Oil well production dynamic analysis method and device, electronic equipment and storage medium

Abstract

The embodiment of the invention discloses an oil well production dynamic analysis method and device, electronic equipment and a storage medium. Comprising the steps that oil reservoir static parameters and oil reservoir physicochemical properties of a target single well are obtained, and estimated covariance threshold values of the permeability, the skin coefficient and the oil drainage area of the target single well are determined according to the oil reservoir static parameters and the oil reservoir physicochemical properties; dynamic system parameters and static reservoir parameters of the target single well under the reservoir numerical simulation model are obtained, and error covariance matrixes of the permeability, the skin coefficient and the oil drainage area of the target single well are determined according to the dynamic system parameters, the static reservoir parameters and an ensemble Kalman filtering algorithm; and selecting a target covariance from the estimated covariance threshold value and the error covariance matrix, and analyzing the production dynamics of the target single well by adopting a mode of fusing the target covariance and an ensemble Kalman filtering algorithm. By adopting the scheme, the future yield of the oil well is effectively predicted, and the problems of low production dynamic analysis efficiency and poor accuracy are solved.

Description

Technical Field

[0001] The embodiments of the present invention relate to the technical field of reservoir dynamic analysis and numerical simulation inversion, and in particular to a method, device, electronic equipment and storage medium for oil well production dynamic analysis. Background Art

[0002] Dynamic analysis of oil and gas production data, also known as single well analysis, refers to the use of oil and gas field production data and information collected by various monitoring methods to analyze and study the movement patterns and development changes of underground oil and gas resources during the development of oil and gas fields, to test the implementation of development plans and related measures, to predict the effects of oil and gas field development, and to provide a basis for adjusting potential.

[0003] Traditional manual trial and error dynamic analysis methods consume a lot of manpower and machine time, often occupying most of the cost of simulation research, and are highly dependent on the simulation experience of operators. In addition, manual trial and error can only ensure that static parameters that are more in line with production rules are found under existing conditions, and cannot give the uncertainty of the static parameters obtained by the conclusion. Summary of the invention

[0004] The embodiments of the present invention provide a method, device, electronic device and storage medium for analyzing oil well production dynamics, so as to provide an efficient and accurate solution for performing dynamic analysis of modern single well production data.

[0005] In a first aspect, an embodiment of the present invention provides a method for analyzing oil well production dynamics, comprising:

[0006] Obtaining the reservoir static parameters and reservoir physicochemical properties of the target single well, and determining the estimated covariance thresholds of the permeability, skin coefficient and oil leakage area of ​​the target single well according to the reservoir static parameters and reservoir physicochemical properties; wherein the reservoir static parameters include reservoir permeability, skin coefficient and oil leakage area;

[0007] Obtain the dynamic system parameters and static reservoir parameters of the target single well under the reservoir numerical simulation model, and determine the error covariance matrix of the permeability, skin coefficient and oil leakage area of ​​the target single well based on the dynamic system parameters, static reservoir parameters and Kalman filtering;

[0008] The target covariance is selected from the estimated covariance threshold and the error covariance matrix, and the production performance of the target single well is analyzed by integrating the target covariance with the ensemble Kalman filter algorithm.

[0009] In a second aspect, an embodiment of the present invention further provides an oil well production dynamic analysis device, comprising:

[0010] An estimated covariance threshold determination module is used to obtain the reservoir static parameters and reservoir physicochemical properties of the target single well, and determine the estimated covariance thresholds of the permeability, skin coefficient and oil leakage area of ​​the target single well according to the reservoir static parameters and reservoir physicochemical properties; wherein the reservoir static parameters include reservoir permeability, skin coefficient and oil leakage area;

[0011] The error covariance matrix determination module is used to obtain the dynamic system parameters and static reservoir parameters of the target single well under the reservoir numerical simulation model, and determine the error covariance matrix of the permeability, skin coefficient and oil leakage area of ​​the target single well according to the dynamic system parameters, static reservoir parameters and Kalman filtering;

[0012] The production dynamics analysis module is used to select the target covariance from the estimated covariance threshold and the error covariance matrix, and analyze the production dynamics of the target single well by fusing the target covariance with the ensemble Kalman filter algorithm.

[0013] In a third aspect, an embodiment of the present invention further provides an electronic device, the electronic device comprising:

[0014] one or more processors;

[0015] A storage device for storing one or more programs;

[0016] When the one or more programs are executed by the one or more processors, the one or more processors implement the oil well production dynamic analysis method described in any embodiment of the present invention.

[0017] In a fourth aspect, an embodiment of the present invention further provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the oil well production dynamic analysis method described in any embodiment of the present invention.

[0018] The embodiment of the present invention provides a method, device, electronic device and storage medium for analyzing the dynamic production of an oil well, which obtains the static parameters of the reservoir and the physical and chemical properties of the target single well, and determines the estimated covariance thresholds of the permeability, skin coefficient and oil leakage area of ​​the target single well according to the static parameters and the physical and chemical properties of the reservoir; wherein the static parameters of the reservoir include the permeability, skin coefficient and oil leakage area of ​​the reservoir; obtains the dynamic system parameters and static reservoir parameters of the target single well under the reservoir numerical simulation model, and determines the error covariance matrix of the permeability, skin coefficient and oil leakage area of ​​the target single well according to the dynamic system parameters, the static reservoir parameters and the ensemble Kalman filter algorithm; selects the target covariance from the estimated covariance threshold and the error covariance matrix, and analyzes the production dynamics of the target single well by fusing the target covariance with the ensemble Kalman filter algorithm. By adopting the technical solution of the embodiment of the present invention, a method for dynamic analysis of oil well production is developed based on the ensemble Kalman filter algorithm and taking into account the estimated covariance threshold. This method can effectively estimate the reservoir permeability, skin coefficient and oil leakage area, predict the future production of the oil well, and effectively solve the problems of low efficiency and poor accuracy of production dynamic analysis. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] Other features, objects and advantages of the present invention will become more apparent by reading the detailed description of non-limiting embodiments made with reference to the following drawings. The drawings are only for the purpose of illustrating preferred embodiments and are not to be considered as limiting the present invention. Also, the same reference symbols are used throughout the drawings to represent the same parts. In the drawings:

[0020] Figure 1 is a flow chart of a method for analyzing oil well production dynamics provided in an embodiment of the present invention;

[0021] Figure 2 is a schematic diagram of a target single well 2D production system provided in an embodiment of the present invention;

[0022] Figure 3 It is a schematic diagram of the fitting process of the crude oil production of a single well in a conceptual oil reservoir under a standard Kalman filter algorithm provided in an embodiment of the present invention;

[0023] Figure 4 It is a schematic diagram of the fitting process of the oil leakage area of ​​a single well in a conceptual reservoir under a standard Kalman filter algorithm provided in an embodiment of the present invention;

[0024] Figure 5 It is a schematic diagram of the fitting process of the wellbore permeability of a single well in a conceptual oil reservoir under a standard Kalman filter algorithm provided in an embodiment of the present invention;

[0025] Figure 6It is a schematic diagram of the fitting process of the reservoir permeability of a single well in a conceptual oil reservoir under a standard Kalman filter algorithm provided in an embodiment of the present invention;

[0026] Figure 7 is a flow chart of another oil well production dynamic analysis method provided in an embodiment of the present invention;

[0027] Figure 8 It is a schematic diagram of a crude oil production fitting process based on an ensemble Kalman filter algorithm and considering a covariance threshold provided in an embodiment of the present invention;

[0028] Fig. 9 1 is a schematic diagram of an oil leakage area fitting process based on an ensemble Kalman filter algorithm and considering a covariance threshold provided in an embodiment of the present invention;

[0029] Fig.10 1 is a schematic diagram of a reservoir permeability fitting process based on an ensemble Kalman filter algorithm and considering a covariance threshold provided in an embodiment of the present invention;

[0030] Fig.11 1 is a schematic diagram of a wellbore permeability fitting process based on an ensemble Kalman filter algorithm and considering a covariance threshold provided in an embodiment of the present invention;

[0031] Fig.12 It is a schematic diagram of a single well production fitting process of a production well provided in an embodiment of the present invention;

[0032] Fig.13 It is a structural schematic diagram of an oil well production dynamic analysis device provided in an embodiment of the present invention;

[0033] Fig.14 It is a structural schematic diagram of an electronic device provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0034] The present invention will be further described in detail below in conjunction with the accompanying drawings and embodiments. It is to be understood that the specific embodiments described herein are only used to explain the present invention, rather than to limit the present invention. It should also be noted that, for ease of description, only parts related to the present invention, rather than all structures, are shown in the accompanying drawings.

[0035] Before discussing the exemplary embodiments in more detail, it should be mentioned that some exemplary embodiments are described as processes or methods depicted as flow charts. Although the flow charts describe the operations (or steps) as sequential processes, many of the operations (or steps) therein can be implemented in parallel, concurrently or simultaneously. In addition, the order of the operations can be rearranged. The process can be terminated when its operation is completed, but can also have additional steps not included in the accompanying drawings. The process can correspond to a method, function, procedure, subroutine, subprogram, etc.

[0036] Among them, the acquisition, storage, use and processing of data in the technical solution of this application are in compliance with the relevant provisions of national laws and regulations.

[0037] Figure 1 This is a flowchart of a method for analyzing oil well production dynamics provided in an embodiment of the present invention. This embodiment is applicable to the case of analyzing oil well production dynamics based on ensemble Kalman filtering and covariance threshold. The method of this embodiment can be executed by an oil well production dynamics analysis device, which can be implemented in hardware and / or software. The device can be configured in a server for oil well production dynamics analysis. The method specifically includes the following steps:

[0038] S110, obtaining reservoir static parameters and reservoir physicochemical properties of the target single well, and determining estimated covariance thresholds of permeability, skin coefficient and oil leakage area of ​​the target single well according to the reservoir static parameters and reservoir physicochemical properties.

[0039] Wherein, the reservoir static parameters include but are not limited to reservoir permeability, skin coefficient and oil leakage area; the reservoir static parameters can be directly obtained from well logging data. Reservoir permeability is used to indicate the ease of fluid flow in the pores of a porous medium, which is called the permeability of the medium, that is, the difficulty of fluid flow in the pores of a reservoir is called reservoir permeability. The skin coefficient can refer to a technical indicator for evaluating the degree of pollution and reservoir damage in the near-wellbore area of ​​an oil and gas well. The size of the skin coefficient is affected by multiple factors such as the intrusion of drilling fluid during drilling and completion, imperfect perforation opening, acidification, fracturing, well deviation, and non-Darcy seepage formed by gas near the wellbore. Mud pollution and dirt blockage around the wellbore will increase the skin coefficient and reduce the oil well production. Fracturing and acidification measures will reduce the skin coefficient and increase the oil well production. The oil leakage area can refer to the area of ​​movable oil affected by a single well; the area affected by a single well is different due to the different trap shapes, well density and method of well field.

[0040] In an optional scheme of the embodiment of the present invention, when determining the estimated covariance threshold of each reservoir static parameter, the estimated covariance threshold of the static parameter can be directly determined based on the reservoir type and the reservoir physicochemical properties, and the estimated covariance threshold can also be determined based on the approximately 95% confidence interval in the normal distribution of the reservoir static parameter obtained by sampling test. For example, when determining the oil leakage area of ​​a target single well, the distance between the target single well and the adjacent wells can be used as the length of the square oil leakage area based on the well layout method of the well site, and then the square oil leakage area of ​​the target single well can be determined, and 3% to 5% of the oil leakage area can be used as the estimated covariance threshold of the oil leakage area.

[0041] As an optional but non-limiting implementation, the estimated covariance thresholds of the target single well permeability, skin factor and oil drainage area are determined respectively according to the reservoir static parameters and reservoir physicochemical properties, including but not limited to steps A1-A3:

[0042] Step A1: Sampling and testing the reservoir static parameters of the target single well to determine the average value and variance of the reservoir static parameters obtained from the sampling and testing.

[0043] Step A2: Determine the normal distribution of each reservoir static parameter under the initial state based on the average value and variance of the reservoir static parameter obtained from sampling test.

[0044] Step A3: According to the physical and chemical properties of the target single well and the normal distribution of the static parameters of each reservoir, the estimated covariance thresholds of the permeability, skin factor and oil leakage area of ​​the target single well are determined.

[0045] Taking the determination of the target single well reservoir permeability as an example, the reservoir permeability of the reservoir near the target single well is sampled and tested, and the normal distribution of the reservoir permeability in the initial state is obtained through the average and variance obtained from the test. The estimated covariance threshold of the reservoir permeability is determined comprehensively based on the normal distribution results obtained from the sampling test results and the physical and chemical properties of the reservoir.

[0046] In an optional scheme of an embodiment of the present invention, the estimated covariance threshold can be determined based on the physical and chemical properties of the oil reservoir of the target single well. For example, for sandstone or carbonate reservoirs with good homogeneity, an estimated covariance closed value of about 3md can be selected; for conventional lithology reservoirs with poor homogeneity, the normal distribution of the sampling test results itself may have a large variance, so the estimated covariance threshold is appropriately increased to about 5md; for unconventional lithology reservoirs, since their permeability is very small, their estimated covariance threshold needs to be selected as a threshold of the same order of magnitude as the sampling test mean.

[0047] S120, obtaining the dynamic system parameters and static reservoir parameters of the target single well under the reservoir numerical simulation model, and determining the error covariance matrix of the permeability, skin coefficient and oil leakage area of ​​the target single well respectively based on the dynamic system parameters, static reservoir parameters and ensemble Kalman filter algorithm.

[0048] The dynamic system parameters may refer to the dynamic system parameters of the target single well determined by the reservoir numerical simulation model, and the dynamic system parameters include but are not limited to the pressure and saturation of the three phases of oil, gas and water. The static reservoir parameters may refer to the static reservoir parameters of the target single well determined by the reservoir numerical simulation model, and the static reservoir parameters include reservoir permeability, skin coefficient and oil leakage area.

[0049] In an optional solution of an embodiment of the present invention, the dynamic system parameters and static reservoir parameters determined by the reservoir numerical simulation model are initialized to determine the initial dynamic system parameters and the initial static reservoir parameters. The crude oil production of the target single well is calculated based on the initial dynamic system parameters, the initial static reservoir parameters and the preset measurement error. The state vector is calculated using the ensemble Kalman filter algorithm to determine the error covariance matrix of the target single well.

[0050] As an optional but non-limiting implementation, the step of obtaining the dynamic system parameters and static reservoir parameters of the target single well under the reservoir numerical simulation model, and determining the error covariance matrix of the permeability, skin coefficient and oil leakage area of ​​the target single well respectively according to the dynamic system parameters, the static reservoir parameters and the ensemble Kalman filter algorithm, includes but is not limited to steps B1-B3:

[0051] Step B1: Establish a reservoir numerical simulation model of the target single well, and use the reservoir numerical simulation model to determine the initial state parameters of the target single well and the reservoir at the current time step; the initial state parameters include dynamic system parameters and static reservoir parameters.

[0052] Step B2: Determine the crude oil production of the target single well based on the dynamic system parameters and static reservoir parameters of the target single well; wherein the dynamic system parameters include the pressure and saturation of oil, gas and water; the static reservoir parameters include reservoir permeability, skin coefficient and oil leakage area.

[0053] Step B3: According to the dynamic system parameters, static reservoir parameters and crude oil production of the target single well, determine the error covariance matrix of the permeability, skin coefficient and oil leakage area of ​​the target single well after being updated by the ensemble Kalman filter algorithm.

[0054] Among them, after establishing the reservoir numerical simulation model of the target single well, the dynamic system parameters and static reservoir parameters of the target single well at time step t-1 are obtained; the dynamic system parameters and static reservoir parameters of time step t-1 are input into the reservoir numerical simulation model to obtain the initial state parameters of the target single well at the current time step t. The crude oil production of the target single well is determined based on the initial state parameters and the preset measurement error. The augmented state vector of the target single well is determined by the ensemble Kalman filter algorithm, and the augmented state vector includes the dynamic system parameters, the static reservoir parameters and the crude oil production; the error covariance matrix of the target single well is determined based on the augmented state vector.

[0055] As an optional but non-limiting implementation, the step of establishing a reservoir numerical simulation model of the target single well and using the reservoir numerical simulation model to determine the initial state parameters of the target single well and the reservoir at the current time step includes but is not limited to steps C1-C2:

[0056] Step C1: Establish a reservoir numerical simulation model of the target single well and obtain the first state parameter of the target single well in the previous time step.

[0057] Step C2: inputting the first state parameter of the target single well into the reservoir numerical simulation model to determine the second state parameter of the target single well; wherein the second state parameter represents the initial state parameter of the target single well at the current time step.

[0058] Among them, the dynamic system parameters x of the target single well at time step t-1 are collected t-1 and the static reservoir parameter θ; the dynamic system parameter x at time step t-1 t-1 And the static reservoir parameter θ is input into the reservoir numerical simulation model of the target single well, and the reservoir numerical simulation model of the target single well is used to predict the state parameters of the target single well at the next time step t; wherein, the reservoir numerical simulation model of the target single well is:

[0059] x t =f(x t-1 ,θ)+ε m ∈R Nx ,ε m ~N(0,Q)

[0060] Among them, x t is the reservoir numerical simulation model, x t-1 is the dynamic system parameter, θ is the static reservoir parameter, ε m is the preset measurement error, and Q is the maximum value of the preset measurement error.

[0061] The initial state parameters are: x(t 0 )=x 0 ,θ(t 0 )=θ0 .

[0062] S130, selecting a target covariance from the estimated covariance threshold and the error covariance matrix, and analyzing the production performance of the target single well by fusing the target covariance with an ensemble Kalman filter algorithm.

[0063] In an optional scheme of an embodiment of the present invention, the production dynamics of a target single well is determined by using a standard ensemble Kalman filter algorithm, taking a conceptual reservoir as an example. The conceptual reservoir is a two-dimensional production system of a production well, and the model is a multiphase flow model. The production method is exhaustion production relying on natural energy. The well layout pattern adopted is a four-point type. The static parameters of the reservoir that need to be inverted are the reservoir permeability, the wellbore perimeter permeability, and the oil leakage area, and the setting values ​​are 18.34md, 33.10md, and 1.28km respectively. 2 The static parameter combination is randomly generated. Figure 2 As shown in the figure, the reservoir thickness is 15 meters, the number of grids in the X and Y directions is 11 and has passed the sensitivity test, and the reservoir depth is -3000m. The initial reservoir pressure is 5000psi, the bubble point pressure is 2800psi, the initial oil phase saturation is 0.8, the initial water phase saturation is 0.2, and the reservoir temperature is 90℃. In the single well example, crude oil production is first maintained at a constant level of 500 barrels / day. As the reservoir pressure is released, the oil well production mode is changed to production under the bottom hole flowing pressure control. The bottom hole flowing pressure is set to 500psi. Taking 10 years of monthly production data as the measured value, the ensemble Kalman filter fitting starts from the first production value after the 500 barrels / day drop. The fitting process contains 100 production data points (representing 100 fitting assimilation processes). The ensemble size used is 50 and has passed the sensitivity test. The reservoir parameters inverted in the fitting include reservoir permeability, wellbore perimeter permeability, oil leakage area, and daily oil production of a single well.

[0064] Figure 3 This is the production data fitting process of the conceptual reservoir. The fitting process contains 100 production data points (representing 100 times of fitting assimilation process). Figure 3 The marked points in the middle are the actual single-well production values ​​of the artificial model. The solid line is the model-estimated single-well production value at each time point. The dotted line includes the uncertainty of the estimate within ±3 standard deviations. It can be seen that due to the adjustment of the model to the reservoir properties, the solid line representing the model-estimated single-well production in the first 8 data points has a large deviation from the dotted solid line representing the "actual" single-well production, but the deviation gradually decreases with the fitting process, and the range of the dotted line representing the uncertainty of the single-well production rapidly decreases, and there is a certain fluctuation. After fitting 10 data points, the solid line and the dotted solid line are matched.

[0065] Figure 4The figure shows the estimated drainage area of ​​the conceptual reservoir. The drainage area is a static constant. The solid line is the ensemble Kalman filter average estimate. Figure 4 The fluctuations over time represent updates to the estimate of the spill area made by the ensemble Kalman filter model after fitting more data points, rather than changes in the spill area itself over time. In theory, the most accurate estimate from the ensemble Kalman filter model is the last estimate given. The dotted line represents the uncertainty of the estimate, which is also ±3 standard deviations. Figure 4 It can be seen that in this hypothetical example, the estimated oil spill area is approximately 1.35km 2 , the uncertainty is only ±0.008km 2 The actual oil leakage area value (straight line) is not within the confidence interval. Figure 5 and Figure 6 is the process of estimating the permeability of the conceptual reservoir. The permeability is also a reservoir constant and does not change with time. Figure 5 It can be seen that the uncertainty of the permeability estimate decreases rapidly after fitting 4 to 5 data points. Although the average estimated value of the well area permeability is close to the true value, the deviation between the estimated value of the reservoir permeability and the true value is 16 md, and the true value is not within the confidence interval.

[0066] From the conceptual reservoir test, it can be observed that the ensemble Kalman filter algorithm itself will cause the variance to decrease, but the rapid reduction of variance will prevent the model from generating enough observation distribution. Although the single well production prediction and the measured single well production quickly reach a certain degree of match, the matching degree of reservoir properties is not high. Therefore, in this conceptual reservoir example, small uncertainty and large estimation error can be observed in the reservoir properties.

[0067] An embodiment of the present invention provides an oil well production dynamics analysis based on an ensemble Kalman filter algorithm and a covariance threshold, selects a target covariance from an estimated covariance threshold and an error covariance matrix, and analyzes the production dynamics of a target single well by integrating the target covariance with the ensemble Kalman filter algorithm.

[0068] As an optional but non-limiting implementation, the step of selecting the target covariance from the estimated covariance threshold and the error covariance matrix includes but is not limited to steps D1-D3:

[0069] Step D1: Determine whether the error covariance matrix is ​​less than an estimated covariance threshold; wherein the estimated covariance threshold and the error covariance matrix belong to the same reservoir static parameter.

[0070] Step D2: If the error covariance matrix is ​​less than the estimated covariance threshold, the estimated covariance threshold is used as the target covariance.

[0071] Step D3: If the error covariance matrix is ​​not less than the estimated covariance threshold, the error covariance matrix is ​​used as the target covariance.

[0072] Among them, since the ensemble Kalman filter algorithm itself will cause the variance to decrease, the rapid decrease in variance will prevent the model from generating sufficient observation distribution. The reduction in variance means that the ensemble Kalman filter algorithm is increasingly "trusting" the prediction results of the numerical model, while ignoring the actual observed crude oil production data, which leads to unsatisfactory fitting of the reservoir static parameters. For example, if the estimated value of the oil leakage area is about 1.35km 2 , the uncertainty is only ±0.008km 2 , the uncertainty has little impact in both the numerical model and the real production environment. Properly expanding the error covariance helps to control the trust of the ensemble Kalman filter algorithm in the numerical model prediction results, thereby improving the estimation of the reservoir state. Therefore, when the error covariance matrix estimated by the module is less than the estimated covariance threshold, the estimated covariance threshold is used as the target covariance; otherwise, the error covariance matrix is ​​used as the target covariance.

[0073] As an optional but non-limiting implementation, the production performance of the target single well is analyzed by fusing the target covariance with the ensemble Kalman filter algorithm, including but not limited to steps E1-E3:

[0074] Step E1: Determine the Kalman iteration gain according to the dynamic system parameters, static reservoir parameters and target covariance of the target single well under the reservoir numerical simulation model.

[0075] Step E2: updating the initial state parameters of the target single well according to the Kalman iteration gain, and determining the updated state parameters of the target single well.

[0076] Step E3: When the dynamic system parameters and static reservoir parameters under the reservoir numerical simulation model meet the iteration termination condition, the target state parameters after iterative update are determined, and the production performance of the target single well is analyzed using the target state parameters.

[0077] Among them, the reservoir state parameters that are more in line with the actual situation are calculated by integrating the numerical model fitting results and the measured crude oil production information. Figure 7 After determining the target covariance, calculate the Kalman gain set; update the initial state parameters of the target single well according to the Kalman gain set to obtain the target state parameters of the current time step t. Perform Kalman iterative gain update on the next time step t+1, determine the iteratively updated target state parameters after the iteration is terminated, and use the target state parameters to analyze the production performance of the target single well.

[0078] By adopting the technical solution of the embodiment of the present invention, the ensemble Kalman filter naturally reduces the covariance during the assimilation process, thereby further reducing the spread and reducing the accuracy of the inversion parameter estimation. The present invention adopts a method of calculating the error covariance matrix and estimating the covariance threshold of the set model, and gives a greater weight to the measured value in the update iteration. This method improves the accuracy of the ensemble Kalman filter mean estimation and maintains a reasonable uncertainty in the inversion parameters.

[0079] In the embodiment of the present invention, in the ensemble Kalman filter oil well production dynamic analysis method considering the target covariance, the estimated covariance thresholds of permeability and oil leakage area are artificially set, and the thresholds are 4md and 0.05km respectively. 2 If the error covariance matrix calculated by the model is less than the estimated covariance threshold, the estimated covariance threshold of the reservoir properties is set to the target covariance and the threshold remains unchanged. The above adjustment ensures that the prediction set has a larger distribution. Figure 8 is the adjusted single well production match; Figure 8 It can be seen that after assimilating about 18 data points, the single well production has achieved a good match. Fig. 9 is the estimated area of ​​oil spill after adjustment; Fig. 9 It can be seen from the figure that the estimated oil spill area after adjustment is similar to the estimated value before adjustment, with little change. However, due to the amplification of the uncertainty of the estimate, the true value falls within the confidence interval of the estimate. Considering the true value (1.28km 2 ) size, artificially enlarged uncertainty (±0.05km 2 ) is more reasonable. Fig.10 and Fig.11 The permeability estimation process of the hypothetical model after the model improvement. It can be seen from the two figures that as the fitting process progresses, the estimated value of the permeability is closer to the true value; after fitting about 50 data points, the absolute error between the estimated value and the true value is less than 1md. It can be observed that the permeability estimation has been significantly improved. The uncertainty of the permeability estimation has been amplified after adjustment, and the true value falls into the confidence interval; the enlarged uncertainty (±4md) is more reasonable in practical applications. In summary, the above adjustments improve the accuracy of the ensemble Kalman filter mean estimation while retaining reasonable uncertainty.

[0080] In an optional solution of the embodiment of the present invention, taking a typical thin-layer porous and fractured carbonate reservoir as an example, some production wells are evaluated in combination with the geological characteristics and production performance data of the reservoir. Fig.12The production history data of the oil well in this typical thin-layer porous carbonate reservoir is shown in Figure 1. The first marked data point is the first fitting point. The pressure drop of the production well is 1 / 4 to 2 / 3 of the difference between the formation pressure and the bubble point pressure or the bottom hole pressure. The reservoir pressure test shows that the reservoir pressure has gradually decreased from about 5000psi to about 3500psi, and the current bottom hole flowing pressure is about 2700psi. The fitting process only involves the fitting of the single well production data. During the fitting process, the measurement error of the single well crude oil production starts from 10 barrels / day; the 33 data points after the anchor point are used for fitting, and the last 10 points are used to verify the estimate. Fig.12 The single well production fitting process of this typical thin-layer porous carbonate reservoir is shown. The large estimation error at the beginning of the fitting rapidly decreases as the assimilation proceeds; after fitting about 20 data points, the fitting of the single well production achieves a very good matching effect; starting from the 30th data point, the reservoir permeability, skin coefficient and oil leakage area estimated by the model at the 30th data point are maintained unchanged, and the remaining production continues to be estimated. The results show that the prediction results are consistent with the actual production data.

[0081] An embodiment of the present invention provides a method for analyzing the dynamic production of an oil well, which obtains the static parameters of the reservoir and the physical and chemical properties of the target single well, and determines the estimated covariance thresholds of the permeability, skin coefficient and oil leakage area of ​​the target single well according to the static parameters and the physical and chemical properties of the reservoir; wherein the static parameters of the reservoir include the permeability, skin coefficient and oil leakage area of ​​the reservoir; the dynamic system parameters and static reservoir parameters of the target single well under the reservoir numerical simulation model are obtained, and the error covariance matrix of the permeability, skin coefficient and oil leakage area of ​​the target single well is determined according to the dynamic system parameters, the static reservoir parameters and the ensemble Kalman filter algorithm; the target covariance is selected from the estimated covariance threshold and the error covariance matrix, and the production dynamics of the target single well are analyzed by fusing the target covariance with the ensemble Kalman filter algorithm. By adopting the technical solution of the embodiment of the present invention, a method for dynamic analysis of oil well production is developed based on the ensemble Kalman filter algorithm and taking into account the estimated covariance threshold. This method can effectively estimate the reservoir permeability, skin coefficient and oil leakage area, predict the future production of the oil well, and effectively solve the problems of low efficiency and poor accuracy of production dynamic analysis.

[0082] Fig.13 This is a schematic diagram of the structure of an oil well production dynamic analysis device provided in an embodiment of the present invention. The technical solution of this embodiment can be applied to the situation of analyzing the oil well production dynamics based on the ensemble Kalman filter and the covariance threshold. The device can be implemented by software and / or hardware, and is generally integrated on any electronic device with network communication function, including but not limited to: servers, computers, personal digital assistants and other devices. Fig.13As shown, the oil well production dynamic analysis device provided in this embodiment may include: an estimation covariance threshold determination module 1310, an error covariance matrix determination module 1320 and a production dynamic analysis module 1330; wherein,

[0083] The estimated covariance threshold determination module 1310 is used to obtain the reservoir static parameters and reservoir physical and chemical properties of the target single well, and determine the estimated covariance thresholds of the permeability, skin coefficient and oil leakage area of ​​the target single well according to the reservoir static parameters and reservoir physical and chemical properties; wherein the reservoir static parameters include reservoir permeability, skin coefficient and oil leakage area;

[0084] The error covariance matrix determination module 1320 is used to obtain the dynamic system parameters and static reservoir parameters of the target single well under the reservoir numerical simulation model, and determine the error covariance matrix of the permeability, skin coefficient and oil leakage area of ​​the target single well according to the dynamic system parameters, static reservoir parameters and Kalman filtering;

[0085] The production performance analysis module 1330 is used to select a target covariance from the estimated covariance threshold and the error covariance matrix, and analyze the production performance of a target single well by fusing the target covariance with the ensemble Kalman filter algorithm.

[0086] Based on the above embodiment, optionally, the estimated covariance threshold determination module includes:

[0087] Sampling and testing the reservoir static parameters of the target single well to determine the average value and variance of the reservoir static parameters obtained from the sampling and testing;

[0088] According to the average value and variance of the reservoir static parameters obtained from sampling tests, the normal distribution of each reservoir static parameter under the initial state is determined;

[0089] According to the physical and chemical properties of the target single well reservoir and the normal distribution of each reservoir static parameter, the estimated covariance thresholds of the permeability, skin coefficient and oil leakage area of ​​the target single well are determined.

[0090] Based on the above embodiment, optionally, the error covariance matrix determination module includes:

[0091] Establishing a reservoir numerical simulation model of the target single well, and using the reservoir numerical simulation model to determine the initial state parameters of the target single well and the reservoir at the current time step; the initial state parameters include dynamic system parameters and static reservoir parameters;

[0092] Determine the crude oil production of the target single well based on the dynamic system parameters and static reservoir parameters of the target single well; wherein the dynamic system parameters include the pressure and saturation of oil, gas and water; the static reservoir parameters include reservoir permeability, skin coefficient and oil leakage area;

[0093] According to the dynamic system parameters, static reservoir parameters and crude oil production of the target single well, the error covariance matrix of the permeability, skin coefficient and oil leakage area of ​​the target single well after being updated by the ensemble Kalman filter algorithm is determined.

[0094] Based on the above embodiment, optionally, the error covariance matrix determination module further includes:

[0095] Establishing a reservoir numerical simulation model of a target single well and obtaining the first state parameters of the target single well at the previous time step;

[0096] The first state parameter of the target single well is input into the reservoir numerical simulation model to determine the second state parameter of the target single well; wherein the second state parameter represents the initial state parameter of the target single well at the current time step.

[0097] Based on the above embodiment, optionally, the production dynamic analysis module includes:

[0098] Determining whether the error covariance matrix is ​​less than an estimated covariance threshold; wherein the estimated covariance threshold and the error covariance matrix belong to the same reservoir static parameter;

[0099] If the error covariance matrix is ​​less than the estimated covariance threshold, the estimated covariance threshold is used as the target covariance;

[0100] If the error covariance matrix is ​​not less than the estimated covariance threshold, the error covariance matrix is ​​used as the target covariance.

[0101] Based on the above embodiment, optionally, the production dynamic analysis module further includes:

[0102] The Kalman iteration gain is determined according to the dynamic system parameters, static reservoir parameters and target covariance of the target single well under the reservoir numerical simulation model;

[0103] The initial state parameters of the target single well are updated according to the Kalman iteration gain to determine the updated state parameters of the target single well;

[0104] When the dynamic system parameters and static reservoir parameters under the reservoir numerical simulation model meet the iteration termination condition, the target state parameters after iterative update are determined, and the production performance of the target single well is analyzed using the target state parameters.

[0105] The oil well production dynamic analysis device provided in the embodiment of the present invention can execute the oil well production dynamic analysis method provided in any embodiment of the present invention mentioned above, and has the corresponding functions and beneficial effects of executing the oil well production dynamic analysis method. For detailed process, please refer to the relevant operations of the oil well production dynamic analysis method in the above-mentioned embodiment.

[0106] Fig.14 1 is a schematic diagram of the structure of an electronic device provided in an embodiment of the present invention. The electronic device 10 is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device may also represent various forms of mobile devices, such as personal digital processing, cellular phones, smart phones, wearable devices (such as helmets, glasses, watches, etc.) and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely examples and are not intended to limit the implementation of the present invention described and / or required herein.

[0107] like Fig.14 As shown, the electronic device 10 includes at least one processor 11, and a memory connected to the at least one processor 11, such as a read-only memory (ROM) 12, a random access memory (RAM) 13, etc., wherein the memory stores a computer program that can be executed by at least one processor, and the processor 11 can perform various appropriate actions and processes according to the computer program stored in the read-only memory (ROM) 12 or the computer program loaded from the storage unit 18 to the random access memory (RAM) 13. In the RAM 13, various programs and data required for the operation of the electronic device 10 can also be stored. The processor 11, the ROM 12, and the RAM 13 are connected to each other through a bus 14. An input / output (I / O) interface 15 is also connected to the bus 14.

[0108] A number of components in the electronic device 10 are connected to the I / O interface 15, including: an input unit 16, such as a keyboard, a mouse, etc.; an output unit 17, such as various types of displays, speakers, etc.; a storage unit 18, such as a disk, an optical disk, etc.; and a communication unit 19, such as a network card, a modem, a wireless communication transceiver, etc. The communication unit 19 allows the electronic device 10 to exchange information / data with other devices through a computer network such as the Internet and / or various telecommunication networks.

[0109] The processor 11 may be a variety of general and / or special processing components with processing and computing capabilities. Some examples of the processor 11 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various dedicated artificial intelligence (AI) computing chips, various processors running machine learning model algorithms, a digital signal processor (DSP), and any appropriate processor, controller, microcontroller, etc. The processor 11 executes the various methods and processes described above, such as an oil well production dynamic analysis method.

[0110] In some embodiments, the oil well production dynamics analysis method may be implemented as a computer program, which is tangibly contained in a computer-readable storage medium, such as a storage unit 18. In some embodiments, part or all of the computer program may be loaded and / or installed on the electronic device 10 via the ROM 12 and / or the communication unit 19. When the computer program is loaded into the RAM 13 and executed by the processor 11, one or more steps of the oil well production dynamics analysis method described above may be performed. Alternatively, in other embodiments, the processor 11 may be configured to execute the oil well production dynamics analysis method in any other appropriate manner (e.g., by means of firmware).

[0111] Various implementations of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), application specific standard products (ASSPs), systems on chips (SOCs), load programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various implementations can include: being implemented in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which can be a special purpose or general purpose programmable processor that can receive data and instructions from a storage system, at least one input device, and at least one output device, and transmit data and instructions to the storage system, the at least one input device, and the at least one output device.

[0112] Computer programs for implementing the methods of the present invention may be written in any combination of one or more programming languages. These computer programs may be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing device, so that when the computer program is executed by the processor, the functions / operations specified in the flow chart and / or block diagram are implemented. The computer program may be executed entirely on the machine, partially on the machine, partially on the machine and partially on a remote machine as a stand-alone software package, or entirely on a remote machine or server.

[0113] In the context of the present invention, a computer-readable storage medium may be a tangible medium that may contain or store a computer program for use by or in combination with an instruction execution system, device or equipment. A computer-readable storage medium may include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, devices or equipment, or any suitable combination of the foregoing. Alternatively, a computer-readable storage medium may be a machine-readable signal medium. A more specific example of a machine-readable storage medium may include an electrical connection based on one or more lines, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

[0114] To provide interaction with a user, the systems and techniques described herein may be implemented on an electronic device having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and a pointing device (e.g., a mouse or trackball) through which the user can provide input to the electronic device. Other types of devices may also be used to provide interaction with the user; for example, the feedback provided to the user may be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user may be received in any form (including acoustic input, voice input, or tactile input).

[0115] The systems and techniques described herein may be implemented in a computing system that includes backend components (e.g., as a data server), or a computing system that includes middleware components (e.g., an application server), or a computing system that includes frontend components (e.g., a user computer with a graphical user interface or a web browser through which a user can interact with implementations of the systems and techniques described herein), or a computing system that includes any combination of such backend components, middleware components, or frontend components. The components of the system may be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: a local area network (LAN), a wide area network (WAN), a blockchain network, and the Internet.

[0116] A computing system may include a client and a server. The client and the server are generally remote from each other and usually interact through a communication network. The client and server relationship is generated by computer programs running on the corresponding computers and having a client-server relationship with each other. The server may be a cloud server, also known as a cloud computing server or cloud host, which is a host product in the cloud computing service system to solve the defects of difficult management and weak business scalability in traditional physical hosts and VPS services.

[0117] It should be understood that the various forms of processes shown above can be used to reorder, add or delete steps. For example, the steps described in the present invention can be executed in parallel, sequentially or in different orders, as long as the desired results of the technical solution of the present invention can be achieved, and this document does not limit this.

[0118] The above specific implementations do not constitute a limitation on the protection scope of the present invention. It should be understood by those skilled in the art that various modifications, combinations, sub-combinations and substitutions can be made according to design requirements and other factors. Any modification, equivalent substitution and improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.

Claims

1. A method for analyzing oil well production dynamics, characterized in that: The method comprises: Obtaining the reservoir static parameters and reservoir physicochemical properties of the target single well, and determining the estimated covariance thresholds of the permeability, skin coefficient and oil leakage area of ​​the target single well according to the reservoir static parameters and reservoir physicochemical properties; wherein the reservoir static parameters include reservoir permeability, skin coefficient and oil leakage area; Obtain the dynamic system parameters and static reservoir parameters of the target single well under the reservoir numerical simulation model, and determine the error covariance matrix of the permeability, skin coefficient and oil leakage area of ​​the target single well based on the dynamic system parameters, static reservoir parameters and ensemble Kalman filter algorithm; The target covariance is selected from the estimated covariance threshold and the error covariance matrix, and the production performance of the target single well is analyzed by integrating the target covariance with the ensemble Kalman filter algorithm.

2. The method according to claim 1, characterized in that The estimated covariance thresholds of target single well permeability, skin coefficient and oil leakage area are determined respectively according to the reservoir static parameters and reservoir physicochemical properties, including: Sampling and testing the reservoir static parameters of the target single well to determine the average value and variance of the reservoir static parameters obtained from the sampling and testing; According to the average value and variance of the reservoir static parameters obtained by sampling test, the normal distribution of each reservoir static parameter under the initial state is determined; According to the physical and chemical properties of the target single well reservoir and the normal distribution of each reservoir static parameter, the estimated covariance thresholds of the permeability, skin coefficient and oil leakage area of ​​the target single well are determined.

3. The method according to claim 1, characterized in that The method of obtaining the dynamic system parameters and static reservoir parameters of the target single well under the reservoir numerical simulation model, and determining the error covariance matrix of the permeability, skin coefficient and oil leakage area of ​​the target single well respectively according to the dynamic system parameters, the static reservoir parameters and the ensemble Kalman filter algorithm, includes: Establishing a reservoir numerical simulation model of the target single well, and using the reservoir numerical simulation model to determine the initial state parameters of the target single well and the reservoir at the current time step; the initial state parameters include dynamic system parameters and static reservoir parameters; Determine the crude oil production of the target single well based on the dynamic system parameters and static reservoir parameters of the target single well; wherein the dynamic system parameters include the pressure and saturation of oil, gas and water; the static reservoir parameters include reservoir permeability, skin coefficient and oil leakage area; According to the dynamic system parameters, static reservoir parameters and crude oil production of the target single well, the error covariance matrix of the permeability, skin coefficient and oil leakage area of ​​the target single well after being updated by the ensemble Kalman filter algorithm is determined.

4. The method according to claim 3, characterized in that The step of establishing a reservoir numerical simulation model of the target single well and using the reservoir numerical simulation model to determine initial state parameters of the target single well and the reservoir at the current time step includes: Establishing a reservoir numerical simulation model of a target single well and obtaining the first state parameters of the target single well at the previous time step; The first state parameter of the target single well is input into the reservoir numerical simulation model to determine the second state parameter of the target single well; wherein the second state parameter represents the initial state parameter of the target single well at the current time step.

5. The method according to claim 1, characterized in that: The step of selecting a target covariance from an estimated covariance threshold and an error covariance matrix includes: Determining whether the error covariance matrix is ​​less than an estimated covariance threshold; wherein the estimated covariance threshold and the error covariance matrix belong to the same reservoir static parameter; If the error covariance matrix is ​​less than the estimated covariance threshold, the estimated covariance threshold is used as the target covariance; If the error covariance matrix is ​​not less than the estimated covariance threshold, the error covariance matrix is ​​used as the target covariance.

6. The method according to claim 3, characterized in that The production performance of the target single well is analyzed by integrating the target covariance with the ensemble Kalman filter algorithm, including: The Kalman iteration gain is determined according to the dynamic system parameters, static reservoir parameters and target covariance of the target single well under the reservoir numerical simulation model; The initial state parameters of the target single well are updated according to the Kalman iteration gain to determine the updated state parameters of the target single well; When the dynamic system parameters and static reservoir parameters under the reservoir numerical simulation model meet the iteration termination condition, the target state parameters after iterative update are determined, and the production performance of the target single well is analyzed using the target state parameters.

7. An oil well production dynamic analysis device, characterized in that: The device comprises: An estimated covariance threshold determination module is used to obtain the reservoir static parameters and reservoir physicochemical properties of the target single well, and determine the estimated covariance thresholds of the permeability, skin coefficient and oil leakage area of ​​the target single well according to the reservoir static parameters and reservoir physicochemical properties; wherein the reservoir static parameters include reservoir permeability, skin coefficient and oil leakage area; The error covariance matrix determination module is used to obtain the dynamic system parameters and static reservoir parameters of the target single well under the reservoir numerical simulation model, and determine the error covariance matrix of the permeability, skin coefficient and oil leakage area of ​​the target single well according to the dynamic system parameters, static reservoir parameters and Kalman filtering; The production dynamics analysis module is used to select the target covariance from the estimated covariance threshold and the error covariance matrix, and analyze the production dynamics of the target single well by fusing the target covariance with the ensemble Kalman filter algorithm.

8. The device according to claim 7, characterized in that The estimated covariance threshold determination module comprises: Sampling and testing the reservoir static parameters of the target single well to determine the average value and variance of the reservoir static parameters obtained from the sampling and testing; According to the average value and variance of the reservoir static parameters obtained by sampling test, the normal distribution of each reservoir static parameter under the initial state is determined; According to the physical and chemical properties of the target single well reservoir and the normal distribution of each reservoir static parameter, the estimated covariance thresholds of the permeability, skin coefficient and oil leakage area of ​​the target single well are determined.

9. An electronic device, characterized in that: include: one or more processors; A storage device for storing one or more programs; When the one or more programs are executed by the one or more processors, the one or more processors implement the oil well production dynamic analysis method described in any one of claims 1-6.

10. A storage medium containing computer executable instructions, characterized in that: The computer executable instructions are used to execute the oil well production dynamics analysis method as described in any one of claims 1-6 when executed by a computer processor.

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