CN118564215A - A method and device for optimizing oil reservoir injection and production - Google Patents

A method and device for optimizing oil reservoir injection and production Download PDF

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Publication number
CN118564215A
CN118564215A CN202410744387.5A CN202410744387A CN118564215A CN 118564215 A CN118564215 A CN 118564215A CN 202410744387 A CN202410744387 A CN 202410744387A CN 118564215 A CN118564215 A CN 118564215A
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control parameter
net present
expression
present value
parameter vector
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周文胜
刘晨
苏彦春
刘丕养
崔永正
张增华
蒋隽哲
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Beijing Research Center of CNOOC China Ltd
CNOOC China Ltd
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Beijing Research Center of CNOOC China Ltd
CNOOC China Ltd
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    • E—FIXED CONSTRUCTIONS
    • E21—EARTH OR ROCK DRILLING; MINING
    • E21B—EARTH OR ROCK DRILLING; OBTAINING OIL, GAS, WATER, SOLUBLE OR MELTABLE MATERIALS OR A SLURRY OF MINERALS FROM WELLS
    • E21B43/00—Methods or apparatus for obtaining oil, gas, water, soluble or meltable materials or a slurry of minerals from wells
    • E—FIXED CONSTRUCTIONS
    • E21—EARTH OR ROCK DRILLING; MINING
    • E21B—EARTH OR ROCK DRILLING; OBTAINING OIL, GAS, WATER, SOLUBLE OR MELTABLE MATERIALS OR A SLURRY OF MINERALS FROM WELLS
    • E21B2200/00—Special features related to earth drilling for obtaining oil, gas or water
    • E21B2200/20—Computer models or simulations, e.g. for reservoirs under production, drill bits

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  • Life Sciences & Earth Sciences (AREA)
  • Engineering & Computer Science (AREA)
  • Geology (AREA)
  • Mining & Mineral Resources (AREA)
  • Physics & Mathematics (AREA)
  • Environmental & Geological Engineering (AREA)
  • Fluid Mechanics (AREA)
  • General Life Sciences & Earth Sciences (AREA)
  • Geochemistry & Mineralogy (AREA)
  • Feedback Control In General (AREA)

Abstract

The invention relates to the technical field of petroleum and natural gas exploitation, and discloses an oil reservoir injection and production optimization method and equipment, which can generate a control parameter vector of a target oil reservoir; the control parameter vector comprises control parameter adjustment data of each of a plurality of oil and gas wells of the target oil reservoir. Constructing a net present value expected expression of a target oil reservoir; wherein the net present value expectation expression includes a control parameter vector. And iterating the element values of the control parameter vector in the net present value expected expression with the aim of maximizing the net present value expected, calculating the net present value expected corresponding to each generation of element values until the iteration stop condition is met, and determining the element value corresponding to the maximum net present value expected as the optimal element value. The invention can maximize the net present value expectation as the target to carry out the oil reservoir injection and production optimization, determine the optimal element value of the control parameter vector and enrich the means of oil reservoir injection and production optimization.

Description

Oil reservoir injection and production optimization method and equipment
Technical Field
The invention relates to the technical field of oil and gas exploitation, in particular to an oil reservoir injection and production optimization method and equipment.
Background
The oil reservoir injection and production optimization refers to a processing mode for optimizing relevant control parameters of a water injection well and an oil production well in an oil reservoir in oil reservoir exploitation research, and is a key technology for improving oil recovery efficiency.
The related technology can improve the yield of an oil deposit production well, reduce the energy consumption and the cost in the oil and gas deposit exploitation process and improve the economic benefit of an oil field through oil deposit injection and extraction optimization. But the related art has fewer technical means for realizing the optimization of the oil reservoir injection and production.
Disclosure of Invention
The invention provides an oil reservoir injection and production optimization method and equipment, which are used for solving the defect of less technical means for realizing oil reservoir injection and production optimization in the related technology and can enrich the means for optimizing the oil reservoir injection and production.
In a first aspect, the present invention provides a method for optimizing oil reservoir injection and production, comprising:
generating a control parameter vector of a target oil reservoir, wherein the control parameter vector comprises control parameter adjustment data of each of a plurality of oil and gas wells of the target oil reservoir;
Constructing a net present value expected expression of the target reservoir based on the control parameter vector; wherein the control parameter vector is included in the net present value expectation expression;
Iterating the element values of the control parameter vector in the net present value expected expression with the aim of maximizing the net present value expectation, and calculating the net present value expectation corresponding to each generation of element values until the iteration stop condition is met;
And determining the maximum net present value expected in the iterative process, and determining the element value corresponding to the maximum net present value expected as the optimal element value.
Optionally, the constructing a net present value expected expression of the target oil reservoir based on the control parameter vector includes:
constructing a net present value expression of the target reservoir based on the control parameter vector and reservoir property variables of the target reservoir;
The net present expected expression is constructed from the plurality of reservoir properties of the target reservoir and the net present expression.
Optionally, the net present value expectation expression is:
Where g Y (x) is the net present value expectation, N e is the total number of reservoir properties, j is the number of reservoir properties, N t is the total number of time steps, i is the time step index, t i is the accumulated time since the start of production, v o and v w are the crude oil price and water treatment cost, respectively, Q oi and Q wi are the total oil and water production in time step t i, y is the reservoir property variable, y j is the reservoir property number j, and x is the control parameter variable.
Optionally, the iterating the element values of the control parameter vector in the expected expression of the net present value, and calculating the expected net present value corresponding to each generation of element values until the iteration stop condition is met, includes:
determining a sensitivity approximation expression for the net present value to the control parameter vector;
Generating an error control expression from the plurality of reservoir properties of the target reservoir and the control parameter vector, the error control expression for reducing errors in the sensitivity approximation expression;
And iterating the element values of the control parameter vector in the net present value expected expression based on a fastest descent method, the sensitivity similarity expression and the error control expression, and calculating a net present value expected corresponding to each generation of element values until an iteration stop condition is met.
Optionally, the determining a sensitivity approximation expression of the net present value to the control parameter vector includes:
Disturbing each piece of control parameter adjustment data in the control parameter vector by using a time-dependent Gaussian random variable to generate a plurality of realization parameters, wherein each realization parameter comprises disturbed data corresponding to each piece of control parameter adjustment data in the control parameter vector;
For any implementation parameter, determining a geological model corresponding to the implementation parameter, calculating a corresponding net present value according to the implementation parameter and the geological model, and taking the whole implementation parameter and the corresponding net present value as a to-be-processed element;
arranging all the elements to be processed to generate a corresponding realization set;
A cross-covariance approximation expression between a net present value expectation and the control parameter vector is determined based on the set of realizations and as the sensitivity approximation expression.
Optionally, the generating an error control expression according to the plurality of reservoir properties of the target reservoir and the control parameter vector includes:
Performing reservoir numerical simulation according to the reservoir properties and the control parameter vector to generate a corresponding accompanying gradient matrix;
Singular value decomposition is carried out on the accompanying gradient matrix to obtain corresponding singular vectors;
And generating a localization matrix for eliminating covariance pseudo-correlation influence based on the singular vector, and taking the localization matrix as the error control expression.
Optionally, the iteration is performed on the element values of the control parameter vector in the net present value expected expression based on the fastest descent method, the sensitivity approximation expression and the error control expression, and the net present value expected corresponding to each generation of element values is calculated until the iteration stop condition is met, including:
Determining the control parameter adjustment data in the control parameter vector as a primary element value of the control parameter vector, and calculating a corresponding net present value expectation according to the primary element value and the net present value expected expression;
Determining next generation element values based on a fastest descent method, the sensitivity approximation expression, the error control expression, and the initial values, and calculating corresponding net present value expectations;
And determining a new next generation element value based on the fastest descent method, the sensitivity approximation expression, the error control expression and the next generation element value until an iteration stop condition is satisfied.
In a second aspect, the present invention provides an oil reservoir injection and production optimization device, comprising:
The generation unit is used for generating a control parameter vector of a target oil reservoir, wherein the control parameter vector comprises control parameter adjustment data of each oil and gas well in a plurality of oil and gas wells of the target oil reservoir;
A construction unit, configured to construct a net present value expected expression of the target reservoir based on the control parameter vector; wherein the control parameter vector is included in the net present value expectation expression;
The iteration unit is used for carrying out iteration on the element values of the control parameter vector in the net present value expected expression with the aim of maximizing the net present value expected value, and calculating the net present value expected value corresponding to each generation of element value until the iteration stop condition is met;
a first determining unit for determining a maximum net present value expectation in an iterative process;
And the second determining unit is used for determining the element value expected to correspond to the maximum net present value as the optimal element value.
In a third aspect, the present invention provides a computer device comprising: the oil reservoir injection and production optimization method comprises a memory and a processor, wherein the memory and the processor are in communication connection, the memory stores computer instructions, and the processor executes the computer instructions so as to execute the oil reservoir injection and production optimization method according to the first aspect or any corresponding implementation mode.
In a fourth aspect, the present invention provides a computer readable storage medium having stored thereon computer instructions for causing a computer to perform the reservoir injection and production optimization method of the first aspect or any of its corresponding embodiments.
The oil reservoir injection and production optimization method and the device provided by the invention can generate the control parameter vector of the target oil reservoir; the control parameter vector comprises control parameter adjustment data of each of a plurality of oil and gas wells of the target oil reservoir. Constructing a net present value expected expression of the target oil reservoir based on the control parameter vector; wherein the net present value expectation expression includes a control parameter vector. And iterating the element values of the control parameter vector in the net present value expected expression with the aim of maximizing the net present value expected expression, and calculating the net present value expected corresponding to each generation of element values until the iteration stop condition is met. And determining the maximum net present value expectation in the iterative process, and determining the element value corresponding to the maximum net present value expectation as the optimal element value. The invention can maximize the net present value expectation as the target to carry out the oil reservoir injection and production optimization, determine the optimal element value of the control parameter vector and enrich the means of oil reservoir injection and production optimization.
Drawings
In order to more clearly illustrate the invention or the technical solutions of the prior art, the following description will briefly explain the drawings used in the embodiments or the description of the prior art, and it is obvious that the drawings in the following description are some embodiments of the invention, and other drawings can be obtained according to the drawings without inventive effort for a person skilled in the art.
FIG. 1 is a flow chart of an oil reservoir injection and production optimization method provided by an embodiment of the invention;
FIG. 2 is a schematic diagram of determining L x and L y by actual correlation area fitting according to an embodiment of the present invention;
FIG. 3 is a schematic diagram of a relationship between ellipse, L x and L y fitting related areas according to an embodiment of the present invention;
FIG. 4 is a schematic diagram of injection and production optimization performed by a correlation model according to an embodiment of the present invention;
FIG. 5 is a schematic structural diagram of an oil reservoir injection and production optimizing device according to an embodiment of the present invention;
Fig. 6 is a schematic structural diagram of a computer device according to an embodiment of the present invention.
Detailed Description
For the purpose of making the objects, technical solutions and advantages of the present invention more apparent, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings, and it is apparent that the described embodiments are some embodiments of the present invention, not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
The reservoir injection and production optimization method of the present invention is described below with reference to fig. 1-4.
As shown in fig. 1, this embodiment proposes a first oil reservoir injection and production optimization method, which may include the following steps:
S101, generating a control parameter vector of a target oil reservoir, wherein the control parameter vector comprises control parameter adjustment data of each of a plurality of oil and gas wells of the target oil reservoir.
The target oil reservoir can be a certain oil reservoir needing injection and production optimization.
The control parameter adjustment data may include adjustment data of each time of the control parameters of the oil and gas well within the target period, for example, adjustment data of each time of the water injection pressure and the temperature of the oil and gas well within the target period. Specifically, the adjustment data of each time may be the difference between the current data and the last data.
Alternatively, the target period may be a period from the first mining time point to the present, or may be a partial period of the period until the first mining time point.
The number of data in the control parameter adjustment data of each oil and gas well may be the same.
Specifically, the elements in the control parameter vector X may be obtained by first traversing the control parameters of one well and then traversing all the wells. For an oil reservoir with N wells, assuming that the adjustment frequency of each well working system is m n, X can be used for representing a control variable vector, namely, a vector with the adjustment value of control parameters such as bottom hole flow pressure and the like as coordinates, and X can contain all well constraints of different control steps, namely, the control parameter adjustment data of each oil and gas well.
Where N x is the total number of control parameters for all wells, thenN is the well number.
S102, constructing a net present value expected expression of a target oil reservoir based on a control parameter vector; wherein the net present value expectation expression includes a control parameter vector.
Optionally, step S102 may include:
constructing a net present value expression of the target oil reservoir based on the control parameter vector and the reservoir property variable of the target oil reservoir;
A net present expected expression is constructed from the plurality of reservoir properties and the net present expression for the target reservoir.
Reservoir properties may include, among other things, permeability and porosity.
Specifically, the embodiment may construct a net present value expression of the target oil reservoir in advance based on the control parameter vector and the plurality of reservoir properties, and then construct a net present value expected expression of the target oil reservoir.
It should be noted that, the expected expression of the net present value may be obtained by optimizing the control parameter variables in the expression of the net present value by applying a set optimization method in this embodiment and considering the uncertainty of the reservoir model. The present embodiment may assume that there are N e different reservoir properties y j, targeting the maximization of net present value expectations to account for reservoir model uncertainty.
Alternatively, the net present value desired expression is:
Where g Y (x) is the net present value expectation, N e is the total number of reservoir properties, j is the number of reservoir properties, N t is the total number of time steps, i is the time step index, t i is the accumulated time since the start of production, v o and v w are the crude oil price and water treatment cost, respectively, Q oi and Q wi are the total oil and water production in time step t i, y is the reservoir property variable, y j is the reservoir property number j, and x is the control parameter variable.
Specifically, the present embodiment may first create a net present value expression:
Where g (x, y) is a net present value expression.
The subscript Y in g Y (x) represents the expected value of the geologic model probability density function. Since the desired way of determination is not changed during the production optimization, g Y (X) can be regarded only as a function of the control parameter variable X.
S103, iterating the element values of the control parameter vector in the net present value expected expression with the aim of maximizing the net present value expected expression, and calculating the net present value expected corresponding to each generation of element values until the iteration stop condition is met.
Specifically, the embodiment may iterate the element values of the control parameter vector in the net present value expected expression with the maximization of the net present value expected expression as a target, and may calculate the corresponding net present value expected according to the element values of the control parameter vector and the net present value expected expression after determining each generation of element values of the control parameter vector.
The iteration stop condition may be that the iteration number reaches a preset number, or that the net present value is expected to reach a preset threshold.
S104, determining the maximum net present value expectation in the iterative process.
Specifically, in this embodiment, the maximum value, that is, the maximum net present value expectation, may be determined from the net present value expectations corresponding to the values of the elements of each generation.
S105, determining the element value corresponding to the maximum net present value expectation as the optimal element value.
Specifically, the embodiment may determine, after determining that the maximum net present value is expected, an element value corresponding to the maximum net present value is expected, and the element value is used as an optimal element value of the control parameter vector.
The oil reservoir injection and production optimization method provided by the embodiment can generate the control parameter vector of the target oil reservoir; the control parameter vector comprises control parameter adjustment data of each of a plurality of oil and gas wells of the target oil reservoir. Constructing a net present value expected expression of the target oil reservoir based on the control parameter vector; wherein the net present value expectation expression includes a control parameter vector. And iterating the element values of the control parameter vector in the net present value expected expression with the aim of maximizing the net present value expected expression, and calculating the net present value expected corresponding to each generation of element values until the iteration stop condition is met. And determining the maximum net present value expectation in the iterative process, and determining the element value corresponding to the maximum net present value expectation as the optimal element value. According to the method and the device, the oil reservoir injection and production optimization can be carried out by taking the maximum net present value as a target, the optimal element value of the control parameter vector is determined, and the oil reservoir injection and production optimization means are enriched.
In the related technology, the oil reservoir injection and production optimization is an important direction in the field of oil reservoir production optimization research, and the oil reservoir injection and production optimization method in the related technology mainly comprises a concomitant gradient method, a no gradient method and a mixed gradient method. The method solves the true gradient with the gradient algorithm, has highest calculation efficiency, but needs to derive the Jacobian matrix in the oil reservoir numerical simulator, has very complex process and can not meet the requirement of actual large-scale complex oilfield optimization. The gradient-free method does not need complex gradient information and has good global property, but a large number of numerical simulation iterative computations are needed in the optimization process to converge to an optimal solution, the time is too long, the method is limited by the discomfort type and the set size of the optimization problem in practical application, the covariance estimation is not easy to cause pseudo-correlation, the convergence and the calculation accuracy are further influenced, and related improvement is needed to be made for the problem.
Based on fig. 1, this embodiment proposes a second method for optimizing oil reservoir injection and production, in which step S103 may include:
determining a sensitivity approximation expression of the net present value expectation to the control parameter vector;
Generating an error control expression according to the plurality of reservoir properties of the target reservoir and the control parameter vector, wherein the error control expression is used for reducing errors of the sensitivity approximation expression;
And iterating the element values of the control parameter vector in the net present value expected expression based on the fastest descent method, the sensitivity approximation expression and the error control expression, and calculating the net present value expected corresponding to each generation of element values until the iteration stop condition is met.
Optionally, the determining the approximate expression of the sensitivity of the net present value to the control parameter vector may include:
Disturbing each piece of control parameter adjustment data in the control parameter vector by using a time-dependent Gaussian random variable to generate a plurality of realization parameters, wherein each realization parameter comprises disturbed data corresponding to each piece of control parameter adjustment data in the control parameter vector;
For any implementation parameter, determining a geological model corresponding to the implementation parameter, calculating a corresponding net present value according to the implementation parameter and the geological model, and taking the implementation parameter and the corresponding net present value as a whole to-be-processed element;
arranging all elements to be processed to generate a corresponding realization set;
A cross-covariance approximation expression between the net present value expectation and the control parameter vector is determined based on the set of realizations and is used as a sensitivity approximation expression.
Optionally, the generating the error control expression according to the plurality of reservoir properties and the control parameter vector of the target reservoir may include:
carrying out oil reservoir numerical simulation according to the plurality of reservoir properties and the control parameter vector so as to generate a corresponding accompanying gradient matrix;
singular value decomposition is carried out on the accompanying gradient matrix to obtain corresponding singular vectors;
a localization matrix for eliminating covariance pseudo-correlation effects is generated based on the singular vectors, and the localization matrix is used as an error control expression.
Optionally, the foregoing method for controlling the element values of the control parameter vector in the net present value expected expression based on the fastest descent method and the sensitivity approximation expression and the error control expression iterates the element values of the control parameter vector in the net present value expected expression, and calculates the net present value expected corresponding to each generation of element values until the iteration stop condition is satisfied, which may include:
determining control parameter adjustment data in the control parameter vector as a primary element value of the control parameter vector, and calculating a corresponding net present value expectation according to the primary element value and the net present value expectation expression;
Determining next generation element values based on the fastest descent method, the sensitivity approximation expression, the error control expression and the initial values, and calculating corresponding net present value expectations;
And determining a new next generation element value based on the fastest descent method, the sensitivity approximation expression, the error control expression and the next generation element value until the iteration stop condition is met.
Specifically, in this embodiment, the element value of the control variable X that maximizes the objective function g Y (X) may be found by using the fastest descent method, where the formula of the fastest descent method is:
where l represents the iteration index, α is the step size of the adjustment parameter determining fastest descent method, G is the sensitivity matrix of G Y (x) to the control parameter variable, and the dimension is 1×n X.
In particular, the set optimization problem is ill-posed without regularization, and the present embodiment may use covariance matrix C x to provide preprocessing for the fastest rise mode before sensitivity matrix G to penalize frequent abrupt changes in control settings. In this case, the present embodiment may choose a gaussian covariance function to describe the time dependence between different controls of the same well to limit the frequency and magnitude of well control variations. Furthermore, depending on the capacity of the facility, different control parameters always have upper and lower limits (e.g., minimum and maximum bottom hole pressures and minimum and maximum liquid production and water injection volumes). The out-of-limit values in this embodiment are truncated and the total injection and production limits are complied with by redistributing the injection and production amounts between the wells proportionally according to the truncated values.
To calculate an approximation of the sensitivity matrix G of G Y (X) to the control parameter variable X, the present embodiment may build an implementation set Z, such as:
Where the set Z contains N e realizations Z k, where k=1, 2, …, N e.Xl,k are realization parameters corresponding to the control parameter variables, all well constraints including each control step can be generated by perturbing the control parameter adjustment data in the currently iterated control parameter variable X using a time-dependent gaussian random variable, where the variance of the gaussian perturbation comes from the desire for frequency and amplitude of well control variations. P k is a geologic model of propagation in EnRML. g Y(Xl,1,P1)、gY(Xl,2,P2) and Combining the set of control parameter variables with the set of geologic models, an implementation of control parameter variables X l,k is applied to a geologic model P k. G Y(Xl,k,Pk may then be calculated from the numerical simulation results), the construction of set Z involving N e simulation runs. It will be appreciated that the present embodiment may be able to take into account uncertainty in reservoir geological properties by way of set optimization.
The present embodiment may represent the cross-covariance between the control parameter variables X and g Y (X) by C x,g(x) and calculate its approximation from the set Z using the following manner.
Wherein:
At this time, the formula of the fastest descent method may be:
it is assumed that, in each iteration, the relationship between g Y (X) and the control parameter variable X may be approximately linear, such as:
since the cross covariance C x,g(x) is estimated using the monte carlo approach, i.e., the correlation approach of set Z, when the scale of set Z is small, it is subject to spurious correlations and the C x,g(x) estimation may be inaccurate. The embodiment can establish a singular value decomposition localization mode to filter out cross covariance exceeding a critical distance so as to improve calculation accuracy.
In the localization approach using singular value decomposition, the elements in the localization matrix may represent the correlation between each control parameter variable X and the numerical simulation observations g Y (X) (total oil and water production).
Wherein,AndIs obtained by numerical simulation calculation of the reservoir based on the control parameter vector X and the reservoir property y, and the gradient matrix G Q obtained in the numerical simulation calculation is the control parameter variable X and the observed valueAndThe gradient matrix between the two is defined as accompanying gradient matrix G QD of gradient matrix C Q in formula (1),AndCollectively denoted as Q (x, y).
(G QDQ(x,y),x)X＝(Q(x,y),GQx)X -) Reinforcement of the reaction products-formula (1);
the gradient matrix G QD,k is computed for each group X l,k,Pk in the set Z by singular value decomposition of the accompanying gradient matrix G QD,k as in equation (2):
Wherein g (x) is shorthand for g Y (x). The embodiment can average the arithmetic of all N e right singular vectors V k for the well where the control variable is located The corresponding columns of (a) are mapped into grid blocks of the reservoir numerical simulation model, and the area obtained after the values of the singular vectors are properly truncated is defined as the total area of the white partial squares in fig. 2. In fig. 2, P1, P2, P3 and P4 each represent an oil well, and I1 represents a water injection well. For each well in which the control parameter variables are located, calculating a minimum value of a least squares formula to determine the correlation lengths L x and L y of the primary and secondary directions, the formula being:
If the observed value is AndThe position is within the relevant area, s is 1, and otherwise is 0.For controlling the Euclidean distance between the well where the parameter variable is located and the position where the observed value is located, the calculation formula is shown as formula (3) and formula (4).
Where θ is the principal direction of geologic modeling, the relationship of θ, L x, and L y may be as shown in FIG. 3. Delta x,δy is the coordinates of the location of the measurement in the north-south coordinate system with the well location of the control parameter variable as the origin. Delta x′ and delta t′ are two intermediate variables in the calculation process.
After obtaining L x,Ly, this embodiment may construct a localization matrix ρ x,g(x) with a size of N X×NX, for each element in the matrix, if the well where the control parameter variable corresponding to the horizontal coordinate is located is the same well as the well where the control parameter variable corresponding to the vertical coordinate is located, the element value is 1, if the wells are different, if the distance between the two wells is smaller than the relevant distance, the corresponding element in the localization matrix is 1, otherwise, it is 0. In the set optimization mode, this embodiment introduces the localization matrix to eliminate the influence of covariance pseudo-correlation, then:
In the embodiment, injection and production optimization is performed on 12 wells in total of 8 injection and 4 production in the egg model shown in fig. 4, and compared with a method without localization, the operation speed of the localization method is improved by 18%, and the iteration times are reduced by 26%. The embodiment establishes a complete oil reservoir injection acquisition and optimization mode based on singular value decomposition localization, can reduce the calculation cost to the greatest extent in the process of automatic optimization of oil field injection and production, avoids sinking into local optimum, effectively guides later development and adjustment, and has a good application prospect.
The method and the device can fully consider the pseudo-correlation caused by inaccurate covariance estimation of a small sample set in the set optimization process, introduce a singular value decomposition localization mode, greatly improve the accuracy of covariance estimation and improve the calculation efficiency of an injection-production optimization algorithm.
The oil reservoir injection and production optimization method provided by the embodiment can form an effective localized matrix calculation mode, reasonably obtains the correlation length of the localized matrix through singular value decomposition, and greatly improves the estimation accuracy of the covariance matrix in the set optimization mode. And an automatic algorithm for all calculation processes can be formed, and automatic optimization adjustment of oil field injection and production is realized through programming.
As shown in fig. 5, this embodiment proposes an oil reservoir injection and production optimization device, which may include:
A generating unit 101, configured to generate a control parameter vector of a target oil reservoir, where the control parameter vector includes control parameter adjustment data of each of a plurality of oil and gas wells of the target oil reservoir;
a construction unit 102, configured to construct a net present value expected expression of the target reservoir based on the control parameter vector; wherein the net present value expectation expression includes a control parameter vector;
An iteration unit 103, configured to iterate the element values of the control parameter vector in the net present value expectation expression with the objective of maximizing the net present value expectation, and calculate the net present value expectation corresponding to each generation of element values until the iteration stop condition is satisfied;
a first determining unit 104 for determining a maximum net present value expectation in an iterative process;
A second determining unit 105, configured to determine an element value corresponding to the maximum net present value expected as an optimal element value.
It should be noted that, the processing procedures of the generating unit 101, the constructing unit 102, the iterating unit 103, the first determining unit 104, and the second determining unit 105 and the beneficial effects thereof may refer to steps S101 to S105 in fig. 1, respectively, and are not described again.
Optionally, the construction unit 102 is further configured to:
constructing a net present value expression of the target oil reservoir based on the control parameter vector and the reservoir property variable of the target oil reservoir;
A net present expected expression is constructed from the plurality of reservoir properties and the net present expression for the target reservoir.
Alternatively, the net present value desired expression is:
Where g Y (x) is the net present value expectation, N e is the total number of reservoir properties, j is the number of reservoir properties, N t is the total number of time steps, i is the time step index, t i is the accumulated time since the start of production, v o and v w are the crude oil price and water treatment cost, respectively, Q oi and Q wi are the total oil and water production in time step t i, y is the reservoir property variable, y j is the reservoir property number j, and x is the control parameter variable.
Optionally, the iteration unit 103 is further configured to:
determining a sensitivity approximation expression of the net present value expectation to the control parameter vector;
Generating an error control expression according to the plurality of reservoir properties of the target reservoir and the control parameter vector, wherein the error control expression is used for reducing errors of the sensitivity approximation expression;
And iterating the element values of the control parameter vector in the net present value expected expression based on the fastest descent method, the sensitivity approximation expression and the error control expression, and calculating the net present value expected corresponding to each generation of element values until the iteration stop condition is met.
Optionally, the iteration unit 103 is further configured to:
Disturbing each piece of control parameter adjustment data in the control parameter vector by using a time-dependent Gaussian random variable to generate a plurality of realization parameters, wherein each realization parameter comprises disturbed data corresponding to each piece of control parameter adjustment data in the control parameter vector;
For any implementation parameter, determining a geological model corresponding to the implementation parameter, calculating a corresponding net present value according to the implementation parameter and the geological model, and taking the implementation parameter and the corresponding net present value as a whole to-be-processed element;
arranging all elements to be processed to generate a corresponding realization set;
A cross-covariance approximation expression between the net present value expectation and the control parameter vector is determined based on the set of realizations and is used as a sensitivity approximation expression.
Optionally, the iteration unit 103 is further configured to:
carrying out oil reservoir numerical simulation according to the plurality of reservoir properties and the control parameter vector so as to generate a corresponding accompanying gradient matrix;
singular value decomposition is carried out on the accompanying gradient matrix to obtain corresponding singular vectors;
a localization matrix for eliminating covariance pseudo-correlation effects is generated based on the singular vectors, and the localization matrix is used as an error control expression.
Optionally, the iteration unit 103 is further configured to:
determining control parameter adjustment data in the control parameter vector as a primary element value of the control parameter vector, and calculating a corresponding net present value expectation according to the primary element value and the net present value expectation expression;
Determining next generation element values based on the fastest descent method, the sensitivity approximation expression, the error control expression and the initial values, and calculating corresponding net present value expectations;
And determining a new next generation element value based on the fastest descent method, the sensitivity approximation expression, the error control expression and the next generation element value until the iteration stop condition is met.
The oil reservoir injection and production optimizing device provided by the embodiment can determine a plurality of reservoir properties of a target oil reservoir and generate a control parameter vector; the control parameter vector comprises control parameter adjustment data of each of a plurality of oil and gas wells of the target oil reservoir. Constructing a net present value expected expression of the target reservoir based on the control parameter vector and the plurality of reservoir properties; wherein the net present value expectation expression includes a control parameter vector. And iterating the element values of the control parameter vector in the net present value expected expression with the aim of maximizing the net present value expected expression, and calculating the net present value expected corresponding to each generation of element values until the iteration stop condition is met. And determining the maximum net present value expectation in the iterative process, and determining the element value corresponding to the maximum net present value expectation as the optimal element value. According to the method and the device, the oil reservoir injection and production optimization can be carried out by taking the maximum net present value as a target, the optimal element value of the control parameter vector is determined, and the oil reservoir injection and production optimization means are enriched.
The reservoir injection and production optimization device in this embodiment is presented in the form of a functional unit, where the unit refers to an ASIC (Application SPECIFIC INTEGRATED Circuit) Circuit, a processor and a memory that execute one or more software or firmware programs, and/or other devices that can provide the above functions.
The embodiment of the invention also provides computer equipment, which is provided with the oil reservoir injection and production optimizing device shown in the figure 5.
Referring to fig. 6, an alternative embodiment of the present invention provides a schematic structural diagram of a computer device, which includes: one or more processors 10, memory 20, and interfaces for connecting the various components, including high-speed interfaces and low-speed interfaces. The various components are communicatively coupled to each other using different buses and may be mounted on a common motherboard or in other manners as desired. The processor may process instructions executing within the computer device, including instructions stored in or on memory to display graphical information of the GUI on an external input/output device, such as a display device coupled to the interface. In some alternative embodiments, multiple processors and/or multiple buses may be used, if desired, along with multiple memories and multiple memories. Also, multiple computer devices may be connected, each providing a portion of the necessary operations (e.g., as a server array, a set of blade servers, or a multiprocessor system). One processor 10 is illustrated in fig. 6.
The processor 10 may be a central processor, a network processor, or a combination thereof. The processor 10 may further include a hardware chip, among others. The hardware chip may be an application specific integrated circuit, a programmable logic device, or a combination thereof. The programmable logic device may be a complex programmable logic device, a field programmable gate array, a general-purpose array logic, or any combination thereof.
Wherein the memory 20 stores instructions executable by the at least one processor 10 to cause the at least one processor 10 to perform a method for implementing the embodiments described above.
The memory 20 may include a storage program area that may store an operating system, at least one application program required for functions, and a storage data area. The storage data area may store data created according to the use of the computer device, etc. In addition, the memory 20 may include high-speed random access memory, and may also include non-transitory memory, such as at least one magnetic disk storage device, flash memory device, or other non-transitory solid-state storage device. In some alternative embodiments, memory 20 may optionally include memory located remotely from processor 10, which may be connected to the computer device via a network. Examples of such networks include, but are not limited to, the internet, intranets, local area networks, mobile communication networks, and combinations thereof.
The memory 20 may include volatile memory, such as random access memory. The memory may also include non-volatile memory, such as flash memory, a hard disk, or a solid state disk. The memory 20 may also comprise a combination of the above types of memories.
The computer device also includes a communication interface 30 for the computer device to communicate with other devices or communication networks.
The embodiments of the present invention also provide a computer readable storage medium, and the method according to the embodiments of the present invention described above may be implemented in hardware, firmware, or as a computer code which may be recorded on a storage medium, or as original stored in a remote storage medium or a non-transitory machine readable storage medium downloaded through a network and to be stored in a local storage medium, so that the method described herein may be stored on such software process on a storage medium using a general purpose computer, a special purpose processor, or programmable or special purpose hardware. The storage medium can be a magnetic disk, an optical disk, a read-only memory, a random access memory, a flash memory, a hard disk, a solid state disk or the like; further, the storage medium may also comprise a combination of memories of the kind described above. It will be appreciated that a computer, processor, microprocessor controller or programmable hardware includes a storage element that can store or receive software or computer code that, when accessed and executed by the computer, processor or hardware, implements the methods illustrated by the above embodiments.
Finally, it should be noted that: the above embodiments are only for illustrating the technical solution of the present invention, and are not limiting; although the invention has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical scheme described in the foregoing embodiments can be modified or some technical features thereof can be replaced by equivalents; such modifications and substitutions do not depart from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims (10)

1.一种油藏注采优化方法，其特征在于，包括：1. A method for optimizing oil reservoir injection and production, comprising: 生成目标油藏的控制参数向量，所述控制参数向量中包括所述目标油藏的多个油气井中每个所述油气井的控制参数调整数据；Generate a control parameter vector of a target oil reservoir, wherein the control parameter vector includes control parameter adjustment data of each of the oil and gas wells in the target oil reservoir; 基于所述控制参数向量构建所述目标油藏的净现值期望表达式；其中，所述净现值期望表达式中包括所述控制参数向量；Constructing a net present value expectation expression of the target reservoir based on the control parameter vector; wherein the net present value expectation expression includes the control parameter vector; 以最大化净现值期望为目标，对所述净现值期望表达式中所述控制参数向量的元素取值进行迭代，并计算每一代元素取值对应的净现值期望，直至满足迭代停止条件为止；With the goal of maximizing the expected net present value, iterate the element values of the control parameter vector in the expected net present value expression, and calculate the expected net present value corresponding to each generation of element values until the iteration stop condition is met; 确定迭代过程中的最大净现值期望，并将所述最大净现值期望对应的所述元素取值确定为最优元素取值。The maximum net present value expectation in the iteration process is determined, and the element value corresponding to the maximum net present value expectation is determined as the optimal element value. 2.根据权利要求1所述的方法，其特征在于，所述基于所述控制参数向量构建所述目标油藏的净现值期望表达式，包括：2. The method according to claim 1, characterized in that the constructing of the expected net present value expression of the target reservoir based on the control parameter vector comprises: 基于所述控制参数向量和所述目标油藏的储层性质变量，构建所述目标油藏的净现值表达式；constructing a net present value expression of the target oil reservoir based on the control parameter vector and the reservoir property variables of the target oil reservoir; 根据所述目标油藏的多个储层性质和所述净现值表达式，构建所述净现值期望表达式。The net present value expectation expression is constructed according to a plurality of reservoir properties of the target oil reservoir and the net present value expression. 3.根据权利要求2所述的方法，其特征在于，所述净现值期望表达式为：3. The method according to claim 2, characterized in that the expected expression of the net present value is: 其中，gY(x)为净现值期望，Ne为所述储层性质的总数，j为所述储层性质的编号，Nt为时间步总数，i为时间步索引，ti为自生产开始以来的累计时间，vo和vw分别是原油价格和水处理成本，Qoi和Qwi是时间步ti内的总石油和水产量，y为所述储层性质变量，yj为编号为j的储层性质，x为控制参数变量。Wherein, g Y (x) is the expected net present value, Ne is the total number of the reservoir properties, j is the number of the reservoir properties, Nt is the total number of time steps, i is the time step index, ti is the cumulative time since the start of production, vo and vw are the crude oil price and water treatment cost respectively, Qoi and Qwi are the total oil and water production in time step ti , y is the reservoir property variable, yj is the reservoir property numbered j, and x is the control parameter variable. 4.根据权利要求1所述的方法，其特征在于，所述对所述净现值期望表达式中所述控制参数向量的元素取值进行迭代，并计算每一代元素取值对应的净现值期望，直至满足迭代停止条件为止，包括：4. The method according to claim 1, characterized in that the step of iterating the element values of the control parameter vector in the expected net present value expression and calculating the expected net present value corresponding to each generation of element values until an iteration stop condition is satisfied comprises: 确定净现值期望对所述控制参数向量的敏感性近似值表达式；Determine an approximate value expression of the sensitivity of the expected net present value to the control parameter vector; 根据所述目标油藏的多个储层性质和所述控制参数向量生成误差控制表达式，所述误差控制表达式用于减少所述敏感性近似值表达式的误差；generating an error control expression according to a plurality of reservoir properties of the target reservoir and the control parameter vector, wherein the error control expression is used to reduce an error of the sensitivity approximation expression; 基于最快下降法和所述敏感性近似值表达式、所述误差控制表达式，对所述净现值期望表达式中所述控制参数向量的元素取值进行迭代，并计算每一代元素取值对应的净现值期望，直至满足迭代停止条件为止。Based on the fastest descent method, the sensitivity approximation expression and the error control expression, the element values of the control parameter vector in the net present value expectation expression are iterated, and the net present value expectation corresponding to each generation of element values is calculated until the iteration stop condition is met. 5.根据权利要求4所述的方法，其特征在于，所述确定净现值期望对所述控制参数向量的敏感性近似值表达式，包括：5. The method according to claim 4, characterized in that the step of determining an approximate value expression of the sensitivity of the expected net present value to the control parameter vector comprises: 使用时间相关的高斯随机变量对所述控制参数向量中的每个所述控制参数调整数据进行扰动，以生成多个实现参数，每个所述实现参数中包括所述控制参数向量中每个所述控制参数调整数据对应的扰动后数据；Using a time-dependent Gaussian random variable to perturb each of the control parameter adjustment data in the control parameter vector to generate a plurality of implementation parameters, each of the implementation parameters including perturbed data corresponding to each of the control parameter adjustment data in the control parameter vector; 对于任一所述实现参数，确定与所述实现参数对应的地质模型，根据所述实现参数和所述地质模型计算相应的净现值，并将所述实现参数和相应的所述净现值整体作为一个待处理元素；For any of the realization parameters, determine a geological model corresponding to the realization parameter, calculate a corresponding net present value according to the realization parameter and the geological model, and treat the realization parameter and the corresponding net present value as a whole as an element to be processed; 对所有所述待处理元素进行排列，生成相应的实现集；Arranging all the elements to be processed to generate a corresponding realization set; 基于所述实现集确定净现值期望与所述控制参数向量间的互协方差近似值表达式，并作为所述敏感性近似值表达式。An approximate expression for the cross-covariance between the expected net present value and the control parameter vector is determined based on the realization set and used as the approximate expression for the sensitivity. 6.根据权利要求5所述的方法，其特征在于，所述根据所述目标油藏的多个储层性质和所述控制参数向量生成误差控制表达式，包括：6. The method according to claim 5, characterized in that the step of generating an error control expression according to a plurality of reservoir properties of the target reservoir and the control parameter vector comprises: 根据所述多个储层性质和所述控制参数向量进行油藏数值模拟，以生成相应的伴随梯度矩阵；Performing reservoir numerical simulation according to the plurality of reservoir properties and the control parameter vector to generate a corresponding adjoint gradient matrix; 对所述伴随梯度矩阵进行奇异值分解，得到相应的奇异向量；Performing singular value decomposition on the adjoint gradient matrix to obtain corresponding singular vectors; 基于所述奇异向量生成用于消除协方差伪相关性影响的局地化矩阵，并将所述局地化矩阵作为所述误差控制表达式。A localized matrix for eliminating the influence of covariance pseudo-correlation is generated based on the singular vectors, and the localized matrix is used as the error control expression. 7.根据权利要求4所述的方法，其特征在于，所述基于最快下降法和所述敏感性近似值表达式、所述误差控制表达式，对所述净现值期望表达式中所述控制参数向量的元素取值进行迭代，并计算每一代元素取值对应的净现值期望，直至满足迭代停止条件为止，包括：7. The method according to claim 4, characterized in that the element values of the control parameter vector in the net present value expectation expression are iterated based on the fastest descent method, the sensitivity approximation expression, and the error control expression, and the net present value expectation corresponding to each generation of element values is calculated until the iteration stop condition is met, comprising: 将所述控制参数向量中的所述控制参数调整数据确定为所述控制参数向量的初代元素取值，根据所述初代元素取值和所述净现值期望表达式计算相应的净现值期望；Determine the control parameter adjustment data in the control parameter vector as the primary element value of the control parameter vector, and calculate the corresponding net present value expectation according to the primary element value and the net present value expectation expression; 基于最快下降法、所述敏感性近似值表达式、所述误差控制表达式和所述初始取值确定下一代元素取值，并计算相应的净现值期望；Determine the next generation element value based on the fastest descent method, the sensitivity approximation expression, the error control expression and the initial value, and calculate the corresponding net present value expectation; 基于最快下降法、所述敏感性近似值表达式、所述误差控制表达式和所述下一代元素取值，确定新下一代元素取值，直至满足迭代停止条件为止。Based on the fastest descent method, the sensitivity approximation expression, the error control expression and the next generation element value, a new next generation element value is determined until an iteration stop condition is met. 8.一种油藏注采优化装置，其特征在于，包括：8. An oil reservoir injection and production optimization device, characterized by comprising: 生成单元，用于生成目标油藏的控制参数向量，所述控制参数向量中包括所述目标油藏的多个油气井中每个所述油气井的控制参数调整数据；A generating unit, configured to generate a control parameter vector of a target oil reservoir, wherein the control parameter vector includes control parameter adjustment data of each of a plurality of oil and gas wells in the target oil reservoir; 构建单元，用于基于所述控制参数向量构建所述目标油藏的净现值期望表达式；其中，所述净现值期望表达式中包括所述控制参数向量；A construction unit, configured to construct an expected net present value expression of the target reservoir based on the control parameter vector; wherein the expected net present value expression includes the control parameter vector; 迭代单元，用于以最大化净现值期望为目标，对所述净现值期望表达式中所述控制参数向量的元素取值进行迭代，并计算每一代元素取值对应的净现值期望，直至满足迭代停止条件为止；An iteration unit, for iterating the element values of the control parameter vector in the net present value expectation expression with the goal of maximizing the net present value expectation, and calculating the net present value expectation corresponding to each generation of element values until an iteration stop condition is met; 第一确定单元，用于确定迭代过程中的最大净现值期望；A first determination unit, used to determine the maximum net present value expectation in the iteration process; 第二确定单元，用于将所述最大净现值期望对应的所述元素取值确定为最优元素取值。The second determining unit is used to determine the element value corresponding to the maximum net present value expectation as the optimal element value. 9.一种计算机设备，其特征在于，包括：9. A computer device, comprising: 存储器和处理器，所述存储器和所述处理器之间互相通信连接，所述存储器中存储有计算机指令，所述处理器通过执行所述计算机指令，从而执行权利要求1至7中任一项所述的油藏注采优化方法。A memory and a processor, wherein the memory and the processor are communicatively connected to each other, the memory stores computer instructions, and the processor executes the reservoir injection and production optimization method according to any one of claims 1 to 7 by executing the computer instructions. 10.一种计算机可读存储介质，其特征在于，所述计算机可读存储介质上存储有计算机指令，所述计算机指令用于使计算机执行权利要求1至7中任一项所述的油藏注采优化方法。10. A computer-readable storage medium, characterized in that computer instructions are stored on the computer-readable storage medium, and the computer instructions are used to enable a computer to execute the oil reservoir injection and production optimization method according to any one of claims 1 to 7.
CN202410744387.5A 2024-06-11 2024-06-11 A method and device for optimizing oil reservoir injection and production Pending CN118564215A (en)

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