# Method of evaluating porosity and penetrability of oil and gas deposits

FIELD: mining.

SUBSTANCE: method consists in boring vertical, horizontal or inclined borehole, in recovery of core samples from collector rock, in applying thermal analysis for identification of separate chemical compounds of collector, in determining connection of per cent concentrations of minerals with parametres of porosity and penetrability by using multi-dimensional correlation-regressive analysis and obtaining plural linear correlation equations for concrete oil and gas deposits facilitating calculation of porosity and penetrability on base of data on mineralogical composition of oil and gas deposits.

EFFECT: reduced labour intensiveness, and upgraded accuracy and validity of determination of mineralogical composition of core material.

1 ex, 2 tbl, 4 dwg

The invention relates to the oil and gas industry, in particular to the field of evaluation and prediction of the productivity of hydrocarbon deposits and fields, and can be used for multipurpose determine the reservoir properties (FES) reservoirs of hydrocarbons.

To assess the current state of development, the calculation of oil and gas, the volume and categories of industrial stocks, research hydrocarbonaceous deposits is carried out using geophysical, parametric studies of wells petrophysical, geochemical and other methods of research core (rock samples).

Known methods of determination of open porosity on the extracted core samples of regular geometric shape, raised from the borehole during drilling method of saturation of their nonpolar kerosene (see OST 39-161-83 Oil. Laboratory method for determining the absolute permeability oil and gas reservoirs and enclosing rocks"), and coefficient of permeability of the core samples of regular geometric shape, raised from the borehole during the drilling process by the method of stationary air filtration (OST 39-181-85 Oil. Laboratory method for determining the porosity hydrocarbon species").

The disadvantage of these methods is that they can be approx the tive only in the presence of conditioned core samples, require time and labor costs. The definition of these parameters using GIW (GIS) involves significant financial costs.

The aim of the invention is to reduce the complexity and cost of the method, improving the accuracy and reliability of the determination of the mineralogical composition of the core material deposits on the minimum and unconditioned specimens of the breed.

This objective is achieved in that in the method of determining the porosity and permeability of the reservoir oil and gas, including the drilling of vertical, horizontal or slanted wells, selected core samples from the rock collector, apply thermal analysis to identify individual chemical compounds, link percentage concentrations of minerals with parameters porosity and permeability due to the use of multivariate regression analysis, get multiple linear correlation equations for specific oil and gas fields, which allow to calculate porosity and permeability, using data on the mineralogical composition of the layer.

The method is as follows.

Are drilling vertical, horizontal or slanted wells (or one hole) with a selection of core samples from the od of the collector. In contrast to the known method, there is no need to strive to ensure continuous and full coring, since the implementation of the proposed method is receiving substandard core material.

The obtained core material is subjected to a complex thermal analysis, consisting of the following steps.

1. Preparation of rock samples

The most characteristic pieces of rock weighing 100-200 g crushed in a steel mortar, and then in a porcelain mortar communicated to the size of grain not exceeding 0,1-0,2 mm

The prepared sample with a volume of 2-3 cm^{3}used for primary thermal analysis. If the data of thermal analysis established that the rock contains organic matter in the amount not exceeding 3%, and the content of the main predopredelyayutsya mineral is at least 95% and does not require clarification on the composition of impurities, then the operation completes.

If the content of the primary rock-forming mineral is not more than 90% and the organic matter content of more than 3%, or require a detailed study of impurities, then carry out the following operations: 1) to remove part of the organic matter extraction alcohol-benzol mixture in to conventional Soxhlet extractions; 2) washing salts from the samples of hot distilled water; 3) to remove carbonates and pyrite dissolution in 10%-nasolenou acid followed by rinsing with distilled water; 4) allocation otmuchivaniem of the terrigenous part of the breed of clay fraction. Selected clay fraction is then investigated thermographically.

2. Conducting thermal studies

The primary mode of research.

1. Corundum crucibles or platinum with a capacity of 1.5-2.5 cm^{3}. Rarely used quartz crucibles with a capacity of 1.5 to 0.5 cm^{3}or small platinum crucibles with a capacity

0.5 to 0.2 cm^{3}.

2. Weighed samples of 1.5 to 3.0,

3. Heating rate: master mode 5°C/min were the least used mode of 2.5; 10; 0.5°C/min

4. The final temperature of 1000°C.

5. Sensitivity differential thermal analysis (DTA) - 1/5; rarely used - 1/10, 1/20, 1/2, 1/1.

6. Sensitivity differenziale-thermogravimetric (DTG) - 1/5; rarely used - 1/2, 1/10, 1/20.

7. Scale thermogravimetric (TG) for preliminary studies variable depending on the type of breed

a) up to 200°C - 20, or 50, or 100 mg

b) from 200-600°C - 100, or 200, or 500 mg

in) from 600-1000°C - 100, or 200, or 500, or 1000, or 2000 mg

Sometimes to clarify the quantitative definitions kaolinitic clays additional studies where the test sample is added to the sample reference kaolinite up to 30%.

In some cases, to clarify the availability and quantity of α-quartz conduct research in cooling mode with the same rate of 5°C/m is N.

3. Chemical analysis of residues breed after thermal studies

If not carried out sectionentry chemical analysis of the core, and the breed is a noticeable amount of thermographically undetectable up to 1000°C anhydrite, then spend the following. Part of the remains of the breed diluted 10-fold washing with hot distilled water. The filtrate is collected and determine the number of SO_{4}^{-2}reaction with baryta water. Sometimes when the analysis of the terrigenous rocks of the need to establish the presence of minerals in the form of iron oxides (limonite, magnetite, etc.). For the remaining boiled in Aqua Regia, the filtrate is separated and carry out chemical analysis method for the presence of oxides of Fe^{2+}and Fe^{3+}.

4. Processing of research results

Processing derivatograph start with a calculation of the total mass loss. A portion of the sample before and after the study weighed on an analytical balance.

Curves TG, DTA, DTG is divided into zones at intervals of temperatures, with the fixing temperatures of the beginning and end of thermal effects, loss of weight or without. Note the temperature peaks thermal effects, DTG peaks, weight loss. Celebrate weight loss in these temperature ranges. In the simplest cases, for example, a fairly pure limestone, it is enough to split into two zones from 20 to 200°C (evaporation of N_{2}O) and from 700 to 1000°C (dis who ociate caso_{
3}) by the reaction of caso_{3}⇔CaO+CO_{2}and calculate % limestone.

If the rock contains a mixture of carbonate minerals, such as magnesite, dolomite, calcite, siderite, ankerite etc., the composition determined using the formula for the calculation of thermal effects the dissociation of Mg^{2+}and CA^{+2}comprising carbonates, and using data loss weight, tabular data ratio calculation.

For terrigenous rocks containing clay minerals, and impurities - anhydrite, carbonates (dolomite, magnesite, calcite, etc), siderite, use the information in a series of studies as with the whole sample, and with a fragment of rock. In some complex cases, when in the initial studies with whole sample was imposing thermal effects of different minerals accompanied by loss of weight, used DTG curve, Ostrava the descending branch is a separate component. Then determine the square DTG individual components and the total area of the corresponding DTG total mass loss. Accounting for the proportion of S_{DTG}- X_{Δmi}SΣ_{DTG}- X_{Δm}and making calculations, determine the mass loss of each component. Then, using a table of estimated coefficients, calculate the percent of a component in the specimen of the breed.

The degree of approximation which can be explored all lithological with the STS collector throughout the volume of the reservoir, ultimately determines the accuracy of forecasting productivity and reservoir recovery. It can be increased either by increasing the number of samples, or the study of samples of large size, or the use of methods of mathematical modeling of the variability of reservoir properties. A large amount of factual material was determined using probabilistic and statistical methods of studying and generalization of material on reservoir properties of rocks, which allowed us to obtain a more objective and reliable assessment results on a quantitative level.

Next, apply one of the methods of mathematical statistics - correlation and regression analysis. The regression is called the dependence of the conditional mathematical random variable Y from the values of X. This dependence is expressed by the corresponding equation. Finding the type and coefficients of this equation represents the task of correlation and regression analysis. View the regression equation is usually chosen based on the physical meaning of the tasks, and the coefficients are determined most often by the method of least squares. This is due to the assumption of normal distributions of Y values (the array of the actual values of porosity/permeability) and X (a multidimensional array of the percentage of the main cell battery (included) is tov in the composition of the core), which is in good agreement with practice. The essence of this method consists in the following: the sum of the squared deviations of calculated values of porosity/permeability from the actual data should be minimal, i.e. to ensure the minimum of the expression:

where Y_{i}- the i-th value of the random variable Y;

X_{i}- the i-th value of a multidimensional random variable X;

i - number of the measured sample;

k is the number of measured samples;

f - linear scalar function of many variables (component X), describing the regression curve.

Assuming that there are k samples with known mineralogical composition consisting of n component with core study of porosity and/or permeability, we obtain that k has measurements of porosity and/or permeability, i.e. the value of Y_{1}, Y_{2}, ..., Y_{k}and as k sets of multicomponent variable X: X_{1}, X_{2}, ..., X_{k}where.

Due to the fact that the breed of collector consists of many minerals, arose the necessity to solve the problem of multiple correlation. The result of these studies was obtained linear correlation equation of the form:

where n is the number of the component of the mineralogical composition of the A.

The coefficients And_{1}And_{2}, ..., A_{n}found by using the least squares method as the solution of a system of linear equations:

where

and

The solution of this system can be performed using numerical methods, and using build identifier Kramer, method, Gauss or other available methods.

Next set of experimental correlation between the percentage of rock-forming minerals with reservoir reservoir properties.

To use the obtained regression curves it is necessary to determine the confidence interval for the values of the content of each component in the composition of the analyte. In our case, the confidence interval was constructed for the mean relative prediction error, defined as the ratio of the difference between predicted and measured values to the predicted value of the investigated trait. The measured value in this case is as true, as it is much nearer to him than predicted. Often when conducting regression analysis introduces the possibility of spurious correlation. This phenomenon takes place in the analysis of so-called prepreg) is closed by a numeric systems belong to all systems in which the sum of all signs shall be equal to one hundred percent. The presence of false correlation leads to significant errors. The proposed method was able to reduce this error to a minimum due to the large number of analyzed traits.

To calculate the bounds of applicability of the proposed method calculates the n-dimensional rectangle in n-dimensional space (on the axes of this space is deferred percentage or other mineral) with center in the point with coordinates equal to the mathematical expectation of the percentage of minerals, and with semi-axes equal to the square root of the variance of the considered variables with appropriate allowances for the physical meaning of quantities. This method allows not only to determine the boundary values of the applicability of the regression analysis, but also to identify the measurements of the mineralogical composition of the core, significantly different from the rest of the "cloud" measurements.

Figure 1 shows the joint presentation of the measured and predicted permeability values for Novo-Klyuchevskaya field; figure 2 is a joint presentation of the measured and predicted values of porosity for Novo-Klyuchevskaya field; figure 3 - distribution measurements of the percentage of the field current is the same in the studied core Novo-Klyuchevskaya field and displaying the boundaries of the applicability of the proposed method for the analysis of the content of feldspar; figure 4 - distribution of permeability in the hydrodynamic model of the object CI+CIa Novo-Klyuchevskaya field.

An example implementation of the proposed method.

Obtained during the drilling of borehole core material Novo-Klyuchevskaya field Samara region subjected to thermal analysis. The results of the research determined the percentage of individual chemical compounds that make up the breed (see Table 1).

After receiving percentage composition, conduct correlation and regression analysis, in result we have the following dependencies for porosity and permeability:

M=-3,962x_{1}+0,h_{2}-0,h_{3}-0,h_{4}-0,100x_{5}+0,271x_{6}+0,100x_{7}+0,h_{8};

K=-1034,9x_{1}+80,1x_{2}-14,4_{3}+2,h_{4}-1,7x_{5}+4,8x_{6}- 0.5_{7}+39,3x_{8};

where M is the calculated porosity, % (see Table 1, column "Porosity calculation");

It is estimated permeability, MD (see Table 1, column "Permeability calculation");

x_{1}- dolomite, x_{2}calcite, x_{3}- hydromica, x_{4}- chlorite, x_{5}- kaolinite, x_{6}quartz, x_{7}- dust particles of feldspar, x_{8}- organic matter - sediment composition in % weight.

Figure 1 and 2 shows a joint presentation of the measured and predicted values of porosity and pronice the spine for Novo-Klyuchevskaya field.

Finding the boundaries of applicability for this study can be presented in table format (see Table 2). In the table calculated boundary values for each of the substances using the values of mathematical expectation and variance, in bold in the table indicates the values that are obtained for the limits. Found the boundary values apply only for the prediction of porosity and/or permeability of core samples, but not to adjust the previously obtained correlation equations.

The obtained limits of applicability is demonstrated by the percentage of feldspar in composition of the samples in figure 3. Icons figure 3 shows measurements of the percentage of feldspar in the studied core samples Novo-Klyuchevskaya field, the squares marked values not included in the interval, applicable for the prediction of porosity or permeability on the mineralogical composition of the core, circles - applicable for calculation of the forecast point. The black solid line shows the expectation parameter, dot-dash and two dots-dash - respectively the upper and lower limit value to the applicability of the parameter.

The application of the method to create and Refine the geological model is to adjust or direct use value the deposits of porosity and permeability in wells, allows you to more accurately describe the picture of the development of reserves in the reservoir or to explain anomalies reserves in the case when the data of porosity and/or permeability in geophysical research wells or reservoir core studies are missing. Clarification of the distribution of porosity reservoir model allows us to estimate the distribution of reserves and, consequently, the direction of their generation. Clarification of permeability distribution in the hydrodynamic model can more accurately determine the nature of promotion of formation and injection waters, the direction reserves, the nature of the irrigation wells.

Using calculated on the basis of correlation and regression analysis of the values of porosity and permeability has been updated geological model of the object CI+CIa Novo-Klyuchevskaya field, allowing to simulate the nature of the oil displacement Samanthurai and injected water and to identify the zone of localization of residual oil reserves (figure 4). Thus, the available information about the exact mineralogical composition of the collector allows to determine the values of porosity and permeability of rocks for individual objects based on the methods of mathematical statistics. In General reduces the complexity and cost of the method through the use of the minimum and not Edizioni rock samples, increases the accuracy and reliability of the determination of the mineralogical composition, including the early and late stages of field development. The proposed method solves the problem of mandatory application of field data (study of core material) in the development of hydrocarbon deposits, as well as obtain the necessary information about the FES collectors to create and Refine geological models, which allows the development of oil and gas fields to significantly increase oil recovery.

The method of determining the porosity and permeability of the reservoir oil and gas, including the drilling of vertical, horizontal or slanted wells, selection of core samples of reservoir rocks, characterized in that the applied thermal analysis to identify individual chemical compounds, communicate percentage concentrations of minerals with parameters porosity and permeability due to the use of multivariate regression analysis, get multiple linear correlation equations for specific oil and gas fields, which allow to calculate porosity and permeability, using data on the mineralogical composition of the aquifer.

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