CN109858196B - A method and system for interpretation of oil and gas reservoir parameters - Google Patents

Abstract

The present application discloses a method for interpreting parameters of oil and gas reservoirs. Calculate the calculated pressure data corresponding to the calculation example; triangulate the multi-dimensional parameter space to obtain multiple regions; based on the multiple points contained in each region and the value of each point, perform high-dimensional linear interpolation in each region respectively, In order to construct the response surface function for each area respectively; construct the objective function for the response surface function of each area; use the optimization algorithm to solve the optimal solution of each objective function and the parameter value of the parameter to be explained corresponding to each objective function; Among the multiple objective functions, the objective function with the smallest optimal solution is determined, and the parameter value of the parameter to be explained corresponding to the objective function is determined as the interpretation result of the parameter to be explained. The method disclosed in the present application can improve the interpretation accuracy of oil and gas reservoir parameters.

Description

A method and system for interpretation of oil and gas reservoir parameters

technical field

The present application belongs to the technical field of petroleum exploitation, and particularly relates to a method and system for interpreting parameters of oil and gas reservoirs.

Background technique

The basic purpose of oil and gas reservoir research is to predict the future behavior of oil and gas reservoirs and find ways to enhance ultimate recovery. Some engineering problems will be encountered in the process of oil exploration, such as how to establish a reliable geological model, so as to solve the problems of oil and gas reservoir evaluation, management and development based on the geological model, and ensure the dynamic prediction of oil and gas reservoirs and oil wells. To establish a geological model, it is necessary to know the formation parameters and wellbore parameters of the oil and gas reservoir.

Well testing is the most commonly used method to obtain formation parameters and wellbore parameters of oil and gas reservoirs during the development of oil and gas reservoirs. Generally speaking, well test analysis is to analyze the measured pressure data combined with production and other data, to study various characteristic parameters of test wells and test layers within the test influence range, and then to accurately predict the formation parameters and wellbore parameters of oil and gas reservoirs.

Numerical well testing is a new well testing interpretation technology developed in recent years. It is a numerical simulation technology that accurately describes the physical process through a large number of mathematical simulation operations. The characteristics of oil and gas reservoirs described by numerical well testing are more realistic and have wider application. But numerical well testing also faces a series of difficulties, with many calculation parameters and long calculation time. In the process of numerical well test interpretation, the well test interpreter needs to manually adjust the uncertain parameters to make the calculated pressure and the measured pressure as close as possible. Often it can take weeks or even months to interpret a well or group of wells.

For those skilled in the art, how to improve the efficiency of numerical well test interpretation and reduce the work intensity of well test interpretation personnel is an urgent problem to be solved.

SUMMARY OF THE INVENTION

In view of this, the purpose of the present application is to provide a method and system for interpreting oil and gas reservoir parameters, so as to improve the efficiency and accuracy of numerical well test interpretation and reduce the work intensity of well test interpreters.

To achieve the above purpose, the application provides the following technical solutions:

The present application provides a method for interpreting oil and gas reservoir parameters, including:

receiving input parameters to be explained and corresponding numerical ranges, wherein the parameters to be explained include formation parameters and wellbore parameters;

Sampling the parameters to be explained within the numerical range to obtain a plurality of trial calculation examples;

Calculating the plurality of trial calculation examples respectively to obtain the calculated pressure data corresponding to the plurality of trial calculation examples;

Triangulate the multi-dimensional parameter space based on the points corresponding to the plurality of trial calculation examples to obtain a plurality of regions, wherein the dimension of the multi-dimensional parameter space is the same as the number of parameters to be explained;

Based on the multiple points contained in each area and the value of each point, high-dimensional linear interpolation is performed in each area, so as to construct a response surface function for each area. The value is: the calculated pressure data corresponding to the trial calculation example;

Constructing an objective function for the response surface function of each area, the objective function indicates the deviation of the calculated pressure data and the measured pressure data;

Use the optimization algorithm to solve the optimal solution of each objective function and the parameter value of the parameter to be explained corresponding to each objective function;

The objective function with the smallest optimal solution is determined among the multiple objective functions, and the parameter value of the parameter to be explained corresponding to the objective function with the smallest optimal solution is determined as the interpretation result of the parameter to be explained.

Optionally, in the above method, the multi-dimensional parameter space is triangulated based on the points corresponding to the multiple trial calculation examples to obtain multiple regions, specifically:

Delaunay triangulation is performed on the multi-dimensional parameter space based on the points corresponding to the multiple trial calculation examples to obtain multiple regions.

Optionally, in the above method, the parameter to be explained is sampled within the numerical range to obtain a plurality of trial calculation examples, including:

The parameters to be explained are sampled within the numerical range by using the Latin hypercube sampling algorithm, and a plurality of trial calculation examples are obtained.

Optionally, in the above method, the constructing an objective function for the response surface function of each region, including:

Based on the principle of minimum error between the calculated pressure data and the measured pressure data of the response surface function of each region under the trial calculation example, the objective function is constructed for the response surface function of each region.

Optionally, in the above method, the optimization algorithm is used to solve the optimal solution of each objective function and the parameter value of the parameter to be explained corresponding to each objective function, including:

The feasible direction algorithm and the Latin hypercube sampling algorithm are used to optimize each objective function respectively, and obtain the optimal solution of each objective function and the parameter values of the parameters to be explained corresponding to each objective function.

The application also provides an oil and gas reservoir parameter interpretation system, including:

a data receiving unit, configured to receive input parameters to be interpreted and corresponding numerical ranges, wherein the parameters to be interpreted include formation parameters and wellbore parameters;

a sampling unit, used for sampling the parameter to be explained within the numerical range to obtain a plurality of trial calculation examples;

a trial calculation example calculation unit, configured to calculate the multiple trial calculation examples respectively, and obtain the calculated pressure data corresponding to the multiple trial calculation examples;

a space division unit, used for triangulating the multi-dimensional parameter space based on the points corresponding to the plurality of trial calculation examples to obtain a plurality of regions;

The response surface function construction unit is used to perform high-dimensional linear interpolation in each region based on the multiple points contained in each region and the value of each point, so as to construct a response surface function for each region, where any one The value of the point corresponding to the trial calculation example is: the calculated pressure data corresponding to the trial calculation example;

an objective function construction unit, used for constructing an objective function for the response surface function of each area, the objective function indicating the deviation between the calculated pressure data and the measured pressure data;

The optimization unit is used to solve the optimal solution of each objective function and the parameter value of the parameter to be explained corresponding to each objective function by using an optimization algorithm;

The parameter interpretation unit is configured to determine the objective function with the smallest optimal solution among the multiple objective functions, and determine the parameter value of the parameter to be explained corresponding to the objective function with the smallest optimal solution as the interpretation result of the parameter to be explained.

Optionally, in the above system, the space division unit is specifically configured to: perform Delaunay triangulation on the multi-dimensional parameter space based on the points corresponding to the multiple trial calculation examples to obtain multiple regions.

Optionally, in the above system, the sampling unit is specifically configured to: use a Latin hypercube sampling algorithm to sample the parameter to be explained within the numerical range to obtain a plurality of trial calculation examples.

Optionally, in the above system, the objective function construction unit is specifically used for: based on the principle of minimum error between the calculated pressure data and the measured pressure data of the response surface function of each area under the trial calculation example, for each area The response surface functions of , respectively, construct objective functions.

Optionally, in the above system, the optimization unit is specifically used for: using the feasible direction algorithm and the Latin hypercube sampling algorithm to optimize each objective function, respectively, to obtain the optimal solution of each objective function and the corresponding waiting for each objective function. Interpret the parameter value of the parameter.

It can be seen that the beneficial effects of the present application are:

In the oil and gas reservoir parameter interpretation method disclosed in the present application, after receiving the parameters to be explained and the numerical range of each parameter to be explained inputted by the well test interpreter, the parameters to be explained are sampled within the corresponding numerical range, and a plurality of trial calculation examples are obtained. , and then perform calculations on multiple trial calculation examples to obtain the corresponding calculated pressure data, and triangulate the multi-dimensional parameter space based on the points corresponding to the multiple trial calculation examples to obtain multiple regions. Each point and the value of each point (that is, the calculated pressure data) are subjected to high-dimensional linear interpolation in each area, so as to construct the response surface function for each area, and then construct the calculated pressure data and measured pressure data for each area separately. The objective function of the deviation between the pressure data, find the optimal solution of each objective function and the parameter value of the parameter to be explained corresponding to each objective function, select the objective function with the smallest optimal solution among the multiple objective functions, and assign the objective function to the objective function. The parameter value of the parameter to be explained corresponding to the function is used as the interpretation result of the parameter to be explained. Based on the oil and gas reservoir parameter interpretation method disclosed in this application, the well test interpreter only needs to input the parameters to be interpreted and the numerical range of each parameter to be interpreted according to the type of oil and gas reservoir, and the electronic equipment can automatically complete the interpretation of formation parameters and wellbore parameters. The interpretation efficiency is greatly improved, and the work intensity of well testing interpreters is reduced; moreover, the application constructs the response surface function by performing high-dimensional linear interpolation in each area, which makes the response surface function up to a quadratic function polynomial, and the response surface The order of the function is effectively lower, which can reduce the interpretation error, thereby improving the interpretation accuracy of oil and gas reservoir parameters.

Description of drawings

In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the following briefly introduces the accompanying drawings that need to be used in the description of the embodiments or the prior art. Obviously, the drawings in the following description are For some embodiments of the present application, for those of ordinary skill in the art, other drawings can also be obtained according to these drawings without any creative effort.

1 is a flow chart of a method for interpreting oil and gas reservoir parameters disclosed in the application;

Figure 2-1 shows the comparison between the measured pressure curve and the estimated pressure curve;

Figure 2-2 is a comparison diagram of the real pressure recovery curve, the estimated pressure recovery curve, the real pressure derivative curve and the estimated pressure derivative curve;

FIG. 3 is a structural diagram of an oil and gas reservoir parameter interpretation system disclosed in the application.

Detailed ways

In the existing numerical well testing interpretation method, firstly, the well testing interpreter sets the parameter values of the parameters to be interpreted (also called parameters to be determined or uncertain parameters), The calculated pressure is calculated from the parameter value, and the calculated pressure and the measured pressure are compared. Then, based on the comparison results, the well test interpreter manually adjusts the parameter value of one or more parameters in the parameters to be explained based on their own experience. The parameter values of the parameters to be explained are calculated to calculate the pressure, and the calculated pressure and the measured pressure are compared again. By repeating the above manual adjustment process in large numbers, the calculated pressure and the measured pressure are as close as possible. When the difference between the calculated pressure and the measured pressure satisfies the predetermined condition, the parameter value of each parameter to be explained currently set is determined as the final interpretation result. It can be seen that the existing numerical well test interpretation process will consume a lot of time, resulting in low efficiency of numerical well test interpretation, and the work intensity of well test interpreters is very high.

The present application discloses a method and system for interpreting oil and gas reservoir parameters, so as to improve the efficiency and precision of numerical well test interpretation and reduce the work intensity of well test interpreters.

In order to make the purposes, technical solutions and advantages of the embodiments of the present application clearer, the technical solutions in the embodiments of the present application will be described clearly and completely below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments It is a part of the embodiments of the present application, but not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present application.

Referring to FIG. 1 , FIG. 1 is a flowchart of a method for interpreting parameters of an oil and gas reservoir disclosed in the present application, and the execution subject of the method for automatically obtaining parameters is an electronic device, such as a computer. The method includes steps S1 to S8.

Step S1: Receive the input parameters to be explained and the corresponding numerical range. The parameters to be explained include formation parameters and wellbore parameters.

In the case of different types of oil and gas reservoirs, there are also differences in the parameters that need to be explained.

The well test interpreter inputs the parameters to be explained according to the type of oil and gas reservoir. In addition, the well test interpreter also needs to input the numerical range of each parameter to be explained, which is an empirical value determined by the well test interpreter. The parameters to be explained include formation parameters and wellbore parameters.

Formation parameters typically include: the reservoir boundary of the reservoir; the SRV (reservoir stimulated volume) of the reservoir; the permeability distribution, porosity distribution and pressure distribution of the reservoir within the SRV range; the reservoir outside the SRV range and Permeability distribution, porosity distribution and pressure distribution within the reservoir boundary; parameters of each main fracture, the parameters of the main fracture include the half-length and azimuth angle of the main fracture.

Wellbore parameters usually include: skin and well storage factor.

Step S2: Sampling the parameters to be explained within the numerical range to obtain multiple trial calculation examples.

After the electronic equipment receives the parameters to be explained and the numerical range of each parameter to be explained input by the well test interpreter, it performs sampling processing to obtain multiple trial calculation examples. For example, the electronic equipment performs a sampling operation to obtain 1000 trial calculation examples.

It should be noted here that each trial calculation example includes a set of parameter values of a plurality of parameters to be explained, and the parameter value of at least one parameter to be explained in any two trial calculation examples is different.

Step S3: Calculating the multiple trial calculation examples respectively to obtain the calculated pressure data corresponding to the multiple trial calculation examples.

Calculations are carried out separately for each trial calculation case to obtain the calculated pressure data. It should be noted here that, in order to distinguish the measured pressure data, the pressure data obtained by calculating the trial calculation example is called the calculated pressure data.

As an embodiment, calculating the pressure data includes: calculating the pressure, calculating the pressure change, and calculating the pressure derivative. Correspondingly, the measured pressure data includes: measured pressure, measured pressure change and measured pressure derivative.

Step S4: Triangulate the multi-dimensional parameter space based on the points corresponding to the multiple trial calculation examples to obtain a plurality of regions, wherein the dimension of the multi-dimensional parameter space is the same as the number of parameters to be explained.

The dimensions of this multidimensional parameter space are the same as the number of parameters to be explained. Each trial calculation case includes a set of parameter values for a plurality of parameters to be explained, and a set of parameter values defines a point in the multidimensional parameter space, that is, each trial calculation case corresponds to a point in the multidimensional parameter space. The multi-dimensional parameter space is triangulated according to the points contained in the multi-dimensional parameter space to obtain multiple regions.

Step S5: Based on the multiple points contained in each area and the value of each point, perform high-dimensional linear interpolation in each area, so as to construct a response surface function for each area.

Wherein, each area includes multiple points, and the value of the point corresponding to any trial calculation example is: the calculated pressure data corresponding to the trial calculation example.

Take the parameters to be explained as parameter A, parameter B and parameter C, and the number of trial calculation examples is 1000 as an example to illustrate:

Each trial calculation case includes a set of parameter values for parameter A, parameter B, and parameter C, and at least one of the parameter values to be explained in any two trial calculation cases is different. Calculations are performed separately for each trial calculation case to obtain the calculated pressure data. A set of parameter values included in each trial calculation example defines a point in the three-dimensional parameter space, and the value of this point is the calculated pressure data corresponding to the trial calculation example, and the total number of points corresponding to 1000 trial calculation examples for 1000. The three-dimensional parameter space is triangulated according to 1000 points contained in the three-dimensional parameter space to obtain a plurality of regions, wherein each region contains a plurality of points. Any area contains multiple points, and the value of each point is known. Based on the multiple points contained in the area and the value of each point, high-dimensional linear interpolation is performed in this area, and the area of the area can be obtained. Response surface function.

It should be noted that the polynomial function is usually used as the response surface function. If the order (ie times) of the response surface function is high, on the one hand, it will lead to a large interpretation error, resulting in a low interpretation accuracy of oil and gas reservoir parameters; The number of times will also increase accordingly. In addition, the higher order of the response surface function will also lead to an increase in the degree of the objective function, resulting in optimization difficulties.

The response surface function is constructed by means of high-dimensional linear interpolation in the region, which makes the response surface function up to a quadratic function polynomial, and the order of the response surface function is effectively lower. Correspondingly, the interpretation error can be reduced, thereby improving the interpretation accuracy of oil and gas reservoir parameters.

Step S6 : constructing an objective function for the response surface function of each region, wherein the objective function indicates the deviation between the calculated pressure data and the measured pressure data.

The objective function OF is usually expressed in the form of a simple sum of squares, which is defined as follows:

为实测压力数据，

为计算压力数据，ω_j表示对应的权值，n为实测压力数据的点数，N是三角剖分得到的区域的总数。这个逆问题的目标是找到一个x，使得计算压力数据与实测压力数据最为接近。Among them, OF _k refers to the objective function corresponding to the response surface function of the kth region,

For the measured pressure data,

In order to calculate the pressure data, ω _j represents the corresponding weight, n is the number of points of the measured pressure data, and N is the total number of regions obtained by triangulation. The goal of this inverse problem is to find an x such that the calculated pressure data is the closest to the measured pressure data.

X_op为全局最优参数向量(即为所有区域中实际观测数据与模拟结果最为接近的点)。remember

X _op is the global optimal parameter vector (that is, the point where the actual observation data is closest to the simulation result in all regions).

Step S7: Use an optimization algorithm to solve the optimal solution of each objective function and the value of the parameter to be explained corresponding to each objective function.

Step S8: Determine the objective function with the smallest optimal solution among the multiple objective functions, and determine the value of the parameter to be explained corresponding to the objective function with the smallest optimal solution as the interpretation result of the parameter to be explained.

The optimal solution of each objective function is the local optimal solution, and the minimum of these optimal solutions is the global optimal solution. Therefore, the optimal solutions of multiple objective functions are compared, the objective function with the smallest optimal solution is determined, and the parameter value of the parameter to be explained corresponding to the objective function is determined as the interpretation result of the parameter to be explained.

In the oil and gas reservoir parameter interpretation method disclosed in the present application, after receiving the parameters to be explained and the numerical range of each parameter to be explained inputted by the well test interpreter, the parameters to be explained are sampled within the corresponding numerical range, and a plurality of trial calculation examples are obtained. , and then perform calculations on multiple trial calculation examples to obtain the corresponding calculated pressure data, and triangulate the multi-dimensional parameter space based on the points corresponding to the multiple trial calculation examples to obtain multiple regions. Each point and the value of each point (that is, the calculated pressure data) are subjected to high-dimensional linear interpolation in each area, so as to construct the response surface function for each area, and then construct the calculated pressure data and measured pressure data for each area separately. The objective function of the deviation between the pressure data, find the optimal solution of each objective function and the parameter value of the parameter to be explained corresponding to each objective function, select the objective function with the smallest optimal solution among the multiple objective functions, and assign the objective function to the objective function. The parameter value of the parameter to be explained corresponding to the function is used as the interpretation result of the parameter to be explained.

Based on the oil and gas reservoir parameter interpretation method disclosed in this application, the well test interpreter only needs to input the parameters to be interpreted and the numerical range of each parameter to be interpreted according to the type of oil and gas reservoir, and the electronic equipment can automatically complete the interpretation of formation parameters and wellbore parameters. The interpretation efficiency is greatly improved, and the work intensity of the well test interpreter is reduced; moreover, the application constructs the response surface function by performing high-dimensional linear interpolation in the region, which makes the response surface function up to a quadratic function polynomial, and the response surface function is The order is effectively lower, which can reduce the interpretation error, thereby improving the interpretation accuracy of oil and gas reservoir parameters.

As an example, in the oil and gas reservoir parameter interpretation method disclosed above in this application, a multi-dimensional parameter space is triangulated based on points corresponding to multiple trial calculation examples to obtain multiple regions, specifically: based on multiple trial calculation examples Delaunay triangulation is performed on the multi-dimensional parameter space for the corresponding points of the example to obtain multiple regions.

Each area obtained by Delaunay triangulation of a multi-dimensional parameter space is an area enclosed by a hyperplane, such as a triangle enclosed by a line segment in a two-dimensional space, and a tetrahedron enclosed by a plane in a three-dimensional space.

Constraints are given separately for each region obtained by the triangulation. Take the hyperplane as an example to construct constraints, and set P ₁ , P ₂ ,…,P _i-1 , P _i+1 ,…P _n+1 to be linearly independent points on the hyperplane, and write:

线性无关，但

线性相关。即：Let P be any point on this plane,

Linearly independent, but

Linear correlation. which is:

Using Equation 1, we get:

The above equation 2 is a hyperplane equation.

Due to the arbitrariness of i, in n-dimensional space, n+1 hyperplane equations of the above form can be obtained for each region. remember:

If f _i (P _i )<0, the corresponding i-th constraint inequality is:

f _i (P)≤0, _P∈Ωj

If f _i (P _i )>0, the corresponding i-th constraint inequality is:

f _i (P)≥0, _P∈Ωj

Similar to the constraints of other n hyperplanes, the constraints of multiple hyperplanes constitute a set of constraint inequalities in the triangular region.

Denote R={i=1, 2,...,n+1|f _i (P _i )>0}, then the constraint inequality group of the triangular region is:

Assuming that there is some relationship between one or more responses y and multiple variables x ₁ , x ₂ ,…x _n , this relationship can be expressed as:

y=f(x ₁ ,x ₂ ,...x _n )+ε Equation (4)

where f is the unknown response surface function and ε represents the error term.

High-dimensional linear interpolation: In each region of the n-dimensional space, the interpolation function can be obtained according to n+1 linearly independent points:

Among them, the coefficient θ _i (i=1, 2, ..., n+1) satisfies:

It should be noted that the above-mentioned interpolation function is the response surface function.

As an example, in the oil and gas reservoir parameter interpretation method disclosed above in this application, the parameters to be interpreted are sampled within the numerical range to obtain multiple trial calculation examples, specifically: using the Latin hypercube sampling algorithm to treat the parameters within the numerical range The interpretation parameters are sampled to obtain multiple trial calculation examples.

Latin hypercube sampling is designed to accurately reconstruct the input distribution with fewer iterations of sampling. The key to Latin Hypercube Sampling is to stratify the input probability distribution. Stratification divides the cumulative curve into equal intervals on a cumulative probability scale (0 to 1.0), then randomly draws samples from each interval or "stratum" of the input distribution. Latin hypercube sampling does not require more samples for more dimensions (variables), and this independence is the main advantage of this sampling scheme.

Simply put, assuming that m samples are to be drawn in an n-dimensional vector space, the steps of Latin hypercube sampling are:

Step1: Divide each dimension into m non-overlapping intervals, so that each interval has the same probability (usually consider a uniform distribution, so that the interval lengths are the same);

Step2: randomly select a point in each interval in each dimension;

Step3: Then randomly extract the points selected in step (2) from each dimension, and form them into a vector.

In the oil and gas reservoir parameter interpretation method disclosed in the present application, the Latin hypercube sampling algorithm is used to sample the parameters to be interpreted multiple times within a given numerical range, which can ensure that the sampling data covers the entire numerical range, and is beneficial to improve the accuracy of the interpretation results.

In implementation, the parameter to be explained can also be sampled within the numerical range by using an equidistant sampling algorithm, a random sampling algorithm, a model Carlo sampling algorithm or a cluster sampling algorithm.

As an example, in the oil and gas reservoir parameter interpretation method disclosed above in this application, an objective function is constructed for the response surface function of each area, specifically: the calculated pressure based on the response surface function of each area under the trial calculation example According to the principle of minimum error between the data and the measured pressure data, the objective function is constructed separately for the response surface function of each area.

Based on the principle of minimum error, the objective function can be constructed quickly.

As an example, in the oil and gas reservoir parameter interpretation method disclosed above in this application, the optimal solution of each objective function and the value of the parameter to be explained corresponding to each objective function are obtained by using an optimization algorithm, specifically: using the feasible direction algorithm and Latin The hypercube sampling algorithm optimizes each objective function respectively, and obtains the optimal solution of each objective function and the value of the parameter to be explained corresponding to each objective function.

According to the objective function and constraints, finding the optimal solution of each objective function is a typical quadratic programming problem. The feasible direction algorithm can be used to optimize the objective function to obtain the optimal solution of the objective function in each region. Thus, the parameter values of the parameters to be explained corresponding to each optimal solution are obtained.

In the oil and gas reservoir parameter interpretation method disclosed in the present application, the feasible direction algorithm and the Latin hypercube sampling algorithm are used to optimize the objective function, which can quickly find the optimal solution of the objective function, thereby further improving the interpretation efficiency of numerical well testing.

The implementation process and the validity of the results of the oil and gas reservoir parameter interpretation method disclosed in the present application will be described below with reference to an example.

A five-point well pattern model is used. The size of the oil and gas reservoir is 600m*400m, the thickness is 10m, and the porosity is 0.2. There is one production well in the middle and four injection wells in the four corners. Surrounding the production well is a compound area with a production well of 240 days on, a production of ^80m3 /day, and a shut-in time of 3 days. All four injection wells were injected for 240 days and shut in for 3 days. The injection volume and flow rate of injection well 1, injection well 2, injection well 3 and injection well 4 are respectively 20m ³ /day, 30m ³ /day, 10m ³ /day and 20m ³ /day.

In order to verify the validity of the oil and gas reservoir parameter interpretation method disclosed in this application, it is assumed that the viscosity of oil and water are equal, the volume coefficients of oil and water are equal, and the phase permeability curve is a straight line with a slope of 1, so the relative The penetration rate is 1. Therefore, the two-phase flow here is equivalent to a single-phase flow.

The four selected parameters to be explained and their value ranges are: formation permeability K, whose value range is (100mD, 700mD); composite regional permeability K ₁ , whose value range is (100mD, 700mD); production wellbore Storage C, whose value range is (0.1 m ³ /MPa, 1.0 m ³ /MPa); production well skin factor S, whose value range is (-3, 3).

Under the premise of the balance of injection and production, the bottom hole pressure recovery curve, pressure change and its derivative curve of the production well were fitted.

First, use the Latin hypercube sampling algorithm to determine 1000 trial calculation cases.

After that, each trial calculation case is brought into the numerical well testing simulator for calculation, and the corresponding calculated pressure data is obtained. The calculated pressure data corresponding to 900 trial calculation examples were randomly selected from 1000 trial calculation examples as sample data, and the multi-dimensional parameter space was triangulated based on the points corresponding to the 900 trial calculation examples to obtain multiple regions.

After that, based on the multiple points contained in each area and the value of each point, high-dimensional linear interpolation is performed in each area, so as to construct a response surface function for each area, and then construct a response surface function for each area. The corresponding objective function OF, and the remaining 100 groups are used as test data. After the optimization of the feasible direction algorithm, the optimal solution of the objective function of each area is obtained, the objective function with the smallest optimal solution is selected among the multiple objective functions, and the parameter value of the parameter to be explained corresponding to the objective function is used as the to-be-interpreted Estimated value of the parameter (i.e. interpreting the result).

The comparison between the estimated value of the optimal parameter to be explained and the actual value is shown in Table 1.

Table 1

parameter name K/mD K₁/mD C/m³/MPa S actual value 130.588 167.810 0.8879 1.6012 estimated value 130.578 169.356 0.8883 1.6989 error 0.010 1.546 0.0004 0.0977

After that, the estimated and actual values of the parameters to be explained are brought into the numerical well testing simulator, and the measured pressure curve and the calculated pressure curve can be obtained, and the measured pressure recovery curve, the measured pressure derivative curve, the calculated pressure recovery curve and the calculated pressure curve can also be obtained. Pressure derivative curve.

Referring to Figure 2-1, Figure 2-1 is a comparison diagram of the measured pressure curve and the estimated pressure curve (that is, the calculated pressure curve). Refer to Figure 2-2. Figure 2-2 shows the real pressure recovery curve (that is, the measured pressure recovery curve), the estimated pressure recovery curve (that is, the calculated pressure recovery curve), the real pressure derivative curve (that is, the measured pressure derivative curve) and A comparison plot of the estimated pressure derivative curve (that is, the calculated pressure derivative curve).

It can be seen that the formation parameters and wellbore parameters can be accurately interpreted based on the oil and gas reservoir parameter interpretation method disclosed in the present application, and the multi-solutions will not be increased.

The present application discloses a method for interpreting oil and gas reservoir parameters above, and correspondingly, the present application also discloses a system for interpreting oil and gas reservoir parameters. The following description of the oil and gas reservoir parameter interpretation system and the above description of the oil and gas reservoir parameter interpretation method may refer to each other.

Referring to FIG. 3, FIG. 3 is a structural diagram of an oil and gas reservoir parameter interpretation system disclosed in the application, including a data receiving unit 10, a sampling unit 20, a trial calculation example calculation unit 30, a spatial division unit 40, and a response surface function structure Unit 50 , objective function construction unit 60 , optimization unit 70 and parameter interpretation unit 80 .

in:

The data receiving unit 10 is used for receiving input parameters to be interpreted and corresponding numerical ranges. The parameters to be explained include formation parameters and wellbore parameters.

The sampling unit 20 is used for sampling the parameters to be explained within the numerical range to obtain a plurality of trial calculation examples.

The trial calculation example calculation unit 30 is configured to perform calculations on a plurality of trial calculation examples, respectively, to obtain calculated pressure data corresponding to the plurality of trial calculation examples.

The space division unit 40 is configured to triangulate the multi-dimensional parameter space based on the points corresponding to the multiple trial calculation examples to obtain multiple regions.

The response surface function construction unit 50 is configured to perform high-dimensional linear interpolation in each region based on a plurality of points contained in each region and the value of each point, so as to construct a response surface function for each region respectively. Among them, the value of the point corresponding to any trial calculation example is: the calculated pressure data corresponding to the trial calculation example.

The objective function constructing unit 60 is configured to construct an objective function for the response surface function of each region, where the objective function indicates the deviation between the calculated pressure data and the measured pressure data.

The optimization unit 70 is configured to use an optimization algorithm to respectively solve the optimal solution of each objective function and the parameter value of the parameter to be explained corresponding to each objective function.

The parameter interpretation unit 80 is configured to determine the objective function with the smallest optimal solution among the multiple objective functions, and determine the parameter value of the parameter to be explained corresponding to the objective function with the smallest optimal solution as the interpretation result of the parameter to be explained.

Based on the oil and gas reservoir parameter interpretation system disclosed in this application, the well test interpreter only needs to input the parameters to be interpreted and the numerical range of each parameter to be interpreted according to the type of oil and gas reservoir, and the electronic equipment can automatically complete the interpretation of formation parameters and wellbore parameters. The interpretation efficiency is greatly improved, and the work intensity of well testing interpreters is reduced; moreover, the application constructs the response surface function by performing high-dimensional linear interpolation in each area, which makes the response surface function up to a quadratic function polynomial, and the response surface The order of the function is effectively lower, which can reduce the interpretation error, thereby improving the interpretation accuracy of oil and gas reservoir parameters.

In another embodiment, the space division unit 40 is specifically configured to: perform Delaunay triangulation on the multi-dimensional parameter space based on the points corresponding to the multiple trial calculation examples to obtain multiple regions.

In another embodiment, the sampling unit 20 is specifically configured to: use the Latin hypercube sampling algorithm to sample the parameters to be explained within the numerical range to obtain a plurality of trial calculation examples.

In another embodiment, the objective function construction unit 60 is specifically configured to: based on the principle of minimum error between the calculated pressure data and the measured pressure data under the trial calculation example of the response surface function of each area, for the response surface of each area The function constructs the objective function respectively.

In another embodiment, the optimization unit 70 is specifically configured to: use the feasible direction algorithm and the Latin hypercube sampling algorithm to optimize each objective function, respectively, to obtain the optimal solution of each objective function and the corresponding to-be-interpreted parameters of each objective function. parameter value.

Finally, it should also be noted that in this document, relational terms such as first and second are used only to distinguish one entity or operation from another, and do not necessarily require or imply these entities or that there is any such actual relationship or sequence between operations. Moreover, the terms "comprising", "comprising" or any other variation thereof are intended to encompass non-exclusive inclusion such that a process, method, article or device comprising a list of elements includes not only those elements, but also includes not explicitly listed or other elements inherent to such a process, method, article or apparatus. Without further limitation, an element qualified by the phrase "comprising a..." does not preclude the presence of additional identical elements in a process, method, article or apparatus that includes the element.

The various embodiments in this specification are described in a progressive manner, and each embodiment focuses on the differences from other embodiments, and the same and similar parts between the various embodiments can be referred to each other. For the system disclosed in the embodiment, since it corresponds to the method disclosed in the embodiment, the description is relatively simple, and the relevant part can be referred to the description of the method.

The above description of the disclosed embodiments enables any person skilled in the art to make or use the present application. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the generic principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present application. Therefore, this application is not intended to be limited to the embodiments shown herein, but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.

Claims (10)

1. A method for reservoir parameter interpretation, comprising:

receiving input parameters to be interpreted and corresponding numerical value ranges, wherein the parameters to be interpreted comprise formation parameters and wellbore parameters;

sampling the parameters to be explained in the numerical range to obtain a plurality of trial calculation examples;

respectively calculating the trial calculation examples to obtain calculated pressure data corresponding to the trial calculation examples;

triangulating a multi-dimensional parameter space based on points corresponding to the trial calculation examples to obtain a plurality of regions, wherein the dimension of the multi-dimensional parameter space is the same as the number of parameters to be explained;

based on the values of a plurality of points and each point contained in each area, respectively carrying out high-dimensional linear interpolation on each area so as to respectively construct a response surface function for each area, wherein the value of the point corresponding to any trial calculation example is as follows: calculating pressure data corresponding to the trial calculation example;

constructing an objective function aiming at the response surface function of each area respectively, wherein the objective function indicates the deviation of the calculated pressure data and the measured pressure data;

respectively solving the optimal solution of each objective function and the parameter value of the parameter to be explained corresponding to each objective function by using an optimization algorithm;

and determining an objective function with the minimum optimal solution in the plurality of objective functions, and determining the parameter value of the parameter to be explained corresponding to the objective function with the minimum optimal solution as the explanation result of the parameter to be explained.

2. The method according to claim 1, wherein the triangulation is performed on the multidimensional parameter space based on the points corresponding to the plurality of trial calculation examples to obtain a plurality of regions, specifically:

and performing Delaunay triangulation on the multi-dimensional parameter space based on the points corresponding to the plurality of trial calculation examples to obtain a plurality of areas.

3. The method according to claim 1 or 2, wherein said sampling said parameter to be interpreted within said range of values, resulting in a plurality of trial calculation examples, comprises:

and sampling the parameters to be explained in the numerical range by utilizing a Latin hypercube sampling algorithm to obtain a plurality of trial calculation examples.

4. The method of claim 1 or 2, wherein constructing the objective function separately for the response surface function of each region comprises:

and respectively constructing a target function aiming at the response surface function of each area based on the principle that the error of the calculated pressure data and the actually measured pressure data of the response surface function of each area under the trial calculation example is minimum.

5. The method according to claim 1 or 2, wherein the using an optimization algorithm to respectively solve the optimal solution of each objective function and the parameter value of the parameter to be interpreted corresponding to each objective function comprises:

and respectively optimizing each target function by using a feasible direction algorithm and a Latin hypercube sampling algorithm to obtain the optimal solution of each target function and the parameter value of the parameter to be explained corresponding to each target function.

6. A reservoir parameter interpretation system, comprising:

the data receiving unit is used for receiving input parameters to be interpreted and corresponding numerical value ranges, wherein the parameters to be interpreted comprise stratum parameters and wellbore parameters;

the sampling unit is used for sampling the parameters to be explained in the numerical range to obtain a plurality of trial calculation examples;

the trial calculation example calculation unit is used for calculating the trial calculation examples respectively to obtain calculation pressure data corresponding to the trial calculation examples;

the space subdivision unit is used for triangulating the multidimensional parameter space based on the points corresponding to the plurality of trial calculation examples to obtain a plurality of areas;

the response surface function constructing unit is used for respectively carrying out high-dimensional linear interpolation on each area based on a plurality of points and values of each point contained in each area so as to respectively construct a response surface function for each area, wherein the value of the point corresponding to any trial calculation example is as follows: calculating pressure data corresponding to the trial calculation example;

the target function constructing unit is used for constructing a target function aiming at the response surface function of each area respectively, and the target function indicates the deviation of the calculated pressure data and the measured pressure data;

the optimization unit is used for respectively solving the optimal solution of each objective function and the parameter value of the parameter to be explained corresponding to each objective function by utilizing an optimization algorithm;

and the parameter interpretation unit is used for determining an objective function with the minimum optimal solution in the objective functions and determining the parameter value of the parameter to be interpreted corresponding to the objective function with the minimum optimal solution as the interpretation result of the parameter to be interpreted.

7. The system of claim 6, wherein the spatial subdivision unit is specifically configured to: and performing Delaunay triangulation on the multi-dimensional parameter space based on the points corresponding to the plurality of trial calculation examples to obtain a plurality of areas.

8. The system according to claim 6 or 7, wherein the sampling unit is specifically configured to: and sampling the parameters to be explained in the numerical range by utilizing a Latin hypercube sampling algorithm to obtain a plurality of trial calculation examples.

9. The system according to claim 6 or 7, characterized in that the objective function construction unit is specifically configured to: and respectively constructing a target function aiming at the response surface function of each area based on the principle that the error of the calculated pressure data and the actually measured pressure data of the response surface function of each area under the trial calculation example is minimum.

10. The system according to claim 6 or 7, wherein the optimization unit is specifically configured to: and respectively optimizing each target function by using a feasible direction algorithm and a Latin hypercube sampling algorithm to obtain the optimal solution of each target function and the parameter value of the parameter to be explained corresponding to each target function.

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