Shale oil yield increasing system and method integrating multiphase gas lift and chemical injection

Abstract

The invention relates to the technical field of shale oil, and discloses a shale oil yield increasing system and method integrating multiphase gas lift and chemical injection, and the system comprises a data acquisition and processing module, a gas lift optimization module, a chemical injection control module, an integrated execution module and a dynamic feedback module; by establishing and optimizing an integrated control scheme of multiphase gas lift parameters and chemical injection parameters in real time, collaborative linkage and integrated decision of gas injection and agent injection processes are achieved, mutual inhibition of gas displacement and chemical modification effects can be avoided, the comprehensive utilization efficiency of reservoir remaining oil is improved, energy and chemical agent consumption is reduced, and meanwhile, the comprehensive utilization efficiency of reservoir remaining oil is improved. The overall economical efficiency and effectiveness of production increasing operation are guaranteed, dynamic deviation analysis and model prediction control are conducted on the production increasing process based on a model prediction control algorithm by collecting and analyzing underground production dynamic data in real time, and therefore the system can adjust injection parameters in real time according to the actual response of an oil reservoir; the stability and the long-term effect of the yield increasing process are ensured.

Description

Technical Field

[0001] This invention relates to the field of shale oil technology, specifically to a shale oil production enhancement system and method integrating multiphase gas lift and chemical injection. Background Technology

[0002] Shale oil refers to low-mature to semi-mature oil and gas formed and retained in source rocks, existing in a free or adsorbed state within the micro-nano-scale reservoir space of the formation. It exhibits minimal or very short-distance migration. With the gradual depletion of traditional oil and gas resources and the increasing demand for energy, shale oil, as a very important unconventional oil and gas resource, has attracted widespread attention. Due to its tight reservoir structure and low oil and gas abundance, shale oil reservoirs typically exhibit poor permeability, low production, and difficulty in energy replenishment during development.

[0003] Currently, in the field of shale oil production enhancement, traditional technical solutions typically treat gas lift enhancement and chemical injection as two independent and simply sequential processes, which has limitations. During production enhancement operations, it is impossible to achieve integrated linkage and synergistic optimization of multiphase gas lift and chemical injection parameters. The gas lift injection rate, pressure, and the type, concentration, and timing of the chemical agent are often set separately based on experience. When parameters are not properly matched, the gas displacement efficiency and the chemical modification effect will inhibit each other, not only failing to utilize the remaining oil in the reservoir but also wasting energy and chemicals, thus affecting the economics of production enhancement.

[0004] Therefore, a shale oil production enhancement system and method integrating multiphase gas lift and chemical injection is proposed to solve the above problems. Summary of the Invention

[0005] To address the shortcomings of existing technologies, this invention provides a shale oil production enhancement system and method that integrates multiphase gas lift and chemical injection, solving the problem mentioned in the background technology of mutual inhibition between gas displacement efficiency and chemical agent modification effect, resulting in waste of energy and chemical agents and affecting the economics of production enhancement.

[0006] To achieve the above objectives, the present invention provides the following technical solution: a shale oil production enhancement system and method integrating multiphase gas lift and chemical injection, the method comprising the following steps: S1. Real-time production data of shale oil wells are collected through wellhead sensors and remote monitoring systems, and geological parameter data are obtained through well logging and seismic interpretation to generate an initial production geological dataset; S2. Based on the initial production geological dataset, a genetic algorithm is used for multi-objective optimization to generate an optimized gas lift parameter dataset, which includes gas injection rate, gas injection pressure, and gas composition parameters. S3. Based on the initial production geological dataset, combined with reservoir numerical simulation and experimental data, chemical injection agent selection and parameter optimization are performed to generate a chemical injection scheme dataset. S4. Based on the optimized gas lift parameter dataset and chemical injection scheme dataset, perform integrated control decisions for multiphase gas lift and chemical injection, and generate integrated control commands. S5. Based on the integrated control instructions, synchronously execute the integrated production enhancement operation of multiphase gas lift and chemical injection, monitor the operation effect in real time, and generate a production enhancement process monitoring dataset. S6. Based on the production increase process monitoring dataset, the model predictive control algorithm is used to dynamically adjust the production increase process, generate a dynamic adjustment parameter dataset, and feed the dynamic adjustment parameter dataset back to step S4 to update the integrated control command. S7. Summarize and analyze the key data from S2 to S6, generate and output a shale oil production enhancement report, which includes production enhancement efficiency, economic assessment and optimization recommendations.

[0007] Preferably, the real-time production data and geological parameter data of shale oil wells collected in S1 include the following steps: S11. Collect real-time production data of shale oil wells through wellhead sensor array, including instantaneous production, bottom hole pressure, wellhead temperature and fluid composition, and generate real-time production dataset; S12. Collect geological parameter data, including oil saturation, porosity, permeability and three-dimensional distribution of fracture network, through well logging equipment and seismic data interpretation system, and generate geological parameter dataset; S13. Import the real-time production dataset and geological parameter dataset into the shale oil production enhancement management platform, perform data cleaning and normalization processing, and generate the initial production geological dataset.

[0008] Preferably, the multiphase gas lift parameter optimization process in S2 includes the following steps: S21. Obtain the initial production geological dataset, extract the characteristic parameters related to multiphase gas lift, and use principal component analysis to reduce the dimensionality, including gas injection efficiency index, gas diffusion coefficient and lifting capacity parameters. S22. A multi-objective optimization model is constructed using a genetic algorithm, with the objective functions of maximizing recovery rate and minimizing energy consumption. The multiphase gas lift parameters are optimized and solved to generate optimized gas lift parameter data. S23. Store the optimized gas lift parameter data in the database to form an optimized gas lift parameter dataset, which includes the gas injection rate setpoint, gas injection pressure curve, and gas component ratio.

[0009] Preferably, the chemical injection agent selection and parameter optimization in S3 includes the following steps: S31. Based on the initial production geological dataset, conduct chemical injection agent adaptability analysis, use fuzzy logic algorithm to consider reservoir temperature, pressure and salinity factors, match the best chemical agent type according to reservoir characteristics, and generate chemical agent selection data. S32. Simulate chemical injection parameters, including injection concentration, injection rate and injection timing, using reservoir numerical simulation software to generate chemical injection parameter data; S33. The chemical injection parameters are corrected by combining experimental data to generate chemical injection scheme data. The experimental data comes from core displacement experiments and microscopic visualization experiments. The chemical agent selection data and the corrected chemical injection parameter data are integrated to generate a chemical injection scheme dataset.

[0010] Preferably, the integrated control decision in S4 includes the following steps: S41. The optimized gas lift parameter dataset and the chemical injection scheme dataset are fused together, and the Kalman filter algorithm is used to reduce data noise. The control signal is generated based on the multivariable control theory. S42. Convert the control signal into an integrated control instruction, execute it through the PLC controller, adjust the operating parameters of the gas injection pump and the chemical injection pump, and realize the synchronous operation of gas lift and chemical injection. S43. Real-time verification of integrated control effect: Based on sensor feedback data, comparison is made to generate control verification data.

[0011] Preferably, performing integrated production ramp-up operations and real-time monitoring in S5 includes the following steps: S51. According to the integrated control command, start the multiphase gas lift system to inject gas, and at the same time start the chemical injection system to inject chemical agent, and record the operation log. S52. Collect dynamic data of the production increase process through a real-time monitoring system, including production change curves, pressure response and fluid composition changes, and generate production increase monitoring data; S53. Based on the collected dynamic data of the production enhancement process, the weighted average method is used to calculate the production enhancement index. The economic benefit index takes into account operating costs and oil price factors, including the percentage increase in recovery rate and the economic benefit index. The dynamic data and production enhancement index are integrated to generate a production enhancement process monitoring dataset.

[0012] Preferably, the dynamic adjustment process in S6 includes the following steps: S61. Obtain the monitoring dataset of the production increase process, perform dynamic deviation analysis, and use the sliding window method to identify the deviation of parameters from the target value. S62. The model predictive control algorithm is used for real-time adjustment to generate adjusted production increase parameter data, including updated gas lift parameters and chemical injection parameters. S63. Encapsulate the dynamic adjustment parameter data into a dynamic adjustment parameter dataset and feed it back to step S4. Exchange data through the OPC protocol and update the integrated control command.

[0013] Preferably, the real-time adjustment using the model predictive control algorithm in S62 includes the following steps: S621. Based on the monitoring dataset of the production enhancement process, establish a prediction model for the oil well production system with production and pressure as the core state variables; S622. With the objective function of maximizing the recovery rate in the future finite time domain, solve for the optimal gas lift and chemical injection operation sequence under system constraints. S623. Apply the first control variable of the optimal operation sequence to the system to generate the updated gas lift parameters and chemical injection parameters, which constitute the adjusted production enhancement parameter data.

[0014] Preferably, the output of shale oil production increase report data in S7 includes the following steps: S71. Integrate and optimize the gas lift parameter dataset, chemical injection scheme dataset, production enhancement process monitoring dataset, and dynamic adjustment parameter dataset, and use data warehouse technology to generate production enhancement summary data; S72. Process the production increase summary data through data visualization tools, generate a shale oil production increase report including charts, curves and text analysis, and output it to the user interface; S73. Export shale oil production increase reports to PDF and Excel formats via API interface.

[0015] Preferably, the system includes: The data acquisition and processing module collects oil well production and environmental data through the sensor unit, preprocesses the collected data using the data cleaning unit, and outputs the initial production geological dataset through the storage unit. The gas lift optimization module receives the initial production geological dataset, obtains feature parameters through the parameter extraction unit, optimizes the parameters using the optimization algorithm unit, and outputs the optimized gas lift parameter dataset through the database unit. The chemical injection control module receives the initial production geological dataset, determines the chemical agent type through the dosage form selection unit, simulates the injection process using the simulation unit, and outputs the chemical injection scheme dataset through the calibration unit. The integrated execution module receives optimized gas lift parameter datasets and chemical injection scheme datasets, performs decision fusion through the control command generation unit, drives field devices using the PLC execution unit, and outputs integrated control commands through the verification unit. The dynamic feedback module collects production increase process data through the monitoring unit, performs dynamic optimization using the adjustment algorithm unit, and outputs a dynamic adjustment parameter dataset through the closed-loop control unit.

[0016] Compared with existing technologies, this invention provides a shale oil production enhancement system and method integrating multiphase gas lift and chemical injection, which has the following beneficial effects: 1. In this invention, by establishing and optimizing an integrated control scheme for multiphase gas lift parameters and chemical injection parameters in real time, the coordinated linkage and integrated decision-making of gas injection and chemical injection processes are realized. This avoids mutual inhibition between the effects of gas displacement and chemical modification, improves the overall utilization efficiency of the remaining oil in the reservoir, and ensures the overall economy and effectiveness of the production enhancement operation while reducing energy and chemical consumption.

[0017] 2. In this invention, by collecting and analyzing downhole production dynamic data in real time, and by performing dynamic deviation analysis and model predictive control on the production enhancement process based on model predictive control algorithms, the system can adjust the injection parameters in real time according to the actual response of the reservoir, thereby intelligently adapting to the dynamic changes in formation conditions and fracture networks, reducing the risk of production enhancement effect attenuation caused by deviations in operating conditions, and ensuring the stability and long-term effectiveness of the production enhancement process.

[0018] 3. In this invention, by integrating and visualizing the multi-dimensional key data of the entire operation process, a production increase report is generated that includes production increase efficiency, economic evaluation and optimization suggestions. This makes the evaluation of the operation effect more accurate and can clearly analyze the synergistic mechanism of "gas lift-chemical injection" and the contribution of each stage, providing reliable decision support for the continuous optimization and efficient large-scale application of subsequent production increase strategies. Attached Figure Description

[0019] Figure 1 This is a flowchart of a shale oil production enhancement method integrating multiphase gas lift and chemical injection according to the present invention; Figure 2 This is a schematic diagram of a shale oil production enhancement system integrating multiphase gas lift and chemical injection according to the present invention. Detailed Implementation

[0020] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0021] For specific implementation examples, please refer to: Figure 1-2 A shale oil production enhancement system and method integrating multiphase gas lift and chemical injection, the method comprising the following steps: S1. Real-time production data of shale oil wells are collected through wellhead sensors and remote monitoring systems, and geological parameter data are obtained through well logging and seismic interpretation to generate an initial production geological dataset; S2. Based on the initial production geological dataset, a genetic algorithm is used for multi-objective optimization to generate an optimized gas lift parameter dataset, which includes gas injection rate, gas injection pressure, and gas composition parameters. S3. Based on the initial production geological dataset, combined with reservoir numerical simulation and experimental data, chemical injection agent selection and parameter optimization are performed to generate a chemical injection scheme dataset. S4. Based on the optimized gas lift parameter dataset and chemical injection scheme dataset, perform integrated control decisions for multiphase gas lift and chemical injection, and generate integrated control commands. S5. Based on the integrated control instructions, synchronously execute the integrated production enhancement operation of multiphase gas lift and chemical injection, monitor the operation effect in real time, and generate a production enhancement process monitoring dataset. S6. Based on the production increase process monitoring dataset, the model predictive control algorithm is used to dynamically adjust the production increase process, generate a dynamic adjustment parameter dataset, and feed the dynamic adjustment parameter dataset back to step S4 to update the integrated control command. S7. Summarize and analyze the key data from S2 to S6, generate and output a shale oil production enhancement report, which includes production enhancement efficiency, economic assessment and optimization recommendations.

[0022] The real-time production data and geological parameter data of shale oil wells collected in S1 include the following steps: S11. Collect real-time production data of shale oil wells through wellhead sensor array, including instantaneous production, bottom hole pressure, wellhead temperature and fluid composition, and generate real-time production dataset; S12. Collect geological parameter data, including oil saturation, porosity, permeability and three-dimensional distribution of fracture network, through well logging equipment and seismic data interpretation system, and generate geological parameter dataset; S13. Import the real-time production dataset and geological parameter dataset into the shale oil production enhancement management platform, perform data cleaning and normalization processing, and generate the initial production geological dataset, including the following steps: S131. Perform outlier detection on the real-time production dataset and geological parameter dataset, and remove outlier data points; S132. Fill in missing data to ensure data continuity; S133. Normalization is performed using the Min-Max standardization method, with the following formula: ; in, Represents the original data value. and These are the minimum and maximum values ​​of the dataset, respectively. This is the normalized value.

[0023] The multiphase gas lift parameter optimization process in S2 includes the following steps: S21. Obtain the initial production geological dataset, extract characteristic parameters related to multiphase gas lift, and use principal component analysis to reduce dimensionality, including gas injection efficiency indicators, gas diffusion coefficients, and lift capacity parameters. This includes the following steps: S211. Standardize the initial production geological dataset to eliminate the dimensional differences of each characteristic parameter and ensure data comparability. S212. Calculate the covariance matrix of the standardized data to represent the linear relationship between the feature parameters. The covariance matrix is ​​calculated using the following formula: ; in, Represents the covariance matrix. This represents the standardized initial production geology dataset. Indicates the number of data points. Indicates the transpose symbol; S213. Perform eigenvalue decomposition on the covariance matrix to obtain eigenvalues ​​and corresponding eigenvectors; S214. Arrange the eigenvalues ​​in descending order and select the eigenvectors corresponding to the k largest eigenvalues ​​as principal components, where k is determined by a preset variance contribution rate threshold. S215. Project the standardized raw data onto the selected principal components to generate a dimensionality-reduced feature parameter dataset. The projection is achieved using the following formula: ; in, This represents the dataset of feature parameters after dimensionality reduction. This represents the standardized initial production geology dataset. This represents the principal component matrix composed of the first k eigenvectors; S22. A multi-objective optimization model is constructed using a genetic algorithm, with the objective functions of maximizing recovery rate and minimizing energy consumption. The multiphase gas lift parameters are then optimized to generate optimized gas lift parameter data, including the following steps: S221. Randomly generate an initial population with a population size of 100. Each individual represents a set of airlift parameters. S222. Calculate the fitness value of each individual based on the objective function, which is: ; in, Indicates the fitness value. Indicates the recovery rate. Indicates energy consumption. and Weighting coefficients

[0024] S223. Use the roulette wheel selection method to select parent individuals based on the proportion of fitness values; S224. Using a single-point crossover method with a crossover probability of 0.8, gene fragments are exchanged at randomly selected crossover points. S225. Using the basic position mutation method, with a mutation probability of 0.1, randomly flip individual gene positions; S226. When the number of iterations reaches 500, output the optimal solution as the optimized airlift parameter data; S23. Store the optimized gas lift parameter data in the database to form an optimized gas lift parameter dataset, which includes the gas injection rate setpoint, gas injection pressure curve, and gas component ratio.

[0025] The selection and parameter optimization of chemical injectors in S3 includes the following steps: S31. Based on the initial production geological dataset, conduct a chemical injection agent suitability analysis. Use a fuzzy logic algorithm to consider reservoir temperature, pressure, and salinity factors, match the optimal chemical agent type according to reservoir characteristics, and generate chemical agent selection data, including the following steps: S311. Convert the input variables of reservoir temperature, pressure, and salinity into fuzzy sets. The membership function adopts a trigonometric function, and the formula is: ; in, This represents the membership function value. Indicates the input value. Represents the center point of the membership function. Indicates the function width; S312. Perform inference based on a predefined fuzzy rule base to generate a fuzzy output set; S313. The centroid method is used to convert the fuzzy output set into accurate chemical agent selection data. The formula is as follows: ; in, This indicates precise data on the selection of chemical agents. Indicates the first The membership degree of a fuzzy set. This indicates the corresponding output value; S32. Simulate chemical injection parameters using reservoir numerical simulation software, including injection concentration, injection rate, and injection timing, to generate chemical injection parameter data, including the following steps: S321. Divide the reservoir area into three-dimensional grids, with the grid size determined based on the geological parameter dataset; S322. Establish mathematical equations describing the fluid flow law to characterize the migration process of the injected agent in the reservoir. The mathematical equations are expressed as follows: ; in, Indicates the concentration of the injectable agent. Indicates time, The Darcy velocity vector represents the fluid. Represents the dispersion coefficient tensor. Indicates source and sink terms. Represents the gradient operator; S323. The mathematical equations are solved using a numerical method with a simulation step size of 1 hour to obtain the parameters of injection concentration, injection rate, and injection timing. The numerical method is implemented through the following discrete formula: ; in, Indicates the first Each grid in The concentration of the injector at each time step. Indicates the first Each grid in The concentration of the injector at each time step. Indicates the simulation step size. Indicates the first Darcy velocity vector of each grid, Represents the dispersion coefficient tensor. Indicates the first The source and sink items of each grid; S33. The chemical injection parameters are corrected by combining experimental data to generate chemical injection scheme data. The experimental data comes from core displacement experiments and microscopic visualization experiments. The chemical agent selection data and the corrected chemical injection parameter data are integrated to generate a chemical injection scheme dataset.

[0026] The integrated control decision-making process in S4 includes the following steps: S41. The optimized gas lift parameter dataset and the chemical injection scheme dataset are fused together, and the Kalman filter algorithm is used to reduce data noise. The control signal is generated based on the multivariable control theory. The process of reducing data noise using the Kalman filter algorithm includes the following steps: S411. Predict the state at the next moment based on the system state equation, which is based on the dynamic model of the air lift and injection system. S412. Update the state estimate using the observed values ​​and calculate the Kalman gain using the following formula: ; in, Indicates Kalman gain, Represents the predicted covariance matrix. Represents the observation matrix. Represents the observation noise covariance matrix. Indicates the transpose symbol; S413. Correct the predicted value, the formula is: ; in, express State estimation at time 10:00 express Predicted state at any given time express The observed value at time; Generating control signals based on multivariable control theory includes the following steps: S414. Establish the state-space model of a multi-input multi-output system, with the following formula: ; ; in, Represents the state vector. Represents the state vector Time derivative, Represents the input vector. Indicates the output vector. , , , For the system matrix; S415. Design a multivariable PID controller with the following output formula: ; in, Indicates the controller output. Represents the error vector. , , This is the gain matrix, representing the proportional, integral, and differential gain parameters; S416. Generate control signals based on controller output and system feedback; S42. Convert the control signal into an integrated control instruction, execute it through the PLC controller, adjust the operating parameters of the gas injection pump and the chemical injection pump, and realize the synchronous operation of gas lift and chemical injection. S43. Real-time verification of integrated control effect: Based on sensor feedback data, comparison is made to generate control verification data.

[0027] Performing integrated boosting operations and real-time monitoring in S5 includes the following steps: S51. According to the integrated control command, start the multiphase gas lift system to inject gas, and at the same time start the chemical injection system to inject chemical agent, and record the operation log. S52. Collect dynamic data of the production increase process through a real-time monitoring system, including production change curves, pressure response and fluid composition changes, and generate production increase monitoring data; S53. Based on the collected dynamic data of the enhanced oil recovery process, a weighted average method is used to calculate the enhanced oil recovery index. The economic benefit index considers operating costs and oil price factors, including the percentage increase in recovery rate and the economic benefit index. The dynamic data and enhanced oil recovery index are integrated to generate a monitoring dataset for the enhanced oil recovery process, including the following steps: S531. Weights are assigned based on operating costs and oil prices, with a total weight of 1. Cost weighting. Oil price weighting ; S532. Calculate the weighted average using the following formula: ; in, Indicating economic efficiency index, Indicates cost indicators, This indicates an oil price index.

[0028] The dynamic adjustment process in S6 includes the following steps: S61. Obtain the monitoring dataset of the production increase process, perform dynamic deviation analysis, and use the sliding window method to identify situations where parameters deviate from the target value, including the following steps: S611. Set the window size to 10 minutes, and the window contains continuous time series data; S612. Slide the window in chronological order, moving one data point at a time. S613. Calculate the average deviation between the parameters within the window and the target value, using the following formula: ; in, Indicates the average deviation. Indicates the number of data points within the window. Indicates the parameter value. Indicates the target value; S62. The model predictive control algorithm is used to make real-time adjustments and generate adjusted production increase parameter data, including updated gas lift parameters and chemical injection parameters. S63. Encapsulate the dynamic adjustment parameter data into a dynamic adjustment parameter dataset and feed it back to step S4. Exchange data through the OPC protocol and update the integrated control command.

[0029] The real-time adjustment using model predictive control algorithms in S62 includes the following steps: S621. Based on the monitoring dataset of the production enhancement process, establish a predictive model for the oil well production system with production and pressure as the core state variables, including the following steps: S6211. Select output and pressure as core state variables; S6212. The least squares method is used for system identification, and the objective function is: ; in, Represents the objective function value. Indicates the measured value. Indicates the predicted value; S6213. Use historical data to verify the model's prediction accuracy and calculate the root mean square error. The formula is: ; in, This represents the root mean square error. Indicates the number of data points; S622. With the objective function of maximizing the recovery rate in the future finite time domain, under system constraints, solve for the optimal gas lift and chemical injection operation sequence. S623. Apply the first control variable of the optimal operation sequence to the system to generate the updated gas lift parameters and chemical injection parameters, which constitute the adjusted production enhancement parameter data.

[0030] The steps involved in outputting shale oil production increase report data in S7 are as follows: S71. Integrate and optimize the gas lift parameter dataset, chemical injection scheme dataset, production enhancement process monitoring dataset, and dynamic adjustment parameter dataset, and use data warehouse technology to generate production enhancement summary data; S72. Process the production increase summary data through data visualization tools, generate a shale oil production increase report including charts, curves and text analysis, and output it to the user interface; S73. Export shale oil production increase reports to PDF and Excel formats via API interface.

[0031] The system includes: The data acquisition and processing module collects oil well production and environmental data through the sensor unit, preprocesses the collected data using the data cleaning unit, and outputs the initial production geological dataset through the storage unit. The gas lift optimization module receives the initial production geological dataset, obtains feature parameters through the parameter extraction unit, optimizes the parameters using the optimization algorithm unit, and outputs the optimized gas lift parameter dataset through the database unit. The chemical injection control module receives the initial production geological dataset, determines the chemical agent type through the dosage form selection unit, simulates the injection process using the simulation unit, and outputs the chemical injection scheme dataset through the calibration unit. The integrated execution module receives optimized gas lift parameter datasets and chemical injection scheme datasets, performs decision fusion through the control command generation unit, drives field devices using the PLC execution unit, and outputs integrated control commands through the verification unit. The dynamic feedback module collects production increase process data through the monitoring unit, performs dynamic optimization using the adjustment algorithm unit, and outputs a dynamic adjustment parameter dataset through the closed-loop control unit.

[0032] The operation steps of this shale oil production enhancement system and method integrating multiphase gas lift and chemical injection are as follows: Step 1: Data Acquisition and Processing Stage The system first collects real-time production data, including instantaneous production, bottomhole pressure, wellhead temperature, and key fluid composition parameters, using sensor arrays deployed at the shale oil wellhead, generating a real-time production dataset. Simultaneously, it acquires geological parameter data, including oil saturation, porosity, permeability, and three-dimensional fracture network distribution information, using logging equipment and a seismic data interpretation system, forming a geological parameter dataset. These two datasets are then imported into the shale oil production enhancement management platform. After data cleaning and normalization, a standardized initial production geological dataset is generated, providing a reliable data foundation for subsequent optimization decisions.

[0033] Step 2: Multiphase gas lift parameter optimization stage: Based on the initial production geological dataset, the system employs a genetic algorithm for multi-objective optimization. First, characteristic parameters related to multiphase gas lift are extracted, including key indicators such as gas injection efficiency, gas diffusion coefficient, and lift capacity. An optimization model is constructed with the objective functions of maximizing recovery and minimizing energy consumption. Multi-parameter collaborative solutions are then performed to generate an optimized gas lift parameter dataset containing complete parameters for gas injection rate, injection pressure, and gas component ratios. This process automatically optimizes parameters using intelligent algorithms, ensuring the scientific rigor and economic viability of the gas lift scheme.

[0034] Step 3: Chemical Injection Scheme Design Stage The system simultaneously performs chemical injection agent selection and parameter optimization based on the same initial production geological dataset. Fuzzy logic algorithms are used to comprehensively analyze reservoir temperature, pressure, and salinity factors to match the most suitable chemical agent type. Injection parameter simulations are performed using reservoir numerical simulation software to determine key parameters such as optimal injection concentration, injection rate, and injection timing. Finally, core displacement experiments and microscopic visualization experimental data are integrated to correct the parameters, forming a complete chemical injection scheme dataset that ensures a high degree of adaptation between chemical injection and geological characteristics.

[0035] Step 4: Integrated Control Decision and Execution Phase The optimized gas lift parameter dataset and chemical injection scheme dataset are fused together, and a Kalman filter algorithm is used to eliminate data noise. Integrated control commands are generated based on multivariable control theory. The operating parameters of the gas injection pump and chemical injection pump are adjusted by a PLC controller to achieve synchronous operation of multiphase gas lift and chemical injection. The system verifies the control effect in real time, and comparative analysis is performed using sensor feedback data to ensure the stability and reliability of the operation process.

[0036] Step 5: Dynamic Monitoring and Feedback Adjustment Phase During the production enhancement operation, the system collects dynamic data on the process in real time, including output change curves, pressure response, and fluid composition changes, generating a production enhancement process monitoring dataset. Based on this data, a model predictive control algorithm is used to perform dynamic deviation analysis, identify deviations of parameters from target values, and promptly generate adjusted production enhancement parameter data. A closed-loop control mechanism feeds the dynamically adjusted parameter dataset back to the control decision-making stage, achieving real-time optimization and adjustment of the injected parameters.

[0037] Step Six: Effectiveness Evaluation and Report Generation Stage The system integrates optimized gas lift parameter datasets, chemical injection scheme datasets, production enhancement process monitoring datasets, and dynamically adjusted parameter datasets from the entire process, and uses data warehouse technology to generate summary production enhancement data. Professional visualization tools transform the data into a comprehensive evaluation report containing charts, curves, and textual analysis, detailing production enhancement efficiency, economic assessments, and optimization recommendations. The final report supports export in multiple formats, providing complete technical support for subsequent decision-making.

[0038] It should be noted that, in this document, relational terms such as "first" and "second" are used merely to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, 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 a process, method, article, or apparatus. Without further limitations, 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 the element.

[0039] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A method for enhancing shale oil production by integrating multiphase gas lift and chemical injection, characterized in that: The method includes the following steps: S1. Real-time production data of shale oil wells are collected through wellhead sensors and remote monitoring systems, and geological parameter data are obtained through well logging and seismic interpretation to generate an initial production geological dataset; S2. Based on the initial production geological dataset, a genetic algorithm is used to perform multi-objective optimization processing to generate an optimized gas lift parameter dataset, which includes gas injection volume, gas injection pressure, and gas composition parameters. S3. Based on the initial production geological dataset, chemical injection agent selection and parameter optimization are performed in combination with reservoir numerical simulation and experimental data to generate a chemical injection scheme dataset. S4. Based on the optimized gas lift parameter dataset and the chemical injection scheme dataset, perform integrated control decisions for multiphase gas lift and chemical injection, and generate integrated control commands. S5. According to the integrated control command, the integrated production enhancement operation of multiphase gas lift and chemical injection is executed synchronously, and the operation effect is monitored in real time to generate a production enhancement process monitoring dataset. S6. Based on the production increase process monitoring dataset, a model predictive control algorithm is used to dynamically adjust the production increase process, generate a dynamic adjustment parameter dataset, and feed the dynamic adjustment parameter dataset back to step S4 to update the integrated control command. S7. Summarize and analyze the key data from S2 to S6, generate and output a shale oil production increase report, which includes production increase efficiency, economic assessment and optimization suggestions.

2. The shale oil production enhancement method integrating multiphase gas lift and chemical injection according to claim 1, characterized in that: The real-time production data and geological parameter data of shale oil wells collected in S1 include the following steps: S11. Collect real-time production data of shale oil wells through wellhead sensor array, including instantaneous production, bottom hole pressure, wellhead temperature and fluid composition, and generate real-time production dataset; S12. Collect geological parameter data, including oil saturation, porosity, permeability and three-dimensional distribution of fracture network, through well logging equipment and seismic data interpretation system, and generate geological parameter dataset; S13. Import the real-time production dataset and geological parameter dataset into the shale oil production enhancement management platform, perform data cleaning and normalization processing, and generate the initial production geological dataset.

3. The shale oil production enhancement method integrating multiphase gas lift and chemical injection according to claim 2, characterized in that: The multiphase gas lift parameter optimization process in S2 includes the following steps: S21. Obtain the initial production geological dataset, extract the characteristic parameters related to multiphase gas lift, and use principal component analysis to reduce the dimensionality, including gas injection efficiency index, gas diffusion coefficient and lift capacity parameters. S22. A multi-objective optimization model is constructed using a genetic algorithm, with the objective functions of maximizing recovery rate and minimizing energy consumption. The multiphase gas lift parameters are optimized and solved to generate optimized gas lift parameter data. S23. The optimized gas lift parameter data is stored in a database to form the optimized gas lift parameter dataset, which includes the gas injection volume set value, the gas injection pressure curve and the gas component ratio.

4. The shale oil production enhancement method integrating multiphase gas lift and chemical injection according to claim 3, characterized in that: The chemical injection agent selection and parameter optimization in step S3 includes the following steps: S31. Based on the initial production geological dataset, perform chemical injection agent adaptability analysis, use fuzzy logic algorithm to consider reservoir temperature, pressure and salinity factors, match the best chemical agent type according to reservoir characteristics, and generate chemical agent selection data; S32. Simulate chemical injection parameters, including injection concentration, injection rate and injection timing, using reservoir numerical simulation software to generate chemical injection parameter data; S33. The chemical injection parameters are corrected by combining experimental data to generate chemical injection scheme data. The experimental data comes from core displacement experiments and microscopic visualization experiments. The chemical agent selection data and the corrected chemical injection parameter data are integrated to generate the chemical injection scheme dataset.

5. The shale oil production enhancement method integrating multiphase gas lift and chemical injection according to claim 4, characterized in that: The integrated control decision-making process in S4 includes the following steps: S41. The optimized gas lift parameter dataset and the chemical injection scheme dataset are fused together, and the Kalman filter algorithm is used to reduce data noise. A control signal is generated based on multivariable control theory. S42. The control signal is converted into the integrated control command and executed by the PLC controller to adjust the operating parameters of the gas injection pump and the chemical injection pump, so as to realize the synchronous operation of gas lift and chemical injection. S43. Real-time verification of integrated control effect: Based on sensor feedback data, comparison is made to generate control verification data.

6. The shale oil production enhancement method integrating multiphase gas lift and chemical injection according to claim 5, characterized in that: The integrated production boosting operation and real-time monitoring in S5 includes the following steps: S51. According to the integrated control command, start the multiphase gas lift system to inject gas, and simultaneously start the chemical injection system to inject chemical agent, and record the operation log. S52. Collect dynamic data of the production increase process through a real-time monitoring system, including production change curves, pressure response and fluid composition changes, and generate production increase monitoring data; S53. Based on the collected dynamic data of the production enhancement process, the weighted average method is used to calculate the production enhancement index. The economic benefit index takes into account operating costs and oil price factors, including the percentage increase in recovery rate and the economic benefit index. The dynamic data and the production enhancement index are integrated to generate the production enhancement process monitoring dataset.

7. The shale oil production enhancement method integrating multiphase gas lift and chemical injection according to claim 6, characterized in that: The dynamic adjustment process in S6 includes the following steps: S61. Obtain the monitoring dataset of the production increase process, perform dynamic deviation analysis, and use the sliding window method to identify the deviation of parameters from the target value. S62. The model predictive control algorithm is used for real-time adjustment to generate adjusted production increase parameter data, including updated gas lift parameters and chemical injection parameters. S63. The dynamic adjustment parameter data is encapsulated into the dynamic adjustment parameter dataset and fed back to step S4. Data exchange is achieved through the OPC protocol to update the integrated control command.

8. The shale oil production enhancement method integrating multiphase gas lift and chemical injection according to claim 7, characterized in that: The real-time adjustment using the model predictive control algorithm in S62 includes the following steps: S621. Based on the production enhancement process monitoring dataset, establish an oil well production system prediction model with production and pressure as the core state variables; S622. With the objective function of maximizing the recovery rate in the future finite time domain, under system constraints, solve for the optimal gas lift and chemical injection operation sequence. S623. Apply the first control variable of the optimal operation sequence to the system to generate the updated gas lift parameters and chemical injection parameters, which constitute the adjusted production enhancement parameter data.

9. A method for enhancing shale oil production by integrating multiphase gas lift and chemical injection according to claim 7, characterized in that: The steps involved in outputting the shale oil production increase report data in S7 are as follows: S71. Integrate the optimized gas lift parameter dataset, chemical injection scheme dataset, production enhancement process monitoring dataset, and dynamic adjustment parameter dataset, and use data warehouse technology to generate production enhancement summary data; S72. Process the summarized production increase data using data visualization tools to generate a shale oil production increase report including charts, curves, and text analysis, and output it to the user interface; S73. Export the shale oil production increase report to PDF and Excel formats, and implement the export function through the API interface.

10. A shale oil production enhancement system integrating multiphase gas lift and chemical injection, characterized in that: A method for enhancing shale oil production by integrating multiphase gas lift and chemical injection as described in any one of claims 1-9, characterized in that the system comprises: The data acquisition and processing module collects oil well production and environmental data through the sensor unit, preprocesses the collected data using the data cleaning unit, and outputs the initial production geological dataset through the storage unit. The gas lift optimization module receives the initial production geological dataset, obtains feature parameters through the parameter extraction unit, optimizes the parameters using the optimization algorithm unit, and outputs the optimized gas lift parameter dataset through the database unit. The chemical injection control module receives the initial production geological dataset, determines the chemical agent type through the dosage form selection unit, simulates the injection process using the simulation unit, and outputs the chemical injection scheme dataset through the correction unit. The integrated execution module receives the optimized gas lift parameter dataset and the chemical injection scheme dataset, performs decision fusion through the control instruction generation unit, drives the field equipment using the PLC execution unit, and outputs integrated control instructions through the verification unit. The dynamic feedback module collects production increase process data through the monitoring unit, performs dynamic optimization using the adjustment algorithm unit, and outputs a dynamic adjustment parameter dataset through the closed-loop control unit.

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