Perforation parameter optimization method and device for carbon dioxide injection well and electronic equipment

Abstract

The invention provides a carbon dioxide injection well perforation parameter optimization method and device and electronic equipment, and the method comprises the steps: carrying out the standardization processing of geological and logging data, and constructing a multi-dimensional data set comprising lithology, porosity, permeability, crack parameters and a ground stress field; a three-dimensional digital twinborn model is constructed based on the data set, and reservoir heterogeneity and fracture network distribution are restored; on the basis of the digital twinborn model, performing multi-physics field coupling simulation including carbon dioxide migration simulation, shaft stress distribution simulation and long-term corrosion prediction; based on a multi-objective optimization algorithm, with the effective wave and volume of the carbon dioxide, the integrity coefficient of the shaft and the anti-channeling safety coefficient as objectives, an optimal perforation parameter combination is screened; and outputting a visual report including perforation parameters, simulation results and risk assessment. Digital twinning, multi-physics field simulation and multi-objective optimization are deeply fused, and a set of complete, accurate and efficient perforation parameter optimization solution from data to decision is formed.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of computer, in particular, relates to a carbon dioxide injection well perforation parameter optimization method and device and electronic equipment. BACKGROUND

[0002] At present, in the process of carbon dioxide geological storage and oil displacement, the optimization of perforation parameters (such as hole density, phase angle, and hole arrangement mode) mainly depends on empirical formula or single physical field simulation, and lacks systematic consideration of multi-dimensional geological characteristics such as reservoir heterogeneity, fracture network, and geostress field. The traditional method often ignores the influence of natural fractures on the migration path of carbon dioxide, and does not include long-term corrosion and stress concentration of wellbore into the parameter optimization system, resulting in low injection efficiency, high risk of wellbore integrity, and easy occurrence of carbon dioxide channeling.

[0003] Existing software is mostly based on the assumption of homogeneity, and cannot realize the dynamic correlation of geological data and perforation parameters. The optimization results often deviate from the actual reservoir conditions, seriously affecting the carbon dioxide injection effect and wellbore life. SUMMARY

[0004] The purpose of the embodiments of the present application is to provide a carbon dioxide injection well perforation parameter optimization method and device and electronic equipment to solve the above technical problems.

[0005] In a first aspect, the present application provides a carbon dioxide injection well perforation parameter optimization method, comprising: standardizing geological and logging data, and constructing a multi-dimensional data set including lithology, porosity, permeability, fracture parameters, and geostress field; constructing a three-dimensional digital twin model based on the data set, and restoring reservoir heterogeneity and fracture network distribution; performing multi-physical field coupling simulation based on the digital twin model, including carbon dioxide migration simulation, wellbore stress distribution simulation, and long-term corrosion prediction; based on a multi-objective optimization algorithm, taking carbon dioxide effective swept volume, wellbore integrity coefficient, and anti-channeling safety coefficient as targets, and screening the optimal perforation parameter combination; outputting a visual report containing perforation parameters, simulation results, and risk assessment.

[0006] In an optional implementation, the standardization processing includes: cleaning the logging data, eliminating invalid sections and completing missing data; performing well logging interpretation correction and interpolation processing on porosity and permeability data; extracting fracture strike, dip angle, and density parameters from imaging logging; inverting the geostress field based on sonic logging and fracturing data and performing three-dimensional interpolation; All parameters are normalized and labeled reservoir quality partition.

[0007] In an optional implementation, constructing the three-dimensional digital twin model comprises: A discrete fracture network is generated based on fracture parameters, and a conductivity attribute is assigned; A three-dimensional geostress field is generated based on geostress data interpolation; An unstructured grid subdivision technique is used to grid the reservoir sweet spot area; Lithology, porosity, fracture, and stress attributes are mapped to the grid model.

[0008] In an optional implementation, the multi-physical field coupling simulation comprises: Carbon dioxide migration simulation is performed based on the perforation-reservoir interaction response coefficient, and the hole density and perforation arrangement are optimized; The phase angle is optimized according to the angle between the geostress direction and the fracture trend to avoid stress concentration and fracture channeling; The wellbore corrosion rate is predicted based on the formation water salinity and the carbon dioxide partial pressure, and the long-term wellbore integrity is evaluated.

[0009] In an optional implementation, the carbon dioxide migration simulation comprises: The hole interference coefficient under different hole densities is calculated to determine the optimal hole density range; Targeted perforation is implemented based on the reservoir quality index distribution, and the hole density is increased in the sweet spot area and decreased in the non-reservoir area; In the vicinity of the interlayer interface, the perforation strategy is compensated by encryption to improve the longitudinal sweep ability of carbon dioxide.

[0010] In an optional implementation, the phase angle optimization comprises: The stress-oriented or fracture-oriented phase angle is selected according to the angle between the geostress direction and the fracture trend; The wellbore stress concentration coefficient under different phase angles is simulated, and the phase angle that meets the safety threshold is selected.

[0011] In an optional implementation, the long-term corrosion prediction comprises: The short-term uniform corrosion rate is calculated based on the formation water salinity and the carbon dioxide partial pressure; The long-term corrosion rate is corrected by introducing the stress concentration coefficient to predict the wellbore wall thickness loss; The remaining wall thickness of the wellbore under different perforation parameter schemes is evaluated to determine whether it meets the safety requirements.

[0012] In an optional implementation, the multi-objective optimization algorithm uses the NSGA-III algorithm, and the effective swept volume of carbon dioxide, the wellbore integrity coefficient, and the anti-channeling safety coefficient are used as targets to generate a Pareto optimal solution set.

[0013] In a second aspect, the present application provides a device for optimizing perforation parameters of a carbon dioxide injection well, comprising: a standardization module configured to standardize geological and logging data and construct a multi-dimensional data set including lithology, porosity, permeability, fracture parameters and geostress field; a reduction module configured to construct a three-dimensional digital twin model based on the data set and restore reservoir heterogeneity and fracture network distribution; a simulation module configured to perform multi-physical field coupling simulation based on the digital twin model, including carbon dioxide migration simulation, wellbore stress distribution simulation and long-term corrosion prediction; a screening module configured to screen an optimal perforation parameter combination based on a multi-objective optimization algorithm and taking effective carbon dioxide swept volume, wellbore integrity coefficient and anti-channeling safety coefficient as targets; an output module configured to output a visual report including perforation parameters, simulation results and risk assessment.

[0014] In a third aspect, the present application provides an electronic device comprising a processor and a memory, wherein the memory stores machine executable instructions capable of being executed by the processor, and the processor is capable of executing the machine executable instructions to implement the method of any one of the preceding embodiments.

[0015] The present application deeply integrates digital twin, multi-physical field simulation and multi-objective optimization, and forms a complete, accurate and efficient perforation parameter optimization solution from data to decision.

[0016] By constructing a digital twin model integrating discrete fracture network (DFN) and three-dimensional geostress field, the reservoir heterogeneity is restored, and the simulation error is reduced.

[0017] Through flow field-stress-corrosion multi-physical field coupling simulation, short-term injection efficiency and long-term wellbore safety are first integrated into a unified optimization framework, and the comprehensive goal of “injection, spreading and long-term safety” is achieved.

[0018] Through “perforation-reservoir interactive response coefficient (PRIC)” and “sweet spot area targeted perforation logic”, the geological parameters (such as lithology, fracture and stress) are dynamically and quantitatively converted into perforation design basis, so that the optimization results are highly consistent with the reservoir characteristics. BRIEF DESCRIPTION OF DRAWINGS

[0019] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the following will briefly introduce the drawings needed to be used in the embodiments of the present application. It should be understood that the following drawings only show some embodiments of the present application, and therefore should not be regarded as a limitation on the scope. For those skilled in the art, other related drawings can also be obtained without creative labor on the basis of these drawings.

[0020] Figure 1 A carbon dioxide injection well perforation parameter optimization method flowchart provided by an embodiment of the present application; Figure 2 A carbon dioxide injection well perforation parameter optimization device structure diagram provided by an embodiment of the present application; Figure 3 An electronic device structure diagram provided by an embodiment of the present application. DETAILED DESCRIPTION

[0021] The technical solutions in the embodiments of the present application will be described below with reference to the drawings in the embodiments of the present application.

[0022] Figure 1 A carbon dioxide injection well perforation parameter optimization method flowchart provided by an embodiment of the present application. As shown in the figure, Figure 1 the method can include: S110, standardizing geological and logging data, and constructing a multi-dimensional data set including lithology, porosity, permeability, fracture parameters and geostress field.

[0023] The standardization processing includes: cleaning the logging data, removing invalid sections and completing missing data; porosity and permeability data are subjected to well logging interpretation correction and interpolation processing; fracture strike, dip angle and density parameters in imaging logging are extracted; geostress field is inverted based on sonic logging and fracturing data and is subjected to three-dimensional interpolation; all parameters are subjected to normalization processing and are labeled reservoir quality zoning.

[0024] In some embodiments, the original geological and logging data have noise, missing and dimension difference, and the standardization processing can improve the data quality and provide reliable input for subsequent modeling and simulation; the multi-dimensional data set integrates key parameters such as lithology, physical property, fracture and geostress, and comprehensively characterizes the reservoir characteristics.

[0025] Invalid data (such as abnormal values caused by logging instrument failure, unmeasured sections) will interfere with subsequent analysis, and completing missing data can ensure data continuity: through visual inspection of logging curves (such as natural gamma, resistivity curves), abnormal sections (such as wellhead sections where the instrument does not go down the well, bad well sections where the borehole collapses) are identified and deleted; for local missing data (such as porosity not measured at a certain depth), interpolation method (linear interpolation, spline interpolation or Kriging interpolation based on adjacent well data) is used to complete, for example: if the porosity at depths 2000m and 2002m is 15% and 18% respectively, the porosity at 2001m is calculated by linear interpolation to be 16.5%.

[0026] In some embodiments, porosity calculated from well logs (e.g. density log, acoustic log) is biased from core measured value and needs to be corrected; discrete well log data needs to be interpolated to continuous profile to meet the requirement of 3D modeling.

[0027] The well logs can be calibrated with core data to establish correction formula. For example, porosity correction: through linear regression of core porosity φ core and well log calculated porosity φ log , the corrected porosity φ log = a x φ frac + b (a, b are correction coefficients determined by core-log data fitting). Discrete porosity and permeability data (e.g. one data point per 0.5m) are interpolated to 3D grid node data using Kriging interpolation method (based on spatial autocorrelation) to generate continuous porosity / permeability 3D field.

[0028] In some embodiments, imaging logs (e.g. FMI, CBIL) can visually display fracture morphology, and extracting fracture parameters is the basis of characterizing fracture network.

[0029] Fractures can be identified from imaging log images: fractures appear as dark strips (open fractures) or bright strips (filled fractures) in images, and edge detection algorithms (e.g. Canny operator) are used to extract fracture contours; Parameters: strike (angle between fracture extension direction and north direction), dip (angle between fracture plane and horizontal plane) are calculated from geometric features of fracture contours; density (number of fractures per unit length) is obtained by counting the number of fractures per unit well section (e.g. 10m).

[0030] In some embodiments, in-situ stress controls fracture propagation direction and wellbore stability, and the true in-situ stress magnitude and direction need to be inverted from well logs and fracturing data and interpolated to 3D continuous field.

[0031] In-situ stress inversion: elastic parameters (Young's modulus E, Poisson's ratio μ) are calculated from P-wave and S-wave velocities of acoustic logs, and combined with fracturing data (e.g. fracture pressure P frac ), the in-situ stress is inverted using Hooke's law. For example, the minimum horizontal principal stress σ h = P frac - P p ( P p is pore pressure); the maximum horizontal principal stress σ H can be calculated using empirical formula σ H = σ h + α P p (α is Biot's coefficient, representing the effect of pore pressure on effective stress); 3D interpolation: discrete in-situ stress data (e.g. one σ H , σh A continuous three-dimensional geo-stress field is generated by a three-dimensional interpolation algorithm (such as inverse distance weighting, Kriging) to ensure that each grid node has a value of σ H , σ h , and σ v .

[0032] In some embodiments, different parameter dimension differences (such as porosity and permeability) can be eliminated by normalization; reservoir quality zoning can identify "sweet spot areas" (high porosity and high permeability) and non-reservoir areas, providing a basis for subsequent targeted hole placement.

[0033] Normalization: min-max normalization is used to unify the parameters to the interval [0, 1], formula: x , where x is the original parameter value, x min , and x max are the minimum and maximum values of the parameter sample (for example, porosity normalization: x min = 5%, x max = 25%, then 15% porosity is normalized to 0.5); Reservoir quality zoning: based on normalized porosity, permeability, and fracture density, a clustering algorithm (such as K-means) is used to divide the reservoir type: sweet spot area (normalized porosity > 0.7, permeability > 0.6), non-reservoir area (normalized porosity < 0.3, permeability < 0.2), and transition zone (between the two).

[0034] S120, based on the data set, a three-dimensional digital twin model is constructed to restore the reservoir heterogeneity and fracture network distribution.

[0035] Constructing a three-dimensional digital twin model includes: generating a discrete fracture network based on fracture parameters and assigning a conductivity attribute; generating a three-dimensional geo-stress field based on geo-stress data interpolation; using unstructured grid subdivision technology to grid the sweet spot area of the reservoir; mapping lithology, porosity, fractures, and stress attributes to the grid model.

[0036] The digital twin model restores the reservoir heterogeneity (lithology, porosity spatial variation) and fracture network through digital mapping of physical properties and geometric morphology, providing a "virtual reservoir" for multi-physical field coupling simulation.

[0037] In some embodiments, natural fractures are the main channels for carbon dioxide migration, and the DFN model can quantify the spatial distribution and flow capacity of fractures; the flow conductivity (fracture permeability x opening) determines the conduction efficiency of the fractures to the fluid.

[0038] DFN generation: Based on the fracture strike, dip, and density extracted in S110, fractures are generated using a random distribution algorithm (e.g., Poisson disk sampling): Set the fracture length to follow a lognormal distribution, and the density to be high in sweet spots and low in non-sweet spots. The strike and dip are randomly assigned according to the statistical rules of imaging logging. Assigning conductivity: Calculate the conductivity k f × w (unit: μm f × cm) based on the fracture opening (calculated from the ground stress and fracture filling degree, e.g., w = 0.1 ~ 0.5 mm for unfilled fractures) and fracture permeability (k 2 = 100 ~ 1000 mD, filled fractures reduce by 1 ~ 2 orders of magnitude), and assign it to each fracture.

[0039] In some embodiments, ground stress controls fracture propagation direction and wellbore stability, and a three-dimensional stress field needs to cover the entire reservoir space to provide continuous input for stress simulation.

[0040] Based on the discrete stress data (σ H , σ h , σ v ) inverted in S110, a three-dimensional stress field covering the reservoir is generated using a three-dimensional interpolation algorithm (e.g., Kriging interpolation, finite element interpolation): Divide the reservoir into a three-dimensional grid, and calculate the σ H (maximum horizontal principal stress), σ h (minimum horizontal principal stress), and σ v (vertical stress) values for each grid node through interpolation.

[0041] In some embodiments, unstructured grids can adapt to complex reservoir morphology (e.g., interlayers, fractures), and sweet spots are key areas for carbon dioxide injection. Grid refinement can improve simulation accuracy (reduce numerical errors), and sparse grids in non-sweet spots can reduce computational cost.

[0042] Use grid partitioning software (e.g., Gmsh) to construct unstructured grids: Use the reservoir top and bottom interfaces and interlayers as boundaries to generate tetrahedral or hexahedral elements; Sweet spot refinement: Set the grid size in sweet spots to 0.1 ~ 0.5 m (e.g., in areas with porosity > 20%), to 1 ~ 5 m in non-reservoir areas, and to 0.5 ~ 1 m in interlayers (considering accuracy and efficiency).

[0043] In some embodiments, the grid model needs to have physical properties to perform simulation. Attribute mapping associates the multi-dimensional data set of S110 with grid elements, so that each grid element has clear lithology, physical properties, and mechanical parameters.

[0044] By matching the spatial coordinates, the lithology (sandstone / mudstone, divided based on the natural gamma curve), porosity, permeability (interpolated three-dimensional field), fracture parameters (whether the fracture in the DFN model passes through the grid), and ground stress (three-dimensional ground stress field) are assigned to the corresponding grid cells. For example, a certain grid cell coordinate corresponds to a reservoir sweet spot area, and its porosity = 22%, permeability = 500 mD, and contains 2 fractures.

[0045] S130, on the basis of the digital twin model, multi-physical field coupling simulation is performed, including carbon dioxide migration simulation, wellbore stress distribution simulation, and long-term corrosion prediction.

[0046] The multi-physical field coupling simulation includes: carbon dioxide migration simulation based on the perforation-reservoir interactive response coefficient to optimize the hole density and perforation pattern; according to the angle between the ground stress direction and the fracture trend, the phase angle is optimized to avoid stress concentration and fracture channeling; based on the formation water salinity and carbon dioxide partial pressure, the wellbore corrosion rate is predicted to evaluate the long-term wellbore integrity.

[0047] The carbon dioxide migration simulation includes: calculating the hole interference coefficient under different hole densities to determine the optimal hole density interval; based on the reservoir quality index distribution, targeted perforation is implemented, the hole density in the sweet spot area is increased, and the hole density in the non-reservoir area is reduced; near the interlayer interface, the encryption compensation perforation strategy is adopted to improve the longitudinal sweep ability of carbon dioxide.

[0048] The phase angle optimization includes: according to the angle between the ground stress direction and the fracture trend, the stress-oriented or fracture-oriented phase angle is selected; the wellbore stress concentration coefficient under different phase angles is simulated to select the phase angle that meets the safety threshold.

[0049] The long-term corrosion prediction includes: based on the formation water salinity and carbon dioxide partial pressure, the short-term uniform corrosion rate is calculated; the stress concentration coefficient is introduced to correct the long-term corrosion rate, and the wellbore wall thickness loss is predicted; the remaining wall thickness of the wellbore under different perforation parameter schemes is evaluated to determine whether it meets the safety requirements.

[0050] The carbon dioxide injection process involves the interaction of fluid flow (carbon dioxide migration), solid mechanics (wellbore stress), and chemical corrosion (carbon dioxide and wellbore material reaction), and the coupling simulation can comprehensively evaluate the influence of perforation parameters on injection effect and wellbore safety.

[0051] In some embodiments, the hole density (number of holes per meter) and perforation pattern directly affect the carbon dioxide injection efficiency and sweep range, and need to be optimized through simulation to maximize the effective swept volume.

[0052] The too dense perforation holes will cause the flow interference (pressure superposition) between adjacent holes, reducing the single-hole injection efficiency; the too sparse perforation holes will cause the insufficient injection, and the optimal hole density needs to balance the interference and injection capacity. The hole interference coefficient calculation: formula I = 1 - exp (-d / D), wherein d is the distance between adjacent holes (d = 1 / hole density, unit: m / hole), and D is the critical interference distance (determined by the reservoir permeability, high-permeability reservoir D is large, such as 5 m; low-permeability reservoir D is small, such as 2 m); Optimal hole density range: simulate the I value under different hole densities (such as 5, 10, 15 holes / m), and select the range of I = 0.3-0.6 (moderate interference), for example, in the high-permeability sweet spot area D = 5 m, when the hole density is 10 holes / m, d = 0.1 m, I = 1 - exp = 0.02 (too small interference), the hole density needs to be increased to 15 holes / m (d = 0.067 m, I = 0.013, still needs to be adjusted, and in practice, the numerical simulation iteration is used to determine).

[0053] The reservoir quality index (RQI) comprehensively represents the advantages and disadvantages of the reservoir, and the targeted perforation can preferentially guide the carbon dioxide into the sweet spot area, improving the sweep efficiency. The reservoir quality index (RQI) calculation: RQI = , wherein k is the permeability, and φ is the porosity; the perforation strategy: the hole density in the sweet spot area (RQI > 0.8) is increased by 20%-50% (such as the base hole density 10 holes / m to 12-15 holes / m), and the hole density in the non-reservoir area (RQI < 0.2) is reduced by 50% (such as 10 holes / m to 5 holes / m).

[0054] The interlayer (low-permeability non-reservoir) hinders the vertical flow of carbon dioxide, and the perforation in the interface vicinity can increase the injection points and promote the carbon dioxide to pass through the interlayer and enter the lower reservoir. In the range of 1-3 m from the top and bottom interfaces of the interlayer, the hole density is increased by 50% (such as the base 10 holes / m to 15 holes / m) than the base value, forming a “penetrating type” perforation, and enhancing the vertical flow channel.

[0055] In some embodiments, the phase angle (the angle between the perforation direction and the wellbore axis) affects the fracture propagation direction and the wellbore stress concentration, and needs to be selected in combination with the in-situ stress and the fracture trend to avoid the stress concentration leading to the wellbore damage or the fracture channeling.

[0056] The maximum principal stress direction controls the artificial fracture propagation, the natural fracture trend controls the natural fracture opening, and the phase angle needs to be matched with both to improve the fracture communication efficiency.

[0057] Stress orientation: if the in-situ stress direction is clear (σ H direction is known), the phase angle is set to be consistent with the σ H direction (such as the azimuth angle of σ H 30°, the phase angle 30°), which is beneficial to the artificial fracture propagation along the σ H direction; Fracture orientation: if natural fractures are developed (fracture orientation 80°), set phase angle as 80° to promote the communication between perforation and natural fracture.

[0058] Improper phase angle will cause local stress concentration in wellbore, which may lead to wellbore rupture. Simulation is needed to ensure that the stress concentration coefficient is lower than the safety threshold.

[0059] Stress concentration coefficient (SCC) calculation: SCC = σ max / σ0, where σ max is the maximum stress on the inner wall of the wellbore (calculated by three-dimensional elastic mechanics), and σ0 is the ground stress; Safety threshold selection: simulate SCC under different phase angles (0°, 30°, 60°, 90°), and select the phase angle with SCC <1.5 (e.g., when the phase angle is 60°, SCC =1.3, which meets the safety requirement).

[0060] In some embodiments, carbon dioxide dissolves in formation water to generate carbonic acid (H2CO3), which can corrode the steel material of the wellbore. Long-term corrosion combined with stress concentration can exacerbate wall thickness loss, and it is needed to predict whether the remaining wall thickness meets the safety requirement.

[0061] Formation water salinity (ion concentration) affects the conductivity of the solution, and carbon dioxide partial pressure (P 二氧化碳 ) determines the concentration of H2CO3, both of which jointly control the corrosion rate. The DeWaard-Milliams model is used: V 短期 =5.8×10^(-5)×P 二氧化碳 ×exp(-4770 / T), where P 二氧化碳 is the carbon dioxide partial pressure (MPa), and T is the reservoir temperature (K); for example, P 二氧化碳 =5 MPa, T=350 K (77°C), then V 短期 =5.8×10^(-5)×5×exp(-4770 / 350)=0.02 mm / year.

[0062] Stress concentration can accelerate corrosion (stress corrosion cracking), and the stress concentration coefficient needs to be introduced to correct the long-term corrosion rate. Long-term corrosion rate calculation: V 长期 =V 短期 ×(1 + k×SCC), where k is the stress corrosion correction coefficient (determined by experiment, k≈0.1 for carbon steel), and SCC is the stress concentration coefficient; wall thickness loss prediction: Δt=V 长期 ×t, t is the corrosion time (years); for example, V 短期 =0.02 mm / year, SCC=1.3, k=0.1, t=10 years, then V 长期 =0.02×(1+0.1×1.3)=0.0226 mm / year, Δt=0.226 mm.

[0063] The wellbore residual wall thickness needs to be greater than the minimum safe wall thickness (according to API standards, such as the minimum wall thickness of tubing = design pressure x inner diameter / (2 x material yield strength)) to ensure the wellbore pressure-bearing capacity. Calculate Δt under different perforation parameters, and the residual wall thickness t 剩余 =t 初始 -Δt, if t 剩余 >t 安全 (for example, the initial wall thickness is 10 mm, Δt = 0.226 mm, t 剩余 =9.774 mm > t 安全 =5 mm, which meets the requirements).

[0064] In some embodiments, perforation can weaken the wellbore structure strength, the phase angle determines the hole position, and thus affects the wellbore stress distribution, and it is necessary to simulate and evaluate whether there is a stress concentration risk. A finite element software (such as ANSYS) is used to establish a wellbore-reservoir coupling model, input the ground stress field and perforation phase angle, calculate the wellbore inner wall stress distribution, and output the stress concentration area (for example, when the phase angle is 90°, the hole is located at the top of the wellbore, the stress concentration coefficient SCC = 2.0, which exceeds the safety threshold of 1.5, and the phase angle needs to be adjusted).

[0065] S140, based on a multi-objective optimization algorithm, taking the effective swept volume of carbon dioxide, the wellbore integrity coefficient and the anti-channeling safety coefficient as the target, to screen the optimal perforation parameter combination.

[0066] The multi-objective optimization algorithm adopts the NSGA-III algorithm, takes the effective swept volume of carbon dioxide, the wellbore integrity coefficient and the anti-channeling safety coefficient as the target, and generates a Pareto optimal solution set.

[0067] The perforation parameters need to meet the requirements of carbon dioxide sweep efficiency (effective swept volume), wellbore safety (integrity coefficient) and anti-channeling (anti-channeling safety coefficient), and the multi-objective optimization can generate the optimal parameter combination that balances each target.

[0068] NSGA-III is an evolutionary algorithm for multi-objective optimization, which divides the target space by reference points to generate uniformly distributed Pareto optimal solutions (solutions that cannot improve one target without deteriorating other targets). The objective functions are: maximizing the effective swept volume of carbon dioxide (V, m 3 , the simulated carbon dioxide contact reservoir volume); maximizing the wellbore integrity coefficient (C, dimensionless, C = t 剩余 / t 初始 , t 剩余 is the long-term corrosion wall thickness, and t 初始 is the original wall thickness); and maximizing the anti-channeling safety coefficient (F, dimensionless, F = 1-channeling probability, the channeling probability is simulated by whether the fracture channels to the interlayer); The perforation parameter population (perforation density 5-20 holes / m, phase angle 0-90°, perforation arrangement mode 3) can be initialized → V, C, F of each individual are calculated → offspring are generated through crossover and mutation → excellent individuals are selected by using reference points (such as V = 800 m 3 , C = 0.9, F = 0.85) → iteration is performed until convergence, and a set of Pareto optimal solutions (such as {(perforation density 12 holes / m, phase angle 30°, V = 900, C = 0.92, F = 0.88),...}) is obtained, and a comprehensive optimal solution (such as V is maximum and C and F meet the safety threshold) is selected from the set.

[0069] S150, output a visual report containing perforation parameters, simulation results and risk assessment.

[0070] Complex simulation results and parameters need to be converted into intuitive charts to facilitate engineers to understand and make decisions, and the report needs to contain perforation schemes, effect evaluation and risk prompts.

[0071] Use visualization software (ParaView, MATLAB) to generate: Perforation parameter table: perforation density, phase angle, perforation arrangement mode (such as "15 holes / m in the sweet spot area, 20 holes / m at the interface of the interlayer, and a phase angle of 30°"); Simulation result chart: three-dimensional cloud chart of carbon dioxide effective swept volume, wellbore stress distribution cloud chart, and corrosion rate curve; Risk assessment: corrosion risk level (low / medium / high), anti-channeling risk probability (such as 10%), and measures such as "recommendation to detect wellbore wall thickness every 5 years" are marked.

[0072] Based on the same inventive concept, the embodiments of the present application also provide a perforation parameter optimization device for a carbon dioxide injection well. As shown in Figure 2 the device can include: The standardization module 201 is configured to perform standardization processing on geological and logging data, and construct a multi-dimensional data set including lithology, porosity, permeability, fracture parameters and geostress field; The restoration module 202 is configured to construct a three-dimensional digital twin model based on the data set, and restore reservoir heterogeneity and fracture network distribution; The simulation module 203 is configured to perform multi-physical field coupling simulation based on the digital twin model, including carbon dioxide migration simulation, wellbore stress distribution simulation and long-term corrosion prediction; The screening module 204 is configured to screen the optimal perforation parameter combination based on a multi-objective optimization algorithm, taking carbon dioxide effective swept volume, wellbore integrity coefficient and anti-channeling safety coefficient as targets; The output module 205 is configured to output a visual report containing perforation parameters, simulation results and risk assessment.

[0073] The device embodiment corresponds to the foregoing method embodiment, and can be understood in conjunction with each other.

[0074] Referring to Figure 3 As shown in the figure, the electronic device 300 provided by the embodiments of the present application at least includes a processor 301, a memory 302, and a computer program stored in the memory 302 and executable on the processor 301, and the processor 301 implements the method provided by the embodiments of the present application when executing the computer program.

[0075] The electronic device 300 provided by the embodiments of the present application can further include a bus 303 connecting different components (including the processor 301 and the memory 302). Among them, the bus 303 represents one or more of several types of bus structures, including a memory bus, a peripheral bus, a local bus, etc.

[0076] The memory 302 can include a readable storage medium in the form of a volatile memory, such as a random access memory (RAM) 3021 and / or a cache memory 3022, and can further include a read-only memory (ROM) 3023. The memory 302 can also include a program tool 3025 having a set of (at least one) program modules 3024, including but not limited to an operating subsystem, one or more application programs, other program modules, and program data, each of these examples or some combination thereof can include the implementation of a network environment.

[0077] The processor 301 can be one processing element, or a collective term for multiple processing elements, for example, the processor 301 can be a central processing unit (CPU), or one or more integrated circuits configured to implement the method provided by the embodiments of the present application. Specifically, the processor 301 can be a general-purpose processor, including but not limited to a CPU, an application specific integrated circuit (ASIC), a ready-to-program gate array (FPGA) or other programmable logic device, a discrete gate or transistor logic device, a discrete hardware component, etc.

[0078] The electronic device 300 can communicate with one or more external devices 304 (such as a keyboard, a mouse, or a voice device) and can communicate with one or more devices that enable a user to interact with the electronic device 300 (such as a phone, a computer, or a wearable device) and / or one or more devices (such as a router, a modem, or a network switch) that enable the electronic device 300 to communicate with one or more other electronic devices. The communication can be facilitated via an Input / Output (I / O) interface 305. Further, the electronic device 300 can communicate with one or more networks (such as a Local Area Network (LAN), a Wide Area Network (WAN), and / or the Internet) via a network adapter 306. As Figure 3 illustrated, the network adapter 306 is in communication with the other modules of the electronic device 300 through the bus 303. As will be appreciated, although not explicitly illustrated, other mobile computing devices, such as laptops, handheld computers, netbooks, netpads, etc., can communicate with the electronic device 300. Figure 3 It will be appreciated that other hardware and / or software modules can be used in conjunction with the electronic device 300, such as, for example, microcode, device drivers, redundant processing units, external disk drive arrays, redundant arrays of independent disks (RAID) subsystems, tape drives, and data archival storage subsystems, etc.

[0079] It should be noted that the electronic device 300 as illustrated is only one example of an electronic device and should not be taken as limiting the scope of functionality or use of embodiments of the present application. Figure 3 It should be noted that the electronic device 300 as illustrated is only one example of an electronic device and should not be taken as limiting the scope of functionality or use of embodiments of the present application.

[0080] A computer readable storage medium provided by an embodiment of the present application is introduced as follows. The computer readable storage medium provided by an embodiment of the present application stores computer instructions. The computer instructions are executed by a processor to implement the method provided by an embodiment of the present application. Specifically, the computer instructions can be built-in or installed in the processor. Thus, the processor can implement the method provided by an embodiment of the present application by executing the built-in or installed computer instructions.

[0081] In addition, the method provided by an embodiment of the present application can also be implemented as a computer program product. The computer program product includes program codes. The program codes are executed on a processor to implement the method provided by an embodiment of the present application.

[0082] The computer program product provided by the embodiments of the present application can adopt one or more computer readable storage media, which can be, but are not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, device or apparatus, or any appropriate combination of the above. Specifically, more specific examples (non-exhaustive list) of the computer readable storage media include an electrical connection having one or more wires, a portable disc, a hard disk, a RAM, a ROM, an erasable programmable read-only memory (EPROM), an optical fiber, a portable compact disc read-only memory (Compact Disc Read-Only Memory, CD-ROM), an optical storage device, a magnetic storage device, or any appropriate combination of the above.

[0083] The computer program product provided by the embodiments of the present application can adopt a CD-ROM and include program codes, and can also run on an electronic device such as a computer. However, the computer program product provided by the embodiments of the present application is not limited to this. In the embodiments of the present application, the computer readable storage medium can be any tangible medium containing or storing program codes, which can be used by or in combination with an instruction execution system, device or apparatus.

[0084] It should be noted that although several units or sub-units of the apparatus are mentioned in the above detailed description, such division is merely exemplary and not mandatory. In fact, according to the embodiments of the present application, the features and functions of two or more units described above can be embodied in one unit. Conversely, the features and functions of one unit described above can be further divided into units for embodiment.

[0085] In addition, although the operations of the method of the present application are described in a specific order in the accompanying drawings, this does not require or imply that the operations must be performed in this specific order, or that all of the shown operations must be performed to achieve the desired result. Additionally or alternatively, certain steps can be omitted, a plurality of steps can be combined into one step, and / or one step can be divided into a plurality of steps.

[0086] Although the preferred embodiments of the present application have been described, those skilled in the art who are informed of the basic inventive concept can make additional changes and modifications to the embodiments. Therefore, the appended claims are intended to include the preferred embodiments and all changes and modifications falling within the scope of the present application.

[0087] It is apparent that a person skilled in the art can make various modifications and variations to the embodiments of the application without departing from the spirit and scope of the application. Therefore, the application is intended to cover the modifications and variations of this application provided they come within the scope of the appended claims and their equivalents.

Claims

1. A method for optimizing parameters of a carbon dioxide injection well perforation, characterized in that, The method comprises the following steps: Standardizing geological and logging data to construct a multi-dimensional data set including lithology, porosity, permeability, fracture parameters and geostress field; Based on the data set, a three-dimensional digital twin model is constructed to restore reservoir heterogeneity and fracture network distribution; Based on the digital twin model, multi-physical field coupling simulation is performed, including carbon dioxide migration simulation, wellbore stress distribution simulation and long-term corrosion prediction; Based on a multi-objective optimization algorithm, the effective swept volume of carbon dioxide, the wellbore integrity coefficient and the anti-channeling safety factor are taken as targets to screen the optimal perforation parameter combination; Output a visual report containing perforation parameters, simulation results and risk assessment.

2. The method of claim 1, wherein, The standardization process includes: Cleaning logging data, removing invalid sections and completing missing data; Logging interpretation correction and interpolation processing of porosity and permeability data; Extracting fracture strike, dip and density parameters from imaging logging; Inversion of geostress field based on acoustic logging and fracturing data and three-dimensional interpolation; Normalization of all parameters and annotation of reservoir quality zoning.

3. The method of claim 1, wherein, The three-dimensional digital twin model includes: Generating a discrete fracture network based on fracture parameters and assigning a flow conductivity attribute; Generating a three-dimensional geostress field based on geostress data interpolation; Using unstructured grid subdivision technology to grid the reservoir sweet spot area; Mapping lithology, porosity, fracture and stress attributes to the grid model.

4. The method of claim 1, wherein, Multi-physical field coupling simulation includes: Carbon dioxide migration simulation based on perforation-reservoir interaction response coefficients to optimize hole density and hole distribution; Optimizing the phase angle according to the angle between the geostress direction and the fracture strike to avoid stress concentration and fracture channeling; Based on the formation water salinity and carbon dioxide partial pressure, the wellbore corrosion rate is predicted to evaluate the long-term wellbore integrity.

5. The method of claim 4, wherein, Carbon dioxide migration simulation includes: Calculating the hole interference coefficient under different hole densities to determine the optimal hole density range; Targeted hole distribution based on reservoir quality index distribution to increase hole density in sweet spots and reduce hole density in non-reservoir areas; Near interlayer interfaces, use the encryption compensation hole strategy to improve the longitudinal sweep ability of carbon dioxide.

6. The method of claim 4, wherein, Phase angle optimization includes: Selecting a stress-oriented or fracture-oriented phase angle according to the angle between the geostress direction and the fracture strike; Simulate the wellbore stress concentration coefficient under different phase angles and select the phase angle that meets the safety threshold.

7. The method of claim 4, wherein, Long-term corrosion prediction includes: Based on the formation water salinity and carbon dioxide partial pressure, the short-term uniform corrosion rate is calculated; Introducing the stress concentration coefficient to correct the long-term corrosion rate and predict the loss of wellbore wall thickness; Evaluate whether the remaining wall thickness of the wellbore under different perforation parameter schemes meets the safety requirements.

8. The method of claim 1, wherein, The multi-objective optimization algorithm uses the NSGA-III algorithm to generate a Pareto optimal solution set with the effective swept volume of carbon dioxide, the wellbore integrity coefficient and the anti-channeling safety factor as targets.

9. A device for optimizing parameters of a carbon dioxide injection well perforation, characterized by, The method comprises the following steps: A standardization module for standardizing geological and logging data to construct a multi-dimensional data set including lithology, porosity, permeability, fracture parameters and geostress field; A restoration module for constructing a three-dimensional digital twin model based on the data set to restore reservoir heterogeneity and fracture network distribution; An analog module is configured to perform multi-physical field coupling simulation based on the digital twin model, including carbon dioxide migration simulation, wellbore stress distribution simulation and long-term corrosion prediction. A screening module is configured to screen an optimal perforation parameter combination based on a multi-objective optimization algorithm, taking carbon dioxide effective swept volume, wellbore integrity coefficient and anti-channeling safety coefficient as targets. An output module is configured to output a visual report containing perforation parameters, simulation results and risk assessment.

10. An electronic device, comprising: A processor and a memory are included, the memory stores machine executable instructions capable of being executed by the processor, and the processor can execute the machine executable instructions to implement the method in any one of claims 1 to 8.

Images