present invention relates to a method of screening a large number of oil production facilities (e.g. reservoirs, wells, well completion, and the like) to determine one or more candidates for a more detailed assessment. Method of screening uses a wide range of types of information, including operational data, examination of public and numerical models, at the same time, still satisfying a range of physical, financial, geopolitical and human limitations.

As an example, is first populated with additional data (filling) is available dataset level reservoirs, and he subsequently analysed using self-organizing maps (SOM), neural network algorithms that are used for a lot of the agreement correlation. Then built a certain number of generalized numerical models with stochastic output from the first stage. These models are used for the formation of the characteristic surfaces for assessment of the sensitivity and uncertainty estimates including parameters. Further, the uncertainty of the reservoir combined with the expert knowledge and environment variables using Bayesian networks (i.e. machines based on probabilistic reasoning). These data are used as proxy models and act as the objective function, where the input parameters are assigned a stochastic manner, and the output data is represented according to the ranking of potential candidates from the reservoir.

As soon as the candidates of the reservoirs identified, each of them may be subject to more detailed assessment to determine which reservoirs can be increased production and recovery through the implementation of oilfield operations on the reservoir (that is, operations of the method of enhanced oil recovery operations to pump water vapor, flooding and the like).

One of the biggest challenges for screening a large number of reservoirs with the aim of planning the development of oil represents the dost shall Pesti and completeness of data. In this field it is well known that it is extremely difficult to have a complete and consistent array of data sets on the field, such as production schedules, distribution or the reverse calculation of the export pipeline to completion, and the like. Even more simplified characteristics of the oil may be incomplete or have questionable accuracy for a large selection of reservoirs. In one or more embodiments of implementation of the present invention, this lack of data about the Deposit and/or its parameters may be the result of low frequency measurements, unknown losses in the system, inaccurate or incorrect measurements, subjective evaluations (i.e. human error), and the like.

In one or more embodiments of implementation of the present invention, the data about the Deposit and/or its parameters can be grouped logically as the main parameters and derived (or estimated) parameters. Figa shows an exemplary data field and/or its parameters in accordance with one or more variants of implementation of the present invention. As shown in figa, the main parameters (601) are measured directly characteristics, such as characteristics of the rocks of the reservoir or the characteristics of the pressure tekokare. Calculated parameters (602) can be extracted from the basic parameters, for example, be formed with the use of very complex processes, such as calculation of rates of return using numerical methods. In one or more embodiments of implementation of the present invention, data and/or parameters at the planning stage of the development to the production stage or other stages of the oilfield operations can be stored as arrays of data about oil field in a database or other usable data storage formats. Each of the data arrays oil field may contain an array of data fields (for example, to contain any of the basic parameters (601) and design parameters (602) figa)associated with a given reservoir in the aggregate reservoir.

Figv shows a statistical chart that shows the completeness of the data arrays oil field in the database for the sample set of reservoirs. This approximate statistical chart contains summarized in table characterizing completeness (603) data the main parameters and the corresponding histogram (604) for the totality of the reservoir. For example, the most affordable option reservoir "source estimated pressure". and the second most the available parameter reservoir "depth of oil", available for 86,7% and 79.5% of the reservoirs in the array, respectively. In one or more embodiments of implementation of the present invention has a missing or blank data fields (for example, the original estimate pressure, the depth of the oil, and the like) in the array of data fields (for example, key parameters on figv) are present in the portion of the array of reservoirs (for example, a 13.3% and 20.5%, respectively) due to technical, natural, subjective, or other contributing factors. These contributing factors may be static or may change in time during the development phase or other phases of oilfield operations. For example, as soon as data is modified, configured, or otherwise replaced over time by newer information, the changed data can create incompatibilities with other parameters in the database. In General, an indication that the data in General has changed throughout history, as a rule, is lost or is not supported in the database. For certain types of characteristics of the oil (for example, the depth of the reservoir) inconsistency and incompleteness can be easily detected; but some other parameters (e.g., derived data, such as volumes in the appropriate location, or total production),craine work is about to be identified as incompatible in the database...

Next, as shown figv, the parameters of the reservoir, which is important for planning of field development, such as oil viscosity and permeability", is almost completely lost (i.e. available to 18.5% and 4.5%, respectively) in the database. In General, the formation of these parameters requires careful measurements and detailed interpretation that is not feasible for the whole array of reservoirs in General. Although the examples In figure 6, describe the completeness of data for the main parameters, the experts will notice that this description applies equally to the calculated parameters and/or other data field/field parameters.

Incomplete and incompatible database is detrimental to the portfolio or inventory for sets of reservoirs, as decisions cannot be made with certainty. For example, if you decide to identify reservoirs with the greatest efficiency (for example, for recovery of the investment) transactions injection, water (e.g., flooding), a large number of reservoirs with the missing parameter is the viscosity of the oil in the database can not be considered. In addition, reservoirs with inconsistent parameter (for example, volume in this position, showing the discrepancy with other ISM is dissolved by pressure parameters) cannot be used in the screening process. Therefore, the final rankings from the screening process could only allocate reservoirs with high occupancy data and consistency without the inclusion of other potentially desirable candidates from reservoirs with no data.

In one or more embodiments of implementation of the present invention, the database may be populated with artificial data, which reflect the best estimate, in order to raise the reservoirs with the scarcity of data, allowing them to pass the screening process. To assess the accuracy of the data obtained when completing the data generated stochastic database, where each parameter is associated with probabilistic information (e.g., a combination of a probability distribution, standard deviation and uncertainty, or with another suitable for use probabilistic information), allowing quantitative determination of the certainty of the data and providing a confidence level for artificial and/or source of data.

Although the above examples and descriptions in relation figa and 6 refer to the data arrays of the level of the reservoir and to the screening of candidates from reservoirs, experts in this field will find that the method can be applied to other oilfield facility, the mayor as arrays of data level of the well/candidates from wells, data completion/candidates for completion, and the like.

Figa and 7B show a block diagram and a sample image of a way to populate additional data stochastic databases in accordance with one or more variants of implementation of the present invention. In one or more embodiments of implementation of the present invention one or more of the steps shown in figa and 7B, may be skipped, repeated and/or performed in a different order. Accordingly, embodiments of the present invention should not be construed as limited to the specific sequence of steps shown figa and 7B.

The method, as shown in figa and 7B, can be carried out in the oil field described above with respect to figa-5. The original can be obtained arrays of data on oil field (for example, organized in the form of data tables (714))associated with the array of field objects (for example, with a large number of reservoirs) (step 701). Each of the data arrays oil field (for example, each row in the data table (714)) corresponds to an oilfield object (for example, a particular first column of the data table (714)) and contains many data fields (for example, defined as a set of fields of the first page the key data tables (714)), such as basic parameters and/or calculated parameters described above in relation to figa and 6B. On the basis of sample statistics described in relation to figv and forth, as shown in the sample data table (714) figa, these data fields are not filled in completely for all reservoirs. In these examples, at least one data field of at least one array of data on oil field is blank for the corresponding field object (e.g., reservoir).

Then may be formed first artificial neural network for these data sets oil field (step 702). As is well known in this field, the artificial neural network is a mathematical model consisting of interconnected groups of neurons (or nodes)that work together to process input data to generate its output, where interconnected neurons have an adaptive structure that varies based on the input/output of the information provided to the network during the training phase. In one or more embodiments of implementation of the present invention, the first artificial neural network can be used as a tool for modeling non-linear statistical data modeling one or more relationships between many fields given the s.

In the example, the data fields of the level of the reservoir (for example, figa and 6B) some relations can be obtained directly, since the data fields of the data level of the reservoir are connected to each other. For example, the depth of the reservoir is generally linearly dependent on reservoir temperature, logarithmically dependent on the permeability and in some cases depends on the power law of the size of the reservoir (assuming that the deeper the groundwater reservoir, the greater the separation of the layers due to the cumulative tectonic events). The oil density, the volume ratio of the reservoir, gas factor (GOR) and the viscosity can also be obtained analytically in one another and tied to the depth. In General, however, this simple correlation is insufficient to describe all statistical examples shown in the data arrays oil field, for a large set of reservoirs.

In one or more embodiments of implementation of the present invention, the first part of the artificial neural network may be constructed using different parts of the data fields (for example, data fields of the level of the reservoir on figa and 6B) as the input and output of the network, where the data training mode based on arrays of data on oil qui the establishment, having these different pieces of data fields completely filled, should be used as input and output of the network during the training phase. In one or more embodiments of implementation of the present invention, for a large set of oilfield facilities (e.g. reservoirs) multidimensional connection of high order between the different data fields corresponding to these input data and output data can be determined on the basis of the ability to non-linear, multi-layer, parallel regression inherent artificial neural network, such as the first artificial neural network. In one or more embodiments of implementation of the present invention, these multidimensional connection of high order for the first artificial neural networks are statistical (or defined) correlation between data fields to complement the more simple and direct relationships described above, for a complete description of all statistical agencies represented in the data arrays oil field, for a set of field objects (for example, for a large number of reservoirs).

Returning to figa on the basis of the description above, the blank data field, at least one array of data about oil field, described otnositelno step 701, then may be filled with estimates obtained from these statistical correlations for forming filled with additional data array of data on oil field (for example, the sample data table (713)) (step 703). In one or more embodiments of implementation of the present invention, the blank data field may represent an output of the first artificial neural network, where the input data correspond to other populated data fields, at least one array of data for the corresponding field object (e.g., reservoir). Accordingly, the estimates (i.e. the reconstructed data or synthetic data) can be obtained for this blank data fields on the basis of these other fields filled with data using ratios for the corresponding part of the first artificial neural network. In one or more embodiments of implementation of the present invention, at least one array of data on oil field can be a filled with additional data the data array using the evaluation data as filled with additional data to fill in the empty data fields. In one or more embodiments of implementation of the present invention is pervonachalno filled data fields can also be compared with the estimated data, obtained from these statistical relationships to generate probabilistic information such as a probability distribution or a combination of average value, standard deviation and uncertainty.

In one or more embodiments of implementation of the present invention, the similarity of the data arrays oil field between populations of field objects (for example, for a large number of reservoirs) can be displayed using self-organizing maps (SOM) (e.g., SOM (711), as shown in figv). As is well known in this field, self-organizing map is a type of artificial neural network, usually represented as a discretized map (for example, characteristic maps (SOM) (711)) data training mode, is presented in color according to the color palette, gradients, these data cards correspond to different colors. The colors of fall in the approximate SOM (711) for clarity. These discretized map may consist of the configuration of the provisions (for example, the position 710) with the usual arrangement in a hexagonal or rectangular grid. Provisions in each of the cards overlap between the different maps to obtain the position of the SOM. Each position is associated with position on the map and with the weight vector with the same dimensionality as the input vectors of the data is x, for the data training mode. In one or more embodiments of implementation of the present invention, the first artificial neural network described with reference to step 702 may be a SOM, and the vectors of the original data represent data arrays oil field (for example, rows in the data table (714) and (713)for oil field facilities included in the training network, where the dimensionality of the input vector represents the number of data fields (defined, for example, on many fields, the first effluent data table (714)) array of data on oil field. Each data field of the data arrays oil field can be represented as a map (SOM), where the vector (i.e. an array of data about oil field oil field object) of the data space (i.e. from the datasets of oil field array of field objects) are placed on the place cards with the weight vector closest to the vector selected from the data space. Typically, for a large set of data training mode, the set of vectors sufficiently close to the weight vectors can be placed in the same position. For example, rather similar amounts of data-level reservoirs for multiple reservoirs may be located in one position on the SOM.

Returning to figa, exploitation of oil can then be carried out at least on the basis of completed "additional data arrays data on oil field (step 704). In one or more embodiments of implementation of the present invention oilfield operations may include the processes of a method of enhanced oil recovery (EOR), such as flooding. In one or more embodiments of implementation of the present invention fill the additional data incomplete data arrays and the formation of stochastic databases capture the levels of confidence for the data field of a large set of field objects (e.g., reservoirs, wells, well completion, and the like). This confidence or certainty which can be used directly for data analysis and interpretation, that usually follow after the accumulation of data and review processes for a variety of technological operations of oil. The classic process of "data validation", for example, can then be displaced and move on technological operation twist, where the uncertainty is reduced.

Flooding is one of the oldest and perhaps the most economical method in the methods of enhanced oil recovery (EOR) to increase the lifetime of the field and increase the total oil recovery from naturally depleting reservoirs. Operating at these high oil prices provide benefits to companies for in-depth consideration of their portfolios reservoirs regarding additional opportunities for the use of flooding. Limitations of time and information can limit the depth and severity of this screening assessment. Time to reflect efforts by screening a huge number of reservoirs, for the implementation of the waterflood, the information reflects data availability (compatible; measured: simulated data), which should be removed a considerable amount of information needed for. the adoption of viable technical solutions.

Figa and 8B shows the try block diagram of the method of screening to identify candidates from. oil field facilities in accordance with one or more variants of implementation of the present invention. In one or more embodiments of implementation of the present invention one or more of the steps shown in figa and 8B, may be skipped, repeated and/or performed in another. order. Accordingly, embodiments of the present invention should not be construed as limited to the specific configuration steps shown figa and 8B.

The method, as shown in figa and 8B, can be used in oil field, described above with reference to figa-5. Initially, figa, can be obtained from the datasets of the oil field. associated with the collection of field objects (step 801). In one or more embodiments of implementation of the present invention, the data arrays oil field can be the same as the original data arrays oil field with blank data fields, as described above relative to step 701. In one or more embodiments of implementation of the present invention, the data arrays oil field can be the same as filled with additional data data on oil field, as described above relative to step 703. In one or more embodiments, the implementation of this izopet is of data arrays oil field may not be blank data fields or can be filled, additional data on the basis of other usable cards.

Data on oil field for large aggregates of oil field facilities, as a rule, show statistical variability and the statistical distribution in the various fields of data. Statistical techniques can be applied to the formation of probabilistic information, such as a probability distribution or a combination of average values, standard deviation and uncertainty, to obtain the stochastic database by using datasets on oil field. In one or more embodiments of implementation of the present invention stochastic database is formed from arrays of data on oil field on the basis of artificial neural network (step 802). In one or more embodiments of implementation of the present invention, the data arrays oil field can be a filled with additional data data on oil field, and stochastic database can be formed on the basis of the first artificial neural network, as described above relative to step 703. In one or more embodiments of implementation of the present invention stochastic database may also contain probabilistic information generated on the basis of Deuteronomy is the second artificial neural network, as described below regarding figv.

Various statistical and modeling techniques can then be applied to stochastic screening databases to identify candidates from oil field facilities for further analysis (step 803). In one or more embodiments of implementation of the present invention proxy model (for example, as described above relative to figure 5) can be used for efficient simulation of oilfield operations (e.g., EOR operations, and the like) for each of a large number of reservoirs for screening purposes. More approximate statistical and modelling technologies are described below relative to Fig.9 Century, Respectively, can then be carried out a detailed analysis of each of the candidates selected through the screening process (step 804). For example, a detailed analysis can be performed using the full model for simulation of reservoir with increased accuracy, but with reduced speed, as described above relative to figure 5.

Returning to figa, one or more objects can be selected and then of the candidates on the basis of detailed analysis (step 805). Thus, these one or more objects are identified based on the two-step method. The first step is fast TFR is ning a large array of field objects based on the stochastic database taking into account the level of consistency and certainty in the data field. In the second stage, a detailed analysis is also used to obtain more accurate estimates for the final selection of one or more candidates.

Oilfield operations may be implemented then these one or more field objects (step 806).

Fig In shows the approximate statistical and modelling technologies for screening a large number of oil production facilities. The original can be obtained datasets of oil field associated with the collection of field objects (step 811). In one or more embodiments of implementation of the present invention, the data arrays oil field can be the same as the original data arrays oil field with blank data fields, as described above relative to step 701. In one or more embodiments of implementation of the present invention, the data arrays oil field can be the same as filled with additional data data on oil field, as described above relative to step 703. In one or more embodiments of implementation of the present invention, the data arrays oil field may not be blank data fields or mo is ut to fill additional data on the basis of other usable cards. In one or more embodiments of implementation of the present invention, the data arrays oil field can be a stochastic database, as described above relative to step 802. In one or more embodiments of implementation of the present invention, the data arrays oil field may not initially make contact with probabilistic information.

As is well known in this field, important information for each specific type of oilfield operations may seem with the help of certain data fields in the data array of the oil field. These critical data fields are the key performance indicators (KPIs) for the relevant oilfield operations. For example, KPI level of the reservoir, such as filling pressure, compressibility factor, the volume ratio of layer (FVF), initial pressure, gas factor (GOR), permeability (K), the ratio of the volume of the gas cap to measure the amount of oil (m-ratio), the thickness of the layer of oil, viscosity, density, porosity and water saturation (Sw), are considered in determining candidates for the flooding of a large number of reservoirs. In one or more embodiments of implementation of the present invention, the second artificial neural network can be formed for arrays d is the R on the oil field, related KPI (step 812). In one or more embodiments of implementation of the present invention, the second artificial neural network contains all KPIs identified as output, so that the determined statistical relationship between KPIs and other data fields in the data arrays oil field.

In one or more embodiments of implementation of the present invention, the second artificial neural network can be a card containing SOM, each of them represents one of the KPI (for example, KPI, described above in relation to flooding), as shown on figa. Colors fall on figa for clarity. As is well known in this field, SOM is particularly suitable for determining the statistical patterns presented in a large set of data. In one or more embodiments of implementation of the present invention, the clusters can be identified on the basis of the second artificial neural network (for example, SOM on figa) (step 813). As shown in figa, clusters (e.g. clusters (910)may each contain many provisions SOM grouped and enclosed inside the boundaries shown by shading. Approximate SOM contain approximately 950 provisions shown as hexagonal cells, which are grouped into 19 clusters identified through the darkened borders.

More details the card approximate SOM are shown on figv. This map is a "log m-ratio", setting KPIs for flooding. On FIGU, color table gradients (920) shows how the four shaded section for a schematic diagram of a continuous color gradations marked on the map in the series from -3,0 to 0.8 for the value of the parameter "log m-ratio". The shaded structure of each hexagonal cell represents the value of the parameter for the respective reservoirs located in its provisions on the basis of the SOM algorithm. As expected, the collectors within a cluster have similar values of the parameter, while the collectors with different parameter values tend to find in different clusters. In one or more embodiments of implementation of the present invention, the clusters can be automatically generated by the SOM algorithm. In one or more embodiments of implementation of the present invention automatic generation of cluster the SOM algorithm can be controlled by the information entered by the user. For example, the total number of clusters can be determined or otherwise restricted information entered by the user. In one or more embodiments of implementation of the present invention, the clusters can be formed using a visual analysis of the SOM manually.

On the basis of the functioning of the SOM oilfield objects (e.g., collectors), according to dtweedie these provisions SOM for each cluster, tend to be similar in behavior to that KPI and interaction KPIs with other data fields of the data arrays oil field. Therefore can be formed of the proxy model, the corresponding clusters for simulation of oilfield operations (step 814). Each proxy model can be used for simulation of field objects associated with the corresponding cluster. In one or more embodiments of implementation of the present invention, each of the proxy model contains the nominal model and the characteristic surface, where the nominal model simulates oilfield operations for representative field object from the corresponding cluster, and the characteristic surface represents the sensitivity of oilfield operations to deviations from the representative oilfield object among oilfield objects within the corresponding cluster. Representative oilfield object can represent a statistical average of the field objects associated with the corresponding cluster, instead of a physical object.

In one or more embodiments of implementation of the present invention deviations in each of the KPI settings for oilfield objects associated with each cluster can Ana is to sirovatka to determine the statistical distribution, for the development of the experiment, when modeling the oilfield operation using proxy models. The statistical distribution obtained for clusters, can also be incorporated in the stochastic database as part of the probabilistic information.

Returning to Fig In may generate a Bayesian network for modeling the objective functions oilfield operations using these proxy models (step 815). The objective function may include numerical analysis of relevant operational output, as well as other aspects of oilfield operations, such as economic, physical, environmental aspects, safety aspects and other important aspects. In one or more embodiments of implementation of the present invention, the simulation is performed for a large set of field objects using these proxy models to accelerate. In one or more embodiments of implementation of the present invention, these proxy models can be complemented with other logical or statistical computing technologies (for example, using expert knowledge) on the basis of the data arrays oil field. In more detail the Bayesian network and simulation of the target function are described below relative to figure 10. Then the objective function can simulate the I to generate a ranking for a set of field objects when considering oilfield operations. In turn, the operation in the oil field can be carried out based on the rank of field objects generated Bayesian network (step 816).

Figure 10 (depicted as Fig, 10A-10C, for purposes of illustration) shows a sample Bayesian network in accordance with one or more variants of implementation of the present invention. As is well known in this field. A Bayesian network is a probabilistic model, which is an array of variables with probabilistic interdependence and, as a rule, is used to control the process of justification when making decisions. As shown in figure 10, the Bayesian network contains variables (1001)-(1015), where each of the variables contains a predetermined number of States associated with a probability. The arrows connecting these variables are base relations of interdependence, which can be obtained probability information for each variable. Although the structure of interrelated variables applies to all considered oilfield objects, the probability associated with each variable, determined individually for each of the field objects array-based data on oil field corresponding to the oil is amylovora object.

In an exemplary Bayesian network shown in figure 10, the variable (1015) is a candidate for flooding, selected or determined from a large number of the considered reservoir. The percent probability of the two provisions "true" and "false" are shown to determine the other three variables (1012)-(1014), which may be considered as the objective function in determining the candidate. Variables (1012), (1013) represent the aspect of profitability and the physical aspect of viability, respectively. specific: reservoirs, for the implementation of the waterflood. The percent probability of the two provisions "true" and "false" - related variables (1012), (1013), show to further define additional variables (1001)-(1011), which represent the original oil in the reservoir, the proximity of the waterflood residual oil in the reservoir, the thickness of the layer of oil, the initial pressure, activity Samanthurai zone, logistics, storage group, the suitability of the operation, the potential of the reservoir, the drive power, respectively. These different variables can be associated with a relevant interest of the probabilities obtained from the data fields of a data array for oil field for a particular reservoir or from percent probability associated with the previous variables, asanami connecting arrows Bayesian networks.

Next, as shown in the exemplary Bayesian network in figure 10, the variable (1014) represents the increase in the production of concrete reservoirs, as a result, if you are flooding. In one or more embodiments of implementation of the present invention, the data arrays oil field represent the stochastic database, and the percent of the probabilities associated with increasing production rates are determined on the basis of simulation by Monte Carlo method with the use of proxy models, as described relative to step 814, on the basis of probabilistic information in a stochastic database (step 815).

Returning to figure 10, the percent probability variable (1015) can then be determined for each of the considered field object as a candidate for the implementation of the oilfield operations based on probabilistic information stochastic database, as described above. The aggregate consideration oilfield objects can then be ranked based on the corresponding percent probability variable (1015). Accordingly, the operation in the oil field can then be carried out on the basis of ranking, as described above relative to step 816 to figv. In one or more embodiments of implementation of the present invention, for the implementation of the nave is promyslovyh operations can be chosen candidates with the highest marks in the ranking. In one or more embodiments of implementation of the present invention candidates with the highest marks ranking can be subjected to further detailed analysis to select the final candidates for the implementation of oilfield operations, as described above relative to step 804 to figa.

Using the screening method described above may be subjected to screening more than 1500 reservoirs, and their number can be reduced to about 100 candidates from reservoirs (i.e. decrease by about an order of magnitude)that is suitable for a more detailed assessment. Also, using the method of screening described above 1700 reservoirs, each with more than 200 parameters can be reduced to a smaller number of candidates from reservoirs suitable for use during flooding to increase productivity and production. The processing time for the ranking of the reservoir may be 3 months, which is significantly less than the time for the previous methods.

Specialists in this field, with the advantages of this detailed description, will appreciate that the previous screening processes have disadvantages in that most databases are incomplete, and therefore, many candidates do not pass the screening process due to incomplete data. In addition to the, specialists in this field, with the advantages of this detailed description, will appreciate that the completed database have a systematic error and skoncentrirovanno more parameters that may affect the terms of the screening. In contrast, in one or more embodiments of implementation of the present invention, the entire process of screening is performed in the stochastic space using stochastic filled with additional data databases associated with proxy models, which can describe complex technical processes or subjective decisions or opinions of expert systems. These proxies are loaded in the Bayesian network, which produces stochastic ranking of each candidate. The advantage of this approach is to include a wide range of important parameters, at the same time there is an increase in the speed of the screening process without making a negative impact on the quality of the results.

Although the examples discussed above relate to the determination of candidates for flooding based on the dataset level reservoir, the methods of the present invention described above can be applied to other oilfield facility and to other operations in the oil fields. For example, introduces a process that uses x is racteristics the surface of the uncertainty analysis in an accurate numerical model of the well. In one or more embodiments of implementation of the present invention the characteristic surface from the model of the well is transmitted to the proxy model, which connects the entire range of input data for each parameter uncertainty with probabilistic output. data for individual process completion. In one or more embodiments of implementation of the present invention the neural network is trained on a stochastic input and output data and has the ability to recalculate the share of production in existing wells in real time.

Speciality, vannoy region, with the advantages of this detailed description, will understand that reverse redistribution from the wellhead to the completed well is difficult when using well - known from the literature methods. Known from the literature methods, which should ensure the separation of products in real time, typically fail to provide accurate results when the characteristics of the tributary one and complete the well changes. Numerical modeling, which takes into account the change of mobility and redistributes pressure in an open system the finished hole is long and usually cannot be used for reverse distribution in real time is I.

From the above description it will be clear that various modifications and changes may be made in the preferred and alternative embodiments of implementation of the present invention without departure from its scope of protection.

The present description is intended only for illustration purposes and should not be construed in a limiting sense. The scope of the present invention should be determined only by the language of the claims which follows. The term "contains" in the claims, as expected, means "contains at least", as set out list of elements in the claims is an open group. Other related singular terms are intended to include the plural, unless it is specifically excluded.

1. The method of performing oilfield operations containing phases in which: receive multiple datasets on oil field, each of which corresponds to a set of field objects and contains many data fields; form a self-organizing map (SOM) by means of: assigning each of the multiple data fields to one of the many maps (SOM); and the purpose of each set of field objects to one of the many provisions of the SOM, based on pre-defined the th SOM algorithm for the presentation of statistical patterns in multiple data arrays oil field; form a stochastic database from multiple arrays of data on oil field on the basis of artificial neural network multiple arrays of data about oil field, and artificial neural network contains a self-organizing map (SOM), which has many provisions SOM and contains many maps; stochastic database contains probabilistic information derived from SOM and associated at least with one of the multiple data fields, and probabilistic information represents at least one selected from the group consisting of a probability distribution and combination of the mean, standard deviation and uncertainty; produce screening of multiple arrays of data on oil field to identify the set of candidates from a variety of oil field facilities, where the screening is based on the stochastic database; perform a detailed assessment of each of the set of candidates; select oilfield object from the set of candidates on the basis of detailed estimates and perform oilfield operations for an oilfield object.

2. The method according to claim 1, wherein the oilfield operation includes at least an operation selected from the group consisting of the operations of the method of enhanced oil recovery (EOR) and operations back what about the distribution of oil production from multiple wells, giving a mixture of products from several horizons.

3. The method of performing oilfield operations includes the steps are: get multiple datasets on oil field associated with multiple field objects, each of the multiple arrays of data on oil field contains many data fields, at least one data field from a variety of data fields, at least one array of data on oil field from multiple arrays of data on oil field is a blank data field; forming the first artificial neural network from a set of data arrays oil field, and the first artificial neural network contains one or more relationships between the data fields from that number; fill the blank data field, at least one array of data on oil field estimated data based on one or more ratios for forming filled with additional data array of data on oil field; and performing the oilfield operation based on at least filled with additional data array of data on oil field.

4. The method according to claim 3, in which the first artificial neural network contains many semiorganized the s cards for multiple arrays of data on oil field and wherein a set of field objects contain, at least one object selected from the group consisting of a reservoir, wells and well completion.

5. The method according to claim 3, which further comprises steps in which: form the probabilistic information estimated data based on the first artificial neural networks, and probabilistic information contains at least one selected from the group consisting of a probability distribution and combination of the mean, standard deviation and uncertainty.

6. The method according to claim 3, additionally containing phases in which: form the second artificial neural network for multiple arrays of data about oil field, the second artificial neural network is a network associated with one or more key performance indicators (KPIs) oilfield operations identified from a variety of fields of data; identify a set of clusters from a variety of oil field facilities on the basis of the second artificial neural network, each of the multiple cluster contains one or more field objects from a variety of oil field facilities; form many proxy models, corresponding to different clusters, each of the many proxy models models oilfield operation for one or more field objects is C the corresponding cluster; and perform the oilfield operation based on multiple proxy models.

7. The method according to claim 6, in which the second artificial neural network contains one or more self-organizing maps one or more KPIs, and a lot of field objects contain at least one object selected from the group consisting of reservoir, drilling wells and well completion.

8. The method according to claim 6, in which each of the many proxy models contains the nominal model and the characteristic surface, and the nominal model simulates the oilfield operation for a representative field object from one or more field objects from the corresponding cluster, and, with characteristic surface displays the sensitivity of oilfield operations to the variance of one or more field objects from representative field object.

9. The method according to claim 6, in which oilfield operation contains at least one operation selected from the group consisting of the operations of the method of enhanced oil recovery (EOR) and reverse distribution of oil production from multiple wells, giving a mixture of products from several horizons.

10. The method according to claim 6, which additionally: identify one or more target functions oil the commercial operations; form a Bayesian network for modeling one or more target functions using at least lots of proxy models; form a ranking of the set of field objects based on Bayesian networks and perform the oilfield operation based on the ranking.

11. The method according to claim 10, which additionally: form a probability distribution, at least for one of the many data fields on the basis of the first artificial neural network and Bayesian network formed on the basis of simulation Monte Carlo probability distribution, using a variety of proxy models.

12. The method according to claim 10, which additionally: identify one or more candidates from a variety of oil field facilities based on the ranking; perform a detailed analysis of one or more candidates and perform the oilfield operation based on a detailed analysis.

13. The method for performing oilfield operations includes the steps are: get multiple datasets on oil field associated with multiple field objects, each of the multiple arrays of data on oil field contains many data fields; form the artificial neural network from a set of data arrays oil field, etc is better than the artificial neural network is associated with one or more key performance indicators (KPIs) oilfield operations identified from a variety of fields of data; identify a set of clusters from a variety of oil field facilities based on artificial neural network, each of the set of clusters contains one or more field objects from a variety of oil field facilities; form many proxy models, corresponding to different clusters, each of the many proxy models models oilfield operations for one or more field objects from the corresponding cluster; and perform the oilfield operation based on multiple proxy models.

14. The method according to item 13, in which artificial neural network contains one or more self-organizing maps one or more KPIs, and wherein a set of field objects contain at least one object selected from the group consisting of a reservoir, wells and well completion.

15. The method according to item 13, in which each of the many proxy models contains the nominal model and the characteristic surface, and the nominal model simulates the oilfield operation for a representative field object from one or more field objects from the corresponding cluster, and the characteristic surface represents the sensitivity of Neftepolis the new operations to the variance of one or more field objects from representative field object.

16. The method according to item 13, in which the oilfield operation contains at least one operation selected from the group consisting of the operations of the method of enhanced oil recovery (EOR) and reverse distribution of many wells, giving a mixture of products from several horizons.

17. The method according to item 13, which additionally: identify one or more target functions oilfield operations; form a Bayesian network for modeling one or more target functions using at least lots of proxy models; form a ranking of the set of field objects based on Bayesian networks and perform the oilfield operation based on the ranking.

18. The method according to 17, in which each of the multiple data fields associated with probability distributions, and the Bayesian network formed on the basis of simulation Monte Carlo probability distribution, using a variety of proxy models.

19. The method according to 17, in which optional: identify one or more candidates from a variety of oil field facilities based on the ranking; perform a detailed analysis of one or more candidates and perform the oilfield operation based on a detailed analysis.

20. Ground installation comprising a memory and a processor, done the store instructions, stored in the memory and executable by the processor to perform steps of a method for performing oilfield operations, and the instructions provide the ability to: obtain a set of data arrays, each of which corresponds to one of the many oil fields, and contains many data fields; forming a self-organizing map (SOM) by means of: assigning each of the multiple data fields to one of the many maps (SOM); and the purpose of each set of field objects to one of the many provisions of the SOM based on a predetermined algorithm SOM to represent statistical patterns in multiple data arrays oil field; generating a stochastic database from multiple datasets about oil field on the basis of artificial neural network from a set of data arrays oil field, and artificial neural network contains SOM, with many provisions SOM and contains many maps; stochastic database contains probabilistic information derived from SOM and associated at least with one of the multiple data fields, and probabilistic information represents at least one selected from the group consisting of a probability distribution and combination among the values, standard deviation and uncertainty; screening of multiple arrays of data on oil field to identify the set of candidates from a variety of oil field facilities, where the screening is based on the stochastic database; perform a detailed evaluation of each of the set of candidates; selection of field object from the set of candidates on the basis of detailed estimates and performing oilfield operations for an oilfield object.

21. Ground installation comprising a memory and a processor that executes instructions stored in memory and executable by the processor to perform steps of a method for performing oilfield operations, and the instructions provide the ability to: obtain multiple arrays of data on oil field associated with multiple field objects, each of the multiple arrays of data on oil field contains many data fields, at least one data field from a variety of data fields, at least one array of data on oil field from multiple arrays of data on oil field is a blank data field; generating a first artificial neural network of multiple arrays of data about oil field, the first artificial neural network provides the one or more relationships between a set of data fields; fill in blank data fields, at least one array of data on oil field estimated data based on one or more ratios for forming filled with additional data array of data on oil field; and performing the oilfield operation based on at least filled with additional data array of data on oil field.

22. Ground installation comprising a memory and a processor that executes instructions stored in memory and executable by the processor to perform steps of a method for performing oilfield operations, and the instructions provide the ability to: obtain multiple arrays of data on oil field associated with multiple field objects, each of the multiple data array for oil field contains many data fields; the formation of the artificial neural network from a set of data arrays oil field, an artificial neural network is associated with one or more key performance indicators (KPIs) oilfield operations identified from a variety of fields of data; identify a set of clusters from a variety of oilfield objects on the basis of artificial neural network, each of the multiple clusters contains one or b is more oilfield objects from a variety of oil field facilities; multiple proxy models, corresponding to different clusters.



 

Same patents:

FIELD: information technology.

SUBSTANCE: method for digital distribution of media content using a distribution main line system comprises steps of receiving a media content request from a client, the request including the profile of the client; performing inventory check and analysis of source assets by iteratively going through the client profile to generate output data; mapping functionalities, wherein several rules enable to map source assets onto the client profile; and scheduling the production process, which determines work elements and execution steps based on functionalities mapped in response to the media content request from the client.

EFFECT: automation of a process which downloads content in digital format and seamlessly manages said content by delivering to the client.

27 cl, 23 dwg

FIELD: radio engineering, communication.

SUBSTANCE: information on characteristics of weapons of each party is switched; the information is stored in a first memory unit; the information is supplemented with characteristics of a backup group with variable input time; information on weapons of all groups is used to pre-evaluate characteristics of their difference and determine coefficients of independent combat superiority of party A over groups B1, B2; the obtained information is used to select a strategy of combat operations; the remaining weapons of all groups are determined; intermediate characteristics of groups and the outcome of combat operations are stored in a second memory unit and read therefrom, and then transmitted to inputs of a display unit, where information on the outcome of combat operations of party A is displayed: win, loss, draw; the remaining weapons in groups: type of strategy, delayed backup, type of difference, values of coefficients of combat superiority and coefficients of distribution of weapons.

EFFECT: high combat efficiency and effectiveness of operations with different groups, rapid planning of the selection of the optimum target distribution strategy.

2 cl, 5 dwg

FIELD: information technology.

SUBSTANCE: method creating an audio scene for an avatar in a virtual environment comprises the following steps: creating a link structure in a virtual environment between a plurality of avatars; reproducing an audio scene for each avatar based on its connection with other avatars connected by the links; wherein the created link structure is configured to determine the angle for reproducing the audio scene and/or the attenuation coefficient for applying to audio streams on input links. The angle for reproducing the audio scene corresponds to angles of links between said each avatar and other avatars connected by links; the link structure includes a minimum spanning tree. Loops are introduced into the minimum spanning tree such that the minimum length of the loops is shorter than a predetermined value so as to prevent echo in the reproduced audio scenes.

EFFECT: solving a task such as creating voices which really seem to originate from avatars in a virtual environment.

12 cl, 6 dwg

FIELD: information technology.

SUBSTANCE: information on unit indicators of compared means is switched, recorded in a first memory unit, sent to a worst quality and best quality reference forming unit, which forms the corresponding beginning and end of a straight line which defines a quality estimation scale; planes perpendicular to that straight line are made through points of the compared means in the space of the unit indicators; parameters of the points of intersection with the estimation scale are found, values of which form complex quality indicators of the compared means, the maximum value of one of which corresponds to the preferred means.

EFFECT: high security of devices.

2 cl, 2 dwg

FIELD: information technology.

SUBSTANCE: system for hosting interactive audio/video (A/V) streaming with short waiting time includes a plurality of servers on which one or more applications are executed. The system also includes a network with input routing, which receives packet streams from users and routes these packets to one or more said servers, wherein said packet streams include user control signal input, wherein one or more said servers is configured to calculate A/V data in response to user control signal input. Furthermore, the system includes a compressing unit which is connected to receive A/V data from one or more servers and derive therefrom streaming compressed A/V data with short waiting time. The system also includes an output routing network which routes streaming compressed A/V data with short waiting time to each user over a communication channel through an interface.

EFFECT: high quality of A/V data transmitted over a communication channel.

29 cl, 40 dwg

FIELD: information technology.

SUBSTANCE: device for simulating the process of choosing a commodity has an array of m*n first registers, second registers whose number equals the number of rows of the array, adders whose number equals the number of rows of the array, AND element units whose number equals the number of rows of the array, third and fourth registers whose number equals the number of columns of the array, an array of m*n divider units, an array of multiplier units, a unit of OR elements, a maximum code selecting unit, a decoder, four delay elements and a flip-flop.

EFFECT: broader functional capabilities by providing selection of the best version of a commodity based on given consumer criteria.

1 dwg

FIELD: information technology.

SUBSTANCE: at least some of illustrated versions of implementation relate to systems having a flow computer configured to monitor a physical process which is external with respect to a data processing unit, an archive server connected to the flow computer over a computer network and configured to receive data related to physical process and store said data in nonvolatile data storage, and a human-machine interface connected to the archive server over a computer network. The human-machine interface is configured to extract values of archive data related to the physical process from the archive server, calculate statistical data not stored in the archive server based on the values of archive data and display the statistical data on a display device.

EFFECT: reduced amount of data stored in archive servers.

32 cl, 8 dwg

FIELD: information technology.

SUBSTANCE: content and metadata associated with the content may be provided to a number of users. The content may be displayed on a display device while the metadata may be transmitted to a remote device corresponding to a receiving user. The user may further request desired information or metadata pertaining to the content and the requested information or metadata may be transmitted to the user's remote device. Different users may request different information on the same or different objects being displayed or presented on a display device. Each requesting user may receive requested information on the same or different objects via corresponding remote devices.

EFFECT: providing content and metadata associated with consumption of the content, providing content and metadata which do not prevent consumption of the content.

27 cl, 6 dwg

FIELD: information technology.

SUBSTANCE: disclosed is a method of generating a customised data viewer in a computer system, where the viewer is configured to display data at any level in a data model. The disclosed method includes a step of receiving a user request indicating that one or more portions of data are to be displayed in a user-customised manner using a data viewer. Further, according to the method, the requested data portions that are to be displayed using the data viewer are accessed. A dynamic data viewer configured to display the accessed data portions in the user-customised manner indicated in the received user request is then generated. The generated dynamic data viewer is also applied to the accessed data portions, such that the generated viewer displays the requested data portions in the user-customised manner.

EFFECT: automating setup of a data viewer for using a user-selected defined data type.

20 cl, 4 dwg

FIELD: information technology.

SUBSTANCE: image container file has at least first and second multimedia streams (MS). The first MS includes first image data representing an image. The second MS includes arbitrary data which can correspond to: a different representation of the same image; annotations to the first image data; second image data that together with the first image data form a new image with greater dynamic range, resolution, field of view or other attributes that can be derived from processing two or more independent images; or an executable file related to the first MS. The image container file can also include extensible metadata to hold information describing one or more multimedia streams of the image container file, as well as DRM information for obtaining a license to access encrypted data or verifying the authenticity of encrypted or unencrypted data.

EFFECT: providing, when creating an image container file, functional linkage of multiple multimedia streams, one of which is received by a receiver and the other includes arbitrary data.

26 cl, 6 dwg

FIELD: oil and gas industry.

SUBSTANCE: method involves drilling of a deposit with production wells crossing the formation with water-saturated and oil-saturated zones separated with a non-permeable natural interlayer, lowering of a casing string with further formation perforation, investigation of its water-oil saturation and their deposit intervals, dimensions of non-permeable natural interlayer, creation of a screen from an insulating compound, which separates water-saturated zone of the formation from oil-saturated zone, cutting of some part of the casing string, enlarging the well shaft at that interval; filling of the enlarged interval of the well shaft with insulating compound, drilling of insulating compound in the well so that the screen remains opposite to oil-saturated zone of the formation after waiting period of the insulating compound curing, perforation opposite to oil-saturated zone of the formation, and development of the well. At arrangement of non-permeable natural interlayer below oil-saturated zone of formation and thickness of non-permeable natural interlayer over 8 m at the bottom interval of non-permeable natural interlayer there installed is a blind packer, and temporary clogging of oil-saturated zone of formation is performed. Some part of the casing string is cut out to 1.0-1.5 m at height of 1.0 m above bottom of non-permeable natural interlayer, and in casing string interval at height 1.0-1.5 m below roof of non-permeable natural interlayer there made are holes across the casing string. Cementing casing string is lowered to well with through drillable packer, packer is installed in casing string opposite to non-permeable natural interlayer in interval between cut out part and holes in casing string, circulation of fresh water is induced on well head along cementing casing string under packer via casing string annulus and intertube space on well head by pumping of fresh water. If there is no fresh water circulation, impulse treatment of non-permeable natural interlayer by mud acid composition is performed. When circulation is available pumping of fresh water is stopped, then insulating compound is pumped via grout casing string and is forced into casing string annulus in interval of non-permeable natural interlayer with formation of insulating bridge in inner space of casing string to the bottom of oil-saturated zone of the formation. Then grout casing string is lifted above the bottom of oil-saturated zone of the formation and surpluses of synthetic resin are washed out from intertube space of casing string. After some period required for synthetic resin curing through and blind packers are drilled as well as insulating bridge, temporary clogging of formation is removed and well is brought into operation.

EFFECT: improving efficiency of the method owing to excluding behind-the-casing flow in the well between water- and oil-saturated zones of the formation and possibility of their simultaneous-separate development.

2 ex, 5 dwg

FIELD: oil and gas industry.

SUBSTANCE: in addition, analysis of isotopic composition of carbon of sum of hydrocarbons C2-C6 is performed and limits of values of isotopic composition of carbon, methane and isotopic composition of carbon of sum of hydrocarbons C2-C6 for reference horizons are determined. Tables and/or graphs represent ranges of values of isotopic composition of gases from reference horizons and gases are represented from inter-string space of wells or drilling fluid; as per the degree of similarity or coincidence of the above ranges of those values (or individual points) there evaluated is nature of investigated inter-string gas shows.

EFFECT: improving reliability in determination of nature of inter-string gas shows.

1 ex, 2 tbl, 1 dwg

FIELD: oil and gas industry.

SUBSTANCE: according to the proposed method, a layout consisting of the following in upward direction is lowered to the well: a lower perforated connection pipe, a valve, a drain valve, a packer, a bottom-hole pump, a tubing string, an upper perforated connection pipe and a line of rods. The bottom-hole pump is brought into operation under action of movements of the line of rods. Supply of high-viscosity oil is performed to the well head via the tubing string and through upper perforated connection pipe via inter-tube space. Periodic straight flushing is performed by pumping of flushing liquid via the tubing string, sampling is performed through upper perforated connection pipe and inter-tube space and back flushing is performed by lifting the pump above upper perforated connection pipe. Pumping of flushing liquid is performed via inter-tube space and sampling is performed through upper perforated connection pipe and tubing string.

EFFECT: providing the possibility of bringing bottom-hole equipment to working position at sticking of the line of rods by straight or back flushing of the well without lifting that equipment from well.

1 ex, 1 dwg

FIELD: oil and gas industry.

SUBSTANCE: in compliance with the procedure qualitative and quantitative composition is defined for water produced at the well mouth or at output from a measuring group unit; concentration is calculated for mixed salts in water coming to the well from different sources considering periodicity of operation for one well or a group of wells in the collecting pipe or a measuring group unit. Calculation of the reaction termination of hard salts formation in a place of measurement, formed at mixture of different waters. A fluid flow velocity on the pipeline and place of depositions of salts is defined. The conclusion about working capacity of the equipment is made. Operation mode is changed for one well or for a group of wells in order to reach concentration of barium sulphate salts mixture at a measuring point less than 0.1 g/l according to calculation results. At that flow rate of the fluid in the pipeline is defined against an analytical expression. Shutdown of wells is considered as a varied mode of their operation. During shutdown period these wells are used for another purpose.

EFFECT: working efficiency of oilfield pipeline at active scale build-up.

1 ex, 4 tbl, 1 dwg

FIELD: oil and gas industry.

SUBSTANCE: according to the method drilling, casing and anchoring of pipe, surface pipe and production string. While drilling the production string a well is drilled with a natural water suspension. When a water-containing formation is open transfer is done to a dense mud in order to prevent spilling of formation water; when the formation is open and after complete loss of mud transfer is done to a natural water suspension maintaining volume of the mud in the upper part over the formation with complete loss of mud. As the well does deeper, volume of mud becomes less in the upper part of the well and intermittent changes in level and spills of borehole fluid occur then volume of mud is added in the upper part of the well till intermittent changes and spills are stopped.

EFFECT: increased efficiency of method.

1 ex

FIELD: oil and gas industry.

SUBSTANCE: during oil deposit development air is pumped through injection wells and oil is extracted through producing wells. Bed shaliness and sintering temperature for clay is predetermined; then in-place permeability is defined after clay sintering; a radius of borehole environment with varied permeability is calculated where balancing of injection pressure with formation pressure takes place. Borehole environment of the injection well is treated thermally by air pumping and arrangement of dry burning in the borehole environment and sintering of clays till the calculated radius is reached in the area treated thermally. At that air pumping is stopped periodically and water is pumped in order to cool and crack sintered clay. Thereafter pumping of a working agent is started.

EFFECT: improvement of oil recovery factor for the oil deposit with low-permeability reservoir.

1 ex, 3 dwg

FIELD: oil and gas industry.

SUBSTANCE: device consists of three chambers of different diameters connected by couplings. At that chambers represent cylindrical tubes. At the bottom of the second chamber there is a spring with large piston fixed on it. The large piston has valves. The valves are installed at connection sleeve between the first and second chamber. A small piston is installed in the third chamber. Small and large pistons are connected by rod assembly. In the third chamber there are through holes for liquid cross-flow at the angle more 45 degrees. The method includes usage of the above device. At that transmission is performed due to difference in volumes of chambers made of cylindrical tubes. Reverse motion of liquid is prevented by closure of valves under liquid pressure between the first and second chamber.

EFFECT: higher operating reliability of the device and efficiency of the method.

2 cl, 1 dwg

FIELD: oil and gas industry.

SUBSTANCE: method involves perforation of a production string below the level of current water-gas contact; lowering to the well of an additional tubing string of small diameter with layout of bottomhole equipment lowered below the operating serving for separation of inner space of the production string, which is filled with gas and water, a working chamber intended for accumulation of liquid condensing in the working face; supply of high-pressure gas to the mall-diameter tubing string from a compressor installed on day surface or a donor well with wellhead pressure above hydrostatic pressure at absolute hypsometric level corresponding to location of process holes of the receiving well more than by 10 atm; gas flushing of excess pressure, liquid condensed in the working face and accumulated in the working chamber to water-saturated interval located below the operating productive interval; as well as installation on day surface of a separator for gas drying, pressure gauges for monitoring of wellhead pressure of the main and minor tubing string, shutoff and control devices for control of gas flow along the main and minor tubing string, a control unit performing the monitoring and control of the process according to the specified algorithm.

EFFECT: increasing efficiency of inventions.

4 cl, 1 dwg

FIELD: oil and gas industry.

SUBSTANCE: product of each individual oil well of the oil well cluster measured by means of group metering stations complete with a controller is supplied to an oil collecting header with a pump multiphase unit installed on it. At the outlet of the pump multiphase unit complete with an electric motor there performed in a real time mode is continuous monitoring of total product flow rate as per the oil well cluster in units of weight by means of a multiphase flow metre installed on the oil collecting header, between the outlet of the pump multiphase unit with an electric motor and a booster pump station and a controller in addition to it, as per a special programme built into it, there preformed is monitoring of differences of total component flow rates of the oil well cluster as a whole, and as per the deviation of difference beyond the limits of the set point specified in the controller, the operator takes this or that decision.

EFFECT: providing higher consumer properties of the site.

2 cl, 1 dwg

FIELD: oil and gas industry.

SUBSTANCE: method involves drilling of exploratory wells, performance of their trial operation, alternating pumping to each of the exploratory wells of the specified volume of portions of water-repellent fluid on water basis or oil with indicator, identification of presence or absence of natural fractures-channels of high conductivity in carbonate deposit and positions of fracturing zones as per pressure pulse transmission speed between exploratory wells at pumping to them of portions of water-repellent fluid on water basis or oil with indicator as per indicator advancing speed from one exploratory well to the other in a single-phase filtration mode, as per dynamics of change of formation pressure at trial operation of carbonate formation and as per determination of the main directions of hydrodynamic interaction of wells with characteristic of interaction degree. Type of carbonate deposit is determined as per the data of the above explorations; production wells are arranged on the deposit depending on the type of carbonate deposit and position of fracturing zones. Explorations are enlarged using production wells and considering the data as per dynamics of change of formation pressure at elastic closed natural development mode of carbonate deposit at early stage. Type of carbonate deposit and position of fracturing zones is confirmed or specified as per the data of additional explorations. Scheme of further development of carbonate deposit - scheme of injection well arrangement is chosen when changing over the elastic closed natural development mode of carbonate deposit to the development method with artificial maintenance of formation pressure.

EFFECT: possible identification of fracturing using a tracer method at the early stage of industrial exploration of oil reserves and trial operation of exploratory and advancing wells and excluding of reconstructions of water-flooding operation during the deposit development.

1 ex, 3 tbl, 3 dwg

FIELD: oil extractive industry.

SUBSTANCE: method includes lowering a tail piece into well with temperature, electric conductivity and pressure sensors placed on tail piece along its length. Pressure sensors are used in amount no less than three and placed at fixed distances from each other. After that, continuously during whole duration of well operation between maintenance procedures, temperature, conductivity of well fluid, absolute value of face pressure and difference of pressures along depth of well in area of productive bed are recorded. Different combinations of pairs of pressure sensors are used for determining special and average values of well fluid density. When absolute pit-face pressure is lower then saturation pressure for well fluid by gas and/or when average values of density deviate from well fluid preset limits and/or when its conductivity deviates from preset limits, adjustment of well operation mode is performed.

EFFECT: higher efficiency, higher safety.

2 cl

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