A method and device for characterizing evolution of a seepage field in a full development cycle of an oil reservoir

Abstract

The present application belongs to the technical field of oil and gas field development, and particularly relates to a method and device for characterizing the evolution of a seepage field in the whole development cycle of an oil reservoir, aiming to solve the problem that the seepage field cannot be simultaneously characterized in a single stage and development history control in the prior art. The method comprises: obtaining data in the whole development cycle of the oil reservoir as input data; based on the input data, checking a dynamic and static multi-information correlation model of the oil reservoir; combining the checked dynamic and static multi-information correlation model of the oil reservoir, calculating a pressure trend field, and then obtaining a velocity field; according to the velocity field, using a particle tracking calculation method, characterizing the seepage field through a streamline field, and generating the seepage field of the oil reservoir in different development stages; combining the seepage field of the oil reservoir in different development stages, constructing the evolution characterization of the seepage field in the whole development cycle and the evolution characterization feature index of the seepage field in the whole development cycle. The present application solves the problem that the seepage field cannot be simultaneously characterized in a single stage and development history control in the prior art.

Description

Technical Field

[0001] This invention belongs to the field of oil and gas field development technology, specifically relating to a method and apparatus for characterizing the evolution of seepage field throughout the entire development cycle of an oil reservoir. Background Technology

[0002] Currently, the main reservoirs in old oilfields are mostly in a "dual-high" stage of high water cut and high recovery, with the seepage field almost "solidified," resulting in prominent contradictions in underground development. Dagang Oilfield, focusing on these contradictions, has continuously explored various seepage field reconstruction models, including water injection treatment, secondary development, and tertiary oil recovery. From the perspective of seepage field formation, evolution, reconstruction, and supporting technologies, the underground seepage field has been divided through classification and grading evaluation using characterization parameters. This division is refined to the well area level in the horizontal plane and to individual sand layers in the vertical plane, yielding substantial results in seepage field reconstruction research and practical development experience.

[0003] However, existing seepage field characterization methods are mostly based on conventional numerical simulations, which leads to difficulties in generating seepage fields during high water-cut periods, long cycles, and a lack of integration with intuitive data such as production logging. Furthermore, existing seepage field characterization methods often focus on the state of a specific stage or the characterization of a single parameter, thus limiting the amount of information contained in the seepage field. To address these technical challenges, based on a thorough study of current domestic and international technological advancements and a deep integration with the geological characteristics, development status, and technological needs of domestic oilfields, a new method for characterizing the evolution of seepage fields throughout the entire development cycle of oil reservoirs is proposed and studied. Summary of the Invention

[0004] To address the aforementioned problems in the prior art, namely, the difficulty in simultaneously characterizing the seepage field under single-stage and development history control, the present invention, in its first aspect, proposes a method for characterizing the seepage field evolution throughout the entire development cycle of an oil reservoir. This method includes:

[0005] S1, acquire data from the entire development cycle of the reservoir as input data; the data includes reservoir geological data, stratigraphic well network data, development dynamic data, and production logging data;

[0006] S2, Based on the input data, verify the reservoir dynamic and static multi-information correlation model;

[0007] S3, combined with the verified reservoir dynamic and static multi-information correlation model, calculates the pressure trend field and then obtains the velocity field;

[0008] S4. Based on the velocity field, the mass tracking calculation method is used to characterize the seepage field through the streamline field and generate the reservoir seepage field at different development stages.

[0009] S5 combines the reservoir seepage field at different development stages to construct a seepage field evolution characterization and characteristic indexes for the seepage field evolution characterization throughout the entire development cycle.

[0010] In some preferred embodiments, the verification of the reservoir dynamic and static multi-information correlation model is as follows:

[0011] S21. Using the input sedimentary microfacies as constraints, a static attribute model is established using the inverse distance weighting method.

[0012] S22, using the static attribute model, output the three-dimensional K distribution at time Ti; where i = 0, Ti represents the model development time corresponding to time i, and K represents the permeability corresponding to the well point location;

[0013] S23, based on the three-dimensional K distribution at time Ti, perform gas-liquid side permeability correction;

[0014] S24. Combine the corrected permeability with the geometric mean method to extract the correction factor, perform permeability-absorption capacity correction according to the correction factor, and determine whether the corrected permeability matches the absorption profile. If yes, proceed to step S26; otherwise, proceed to step S25.

[0015] S25, calculate the permeability fluctuation range and correct the permeability at the well point location;

[0016] S26, determine whether the injection-production pressure difference matches. If it matches, let i = i + 1 and jump to S24; otherwise, perform water washing variation calculation through the pre-constructed permeability water washing variation model.

[0017] S27, output the three-dimensional K distribution at time Ti, and end the verification.

[0018] In some preferred embodiments, the correction factor is extracted using the geometric mean method, as follows:

[0019]

[0020] Where C is the reservoir profile attribute verification correction factor, N is the number of tests, j is the test number, i is the sub-layer number, q is the daily liquid production, and K is the reservoir permeability.

[0021] In some preferred embodiments, the verification of the reservoir dynamic and static multi-information association model further includes: association attribute field verification and dynamic and static integrated attribute field verification;

[0022] Correlation attribute field verification: Correlation attribute field verification: other correlation attributes are corrected based on permeability; the other correlation attributes include: saturation, porosity, and seepage physical characteristic parameters;

[0023] Based on structural and lithological characteristics, saturation correction based on permeability is performed;

[0024] Based on the singularity elimination principle, porosity correction based on permeability is performed;

[0025] Based on the characteristic parameters of multiphase seepage, the seepage physical characteristic parameters are corrected according to permeability;

[0026] The integrated dynamic and static attribute field verification includes hierarchical verification and real-time verification;

[0027] The hierarchical verification is conducted at four levels: the entire area, near-well, profile, and between wells.

[0028] The regional-level verification in the hierarchical verification refers to the overall verification of the attribute field of the entire region; the regional attribute field includes porosity, reservoir permeability, effective reservoir thickness, and oil saturation.

[0029] The near-wellbore layer verification in the graded verification refers to the verification of the near-wellbore attribute field considering dynamic and static production characteristics; the near-wellbore attribute field includes near-wellbore permeability and oil saturation;

[0030] The verification of the profile layer in the graded verification refers to verifying the attribute field of a single layer of the profile, taking into account the heterogeneity between reservoir layers; the attribute field of a single layer of the profile includes near-wellbore permeability and oil saturation.

[0031] The inter-well level verification in the graded verification refers to the verification of the inter-well attribute field considering the reservoir planar heterogeneity and the water washing variation characteristics of permeability; the inter-well attribute field includes inter-well permeability;

[0032] The real-time verification is carried out according to the set verification frequency, and dynamic comprehensive verification is performed in each verification stage, and cyclical comparative analysis is performed to continuously correct the main attribute parameters of the phase control constraint model. The main attribute parameters include porosity, reservoir permeability, effective reservoir thickness, and oil saturation. The comprehensive verification is carried out from four levels: whole area, near-well, profile, and inter-well.

[0033] In some preferred embodiments, the pressure trend field is calculated using the following method:

[0034] S31, obtain the mesh parameters, fluid parameters, pressure distribution, and saturation distribution as input parameters;

[0035] S32, determine whether initialization is required. If yes, determine the initial field parameters first, then jump to S33. If no, jump directly to S33.

[0036] S33, starting from time T=0, input well information and control parameters;

[0037] S34. Based on the well information, the control parameters, and the input parameters, calculate the relative permeability and conductivity to form a pressure matrix;

[0038] The pressure equations are solved by preprocessing the conjugate gradient method.

[0039] The saturation solution step size is determined, and the saturation is solved explicitly; in the saturation calculation, a method of one-step pressure and multi-step saturation calculation is adopted.

[0040] S35, determine whether the iterative pressure difference meets the set pressure difference threshold. If it does, perform MBE calculation and jump to S36; otherwise, jump to S34.

[0041] S36. If the calculation time has not reached the maximum set time, then jump to S34; otherwise, jump to S37.

[0042] S37 outputs the pressure trend field for this time period and ends the calculation; otherwise, jump to S33.

[0043] In some preferred embodiments, the characterization of the seepage field evolution throughout the entire development cycle includes: characterization of the instantaneous flow field and the historical superimposed flow field at each stage, characterization of the time-varying heterogeneous characteristics of the reservoir skeleton, and characterization of the water flooding field during the development history.

[0044] In some preferred embodiments, the time-varying heterogeneous characteristics of the reservoir skeleton include a permeability water washing variation model, a permeability elastoplastic deformation model, and a porosity-permeability correlation model.

[0045] The permeability washing variation model is as follows:

[0046]

[0047] Where K is the instantaneous permeability of the reservoir; t is the time at a certain moment of development; C1(K) and C2(K) are permeability correction coefficients; V w V is the seepage velocity of the aqueous phase. w0 (K) represents the critical seepage velocity;

[0048] The permeability elastic-plastic deformation model is as follows:

[0049]

[0050] Where K0 is the initial permeability, α is the deformation factor, and ΔP(t) is the pressure change amplitude;

[0051] The porosity-permeability correlation model is as follows:

[0052]

[0053] Where Φ is porosity and Φ0 is the original porosity.

[0054] In some preferred embodiments, the flooded field of the development history process is characterized as follows: based on the existing stage flow field and the historical flow field, the distribution of the flooded field is calculated using a non-piston water-driven oil displacement model, and the water production rate is used as the characterization.

[0055] The water production rate is:

[0056]

[0057] Among them, f w f is the water production rate. w0 denoted as , where is the initial water production rate of the reservoir; x is the distance from the displacement front to the injection well; u is the seepage velocity of the injected fluid underground; D is the equivalent diffusion coefficient; t′ is the derivative of the injection time; erfc(x) is the Gaussian error function; α is the dilution factor; T k b represents the permeability difference of the crossflow channel; b represents the proportion of the thickness of the production layer occupied by the crossflow channel layer; μ w The viscosity of the aqueous phase is μ. o This represents the viscosity of the oil phase.

[0058] In some preferred embodiments, the characteristic indicators for the evolution of the seepage field throughout the entire development cycle include dynamic indicators for zoned development, dynamic indicators for stratified development, dynamic indicators for inter-well development, indicators for dominant channels throughout the entire cycle, indicators for stratified and overall dominant channels, characteristic indicators for driving index, and characteristic indicators for reservoir parameters.

[0059] In a second aspect, the present invention provides a device for characterizing the evolution of seepage field throughout the entire development cycle of an oil reservoir, the device comprising:

[0060] The operation and control system is used to acquire data throughout the entire development cycle of the reservoir and store it according to a set working directory; it is also used to modify the working directory and the set control parameters; the working directory includes well network stratification, well logging interpretation, reservoir characteristics, reservoir zoning, PVT and seepage physics, perforation completion, injection and production dynamics, sedimentary microfacies, production and absorption profiles, and single-well fracturing; the revision includes modification and deletion.

[0061] A basic data input system is used for data management of the data; the data management includes adding, deleting, and modifying.

[0062] The basic data processing system is used to verify the reservoir dynamic and static multi-information correlation model of the data input into the basic data input system;

[0063] The integrated inversion processing system is used to combine the verified reservoir dynamic and static multi-information correlation model to calculate the pressure trend field and then obtain the velocity field. Based on the velocity field, the mass point tracking calculation method is used to characterize the seepage field through the streamline field to generate reservoir seepage fields at different development stages. Combining the reservoir seepage fields at different development stages, the system constructs a seepage field evolution characterization for the entire development cycle and a seepage field evolution characterization feature index for the entire development cycle.

[0064] The graphical analysis system is used to analyze and simulate the reservoir characteristics of a block based on the reservoir seepage field at different development stages, the constructed seepage field evolution characterization throughout the entire development cycle, and the characteristic indicators of the seepage field evolution characterization throughout the entire development cycle. The reservoir characteristics include water content, flow field, and production.

[0065] The beneficial effects of this invention are:

[0066] 1) This invention utilizes reservoir geology, stratigraphic well network, development dynamics, and production logging data throughout the entire reservoir development cycle to generate a seepage field under multi-information constraints, improving the scientific rigor and reliability of seepage field evolution characterization. Simultaneously considering the synergistic effects of various factors throughout the entire reservoir development cycle, it establishes a method and index for characterizing the seepage field evolution throughout the entire development cycle that can simultaneously achieve single-stage and development history control, solving the problem that existing technologies struggle to simultaneously characterize seepage fields controlled by a single stage and development history.

[0067] 2) This invention is simple to operate, stable and effective, and easy to promote and use, providing strong technical support for improving the dynamic tracking and characterization of high water-cut oilfields in my country.

[0068] 3) This invention is based on a method for characterizing the seepage field evolution throughout the entire development cycle of an oil reservoir, and uses computer programming to create operating software. It not only achieves the automatic generation and evolution characterization of the seepage field throughout the entire development cycle of an oil reservoir, but also solves the problems of difficulty and long cycle in generating the seepage field during the high water-cut period caused by the limited information contained in the seepage field in existing technologies, thus improving the accuracy and effectiveness of seepage field evolution characterization. Attached Figure Description

[0069] Other features, objects, and advantages of this application will become more apparent from the following detailed description of non-limiting embodiments with reference to the accompanying drawings.

[0070] Figure 1 This is a schematic flowchart of a reservoir seepage field evolution characterization method according to an embodiment of the present invention.

[0071] Figure 2 This is a simplified flowchart illustrating a reservoir seepage field evolution characterization method according to an embodiment of the present invention.

[0072] Figure 3 This is a flowchart of a method for generating a seepage field throughout the entire development cycle according to an embodiment of the present invention.

[0073] Figure 4 This is a flowchart of a wellpoint permeability verification method according to an embodiment of the present invention;

[0074] Figure 5 This is a flowchart of a near-wellbore permeability dynamic comprehensive correction method according to an embodiment of the present invention;

[0075] Figure 6 This is a flowchart of the correlation attribute field verification process according to an embodiment of the present invention;

[0076] Figure 7 This is a flowchart of the dynamic and static integrated attribute field verification process according to an embodiment of the present invention;

[0077] Figure 8 This is a flowchart of the energy potential calculation for the seepage field generation throughout the entire development cycle according to an embodiment of the present invention;

[0078] Figure 9 This is a flowchart of the seepage field calculation process for generating the seepage field throughout the entire development cycle according to an embodiment of the present invention;

[0079] Figure 10 This is a diagram illustrating a historical flow field tracing and calculation design method for generating seepage fields throughout the entire development cycle, according to an embodiment of the present invention.

[0080] Figure 11 This is a design framework diagram of the reservoir seepage field evolution characterization index according to an embodiment of the present invention;

[0081] Figure 12 This is a flow field diagram and injection-production correspondence diagram of the main oil layer stage in reservoir M according to an embodiment of the present invention;

[0082] Figure 13 This is a diagram showing the historical flow field and injection-production correspondence of the main oil layer in reservoir M according to an embodiment of the present invention;

[0083] Figure 14 This is an analysis diagram of the development of dominant channels in the M reservoir when significant water channeling occurs in the main oil layer according to an embodiment of the present invention.

[0084] Figure 15 This is an analysis diagram of the historical dominant channel development status when significant water channeling occurs in the main oil layer of the M reservoir according to an embodiment of the present invention. Detailed Implementation

[0085] To make the objectives, technical solutions, and advantages of this invention clearer, the technical solutions in the embodiments of this invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this invention, not all embodiments. Based on the embodiments of this invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this invention.

[0086] The present application will now be described in further detail with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are for illustrative purposes only and are not intended to limit the invention. Furthermore, it should be noted that, for ease of description, only the parts relevant to the invention are shown in the accompanying drawings.

[0087] It should be noted that, unless otherwise specified, the embodiments and features described in this application can be combined with each other.

[0088] A method for characterizing the seepage field evolution throughout the entire development cycle of an oil reservoir, as described in the first embodiment of the present invention, is as follows: Figure 1 As shown, it includes the following steps:

[0089] S1, acquire data from the entire development cycle of the reservoir as input data; the data includes reservoir geological data, stratigraphic well network data, development dynamic data, and production logging data;

[0090] S2, Based on the input data, verify the reservoir dynamic and static multi-information correlation model;

[0091] S3, combined with the verified reservoir dynamic and static multi-information correlation model, calculates the pressure trend field and then obtains the velocity field;

[0092] S4. Based on the velocity field, the mass tracking calculation method is used to characterize the seepage field through the streamline field and generate the reservoir seepage field at different development stages.

[0093] S5 combines the reservoir seepage field at different development stages to construct a seepage field evolution characterization and characteristic indexes for the seepage field evolution characterization throughout the entire development cycle.

[0094] To more clearly illustrate the method for characterizing the seepage field evolution throughout the entire development cycle of an oil reservoir according to the present invention, the steps of one embodiment of the method of the present invention will be described in detail below with reference to the accompanying drawings.

[0095] This invention provides a method for characterizing the seepage field evolution throughout the entire development cycle of an oil reservoir. First, it utilizes information and data from reservoir geology, stratigraphic well network, development dynamics, and production logging throughout the entire development cycle to generate a seepage field under multiple information constraints during the high water-cut period, thus solving the problem of automated and rapid generation of seepage fields at different stages of development. Then, considering the synergistic effects of various factors throughout the entire development cycle, a method and index for characterizing the seepage field evolution throughout the entire development cycle are established, capable of simultaneously representing the state of a single stage and the control of development history. This addresses the problem of limited information content in the seepage field, making it difficult to simultaneously characterize the seepage field under the control of a single stage and development history. Based on this, a classified data file structure is designed to generate static, dynamic, and measurement data sets, establishing a seepage field evolution characterization device for the entire development cycle of the oil reservoir, thereby achieving automatic generation and evolution characterization of the seepage field throughout the entire development cycle. This solves the challenge of generating and characterizing the seepage field under multiple information constraints during the entire development cycle of a high water-cut oil reservoir. Specifically:

[0096] S1, acquire data from the entire development cycle of the reservoir as input data; the data includes reservoir geological data, stratigraphic well network data, development dynamic data, and production logging data;

[0097] In this embodiment, information and data from reservoir geology, stratigraphic well network, development dynamics, and production logging throughout the entire development cycle of the reservoir are used to generate a seepage field under multi-information constraints for the entire development cycle of a high water-cut reservoir. Specifically, generating a seepage field under multi-information constraints for the entire development cycle of a high water-cut reservoir requires various basic information, including geology, reservoir dynamics, testing, technology, and experimental data. These various data points span different time and space periods, necessitating innovative integration of reservoir engineering methods. The basic data includes structural well locations, geological stratification, logging data, perforation layer adjustment, production dynamics, production logging, pressure testing, and reservoir stimulation.

[0098] To achieve rapid generation of seepage fields under multiple information constraints throughout the entire development cycle, a process framework for a seepage field generation method throughout the entire development cycle was constructed based on the fundamental principles of seepage field generation, such as... Figure 3 For details, please refer to S2-S4;

[0099] S2, Based on the input data, verify the reservoir dynamic and static multi-information correlation model;

[0100] The definition of reservoir permeability varies, and different information sources (well logging, core samples, production capacity, testing, etc.) have different physical meanings. To achieve uniformity of various permeabilities at the engineering level, multi-level, full-process, and multi-scale permeability correction and calibration are required. In this embodiment, the reservoir dynamic and static multi-information correlation model is calibrated from seven aspects: phase control constraint modeling, wellpoint permeability verification, profile test single-layer attribute verification, near-well dynamic comprehensive correction, inter-well attribute dynamic verification, correlated attribute field verification, and dynamic and static integrated attribute field verification, thereby forming the verified reservoir geological foundation. Details are as follows:

[0101] S21. Using the input sedimentary microfacies as constraints, a static attribute model is established using the inverse distance weighting method.

[0102] In this invention, during the phase control constraint modeling process, the reverse distance weighting method is adopted. Under multi-well constraints, the input sedimentary microfacies is used as the constraint to establish a static attribute model. For details on the process of constructing the static attribute model, please refer to the following references: [1] Lü Jianrong, Wang Xiaoguang, Qian Xin, et al. Sedimentary microfacies constraints in geological modeling technology [J], Fault Block Oil and Gas Field, 2009, 16(03): 14-16. and [2] Wei Huajing. Discrete element post-processing technology and its application in structural simulation [D], Nanjing University, 2020.

[0103] The formula for the reverse distance weighted method is:

[0104]

[0105] Among them, w i =d i -2 , z p To determine the value of the point to be interpolated; z i The values ​​of the known points surrounding the point to be interpolated are: n = n / w. i The weight coefficients for the interpolation points are given by each known point, and are inversely proportional to the distance from each point to that point; d i This represents the distance from each known point to the interpolation point.

[0106] S22, using the static attribute model, output the three-dimensional K distribution at time Ti; where i = 0, Ti represents the model development time corresponding to time i, and K represents the permeability corresponding to the well point location;

[0107] S23, based on the three-dimensional K distribution at time Ti, perform gas-liquid side permeability correction;

[0108] S24. Combine the corrected permeability with the geometric mean method to extract the correction factor, perform permeability-absorption capacity correction according to the correction factor, and determine whether the corrected permeability matches the absorption profile. If yes, proceed to step S26; otherwise, proceed to step S25.

[0109] In this invention, the definition of reservoir permeability varies, and different information sources (well logging, core analysis, production capacity analysis, testing, etc.) have different physical meanings. To achieve uniformity of various permeabilities at the engineering level, multi-level, full-process, and multi-scale permeability correction and calibration are required. Methods for wellpoint permeability verification include... Figure 4 As shown. The key to reservoir profile attribute verification is to correct single-layer attributes, especially permeability, based on multiple production-absorption profiles from a single well. Required data include production-absorption profile data and production dynamic data. Considering the potential impact of inter-layer interference and permeability fluctuations during development, a correction factor is extracted using the geometric mean method, and verification is performed based on the median attribute values ​​of the production-absorption profile.

[0110] The functional expression of the correction factor (i.e., the reservoir profile attribute verification correction factor) is as follows:

[0111]

[0112] Where C is the reservoir profile attribute verification correction factor; N is the number of tests; j is the test number sequence; i is the sub-layer sequence; q is the daily liquid production; and K is the reservoir permeability.

[0113] S25, calculate the permeability fluctuation range and correct the permeability at the well point location;

[0114] Wellpoint, near-well, and inter-well permeability are three key parameters of permeability on a plane. In this invention, near-well permeability dynamic comprehensive correction primarily utilizes dynamic information such as production capacity, dynamics, and testing during the development process, targeting dynamic fluid production ratio and pressure to correct near-well permeability. Required basic data include inter-layer interference correlation, seepage physics data, daily fluid production, production cut, and empirical correlation of production-absorption profiles. The method flow for near-well permeability dynamic comprehensive correction is as follows: Figure 5 As shown.

[0115] S26, determine whether the injection-production pressure difference matches. If it matches, let i = i + 1 and jump to S24; otherwise, perform water washing variation calculation through the pre-constructed permeability water washing variation model.

[0116] In this invention, the dynamic verification of inter-well attributes takes into account the water washing variation characteristics of reservoirs in the high water-cut period, and performs an overall verification of inter-well permeability to establish a permeability water washing variation model.

[0117] The functional expression for the permeability washing variation model is:

[0118]

[0119] In the formula, K is the instantaneous permeability of the reservoir; t is the time at a certain moment of development; C1(K) and C2(K) are permeability correction coefficients; V w V is the seepage velocity of the aqueous phase. w0 (K) represents the critical seepage velocity.

[0120] S27, output the three-dimensional K distribution at time Ti, and end the verification.

[0121] In addition, the reservoir dynamic and static multi-information correlation model also includes correlation attribute field verification and dynamic and static integrated attribute field verification;

[0122] Correlation Attribute Field Verification: Reservoir attribute field parameters are interconnected. Based on the correlations between different attributes obtained from core experiments, dynamic analysis, and dynamic monitoring, other correlated attribute fields are sequentially corrected around the permeability field. The correction objects include: saturation, porosity, and seepage physical characteristic parameters. The correction process is as follows: Figure 6 As shown. Specifically, based on structural-lithological characteristics, saturation correction based on permeability is performed; based on the singularity elimination principle, porosity correction based on permeability is performed; and based on multiphase flow characteristic parameters, seepage physical characteristic parameter correction based on permeability is performed.

[0123] Integrated dynamic and static attribute field verification: Integrated dynamic and static attribute field verification helps to unify geological information with trend dynamic information, and can serve as the reservoir basis for dominant channel research. The verification process is as follows: Figure 7 As shown (Φ is porosity, K is reservoir permeability, H is effective reservoir thickness, S...), o(This refers to oil saturation). The verification method includes two stages: hierarchical verification and real-time verification. Hierarchical verification is conducted at four levels: whole area, near-well, profile, and inter-well. The whole area level verification refers to the overall verification of the attribute field of the entire area, which includes porosity, reservoir permeability, effective reservoir thickness, and oil saturation. The near-well level verification in hierarchical verification refers to verifying the near-well attribute field considering dynamic and static production characteristics, which includes near-well permeability and oil saturation. The profile level verification in hierarchical verification refers to verifying the attribute field of a single layer in the profile, considering the heterogeneity between reservoir layers, which includes near-well permeability and oil saturation. Saturation; the verification of inter-well layers in the graded verification refers to the verification of the inter-well attribute field considering the heterogeneity of the reservoir plane and the water washing variation characteristics of permeability. The inter-well attribute field includes inter-well permeability. That is, based on the magnitude of the permeability value, unreasonable saturation / porosity / seepage physical characteristic parameters are modified. When modifying, in addition to considering whether the values ​​of saturation / porosity / seepage physical characteristic parameters are reasonable under the corresponding permeability, the constraints of structural-lithological characteristics / singularity elimination principle / multiphase seepage characteristic parameters on the magnitude of saturation / porosity / seepage physical characteristic parameter values ​​should also be considered. For the specific verification process, please refer to S21-S27.

[0124] Real-time verification involves conducting dynamic comprehensive verification at each stage, with iterative comparative analysis to continuously correct the main attribute parameters of the phase-controlled constraint modeling. These main attribute parameters include porosity, reservoir permeability, effective reservoir thickness, and oil saturation, all possessing clear physical meaning. The verification frequency is set, such as once every month or once a year, determined based on production needs. Based on this frequency, dynamic comprehensive verification (verification at four levels: full area, near-wellbore, profile, and inter-well) is conducted at each verification stage (i.e., each combined time period), with iterative comparative analysis (comparing the current level verification result with previous (excluding the first) or subsequent (excluding the last) verification results). The main attribute parameters of the phase-controlled constraint modeling are continuously corrected based on the comparison analysis results.

[0125] S3, combined with the verified reservoir dynamic and static multi-information correlation model, calculates the pressure trend field and then obtains the velocity field;

[0126] In this embodiment, the implicit pressure calculation method is used, with single-well production, water cut, and pressure as constraints. Based on model calibration, energy potential calculation and analysis are performed during reservoir development, i.e., pressure trend field calculation. The implicit pressure calculation method implicitly solves for pressure and explicitly solves for saturation. The approach is as follows: merge the oil, gas, and water phase equations, eliminate the oil, gas, and water phase saturation and water and gas phase pressures, retaining only the oil phase pressure. The Darcy coefficient and capillary pressure on the left-hand side of the equation use the values ​​from the previous time stage, i.e., explicitly processed coefficients. After calculating the oil phase pressure, substitute it into the capillary force equation to obtain the water and gas phase pressures. Then, from the oil, water, and gas phase equations, the saturation of each phase is explicitly calculated. In the saturation calculation, a one-step pressure, multi-step saturation calculation method is used. Considering the uncertainty of saturation, a method of basically fixing the saturation field in a single stage is used to generate the pressure trend field, thereby obtaining the velocity field. Figure 8 As shown, the details are as follows:

[0127] S31, obtain the mesh parameters, fluid parameters, pressure distribution, and saturation distribution as input parameters;

[0128] S32, determine whether initialization is required. If yes, determine the initial field parameters first, then jump to S33. If no, jump directly to S33.

[0129] S33, starting from time T=0, input well information and control parameters;

[0130] S34. Based on the well information, the control parameters, and the input parameters, calculate the relative permeability and conductivity to form a pressure matrix, and then construct a set of pressure equations.

[0131] The pressure equations were solved using the pre-processed conjugate gradient method.

[0132] The step size for solving saturation is determined, and saturation is solved explicitly. In the saturation calculation, a method of one-step pressure and multi-step saturation calculation is adopted. The preprocessing conjugate gradient method and the explicit solution method are existing technologies, which will not be described in detail here.

[0133] S35, determine whether the iterative pressure difference meets the set pressure difference threshold. If it does, perform MBE calculation and jump to S36; otherwise, jump to S34.

[0134] S36. If the calculation time has not reached the maximum set time, then jump to S34; otherwise, jump to S37.

[0135] S37 outputs the pressure trend field for this time period and ends the calculation; otherwise, jump to S33.

[0136] S4. Based on the velocity field, the mass tracking calculation method is used to characterize the seepage field through the streamline field and generate the reservoir seepage field at different development stages.

[0137] In this embodiment, based on the velocity field, a particle tracking calculation method is used, and the streamline field is used to characterize the seepage field, such as... Figure 9 As shown. Based on the actual classification of planar flow, particle tracking calculations are performed; in the case of no flow, the condition for ending streamline particle tracking is considered.

[0138] S5 combines the reservoir seepage field at different development stages to construct a seepage field evolution characterization and characteristic indexes for the seepage field evolution characterization throughout the entire development cycle.

[0139] In this embodiment, considering the synergistic effect of various factors throughout the entire development cycle of the reservoir, a characterization and index for the evolution of the seepage field throughout the entire development cycle are established to simultaneously achieve control over a single stage state and development history.

[0140] 1. Establish an integrated evolutionary characterization of seepage field, formation energy, and well water flooding, i.e., a seepage field evolution characterization throughout the entire development cycle.

[0141] Considering the synergistic effects of various factors throughout the entire development cycle of the reservoir, an integrated evolutionary characterization method for seepage field, formation energy, and inter-well water flooding is established from three aspects: instantaneous flow field and historical superimposed flow field of each stage, characterization of time-varying heterogeneous characteristics of the reservoir skeleton, and characterization of water flooding field during the development history process. This method aims to achieve the evolutionary characterization of seepage field throughout the entire development cycle under the control of a single stage state and development history.

[0142] 1) Instantaneous flow field and superimposed historical flow field at each stage

[0143] To clearly distinguish between instantaneous flow fields at different stages and superimposed historical flow fields, and to achieve single-stage flow field tracking and historical stage flow field quantification based on reservoir conditions, the concept of historical flow field is proposed, and its formulaic form is as follows:

[0144] The instantaneous injection / sampling relationship in this phase + the historical injection / sampling relationship in the previous phase = the historical injection / sampling relationship in this phase.

[0145] After completing the calculation and analysis of the stage flow field, the historical flow field tracing calculation can begin, such as... Figure 10 As shown. The historical flow field tracking calculation is based on the overall dynamics of the block, constrained by the dynamic adjustment of individual wells, and the seepage field is tracked and analyzed after time-weighted superposition.

[0146] 2) Characterization of time-varying heterogeneous features of reservoir framework

[0147] Characterization of time-varying heterogeneous features of reservoir skeleton, including permeability water washing variation model, permeability elastoplastic deformation model, and porosity-permeability correlation model.

[0148] The permeability elastic-plastic deformation model is as follows:

[0149]

[0150] Where K0 is the initial permeability, α is the deformation factor, and ΔP(t) is the pressure change amplitude;

[0151] The porosity-permeability correlation model is as follows:

[0152]

[0153] Where Φ is porosity and Φ0 is the original porosity.

[0154] 3) Develop historical flood field characterization

[0155] Based on existing stage flow fields and historical flow fields, a non-piston water-drive oil displacement model is used to calculate the distribution of the flooding field, with water production rate as the characterization index.

[0156] The functional expression for calculating water production rate is:

[0157]

[0158] in, f w f is the water production rate. w0 denoted as , where is the initial water production rate of the reservoir; x is the distance from the displacement front to the injection well; u is the seepage velocity of the injected fluid underground; D is the equivalent diffusion coefficient; t′ is the derivative of the injection time; erfc(x) is the Gaussian error function; α is the dilution factor; T k b represents the permeability difference of the crossflow channel; b represents the proportion of the thickness of the production layer occupied by the crossflow channel layer; μ w The viscosity of the aqueous phase is μ. o This represents the viscosity of the oil phase.

[0159] 2. Establish characteristic indicators for the evolution of seepage field throughout the entire development cycle.

[0160] Considering the synergistic effects of various factors throughout the entire reservoir development cycle, this study designs a characterization framework for the seepage field evolution across the entire development cycle, encompassing seven aspects: the injection-production relationship, inter-well injection-production intensity, water drive at each unit stage, historical water drive within a unit, water flooding status within a unit, pressure distribution within a unit, and permeability distribution. Corresponding characteristic indicators for this evolutionary characterization framework are established, including dynamic indicators for zoned development, dynamic indicators for stratified development, dynamic indicators for inter-well development, indicators for dominant channels throughout the entire cycle, indicators for stratified and overall dominant channels, characteristic indicators for driving indexes, and characteristic indicators for reservoir parameters, such as... Figure 11 As shown, this enables a quantitative characterization of the seepage field evolution throughout the entire development cycle of the reservoir.

[0161] A reservoir seepage field evolution characterization device according to a second embodiment of the present invention includes:

[0162] The operation and control system is used to acquire data throughout the entire development cycle of the reservoir and store it according to a set working directory; it is also used to modify the working directory and the set control parameters; the working directory includes well network stratification, well logging interpretation, reservoir characteristics, reservoir zoning, PVT and seepage physics, perforation completion, injection and production dynamics, sedimentary microfacies, production and absorption profiles, and single-well fracturing; the revision includes modification and deletion.

[0163] In this embodiment, the main function of the operation control system is to select a text editor to determine the working directory and control parameters for the generation, storage, modification, and deletion of data files, thus preparing for the input of the basic data input system.

[0164] The work catalog includes ten categories: well network stratification, well logging interpretation, reservoir characteristics, reservoir zoning, PVT and flow physics, perforation completion, injection-production dynamics, sedimentary microfacies, production-absorption profiles, and single-well fracturing. Based on this, a collection of static, dynamic, and logistic data volumes is generated, providing a fundamental database for characterizing the flow field evolution throughout the entire reservoir development cycle, such as... Figure 2 As shown.

[0165] A basic data input system is used for data management of the data; the data management includes adding, deleting, and modifying.

[0166] The basic data processing system is used to verify the reservoir dynamic and static multi-information correlation model of the data input into the basic data input system;

[0167] In this embodiment, the main function of the basic data processing system is to complete the verification and correction of various data, including: data processing and verification, well logging interpretation data correction, reservoir development data correction, FIP zoning after data correction, accurate sedimentary microfacies data, and post-calibration inspection of fracturing data, that is, to verify the dynamic and static multi-information correlation model of the reservoir.

[0168] The integrated inversion processing system is used to combine the verified reservoir dynamic and static multi-information correlation model to calculate the pressure trend field and then obtain the velocity field. Based on the velocity field, the mass point tracking calculation method is used to characterize the seepage field through the streamline field to generate reservoir seepage fields at different development stages. Combining the reservoir seepage fields at different development stages, the system constructs a seepage field evolution characterization for the entire development cycle and a seepage field evolution characterization feature index for the entire development cycle.

[0169] The graphical analysis system is used to analyze and simulate the reservoir characteristics of a block based on the reservoir seepage field at different development stages, the constructed seepage field evolution characterization throughout the entire development cycle, and the characteristic indicators of the seepage field evolution characterization throughout the entire development cycle. The reservoir characteristics include water content, flow field, and production.

[0170] In this embodiment, based on the calculation and inversion analysis results obtained from the previous modules (seepage field of reservoir at different development stages, seepage field evolution characterization throughout the entire development cycle, and seepage field evolution characterization feature indexes throughout the entire development cycle), the water content, flow field, production, and other conditions of the block are analyzed and simulated using graphical analysis.

[0171] Taking the seepage field evolution of reservoir M in an eastern oilfield throughout its entire development cycle as an example, this study uses a reservoir seepage field evolution characterization device to sequentially display the results from several aspects: basic geological modeling of the main oil-bearing layer, water flooding distribution, stage flow field and injection-production correspondence, historical flow field and injection-production correspondence, development of stage dominant channels, and evolution of historical dominant channels. The stage flow field and injection-production correspondence diagram of the main oil-bearing layer in reservoir M is shown below. Figure 12 As shown in the diagram. Light gray represents the production line, and dark gray represents the water injection line. The historical flow field and injection-production relationship of the main oil-bearing layer in reservoir M are shown in the diagram. Figure 13 As shown in the figure. Light gray represents the production line, and dark gray represents the water injection line. This is an analysis of the development of the dominant channels during a significant water channeling event in the main oil layer of reservoir M (day 1080). Figure 14 As shown in the diagram. Small circles represent production wells, and large circles represent water injection wells; color represents water content, and thickness represents the strength of dominant channel development. The diagram shows the historical dominant channel development status of the main oil-bearing layer in the M reservoir when significant water channeling occurred (day 1080), as shown below. Figure 15 As shown, the small circles represent production wells, and the large circles represent injection wells; the color represents water content, and the thickness represents the strength of the dominant channel development.

[0172] In summary, this invention solves the problems of existing methods, such as the difficulty and long cycle of generating seepage fields during high water cut periods, the difficulty in combining production logging data, and the limited information contained in the seepage field. It realizes the automated and rapid generation of seepage fields at different stages of development, as well as the simultaneous characterization of seepage field evolution under the control of single-stage state and development history. It is a practical and efficient method and device for characterizing seepage field evolution throughout the entire development cycle of oil reservoirs.

[0173] Those skilled in the art will understand that, for the sake of convenience and brevity, the specific working process and related descriptions of the device described above can be found in the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0174] It should be noted that the reservoir seepage field evolution characterization device provided in the above embodiments is only an example of the division of the above functional modules. In practical applications, the above functions can be assigned to different functional modules as needed, that is, the modules or steps in the embodiments of the present invention can be further decomposed or combined. For example, the modules in the above embodiments can be merged into one module, or further divided into multiple sub-modules to complete all or part of the functions described above. The names of the modules and steps involved in the embodiments of the present invention are only for distinguishing the various modules or steps and are not considered as an improper limitation of the present invention.

[0175] An electronic device according to a third embodiment of the present invention includes: at least one processor; and a memory communicatively connected to at least one of the processors; wherein the memory stores instructions executable by the processor, the instructions being executed by the processor to implement the above-described method for characterizing the seepage field evolution of a reservoir throughout its entire development cycle.

[0176] A computer-readable storage medium according to a fourth embodiment of the present invention stores computer instructions, which are executed by the computer to implement the above-described method for characterizing the seepage field evolution throughout the entire development cycle of an oil reservoir.

[0177] Those skilled in the art will understand that, for the sake of convenience and brevity, the specific working process and related descriptions of the electronic devices and computer-readable storage media described above can be referred to the corresponding processes in the foregoing method examples, and will not be repeated here.

[0178] Those skilled in the art will recognize that the modules and method steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, computer software, or a combination of both. The programs corresponding to the software modules and method steps can be placed in random access memory (RAM), main memory, read-only memory (ROM), electrically programmable ROM, electrically erasable programmable ROM, registers, hard disks, removable disks, CD-ROMs, or any other form of storage medium known in the art. To clearly illustrate the interchangeability of electronic hardware and software, the components and steps of the various examples have been generally described in terms of functionality in the foregoing description. Whether these functions are implemented in electronic hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of the invention.

[0179] The terms “first”, “second”, etc., are used to distinguish similar objects, not to describe or indicate a specific order or sequence.

[0180] The term "comprising" or any other similar term is intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus / device that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent in such process, method, article, or apparatus / device.

[0181] The technical solution of the present invention has been described above with reference to the preferred embodiments shown in the accompanying drawings. However, it will be readily understood by those skilled in the art that the scope of protection of the present invention is obviously not limited to these specific embodiments. Without departing from the principles of the present invention, those skilled in the art can make equivalent changes or substitutions to the relevant technical features, and the technical solutions after such changes or substitutions will all fall within the scope of protection of the present invention.

Claims

1. A method for characterizing the evolution of seepage field throughout the entire development cycle of an oil reservoir, characterized in that, The method includes the following steps: S1, acquire data from the entire development cycle of the reservoir as input data; the data includes reservoir geological data, stratigraphic well network data, development dynamic data, and production logging data; S2, Based on the input data, verify the reservoir dynamic and static multi-information correlation model; S3, combined with the verified reservoir dynamic and static multi-information correlation model, calculates the pressure trend field, and then obtains the velocity field; S4. Based on the velocity field, the mass tracking calculation method is used to characterize the seepage field through the streamline field and generate the reservoir seepage field at different development stages. S5 combines the reservoir seepage field at different development stages to construct a seepage field evolution characterization and characteristic indexes for the seepage field evolution characterization throughout the entire development cycle.

2. The method for characterizing the seepage field evolution throughout the entire development cycle of an oil reservoir according to claim 1, characterized in that, The verification of the reservoir dynamic and static multi-information correlation model is as follows: S21. Using the input sedimentary microfacies as constraints, a static attribute model is established using the inverse distance weighting method. S22, using the static attribute model, output the three-dimensional K distribution at time Ti; where i = 0, Ti represents the model development time corresponding to time i, and K represents the permeability corresponding to the well point location; S23, based on the three-dimensional K distribution at time Ti, perform gas-liquid side permeability correction; S24. Combine the corrected permeability with the geometric mean method to extract the correction factor, perform permeability-absorption capacity correction according to the correction factor, and determine whether the corrected permeability matches the absorption profile. If yes, proceed to step S26; otherwise, proceed to step S25. S25, calculate the permeability fluctuation range and correct the permeability at the well point location; S26, determine whether the injection-production pressure difference matches. If it matches, let i = i + 1 and jump to S24; otherwise, perform water washing variation calculation through the pre-constructed permeability water washing variation model. S27, output the three-dimensional K distribution at time Ti, and end the verification.

3. The method for characterizing the seepage field evolution throughout the entire development cycle of an oil reservoir according to claim 2, characterized in that, The correction factor is extracted using the geometric mean method, which is as follows: Where C is the reservoir profile attribute verification correction factor, N is the number of tests, j is the test number, i is the sub-layer number, q is the daily liquid production, and K is the reservoir permeability.

4. The method for characterizing the seepage field evolution throughout the entire development cycle of an oil reservoir according to claim 3, characterized in that, The verification of the reservoir dynamic and static multi-information correlation model also includes: correlation attribute field verification and dynamic and static integrated attribute field verification; Correlation attribute field verification: other correlation attributes are corrected based on permeability; the other correlation attributes include: saturation, porosity, and seepage physical characteristic parameters; Based on structural and lithological characteristics, saturation correction based on permeability is performed; Based on the singularity elimination principle, porosity correction based on permeability is performed; Based on the characteristic parameters of multiphase seepage, the seepage physical characteristic parameters are corrected according to permeability; The integrated dynamic and static attribute field verification includes hierarchical verification and real-time verification; The hierarchical verification is conducted at four levels: the entire area, near-well, profile, and between wells. The regional-level verification in the hierarchical verification refers to the overall verification of the attribute field of the entire region; the regional attribute field includes porosity, reservoir permeability, effective reservoir thickness, and oil saturation; The near-wellbore layer verification in the graded verification refers to the verification of the near-wellbore attribute field considering dynamic and static production characteristics; the near-wellbore attribute field includes near-wellbore permeability and oil saturation; The verification of the profile layer in the graded verification refers to verifying the attribute field of a single layer of the profile, taking into account the heterogeneity between reservoir layers; the attribute field of a single layer of the profile includes near-wellbore permeability and oil saturation. The inter-well level verification in the graded verification refers to the verification of the inter-well attribute field considering the reservoir planar heterogeneity and the water washing variation characteristics of permeability; the inter-well attribute field includes inter-well permeability; The real-time verification is carried out according to the set verification frequency, and dynamic comprehensive verification is performed in each verification stage, and cyclical comparative analysis is performed to continuously correct the main attribute parameters of the phase control constraint model. The main attribute parameters include porosity, reservoir permeability, effective reservoir thickness, and oil saturation. The comprehensive verification is carried out from four levels: whole area, near-well, profile, and inter-well.

5. The method for characterizing the seepage field evolution throughout the entire development cycle of an oil reservoir according to claim 4, characterized in that, The method for calculating the pressure trend field is as follows: S31, obtain the mesh parameters, fluid parameters, pressure distribution, and saturation distribution as input parameters; S32, determine whether initialization is required. If yes, determine the initial field parameters first, then jump to S33. If no, jump directly to S33. S33, starting from time T=0, input well information and control parameters; S34. Based on the well information, the control parameters, and the input parameters, calculate the relative permeability and conductivity to form a pressure matrix; The pressure equations are solved by preprocessing the conjugate gradient method. The saturation solution step size is determined, and the saturation is solved explicitly; in the saturation calculation, a method of one-step pressure and multi-step saturation calculation is adopted. S35, determine whether the iterative pressure difference meets the set pressure difference threshold. If it does, perform MBE calculation and jump to S36; otherwise, jump to S34. S36. If the calculation time has not reached the maximum set time, then jump to S34; otherwise, jump to S37. S37 outputs the pressure trend field for this time period and ends the calculation; otherwise, jump to S33.

6. The method for characterizing the evolution of seepage field throughout the entire development cycle of an oil reservoir according to claim 5, characterized in that, The evolution characterization of the seepage field throughout the entire development cycle includes: characterization of the instantaneous flow field at each stage and the superimposed historical flow field, characterization of the time-varying heterogeneous characteristics of the reservoir skeleton, and characterization of the water flooding field during the development history.

7. The method for characterizing the seepage field evolution throughout the entire development cycle of an oil reservoir according to claim 6, characterized in that, The time-varying heterogeneous characteristics of the reservoir skeleton include a permeability water washing variation model, a permeability elastoplastic deformation model, and a porosity-permeability correlation model. The permeability washing variation model is as follows: Where K is the instantaneous permeability of the reservoir; t is the time at a certain moment of development; C1(K) and C2(K) are permeability correction coefficients; V w V is the seepage velocity of the aqueous phase. w0 (K) represents the critical seepage velocity; The permeability elastic-plastic deformation model is as follows: Where K0 is the initial permeability, α is the deformation factor, and ΔP(t) is the pressure change amplitude; The porosity-permeability correlation model is as follows: Where Φ is porosity and Φ0 is the original porosity.

8. The method for characterizing the seepage field evolution throughout the entire development cycle of an oil reservoir according to claim 6, characterized in that, The flood field characterization of the development history process is as follows: based on the existing stage flow field and historical flow field, the distribution of the flood field is calculated using a non-piston water-drive oil model, and the water production rate is used as the characterization. The water production rate is: Among them, f w f is the water production rate. w0 denoted as , where is the initial water production rate of the reservoir; x is the distance from the displacement front to the injection well; u is the seepage velocity of the injected fluid underground; D is the equivalent diffusion coefficient; t′ is the derivative of the injection time; erfc(x) is the Gaussian error function; α is the dilution factor; T k b represents the permeability difference of the crossflow channel; b represents the proportion of the thickness of the production layer occupied by the crossflow channel layer; μ w The viscosity of the aqueous phase is μ. o This represents the viscosity of the oil phase.

9. The method for characterizing the seepage field evolution throughout the entire development cycle of an oil reservoir according to claim 6, characterized in that, The characteristic indicators for the evolution of the seepage field throughout the entire development cycle include dynamic indicators for zoned development, dynamic indicators for stratified development, dynamic indicators for inter-well development, indicators for dominant channels throughout the entire cycle, indicators for stratified and overall dominant channels, characteristic indicators for driving index, and characteristic indicators for reservoir parameters.

10. A device for characterizing the evolution of seepage field throughout the entire development cycle of an oil reservoir, characterized in that, The device includes: The operation and control system is used to acquire data throughout the entire development cycle of the reservoir and store it according to a set working directory; it is also used to modify the working directory and the set control parameters; the working directory includes well network stratification, well logging interpretation, reservoir characteristics, reservoir zoning, PVT and seepage physics, perforation completion, injection and production dynamics, sedimentary microfacies, production and absorption profiles, and single-well fracturing; the revision includes modification and deletion. A basic data input system is used for data management of the data; the data management includes adding, deleting, and modifying. The basic data processing system is used to verify the reservoir dynamic and static multi-information correlation model of the data input into the basic data input system; The integrated inversion processing system is used to combine the verified reservoir dynamic and static multi-information correlation model to calculate the pressure trend field and then obtain the velocity field. Based on the velocity field, the mass point tracking calculation method is used to characterize the seepage field through the streamline field to generate reservoir seepage fields at different development stages. Combining the reservoir seepage fields at different development stages, the system constructs a seepage field evolution characterization for the entire development cycle and a seepage field evolution characterization feature index for the entire development cycle. The graphical analysis system is used to analyze and simulate the reservoir characteristics of a block based on the reservoir seepage field at different development stages, the constructed seepage field evolution characterization throughout the entire development cycle, and the characteristic indicators of the seepage field evolution characterization throughout the entire development cycle. The reservoir characteristics include water content, flow field, and production.

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