RU2399070C2 - Method for determination of residual water saturation and permeability of bed - Google Patents

Abstract

FIELD: mining.

SUBSTANCE: geophysical survey (GS) of wells are carried out, as well as laboratory research of core, parametres of porosity and residual water saturation are determined. Permeability of bed is determined on the basis of functionally correlation relation of permeability with parametres of porosity and residual water saturation produced on the basis of analysis of core laboratory research data. Residual water saturation is estimated as a result of determination of direct correlation-regression dependence of data of core residual water saturation on data of GS set. Besides to determine residual water saturation of bed, only those types of GS are carried out, parametres of which correlate dependence of residual water saturation with residual water saturation of core.

EFFECT: increased validity of results, increased efficiency.

2 dwg

Description

The invention relates to the study of reservoirs, namely, to restore their residual water saturation and permeability by constructing a correlation-regression model of the relationship of a complex of logging geophysical surveys (GIS) and laboratory data for core research.

A known method for determining the permeability of the reservoir (US patent No. 6691037, G01V 11/00, 02/10/2004 - prototype), where the first stage is the determination of the porosity parameter (total, effective) by GIS, the second - restoration of the corresponding parameter of residual water saturation, and the type of relationship between the two specified parameters - porosity and residual water saturation - is established from laboratory data on core research. A subsequent assessment of permeability is carried out through the parameters of porosity and residual water saturation based on the formulas of Cozeny-Carmen, Coats, Timur or others, taking into account not only porosity, but also residual water saturation. In this case, the parameters of the dependence of permeability on porosity and residual water saturation are adjusted so that this dependence is consistent with the data obtained by laboratory studies of the core.

The prototype method is not effective enough for the following reasons:

the correlation porosity - residual water saturation has less reliability than permeability - residual water saturation, which is proved by numerous studies of petrophysical dependences of the core-core type. However, it is the first one that is proposed as key in the joint interpretation of GIS complex data and core data. As a result of a two-stage interpretation scheme, porosity - residual water saturation - permeability, the reliability of the interpretation of permeability as a whole is controlled by a weak correlation of the type porosity - residual water saturation.

The object of the invention and the expected technical result are to increase the efficiency of the method for determining the residual water saturation and permeability of the reservoir by directly determining the residual water saturation according to GIS. In turn, the direct determination of residual water saturation from GIS data provides an increase in the reliability of determining both residual water saturation and permeability.

Based on a standard well logging system and laboratory core tests, the parameters of porosity and residual water saturation are determined. The permeability of the formation is determined on the basis of the functional correlation between the permeability and the parameters of porosity and residual water saturation, obtained based on the analysis of core data. The principal difference of this method is the determination of the parameter of residual water saturation as a result of constructing a direct correlation and regression dependence of the data of the standard GIS complex with the data of the residual water saturation of the core. The reliability of the method for determining the residual water saturation and permeability of the formation increases; and without the use of additional research methods relative to the standard GIS complex; on the contrary, the necessary and sufficient complex of GIS according to the claimed method is narrowed relative to the standard.

The problem is solved in that the proposed method for determining the residual water saturation and permeability of the formation based on a standard GIS complex and laboratory core tests, in which the parameters of porosity and residual water saturation are determined, and the permeability of the formation is determined based on the functional correlation of permeability with the parameters of porosity and residual water saturation obtained based on the analysis of core data, characterized in that the parameter of residual water saturation is evaluated in Performan determining direct correlation-regression GIS communication standard complex data with residual water saturation of the core, and for determining the residual water saturation of the formation is carried out only those kinds of GIS GIS standard complex dependence on parameters which residual water saturation of the formation correlates with residual water saturation of the core.

The authors found and verified on a large amount of factual material that there is a certain set of geophysical surveys included in the standard set of geophysical surveys carried out in the vast majority of wells, which reveals a stable correlation to the data on the residual water saturation of the core and, thus, allows building an independent correlation -regressive interpretation of the residual water saturation for this set of logging studies.

The standard GIS complex includes: gamma-ray logging (GK, GK), self-polarizing logging (SP, SP), neutron logging (HK, NK), induction logging (IR, IK), lateral logging (BK, VK), acoustic logging ( AK, DT), density logging (GGK, GGK), cavernometry (KB, CALI). Choosing different combinations of well logs from a standard set, they determine the correlation between the parameters obtained by each specific combination of well logs with porosity and residual water saturation. Enumeration of all possible combinations gives the best set of GIS (from the standard) for determining porosity, as well as the optimal set of GIS for determining residual water saturation. Different fields may have different optimal GIS sets for determining porosity. Also, in different fields there may be different optimal GIS sets for determining residual water saturation. Enumeration of combinations can be carried out both in an automated mode and manually.

The type of direct correlation and regression relationship between porosity (or residual water saturation) and parameters (measurements) obtained as a result of GIS is set by the expert in the form of a functional dependence or a multidimensional table. The functional dependence can be linear with respect to the GIS parameters (measurements) or non-linear. For example, in the case of a linear relationship, the correlation-regression relationship “porosity (or residual water saturation) - GIS parameters (measurements)” will be presented in general form as follows:

(j, d_i) - оценка пористости (или остаточной водонасыщенности) на глубине d_i j-той скважины, WL_k(j, d_i) - измерения k-го вида ГИС на глубине d_i j-той скважины, а_1..a_n+1 - искомые параметры регрессии.Where

(j, d _i ) - assessment of porosity (or residual water saturation) at a depth d _{i of the} j-th well, WL _k (j, d _i ) - measurements of the k-type well logging at a depth d _{i of the} j-th well, and _{1. .} a _{n + 1} are the desired regression parameters.

In this case, the regression parameters are based on minimizing a certain quality functional, for example, quadratic

- оценка соответствующего свойства (пористости, остаточной водонасыщенности) j-го образца по ГИС.where Y _j - the measured property of the j-th sample of laboratory tests of core (porosity, residual water saturation),

- assessment of the corresponding property (porosity, residual water saturation) of the j-th sample according to well logging.

If there is some a priori information about the correlation-regression relationship of a particular core parameter (obtained from laboratory core studies) with GIS parameters (measurements), then it is also advisable to take them into account. For example, if it is known that with increasing HA, porosity (residual water saturation) should fall (increase), then the sign in the regression with HA should be negative (positive). Thus, functional (2) is minimized taking into account additional restrictions in the form of inequalities

where A = [a ₁ a ₂ a ₃ a _{n + 1} ] - regression parameters (1);

C, d are the specified parameters of constraints (based on a priori information, for example, on the maximum and minimum values of the corresponding GIS parameters and core data from laboratory tests).

For example, if it is necessary to take into account that with the increase in the parameter (measurement) of HA (for example, WL ₄ - HA), the residual water saturation should increase, then parameter a ₄ for HA in regression (1) should be positive. This condition can be expressed as inequality (3), where C = [0 0 0-1 0 ... 0], d = 0.

Also, the problem of sample non-representativeness often arises when the data after crossing the core laboratory depths with the GIS data is much smaller than the initial data. In this case, when optimizing functional (2), it is recommended to add, in addition to restrictions such as inequalities (3), constraints in the form of equalities, which allow preserving some extreme characteristics of the desired petrophysical dependence (1). For example, it is known from an analysis of laboratory core studies that porosity has a maximum value of 27% and a minimum of 3%. Then you can set these restrictions:

where A are the regression parameters (1),

C ₂ , d ₂ - set parameters of restrictions (based on a priori information).

Thus, the optimal regression parameters (1) are found not only as a result of optimization of functional (2), but as a result of solving the system

It should be noted that the regression can be not only linear (1), but also of any type of non-linearity, the choice of which depends on a priori information about the nature of the relationship between the core characteristics and the parameters (measurements) of the GIS, as well as on the results of the correlation analysis of core data and GIS data.

In the prototype method, the residual water saturation is determined through porosity (effective, total) either graphically or by the following formulas:

where S _wi is the residual water saturation, φ _e is the effective porosity, and B is the regression parameter (6).

where S _wi is the residual water saturation, φ _t is the total porosity, BVW is the regression parameter (7).

The main difference between our proposed method and the prototype method is that the residual water saturation is determined directly through the correlation and regression dependence according to formula (1) through the GIS parameters (measurements), and not through porosity (6), (7).

It should be noted that formula (1) includes the entire set of GIS from the standard complex. However, it is often sufficient to use a narrower GIS complex from a standard GIS. Thus, a direct diagram of the correlation-regression model of linking data from a logging complex and data on porosity (residual water saturation) from laboratory core studies is built by selecting a suitable logging complex from a standard complex.

The next step involves determining permeability through porosity and residual water saturation.

Permeability assessment is carried out by functional correlation through the parameters of porosity and residual water saturation based on the formulas of Cozeny-Carmen, Coats, Timur or others, taking into account not only porosity, but also residual water saturation (formula (8) below). The parameters of this connection are adjusted so that the dependence {φ, S _wi , k} is consistent with the data of laboratory core studies. Thus, the equation and those parameters of the equation are selected that are in the best way (in the sense of some given criterion, for example, standard deviation) consistent with the data of laboratory core studies.

The dependence of permeability on other core characteristics is well studied and the most preferred equations are those that take into account both porosity and residual water saturation.

In general, the permeability assessment can be expressed as follows:

- остаточная водонасыщенность из лабораторных исследований j-го образца керна,

- оценка проницаемости через пористость - остаточную водонасыщенность.where A _k - dependence parameters, φ _j - porosity from laboratory studies of the j-th core sample,

- residual water saturation from laboratory tests of the j-th core sample,

- assessment of permeability through porosity - residual water saturation.

Quality criterion, for example, quadratic (or others):

The problem of determining the parameters of dependence (8) is reduced to minimizing the functional (9). The minimization problem is solved by any of the optimization methods. If functional (9) has only one minimum with respect to the parameters, then it suffices to use gradient algorithms. If the functionality is multi-extreme, then it is necessary to use global optimization methods, for example, Monte Carlo, genetic algorithms, etc.

Examples of the method

The proposed method for determining permeability has been successfully tested at a number of Rosneft fields.

A group of wells was selected for laboratory core tests in the interval of the section under consideration.

Based on the standard GIS complex and core laboratory research data (after the intersection of the GIS data and the core laboratory research in depth), we obtained the equations of relationship of the form (1).

Example 1 (hereinafter “in the depth interval of example 1”)

Based on the analysis of the regression coefficients (1) and statistical characteristics of the distribution of logging data (IK, ..., VK), two (three) logging types were determined that have the highest degree of correlation with laboratory measurements of core residual water saturation (porosity).

It turned out that to estimate the residual water saturation, it is sufficient to conduct only GK gamma-ray logging and SP self-polarization logging in the well, and neutron logging NK, in addition to the two indicated, to evaluate porosity.

Thus, generalized regression (1) is reduced to the equation for residual water saturation

- оценка остаточной водонасыщенности по ГИС, j - номер скважины, d_i - i-я глубина d, на которой проведено измерение, GK - измерения гамма-каротажа, SP - измерения каротажа собственной поляризации;Where

- GIS residual water saturation estimation, j - well number, d _i - i-th depth d, at which the measurement was carried out, GK - gamma-ray measurements, SP - self-polarization measurements;

and equation for porosity

- оценка пористости по ГИС, j - номер скважины, d_i - i-я глубина d, на которой проведено измерение, GK - измерения гамма-каротажа, SP - измерения каротажа собственной поляризации, NK - измерения нейтронного каротажа. Затем, при решении системы (5) для остаточной водонасыщенностиWhere

- well porosity assessment, j - well number, d _i - i-th depth d, at which the measurement was made, GK - gamma-ray measurements, SP - self-polarization logs, NK - neutron logs. Then, when solving system (5) for residual water saturation

as well as porosity

the optimal regression parameters (10), (11) were determined.

Further, according to the obtained dependences (10), (11), the residual water saturation and porosity were estimated for all wells where there is a corresponding well logging complex.

In a newly drilled well of a given formation in a given depth interval, the determination of residual water saturation and permeability was carried out exclusively using GK - gamma ray logging, SP - self-polarizing logging and NK - neutron logging.

For different reservoirs, depth intervals, the parameters of dependencies (10), (11) will be different, for example, as in example 1 and example 2 (below).

Example 2 (in the interval of depths of the Bazhenov Formation - another relative to the interval of depths in example 1)

The coefficient at SP is zero, since the residual water saturation according to the measurements of the SP logs in this depth interval does not correlate with the residual water saturation of the core.

In this regard, SP-logging is excluded in the study of this interval and to determine the residual water saturation, it is sufficient to conduct only one type of well logging - GK-logging.

Further, according to the obtained dependences (10), (11), the residual water saturation and porosity were estimated for all wells where there is a corresponding well logging complex.

In a newly drilled well of a given formation in a given depth interval, the determination of residual water saturation and permeability was carried out exclusively using GK - gamma ray and NK - neutron logs.

Based on the data of laboratory core studies, a functional-correlation model of communication was selected, including for the conditions of examples 1.2

- оценка проницаемости, φ -пористость и S_wi -остаточная водонасыщенность.permeability with porosity and residual water saturation (8), the parameters A _k1 ... A _{k3 of} which are determined by minimizing the functional (9), where

- assessment of permeability, φ-porosity and S _{wi -} residual water saturation.

Accordingly, the permeability parameters of the formation are determined, including for the conditions of examples 1, 2.

Figure 1 shows an example of comparing the interpretation of residual water saturation according to the claimed method (Fig. 1 points) and an alternative interpretation method based on an expanded GIS complex using additional methods relative to a standard GIS complex, including nuclear magnetic logging (NMR) (in Fig. 1). .1 - crosses). The analysis results show a high correlation between the two indicated methods: the correlation coefficient is 0.86. This confirms the reliability of the proposed method.

As for the comparison of the reliability of determining residual water saturation and permeability of the claimed method and the prototype method, figure 2 presents typical scattering diagrams of estimates of residual water saturation (residual water) for the prototype (figure 2, left) and for the inventive method (figure 2, right) under comparable conditions similar to the conditions of example 1.

It can be seen that the relationship between the residual water saturation and the measurements of gamma-ray (GC) and self-potential (SP) logs is really closer (reliable) than the relationship between the residual water saturation and porosity used in the prototype method. Accordingly, the correlation of the estimate of residual water saturation with the residual water saturation of the core is: 0.70939 for the prototype method and 0.85327 for the inventive method. This confirms the advantage of the proposed method for the reliability of determining residual water saturation; accordingly, the determination of permeability is more reliable.

While in order to more reliably determine the residual water saturation, an expensive method based on nuclear magnetic resonance (NMR) is often used in practice, the claimed method can significantly reduce the cost of studying the filtration-capacitive properties (PES) of the formation - its residual water saturation and permeability, since it not only does not require the use of NMR, but allows you to get by with a significantly narrowed relatively standard GIS complex.

The claimed method allows you to:

- make a complete and consistent reinterpretation of many old wells using the most common standard well logging system and core data to obtain more reliable data;

- reduce the cost of researching newly drilled wells by conducting on most of them only a standard GIS complex, and on a part of the wells - a narrowed GIS complex;

- use this method as an alternative comparison technique when conducting an extended logging complex.

Claims (1)

A method for determining residual water saturation and permeability of a formation, including conducting geophysical well surveys (GIS) and laboratory core tests, determining porosity and residual water saturation parameters, determining permeability of a formation based on a functional correlation of permeability with porosity and residual water saturation obtained from data analysis core laboratory tests, characterized in that the parameter of residual water saturation is evaluated as a result of To determine the direct correlation and regression dependence of the residual water saturation data of the core on the data of the standard GIS complex, moreover, to determine the residual water saturation of the formation, only those types of GIS from the standard GIS complex are used, on the parameters of which the dependence of the residual water saturation of the formation correlates with the residual water saturation of the core.

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