Carbon dioxide oil displacement and storage optimization method and system based on numerical simulation

Abstract

The invention provides a carbon dioxide oil displacement and storage optimization method and system based on numerical simulation, and belongs to the technical field of carbon dioxide oil displacement and storage. The method comprises the steps that an oil reservoir economic market fusion database is established based on preprocessed data, and the economic threshold value of unit oil exploitation is quantified; according to the reservoir economic market fusion database and the economic threshold, a carbon dioxide flooding and storage numerical simulation model including economic cost is constructed, a non-dominated sorting genetic algorithm is adopted to perform optimization calculation on carbon dioxide injection parameters, and a Pareto optimal solution set is obtained through iterative solution; and performing sensitivity analysis and dynamic weight adjustment on the Pareto optimal solution set according to market oil price fluctuation to obtain a carbon dioxide flooding and storage optimization scheme. According to the method, oil reservoir numerical simulation is deeply coupled with full-chain economic cost and market oil price fluctuation, and the final scheme has the capability of resisting market risks through a multi-objective optimization algorithm.

Description

A Numerical Simulation-Based Optimization Method and System for Carbon Dioxide Enhanced Oil Recovery and Storage Technical Field

[0001] This invention relates to the field of carbon dioxide enhanced oil recovery and storage technology, specifically to an optimized method and system for carbon dioxide enhanced oil recovery and storage based on numerical simulation. Background Technology

[0002] Carbon dioxide enhanced oil recovery (CEM) and its sequestration (SOR) technology is a key technological approach for achieving low-carbon utilization of fossil fuels and addressing climate change. This technology injects carbon dioxide emitted from industrial sources into underground oil reservoirs, simultaneously enhancing oil recovery and achieving geological sequestration of carbon dioxide, thus offering both economic and environmental benefits. Numerical simulation technology plays an indispensable role in the design and optimization of this technology. By establishing numerical models describing reservoir geological characteristics, fluid flow patterns, and phase changes, the dynamics of oil extraction and the effectiveness of carbon dioxide sequestration under different injection schemes can be predicted. Currently, optimization research in this field mainly focuses on improving technical indicators.

[0003] Most existing optimization models focus only on technical indicators (recovery rate, storage rate) and fail to deeply couple economic costs as the core optimization objective into numerical simulation. Even when some studies conduct economic evaluations, these are often simple post-evaluations after technical optimization is completed. This sequential process of "technology first, economy second" cannot balance technical performance and economic benefits in real time during the optimization process, which may result in the "technically optimal solution" being economically infeasible. Summary of the Invention

[0004] The purpose of this invention is to provide a method and system for optimizing carbon dioxide enhanced oil recovery and storage based on numerical simulation. By coupling oil price scenario analysis with a time-varying weight adjustment mechanism, dynamic synergistic optimization of technical effects and economic benefits can be achieved.

[0005] To achieve the above objectives, this invention provides a numerical simulation-based optimization method for carbon dioxide enhanced oil recovery and storage (CEM), comprising: collecting and preprocessing reservoir geological data, development data, economic cost data, and market and policy data of a target oilfield; establishing a reservoir economic market fusion database based on the preprocessed data and quantifying the economic threshold per unit of oil extraction; constructing a CEM numerical simulation model including economic costs based on the reservoir economic market fusion database and the economic threshold, and setting the economic threshold, reservoir safety pressure boundary, and environmental protection storage indicators for the model; using a non-dominated sorting genetic algorithm to optimize the carbon dioxide injection parameters based on the CEM numerical simulation model, and obtaining a Pareto optimal solution set where the oil recovery rate, carbon dioxide storage rate, and extraction cost all meet the design requirements through iterative solution; and performing sensitivity analysis and dynamic weight adjustment on the Pareto optimal solution set according to market oil price fluctuations to obtain an optimized CEM scheme that meets the economic threshold constraints.

[0006] Optionally, the establishment of the reservoir economic market integration database based on the collected data includes: cleaning the collected reservoir geological data, development data, economic cost data, and market and policy data to remove outliers; using interpolation to complete the missing items in the above data; standardizing the cleaned and completed multi-source heterogeneous data; and uniformly collecting and integrating the standardized data to obtain the reservoir economic market integration database.

[0007] Optionally, the economic threshold for quantifying a unit of oil extraction includes: calculating the marginal cost of a unit of oil extraction based on economic cost data; setting the marginal cost as the benchmark value of the economic threshold and constructing an economic threshold adjustment model; the economic threshold adjustment model compares the predicted oil price with the benchmark value of the economic threshold and dynamically adjusts the benchmark value of the economic threshold.

[0008] Optionally, the construction of the numerical simulation model for carbon dioxide enhanced oil recovery and storage, which includes economic costs, includes: establishing a basic physical model using numerical simulation methods based on reservoir geological data and development data; coupling the full-chain cost data of carbon dioxide capture, transportation and injection extracted from the economic cost data to the basic physical model to construct a quantitative mapping relationship between carbon dioxide injection parameters and oil extraction costs and economic benefits; and setting the economic threshold set based on economic benefit targets, the reservoir safety pressure boundary determined based on reservoir engineering safety standards, and the environmental protection storage index determined based on environmental protection requirements as constraints.

[0009] Optionally, the quantitative mapping relationship between carbon dioxide injection parameters and oil extraction costs and economic benefits includes: establishing a mathematical correlation between carbon dioxide injection parameters and total chain costs and the revenue generated by changes in oil recovery rate, wherein the carbon dioxide injection parameters include at least injection intensity, injection pressure and injection well opening / closing sequence.

[0010] Optionally, the optimization calculation of carbon dioxide injection parameters using a non-dominated sorting genetic algorithm includes: converting the carbon dioxide injection parameters into chromosome individuals using numerical encoding; randomly generating an initial population containing multiple individuals based on the constraints of the carbon dioxide enhanced oil recovery and storage numerical simulation model; constructing a multi-objective fitness function with the objectives of maximizing oil recovery, maximizing carbon dioxide storage, and minimizing extraction costs, wherein the recovery and storage rates are calculated using the carbon dioxide enhanced oil recovery and storage numerical simulation model, and the extraction cost is solved based on economic cost accounting logic; using tournament selection as the selection operator, randomly selecting a certain number of individuals from the population, and selecting the individual with the best fitness to enter the next generation; setting a crossover probability, with the crossover operator being a continuous variable crossover operator, controlling the dispersion of offspring individuals by adjusting the crossover distribution index; and setting a mutation probability, with the mutation operator being a multinomial function-based mutation operator, adjusting the mutation distribution index to adjust the mutation length.

[0011] Optionally, the step of obtaining the Pareto optimal solution set that satisfies the design requirements for oil recovery, carbon dioxide sequestration, and extraction cost through iterative solution includes: performing non-dominated sorting on individuals in each generation of the population, dividing individuals into different front layers according to fitness function values, and calculating the crowding distance for individuals in the same front layer; merging the parent and offspring populations, retaining the next generation population through non-dominated sorting and crowding selection to ensure that the algorithm converges to the Pareto front; setting an iteration termination condition, and outputting the Pareto optimal solution set composed of all non-dominated solutions after iteration termination.

[0012] Optionally, the step of performing sensitivity analysis and dynamic weight adjustment on the Pareto optimal solution set based on market oil price fluctuations includes: setting multiple market oil price fluctuation scenarios covering the current and forecast intervals; calculating the economic indicators of each solution in the Pareto optimal solution set for each market oil price fluctuation scenario; quantifying the sensitivity of each solution to oil price fluctuations using the elasticity coefficient method, and determining the solution with the highest sensitivity.

[0013] Optionally, the step of performing sensitivity analysis and dynamic weight adjustment on the Pareto optimal solution set based on market oil price fluctuations further includes: setting a preset economic indicator baseline threshold and an acceptable maximum fluctuation range threshold based on the solution with the highest sensitivity; conducting an oil price fluctuation stress test by comparing the calculated economic indicators with the preset economic indicator baseline threshold; if the economic indicator of the solution with the highest sensitivity in the oil price fluctuation stress test is lower than the preset economic indicator baseline threshold, or the fluctuation range exceeds the acceptable maximum fluctuation range threshold, then increasing the weight of the economic objective in the multi-objective optimization model; if the economic indicator of the solution with the highest sensitivity in the oil price fluctuation stress test is higher than or equal to the preset economic indicator baseline threshold, and the fluctuation range does not exceed the acceptable maximum fluctuation range threshold, then keeping the current weight of the multi-objective optimization model unchanged; calculating the comprehensive evaluation value of each solution in the original Pareto optimal solution set based on the adjusted or unchanged weight of the multi-objective optimization model and reordering them; selecting the solution with the best comprehensive evaluation value and whose economic indicators meet the preset economic threshold constraints from the reordered original Pareto optimal solution set as the carbon dioxide flooding and storage optimization scheme.

[0014] On the other hand, the present invention provides a numerical simulation-based carbon dioxide enhanced oil recovery and storage optimization system for implementing a numerical simulation-based carbon dioxide enhanced oil recovery and storage optimization method. The system includes a control module, which includes a memory, a processor, and a computer program stored in the memory and executable on the processor. The processor executes the computer program to implement the numerical simulation-based carbon dioxide enhanced oil recovery and storage optimization method.

[0015] The above technical solution deeply couples reservoir numerical simulation with the entire chain of economic costs and market oil price fluctuations. Through multi-objective optimization algorithms, it automatically generates a set of Pareto optimal solutions that perform well in terms of oil recovery rate, carbon dioxide sequestration rate, and extraction cost. By introducing sensitivity analysis based on oil price fluctuations and a dynamic weight adjustment mechanism, the final solution has the ability to resist market risks and improves the economic stability of the solution in the context of oil price fluctuations.

[0016] Other features and advantages of the present invention will be described in detail in the following detailed description section. Attached Figure Description

[0017] The accompanying drawings are provided to further illustrate the invention and form part of the specification. They are used together with the following detailed description to explain the invention, but do not constitute a limitation thereof. In the drawings: Figure 1 is a flowchart of a carbon dioxide enhanced oil recovery and storage optimization method based on numerical simulation.

[0018] Figure 2 is a flowchart of the multi-objective optimization algorithm. Detailed Implementation

[0019] The specific embodiments of the present invention will be described in detail below with reference to Figures 1 and 2. It should be understood that the specific embodiments described herein are for illustration and explanation only and are not intended to limit the embodiments of the present invention.

[0020] It should be noted that the acquisition, transmission, storage, use, and processing of data in the technical solution of this application all comply with the relevant provisions of national laws and regulations. In the embodiments of this application, certain existing industry solutions such as software, components, and models may be mentioned. These should be considered exemplary, intended only to illustrate the feasibility of implementing the technical solution of this application, and do not imply that the applicant has already used or necessarily used such solutions.

[0021] In the process of realizing this invention, the inventors of this application discovered that the existing carbon dioxide flooding and storage optimization schemes are designed based on static economic parameters, which are seriously out of touch with the dynamic and fluctuating market environment, resulting in insufficient economic robustness of the schemes in the face of actual oil price fluctuations.

[0022] Example 1 refers to Figures 1-2, which is the first embodiment of the present invention. This embodiment provides a carbon dioxide enhanced oil recovery and storage optimization method based on numerical simulation, including: S100: collecting and preprocessing reservoir geological data, development data, economic cost data and market and policy data of the target oil field, establishing a reservoir economic market fusion database based on the preprocessed data, and quantifying the economic threshold of unit oil extraction.

[0023] In the embodiments of this application, the collected reservoir geological data, development data, economic cost data, and market and policy data are cleaned to remove outliers; interpolation is used to complete the missing items in the above data; the cleaned and completed multi-source heterogeneous data are standardized, and the standardized data are uniformly collected and integrated to obtain a reservoir economic market fusion database.

[0024] In the preferred embodiments of this application, reservoir geological data is obtained through core experiments, well logging, seismic exploration, and reservoir dynamic monitoring, covering core parameters such as porosity, permeability, and oil saturation; development data is collected from the oilfield production system, including time-series data such as historical injection volume, oil production, and bottom hole pressure; economic cost data covers the costs of equipment, manpower, and energy consumption for carbon dioxide capture, transportation, injection, and oil extraction; market and policy data include historical oil prices, future oil price forecasts, and policy documents such as carbon trading and environmental subsidies.

[0025] Furthermore, the collected data is cleaned using 3D printing technology. The criteria remove outliers from reservoir geological and economic cost data, and verify abnormal fluctuations in development data using the experience of domain experts. For missing data, kriging interpolation is used to complete numerical data, while moving average interpolation is used to complete time-series data. Then, the multi-source heterogeneous data are standardized. Reservoir geological and development data are normalized according to industry exploration and development standards, and economic cost and market and policy data are uniformly converted according to currency units and time dimensions, outputting a standardized dataset.

[0026] Furthermore, a four-dimensional modular reservoir economic market integrated database is constructed, encompassing geology, development, economy, and market. Each module establishes a correlation index through oilfield block number and timestamp, with built-in data call interface and update module, and supports real-time synchronization of newly collected data, policy and oil price dynamics data.

[0027] In the embodiments of this application, the marginal cost of unit oil extraction is calculated based on economic cost data; the marginal cost is set as the benchmark value of the economic threshold, and an economic threshold adjustment model is constructed; the economic threshold adjustment model compares the predicted oil price with the benchmark value of the economic threshold and dynamically adjusts the benchmark value of the economic threshold.

[0028] In a preferred embodiment of this application, based on economic cost data, the marginal cost per unit of oil extraction is calculated using the activity-based costing method (ABC) according to the entire process cost structure of carbon dioxide capture → transportation → injection → oil extraction. This marginal cost is then used as a benchmark value to construct an economic threshold adjustment model that integrates time series forecasting and policy factors. The economic threshold adjustment model has a three-tiered coherent architecture consisting of an input layer, a processing layer, and an output layer. The input layer takes into account two-dimensional quantitative parameters: time series parameters and policy parameters. Time series parameters cover at least three years of monthly data, including historical oil prices and the marginal cost per unit of oil extraction, to capture market fluctuation trends. Policy parameters include quantifiable indicators such as carbon subsidies, carbon trading prices, and environmental compliance cost coefficients. The processing layer contains two core modules: first, an autoregressive integral moving average model (p-order autoregression, d-order differencing, and q-order moving average) is used to fit the trend of the time series parameters, outputting an initial threshold value adapted to market fluctuations. Then, policy parameters are quantified into positive (e.g., carbon subsidies) or negative (e.g., compliance costs) correction coefficients. The prediction equation is embedded in the autoregressive integral moving average model, and the calculation formula is used to... After completing policy adaptation and correction, the output layer outputs the economic threshold. And it has a built-in verification module, if If the value exceeds a reasonable range of 0-2 times the marginal cost, parameter recalibration is triggered to ensure the accuracy and practicality of the economic threshold.

[0029] The aforementioned solution cleanses, completes, and standardizes the collected data, constructing a four-dimensional fusion database. While ensuring data consistency and availability, it breaks down the fragmentation of data across different fields in traditional research, providing a single, reliable data source for subsequent integrated optimization. Furthermore, it dynamically adjusts economic thresholds based on overall process costs and market and policy factors, making economic constraints more aligned with real-world scenarios and preventing optimization schemes from deviating from economic feasibility. Through built-in data access interfaces and update modules, it supports real-time synchronization of new data, enabling the entire optimization process to continuously evolve and adapt to changes in oilfield production and the external environment.

[0030] S200: Based on the integrated database of reservoir economic market and economic thresholds, a numerical simulation model of carbon dioxide flooding and storage, including economic costs, is constructed, and the economic threshold, reservoir safety pressure boundary and environmental protection storage indicators are set for the model.

[0031] In the embodiments of this application, a basic physical model is established using numerical simulation based on reservoir geological data and development data; and the full-chain cost data of carbon dioxide capture, transportation and injection extracted from economic cost data is coupled to the basic physical model to construct a quantitative mapping relationship between carbon dioxide injection parameters and oil extraction costs and economic benefits; and the economic threshold set based on economic benefit targets, the reservoir safety pressure boundary determined based on reservoir engineering safety standards, and the environmental protection sequestration index determined based on environmental protection requirements are jointly set as constraints.

[0032] In a preferred embodiment of this application, based on reservoir geological data and development data, a basic physical model of carbon dioxide flooding and storage for the target oilfield is established using reservoir numerical simulation software (such as CMG and Eclipse). This model can characterize the heterogeneity of the reservoir (such as the spatial distribution of porosity and permeability), fluid PVT properties (such as phase equilibrium, density, and viscosity), relative permeability curves, and relative permeability curves of multiphase fluids in porous media, so as to simulate the oil displacement mechanism of carbon dioxide (such as viscosity reduction, extraction, and miscibility effect), and also simulate the migration, diffusion, and retention processes of carbon dioxide in underground space.

[0033] Furthermore, the quantitative mapping relationship is achieved by establishing a mathematical correlation between carbon dioxide injection parameters, total chain costs, and the revenue generated by changes in oil recovery. The carbon dioxide injection parameters include at least injection intensity, injection pressure, and injection well opening / closing timing. This quantitative mapping relationship is implemented through a built-in economic accounting submodule. The calculation engine unit within this submodule contains a built-in cost-revenue mapping function for the entire chain. The calculation engine unit performs dynamic economic accounting, enabling real-time calculation of carbon dioxide capture costs, transportation costs, injection energy costs, and the revenue generated by increased crude oil production for any given injection scheme.

[0034] In a preferred embodiment of this application, a quantified economic threshold is used as a hard constraint, ensuring that the predicted unit crude oil extraction cost of any injection scheme at the end of the simulation must not exceed this threshold. The reservoir safety pressure boundary is set according to reservoir engineering safety standards, ensuring that carbon dioxide injection does not cause mechanical damage to the reservoir or caprock by setting the maximum allowable bottomhole flowing pressure and reservoir fracture pressure for the reservoir and caprock. A minimum carbon dioxide sequestration rate is set as an environmental sequestration indicator to ensure that the vast majority of the injected carbon dioxide is effectively sequestered underground, meeting the project's environmental protection requirements.

[0035] The above-mentioned scheme, building upon the traditional approach of simulating only the physical processes of oil displacement and storage, incorporates the economic costs of the entire supply chain and establishes a quantitative mapping relationship between injection parameters and economic benefits. This allows the scheme evaluation to move beyond technical indicators and directly focus on ultimate profitability and economic feasibility. By setting three hard constraints—economic thresholds, safety pressure boundaries, and environmentally friendly storage indicators—it ensures that any scheme generated during the optimization process simultaneously satisfies economic efficiency (no losses), safety (no formation damage), and environmental friendliness (effective storage). This avoids sacrificing other key requirements in pursuit of a single objective (such as recovery rate), thus guaranteeing comprehensive benefits and sustainability.

[0036] S300: Based on the numerical simulation model of carbon dioxide enhanced oil recovery and storage, a non-dominated sorting genetic algorithm is used to optimize the carbon dioxide injection parameters. Through iterative solution, the Pareto optimal solution is obtained in which the oil recovery rate, carbon dioxide storage rate and extraction cost all meet the design requirements.

[0037] In the embodiments of this application, carbon dioxide injection parameters are converted into chromosome individuals using numerical encoding; an initial population containing multiple individuals is randomly generated based on the constraints of the carbon dioxide enhanced oil recovery and storage numerical simulation model; a multi-objective fitness function is constructed with the objectives of maximizing oil recovery, maximizing carbon dioxide storage, and minimizing extraction costs, wherein the recovery and storage rates are calculated using the carbon dioxide enhanced oil recovery and storage numerical simulation model, and the extraction cost is solved based on economic cost accounting logic; the selection operator is a tournament selection method, randomly selecting a certain number of individuals from the population, and selecting the individual with the best fitness to enter the next generation; a crossover probability is set, and the crossover operator is a continuous variable crossover operator, controlling the dispersion of offspring individuals by adjusting the crossover distribution index; a mutation probability is set, and the mutation operator is a multinomial function-based mutation operator, adjusting the mutation distribution index to adjust the mutation length.

[0038] In the embodiments of this application, individuals in each generation of the population are sorted non-dominated, and individuals are divided into different front layers according to the fitness function value. Crowding distance is calculated for individuals in the same front layer. The parent and offspring populations are merged, and the next generation population is retained through non-dominated sorting and crowding screening to ensure that the algorithm converges to the Pareto front. An iteration termination condition is set, and after the iteration terminates, the Pareto optimal solution set composed of all non-dominated solutions is output.

[0039] In a preferred embodiment of this application, the carbon dioxide injection parameters to be optimized are used as decision variables, and are converted into chromosome individuals in a genetic algorithm (NSGA-II) using real-number encoding. Based on the set constraints such as reservoir safety pressure boundaries, the value range of each decision variable is defined, and N initial individuals are randomly generated within this range to form an initial population. For each generation of the population, the following steps are performed: S301: Each individual in the current population (i.e., a set of injection parameters) is substituted into the carbon dioxide enhanced oil recovery and storage numerical simulation model constructed in S200 for calculation. This model outputs the oil recovery rate under this scheme (…). ) and carbon dioxide sequestration rate ( At the same time, the economic accounting submodule outputs the mining cost ( Based on these three factors, a multi-objective fitness function is constructed: maximizing... ,maximize , minimize .

[0040] S302: Using the tournament selection method, k individuals are randomly selected from the current population, and the individual with the best fitness is selected as the parent. This process is repeated until a parent population of size N is generated.

[0041] S303: Perform crossover operation on the generated parent population with a preset crossover probability; the crossover operator adopts a simulated binary crossover operator, and controls the similarity between offspring individuals and parent individuals by adjusting the crossover distribution index, thereby balancing global exploration and local development capabilities.

[0042] S304: Perform a mutation operation on the offspring population generated after crossover with a preset mutation probability. The mutation operator is a multinomial mutation operator, which controls the variation length by adjusting the mutation distribution exponent to maintain population diversity.

[0043] S305: After crossover and mutation operations, the offspring populations are merged to form a mixed population of size 2N. The mixed population is then subjected to non-dominated ordination, and divided into multiple non-dominated layers based on the fitness values ​​of individuals. For individuals within the same non-dominated layer, their crowding distance is calculated to assess the distribution density of individuals in the target space. Based on the non-dominated ordination layer (prioritizing individuals with higher layers) and crowding distance (prioritizing individuals with higher crowding within the same layer to maintain distribution), N optimal individuals are selected from the mixed population to form a new generation population.

[0044] Repeat steps S301 to S305 until a preset iteration termination condition is met (such as maximum number of generations or solution set convergence criterion). After iteration termination, output the Pareto optimal solution set consisting of all non-dominated solutions in the final population. Each solution in this set represents a set of solutions that achieve the optimal balance between oil recovery, carbon dioxide sequestration, and extraction costs.

[0045] The aforementioned scheme, employing the NSGA-II algorithm, can simultaneously optimize three conflicting objectives: recovery rate, storage rate, and cost, ultimately outputting a Pareto-optimal solution set. This solution set provides multiple "optimal" choices rather than a single solution, allowing decision-makers to flexibly choose based on their preferences at different stages (e.g., prioritizing profitability versus environmental impact). Furthermore, non-dominated sorting ensures convergence towards the true optimal frontier, while crowding calculation guarantees a uniform distribution of solutions in the objective space. This results in a high-quality solution set that covers various possible trade-offs, avoiding getting trapped in local optima. Moreover, by combining complex numerical simulations with efficient search algorithms, it replaces the traditional trial-and-error method relying on engineer experience, improving optimization efficiency and scientific rigor.

[0046] S400: Based on market oil price fluctuations, perform sensitivity analysis and dynamic weight adjustment on the Pareto optimal solution set to obtain an optimized scheme for carbon dioxide flooding and storage that meets economic threshold constraints.

[0047] In the embodiments of this application, multiple market oil price fluctuation scenarios covering the current and predicted intervals are set; for each market oil price fluctuation scenario, the economic indicators of each solution in the Pareto optimal solution set are calculated; the sensitivity of each solution to oil price fluctuations is quantified by the elasticity coefficient method, and the solution with the highest sensitivity is determined.

[0048] In a preferred embodiment of this application, three market oil price fluctuation scenarios are defined: a baseline scenario, a conservative scenario, and an optimistic scenario. The baseline scenario uses the median oil price forecast published by an authoritative institution as the benchmark oil price, representing the most likely future oil price level as generally expected by the market. This provides an objective benchmark for sensitivity analysis. A fluctuation of less than 20% above or below the benchmark oil price falls under the baseline scenario. A 20% to 30% decrease below the benchmark oil price constitutes the conservative scenario, used to simulate unfavorable market conditions such as weak demand. The optimistic scenario involves a 20% to 30% increase above the benchmark oil price, used to simulate favorable conditions such as tight supply or geopolitical risks.

[0049] Furthermore, for three market oil price fluctuation scenarios—baseline, conservative, and optimistic—each solution in the Pareto optimal solution set is traversed. The numerical simulator of the integrated economic accounting submodule is called to calculate the net present value of each solution under different oil price scenarios. The elasticity coefficient method is used, and the calculation formula is as follows:

[0050] in, This represents the sensitivity of the i-th solution. This indicates that the i-th solution represents the oil price under the optimistic scenario. The net present value is calculated below. This indicates that the i-th solution represents the oil price under the conservative scenario. The net present value is calculated below. This indicates that the i-th solution is based on the baseline scenario of oil price. The net present value is calculated below.

[0051] The formula for calculating net present value is as follows:

[0052] in, denoted as net present value, and r represents the discount rate (from the Reservoir Economic Market Integration Database). This represents the amount of crude oil produced by carbon dioxide flooding within a time step t (from the numerical simulation output). This represents the oil price at time step t. This represents the amount of carbon dioxide injected within time step t (from the numerical simulation output). This represents the cost of capturing carbon dioxide per unit mass. This represents the transportation cost per unit mass of carbon dioxide. This represents the cost per unit mass of carbon dioxide injected. T represents the initial fixed costs (from economic cost data), and T represents the total evaluation period of the project.

[0053] In the embodiments of this application, based on the solution with the highest sensitivity, a preset economic indicator baseline threshold and an acceptable maximum fluctuation range threshold are established. An oil price fluctuation stress test is conducted by comparing the calculated economic indicators with the preset economic indicator baseline threshold. If the economic indicator of the solution with the highest sensitivity in the oil price fluctuation stress test is lower than the preset economic indicator baseline threshold, or the fluctuation range exceeds the acceptable maximum fluctuation range threshold, the weight of the economic objective in the multi-objective optimization model is increased. If the economic indicator of the solution with the highest sensitivity in the oil price fluctuation stress test is higher than or equal to the preset economic indicator baseline threshold, and the fluctuation range does not exceed the acceptable maximum fluctuation range threshold, the weight of the current multi-objective optimization model remains unchanged. Based on the adjusted or unchanged weights of the multi-objective optimization model, the comprehensive evaluation value of each solution in the original Pareto optimal solution set is calculated and reordered. From the reordered original Pareto optimal solution set, the solution with the best comprehensive evaluation value and whose economic indicators meet the preset economic threshold constraints is selected as the carbon dioxide flooding and storage optimization scheme.

[0054] In a preferred embodiment of this application, the preset economic indicator baseline threshold is that, under a conservative scenario, the net present value (NPV) of the solution is not lower than zero. The preset maximum acceptable volatility threshold is the maximum allowable relative decline rate of the NPV from the baseline scenario to the conservative scenario. This percentage can be set to a specific value based on the project's risk tolerance, such as 40%. An oil price volatility stress test is performed on the aforementioned identified key risk solutions with the highest sensitivity to determine whether they meet any of the following conditions: 1. The NPV of the key risk solution under the conservative scenario is lower than the baseline threshold; 2. The relative decline rate of the NPV of the key risk solution from the baseline scenario to the conservative scenario exceeds the maximum volatility threshold.

[0055] If any condition is met, the overall economic robustness of the current Pareto solution set is deemed insufficient. In this case, the weight of the objective of minimizing mining costs in S300 is increased, and other weights are reduced accordingly. The adjusted weights are then fed back to step S300, and the optimization process of the non-dominated sorting genetic algorithm is restarted to generate a Pareto optimal solution set under the new weight preferences. Sensitivity analysis and screening are then performed on this new solution set to guide the optimization direction towards greater economic robustness.

[0056] If none of the above conditions are met, the economic robustness of the current solution set is deemed to meet the requirements, and the objective weights in step S300 remain unchanged.

[0057] It should be noted that the adjusted weights are fed back to step S300 to restart the optimization process of the non-dominated sorting genetic algorithm. The loop is terminated by setting the maximum number of restarts or the economic robustness convergence threshold.

[0058] Subsequently, based on the final target weights determined by the stress test results (i.e., the adjusted or unchanged weights), a weighted comprehensive evaluation method is used to quantify and rank each solution in the original Pareto optimal solution set obtained in step S300. Specifically, the recovery rate (profitability target), storage rate (profitability target), and extraction cost (cost target) of each solution are normalized to eliminate the influence of dimensions. Then, the normalized target values ​​are multiplied by their corresponding dynamic weights and summed to calculate the comprehensive evaluation value of each solution. All solutions are then ranked from highest to lowest based on this comprehensive evaluation value. Finally, from the ranked solution set, the solution with the highest comprehensive evaluation value that satisfies the economic constraints under all preset oil price scenarios is selected as the final CO2 enhanced oil recovery and storage optimization scheme.

[0059] The aforementioned scheme, through multi-scenario oil price settings and the elasticity coefficient method, accurately identifies the scheme most sensitive to oil price fluctuations in the Pareto solution set and provides crucial risk warnings. By introducing a stress testing mechanism, when the current optimization result is found to lack economic robustness, the weights of the optimization model are dynamically adjusted (increasing the weight of cost minimization), guiding the optimization direction to generate more robust solutions. This ensures that the final recommended scheme is not only optimal under ideal conditions but also maintains economic feasibility under unfavorable market conditions. Finally, a weighted comprehensive evaluation is used to select the optimal scheme, ensuring that the final output scheme balances comprehensive benefits and economic constraints.

[0060] The present invention also provides a numerical simulation-based carbon dioxide enhanced oil recovery and storage optimization system for implementing a numerical simulation-based carbon dioxide enhanced oil recovery and storage optimization method. The system includes a control module, which includes a memory, a processor, and a computer program stored in the memory and executable on the processor. The processor executes the computer program to implement the numerical simulation-based carbon dioxide enhanced oil recovery and storage optimization method.

[0061] This invention provides a storage medium storing a program that, when executed by a processor, implements a numerical simulation-based method for optimizing carbon dioxide enhanced oil recovery and sequestration.

[0062] This invention provides a processor for running a program, wherein the program executes a carbon dioxide enhanced oil recovery and storage optimization method based on numerical simulation.

[0063] This invention provides a device including a processor, a memory, and a program stored in the memory and executable on the processor. When the processor executes the program, it implements a numerical simulation-based optimization method for carbon dioxide enhanced oil recovery and sequestration. The device described herein can be a server, PC, tablet, mobile phone, etc.

[0064] This application also provides a computer program product that, when executed on a data processing device, is suitable for performing a numerical simulation-based carbon dioxide enhanced oil recovery and storage optimization method.

[0065] Those skilled in the art will understand that embodiments of this application can provide methods, systems, or computer program products. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application can take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0066] This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this application. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in one or more flowchart illustrations and / or one or more block diagrams.

[0067] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means that implement the functions specified in one or more flowcharts and / or one or more block diagrams.

[0068] These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer-implemented process, such that the instructions, which execute on the computer or other programmable apparatus, provide steps for implementing the functions specified in one or more flowcharts and / or one or more block diagrams.

[0069] In a typical configuration, a computing device includes one or more processors (CPU), input / output interfaces, network interfaces, and memory.

[0070] Memory may include non-persistent memory in computer-readable media, such as random access memory (RAM) and / or non-volatile memory, such as read-only memory (ROM) or flash RAM. Memory is an example of computer-readable media.

[0071] Computer-readable media includes both permanent and non-permanent, removable and non-removable media that can store information using any method or technology. Information can be computer-readable instructions, data structures, modules of programs, or other data. Examples of computer storage media include, but are not limited to, phase-change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technologies, CD-ROM, digital versatile optical disc (DVD) or other optical storage, magnetic tape, magnetic disk storage or other magnetic storage devices, or any other non-transferable medium that can be used to store information accessible by a computing device. As defined herein, computer-readable media does not include transient computer-readable media, such as modulated data signals and carrier waves.

[0072] It should also be noted that the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article, or apparatus. Unless otherwise specified, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes that element.

[0073] The above are merely embodiments of this application and are not intended to limit the scope of this application. Various modifications and variations can be made to this application by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the scope of the claims of this application.

Claims

1. An optimized method for carbon dioxide enhanced oil recovery and storage based on numerical simulation, characterized in that, include: Data on reservoir geology, development, economic costs, and market and policy information for the target oilfield are collected and preprocessed. Based on the preprocessed data, a reservoir economic market fusion database is established, and the economic threshold for unit oil extraction is quantified. Based on the reservoir economic market fusion database and the economic threshold, a numerical simulation model of carbon dioxide enhanced oil recovery (CEM) and carbon sequestration (CFS) including economic costs is constructed, and the economic threshold, reservoir safety pressure boundary, and environmental sequestration indicators are set for this model. Based on the CEM numerical simulation model, a non-dominated sorting genetic algorithm is used to optimize the CEM injection parameters. Through iterative solutions, a Pareto optimal solution set is obtained where oil recovery, CFS, and extraction costs all meet the design requirements. Sensitivity analysis and dynamic weight adjustment are performed on the Pareto optimal solution set based on market oil price fluctuations to obtain an optimized CEM scheme that satisfies the economic threshold constraints.

2. The carbon dioxide enhanced oil recovery and storage optimization method based on numerical simulation according to claim 1, characterized in that, The establishment of the reservoir economic market integrated database based on the collected data includes: cleaning the collected reservoir geological data, development data, economic cost data, and market and policy data to remove outliers; using interpolation to fill in the missing items in the above data; standardizing the cleaned and filled multi-source heterogeneous data; and uniformly collecting and integrating the standardized data to obtain the reservoir economic market integrated database.

3. The carbon dioxide enhanced oil recovery and storage optimization method based on numerical simulation according to claim 1, characterized in that, The economic threshold for quantifying a unit of oil extraction includes: calculating the marginal cost of a unit of oil extraction based on economic cost data; setting the marginal cost as the benchmark value of the economic threshold and constructing an economic threshold adjustment model; the economic threshold adjustment model compares the predicted oil price with the benchmark value of the economic threshold and dynamically adjusts the benchmark value of the economic threshold.

4. The carbon dioxide enhanced oil recovery and storage optimization method based on numerical simulation according to claim 1, characterized in that, The construction of the numerical simulation model for carbon dioxide enhanced oil recovery and storage, which includes economic costs, includes: establishing a basic physical model using numerical simulation methods based on reservoir geological data and development data; coupling the full-chain cost data of carbon dioxide capture, transportation and injection extracted from the economic cost data to the basic physical model to construct a quantitative mapping relationship between carbon dioxide injection parameters and oil extraction costs and economic benefits; and setting the economic threshold based on economic benefit targets, the reservoir safety pressure boundary determined based on reservoir engineering safety standards, and the environmental protection storage index determined based on environmental protection requirements as constraints.

5. The carbon dioxide enhanced oil recovery and storage optimization method based on numerical simulation according to claim 4, characterized in that, The quantitative mapping relationship between carbon dioxide injection parameters and oil extraction costs and economic benefits includes: establishing a mathematical correlation between carbon dioxide injection parameters and total chain costs and the revenue generated by changes in oil recovery rate. The carbon dioxide injection parameters include at least injection intensity, injection pressure, and injection well opening / closing sequence.

6. The carbon dioxide enhanced oil recovery and storage optimization method based on numerical simulation according to claim 1, characterized in that, The optimization calculation of carbon dioxide injection parameters using a non-dominated sorting genetic algorithm includes: converting the carbon dioxide injection parameters into chromosome individuals using numerical encoding; randomly generating an initial population containing multiple individuals based on the constraints of the carbon dioxide enhanced oil recovery and storage numerical simulation model; constructing a multi-objective fitness function with the objectives of maximizing oil recovery, maximizing carbon dioxide storage, and minimizing extraction costs, where the recovery and storage rates are calculated using the carbon dioxide enhanced oil recovery and storage numerical simulation model, and the extraction cost is solved based on economic cost accounting logic; using a tournament selection method, randomly selecting a certain number of individuals from the population, and selecting the individual with the best fitness to enter the next generation; setting a crossover probability, with the crossover operator being a continuous variable crossover operator, controlling the dispersion of offspring individuals by adjusting the crossover distribution index; and setting a mutation probability, with the mutation operator being a multinomial function-based mutation operator, adjusting the mutation distribution index to adjust the mutation length.

7. The carbon dioxide enhanced oil recovery and storage optimization method based on numerical simulation according to claim 1, characterized in that, The process of obtaining a Pareto optimal solution set that satisfies the design requirements for oil recovery, carbon dioxide sequestration, and extraction cost through iterative solution includes: performing non-dominated sorting on individuals in each generation of the population; dividing individuals into different front layers based on fitness function values; calculating the crowding distance for individuals in the same front layer; merging the parent and offspring populations; retaining the next generation population through non-dominated sorting and crowding selection to ensure that the algorithm converges to the Pareto front; setting an iteration termination condition; and outputting the Pareto optimal solution set composed of all non-dominated solutions after iteration termination.

8. The optimized method for carbon dioxide enhanced oil recovery and storage based on numerical simulation according to claim 1, characterized in that, The process of performing sensitivity analysis and dynamic weight adjustment on the Pareto optimal solution set based on market oil price fluctuations includes: setting multiple market oil price fluctuation scenarios covering the current and forecast intervals; calculating the economic indicators of each solution in the Pareto optimal solution set for each market oil price fluctuation scenario; quantifying the sensitivity of each solution to oil price fluctuations using the elasticity coefficient method, and determining the solution with the highest sensitivity.

9. The optimized method for carbon dioxide enhanced oil recovery and storage based on numerical simulation according to claim 8, characterized in that, The process of performing sensitivity analysis and dynamic weight adjustment on the Pareto optimal solution set based on market oil price fluctuations further includes: setting a preset economic indicator baseline threshold and an acceptable maximum fluctuation range threshold based on the solution with the highest sensitivity; conducting an oil price fluctuation stress test by comparing the calculated economic indicators with the preset economic indicator baseline threshold; if the economic indicator of the solution with the highest sensitivity in the oil price fluctuation stress test is lower than the preset economic indicator baseline threshold, or the fluctuation range exceeds the acceptable maximum fluctuation range threshold, then the weight of the economic objective in the multi-objective optimization model is increased; if the economic indicator of the solution with the highest sensitivity in the oil price fluctuation stress test is higher than or equal to the preset economic indicator baseline threshold, and the fluctuation range does not exceed the acceptable maximum fluctuation range threshold, then the weight of the current multi-objective optimization model remains unchanged; based on the adjusted or unchanged weights of the multi-objective optimization model, calculating the comprehensive evaluation value of each solution in the original Pareto optimal solution set and reordering them; from the reordered original Pareto optimal solution set, selecting the solution with the best comprehensive evaluation value and whose economic indicators meet the preset economic threshold constraints as the carbon dioxide flooding and storage optimization scheme.

10. A carbon dioxide enhanced oil recovery and storage optimization system based on numerical simulation, characterized in that, The system includes a control module, which includes a memory, a processor, and a computer program stored in the memory and executable on the processor. The processor executes the computer program to implement the carbon dioxide enhanced oil recovery and storage optimization method based on numerical simulation according to any one of claims 1-9.

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