Oil and gas reservoir underground in-situ hydrogen production method based on gas compounding

Abstract

The invention provides an oil and gas reservoir underground in-situ hydrogen production method based on gas compounding, and the method comprises the steps: collecting multi-source original data, and constructing a three-dimensional geology-thermodynamic digital model; a thermal-mass multiphase CFD simulation grid is constructed, real-time coupling simulation is carried out, a space-time distribution prediction variable is obtained, and periodic inversion of the optimal gas ratio and process parameters is carried out, so that an on-site gas injection system is automatically adjusted; controlling each gas source to pressurize, store, mix and inject into the oil and gas reservoir, and monitoring the reaction temperature, pressure and output mixed gas composition of the oil and gas reservoir in real time; according to the hydrogen concentration in the produced gas, the control instruction is optimized and adjusted, and the hydrogen production concentration is further improved; and carrying out multi-stage condensation impurity removal and purification on the product gas to obtain high-purity hydrogen. The in-situ intelligent hydrogen production of the oil and gas reservoir can be realized, the hydrogen production efficiency and purity are remarkably improved, the energy consumption and carbon emission are reduced, the whole process is more digital, intelligent and sustainable, and the safety of hydrogen production is further improved.

Description

Technical Field

[0001] The present invention relates to the technical field of hydrogen production, and in particular to an underground in-situ hydrogen production method in oil and gas reservoirs based on gas compounding. Background Art

[0002] Underground in-situ hydrogen production in oil and gas reservoirs refers to the use of natural oil and gas reservoirs as the reaction site. By injecting oxidants (such as air, oxygen, steam, carbon dioxide, etc.) or catalysts, hydrocarbons are promoted to undergo partial oxidation, steam reforming and other reactions in an underground high-temperature and high-pressure environment, and the in-situ hydrocarbons are cracked or converted into hydrogen on site, and the hydrogen is extracted to the ground. This is a new "underground original plant hydrogen production" method that does not require oil and gas to be mined and then hydrogen is produced on the ground.

[0003] Conventional methods for in-situ hydrogen production in oil and gas reservoirs include pyrolysis, partial oxidation, and steam reforming. Due to the complex underground environment, these methods struggle with precise temperature and oxygen control, are prone to carbonization and coking, and thus reduce hydrogen yields. They also suffer from low energy efficiency and low hydrogen gas purity. While some research has explored "gas compounding" approaches, the technology is relatively underdeveloped and immature. These methods present the following challenges: 1) Complex gas synergy mechanisms: The window of multi-gas synergistic reaction conditions is narrow, making injection ratios difficult to control, and errors can lead to carbon deposition or reaction stagnation. 2) Uneven gas transport and distribution: Heterogeneous porosity and layered structure in the subsurface result in uneven gas mixture dispersion and a discontinuous reaction space. 3) High energy consumption: Multiple high-energy-consuming gases (such as steam and oxygen) must be synthesized aboveground and injected at high pressure, resulting in high energy consumption and costs. 4) Difficult safety control: Gas combinations present explosion and leakage risks, complex pressure management, and wellbore corrosion. 5) Limited hydrogen product purity: Strong miscibility and some side reactions prevent the purity of the hydrogen gas stream from being improved, necessitating high back-end purification requirements. Summary of the Invention

[0004] In order to overcome the shortcomings of the existing technology, the purpose of the present invention is to provide an underground in-situ hydrogen production method in oil and gas reservoirs based on gas compounding, which can realize in-situ intelligent hydrogen production in oil and gas reservoirs, significantly improve the efficiency and purity of hydrogen production, reduce energy consumption and carbon emissions, make the entire process more digital, intelligent and sustainable, and further improve the safety of hydrogen production.

[0005] To achieve the above objectives, the present invention provides the following solution: a method for producing hydrogen underground in situ in oil and gas reservoirs based on gas compounding, comprising:

[0006] Collect relevant data of the target oil and gas reservoir area to obtain multi-source raw data, and perform intelligent processing and spatial mapping based on the multi-source raw data to obtain a three-dimensional geological-thermal digital model;

[0007] Based on the three-dimensional geological-thermal digital model and the target oil and gas reservoir regional structure, a thermal-mass multiphase CFD simulation grid is constructed, and then the thermal-mass multiphase CFD simulation grid is used for cloud deployment and real-time coupled simulation to obtain spatial-temporal distribution prediction variables;

[0008] According to the set optimization objectives and physical boundaries, the optimal gas ratio and process parameters are periodically inverted using the space-time distribution prediction variables to output control instructions and automatically adjust the on-site gas injection system;

[0009] The control instructions are disassembled and distributed using a main control system to control the pressurized injection, storage, mixing, and real-time monitoring of each gas source to obtain a mixed gas, which is then injected into a sealed oil and gas reservoir for reaction to obtain a product gas including hydrogen;

[0010] Deploy a variety of monitoring sensors inside the sealed oil and gas reservoir to monitor the temperature, pressure and output gas components of the reaction process, and transmit the monitoring data back to the main control system in real time to optimize and adjust the control instructions;

[0011] The product gas is directed to a ground separation module for multi-stage condensation, impurity removal and purification to obtain high-purity hydrogen.

[0012] Optionally, relevant data of the target oil and gas reservoir area is collected to obtain multi-source raw data, and intelligent processing and spatial mapping are performed based on the multi-source raw data to obtain a three-dimensional geological-thermal digital model, including:

[0013] Based on the target oil and gas reservoir area, porosity, permeability, lithology and mineral composition, in-situ pressure, temperature field distribution, fracture development characteristics and fault development characteristics are collected to obtain multi-source raw data;

[0014] Unifying the coordinate system and numerical format of the multi-source raw data, using a machine learning algorithm to denoise the multi-source raw data and fill in abnormal and missing values, and then using GIS spatial management to dynamically superimpose the physical properties, temperature, pressure and stress fields in the multi-source raw data to obtain well logging geological data;

[0015] Based on the modeling software, the grid cells are divided, and the logging geological data are assigned values ​​grid by grid, and then the temperature-pressure-formation seepage correlation model is constructed by combining the formation stress-thermal coupling effect;

[0016] Based on the temperature-pressure-formation seepage correlation model, and according to mineral distribution and in-situ temperature changes, a geochemical simulation algorithm is used to obtain the reaction activity, CO2 storage capacity, and heat transfer efficiency of each layer. The spatial partitions are then coded to dynamically set target heat input, gas flow, and monitoring point priorities for different regions, completing the spatial mapping of areas prone to reaction, carbon deposition, and failure.

[0017] The easily reactive areas, the easily carbonized areas and the easily failed areas are mapped to the temperature-pressure-formation seepage correlation model to obtain a three-dimensional geological-thermal digital model.

[0018] Optionally, based on the three-dimensional geological-thermal digital model and the target oil and gas reservoir regional structure, a thermal-mass multiphase CFD simulation grid is constructed, and then the thermal-mass multiphase CFD simulation grid is used for cloud deployment and real-time coupled simulation to obtain spatial-temporal distribution prediction variables, including:

[0019] Based on the three-dimensional geological-thermal digital model, standardized multi-dimensional spatial distribution parameters are extracted through spatial resampling and automatic conversion of boundary conditions; the standardized multi-dimensional spatial distribution parameters include porosity, permeability, mineral distribution, temperature and pressure;

[0020] Based on the regional structure of the target oil and gas reservoir, a physical grid is constructed and a multi-physics sub-model is embedded in the physical grid. Then, a geochemical reaction database is combined with the standardized multi-dimensional spatial distribution parameters to obtain dynamic reflection properties. The multi-physics sub-model includes a heat conduction and convection model, a multiphase fluid seepage model, and a gas composite component transport model. The dynamic reflection properties include reaction rate, product distribution, and thermal effect.

[0021] The dynamic reflection attributes are assigned to the physical grid to obtain a heat-mass multiphase CFD simulation grid, and the heat-mass multiphase CFD simulation grid is deployed to the cloud for calculation. A 30-minute heat-mass-reaction process prediction is performed in less than 1 minute to complete the real-time coupled simulation and obtain space-time distribution prediction variables; the space-time distribution prediction variables include temperature field, gas seepage field, hydrogen production rate and spatial distribution, carbon deposition hotspot zoning, and mineralized crust risk zoning.

[0022] Optionally, the on-site gas injection system includes a gas phase injection subsystem for injecting air, oxygen and CO2 and a liquid phase injection subsystem for injecting steam;

[0023] The gas phase injection subsystem includes an air compressor, an oxygen cylinder, a CO2 cylinder, an air booster pump, an oxygen booster pump, a CO2 booster pump, an air storage tank, an oxygen storage tank, a CO2 storage tank and a gas flow meter;

[0024] The liquid phase injection subsystem includes a high-pressure plunger pump, a water container and a steam generator.

[0025] Optionally, according to the set optimization objectives and physical boundaries, the space-time distribution prediction variables are used to periodically invert the optimal gas ratio and process parameters to output control instructions and automatically adjust the on-site gas injection system, including:

[0026] Combined with the space-time distribution prediction variables, the optimization objective function and constraints are set, and a multi-objective model predictive control algorithm is constructed; the calculation expression of the optimization objective function is:

[0027]

[0028] in, is the product gas hydrogen gas integral fraction predicted for the new cycle, is the preset target hydrogen production purity, is the average temperature of the reaction zone, T opt is the theoretical optimal reaction temperature, Δ C is the carbon accumulation and coking factor, E inj is the injection energy consumption index, ω1, ω2, ω3, and ω4 are all dynamic weight parameters;

[0029] Combining the target model predictive control algorithm and the sequence optimization algorithm, automatically searching for the optimal gas ratio and injection flow pattern that meet the target oil and gas reservoir area, and dynamically correcting the optimal gas ratio and injection flow pattern using the space-time distribution prediction variables to obtain the optimal solution, and then outputting the optimal solution as a control instruction;

[0030] The control instructions are sent to the on-site gas injection system in a standardized format for high-pressure injection of multiple gases; the control instructions include the ratio of five gas sources, the total gas injection flow rate and real-time adjustment parameters. The five gas sources include air, oxygen, CO2 and steam.

[0031] Optionally, the control instructions are disassembled and distributed using a main control system to control the pressurized injection, storage, mixing, and real-time monitoring of each gas source to obtain a mixed gas, which is then injected into a sealed oil and gas reservoir for reaction to obtain a product gas including hydrogen, including:

[0032] Using the main control system, the control instructions are decomposed to obtain the quality set value, flow set value and response time of each gas source in the current cycle;

[0033] Use a booster pump to boost the pressure of each gas source to the target pressure. After the boosting is completed, the gas from each gas source enters its own high-pressure buffer storage tank. The mass flow meter and high-frequency proportional valve are used to adjust the injection flow of each gas source in a closed loop to complete the boosted injection and storage of the gas source.

[0034] The gases in the high-pressure buffer storage tanks are mixed evenly through a high-pressure pipeline to obtain a mixed gas, and a component analyzer or infrared sensor is arranged at the outlet of the high-pressure pipeline to monitor and feedback the individual proportions, pressure and temperature of the mixed gas in real time;

[0035] The mixed gas is injected into a sealed oil and gas reservoir for reaction to obtain product gas including hydrogen.

[0036] Optionally, multiple monitoring sensors are deployed inside the sealed oil and gas reservoir to monitor the temperature, pressure and output gas components of the reaction process, and the monitoring data are transmitted back to the main control system in real time to optimize and adjust the control instructions, including:

[0037] Deploying optical fiber sensors, MEMS sensor arrays, and micro gas analyzers inside the sealed oil and gas reservoir to monitor the temperature, pressure, and produced gas components of the reaction process and obtain multi-channel data;

[0038] Based on the multi-channel data, an AI model is used to automatically analyze temperature mutations, pressure mutations, and component mutations to obtain anomaly detection results, and the anomaly detection results are transmitted back to the main control system in real time;

[0039] The main control system is used to determine whether the abnormal monitoring results have hidden dangers. If so, an automatic alarm is issued, and the control instructions are optimized and adjusted according to the abnormal detection results through the optimal matching strategy.

[0040] Optionally, the main control system is used to determine whether the abnormal monitoring result indicates a hidden danger. If so, an automatic alarm is issued, and the control instructions are optimized and adjusted according to the abnormal detection result through an optimal matching strategy, including:

[0041] Extract monitoring indicators based on the abnormal monitoring results; the monitoring indicators include the maximum value of the temperature section, the minimum value of the temperature section, the temperature gradient, the hydrogen purity and the carbon deposition blockage risk;

[0042] The optimal matching strategy is preset, and the control instructions are optimized and adjusted according to the monitoring indicators; the calculation expression of the optimal matching strategy is:

[0043]

[0044] Among them, x1, x2, x3, and x4 are the proportions of oxygen, carbon dioxide, steam, and methane in the mixed gas, respectively. inj is the total injection flow, L seg is the gas injection length of each segment, is the product gas hydrogen gas integral fraction predicted for the new cycle, is the preset target hydrogen purity, T peak is the maximum value of the temperature segment, T opt is the theoretical optimal reaction temperature, φ C The carbon deposition and blockage risk predicted for the new cycle, E inj is the injection energy consumption index, θ1, θ2, θ3, and θ4 are all weight factors.

[0045] Optionally, the product gas is directed to a ground separation module for multi-stage condensation, impurity removal, and purification to obtain high-purity hydrogen, including:

[0046] The product gas is directed to a surface separation module for primary condensation, secondary condensation, and tertiary condensation to remove impurities from the product gas and obtain an initial product; the primary condensation includes crude dehydration or light hydrocarbon condensation, the secondary condensation includes water removal or deep removal of light hydrocarbons, and the tertiary condensation includes enhanced pre-separation of extreme hydrocarbons or CO2;

[0047] The initial product is purified by using temperature swing adsorption technology and pressure swing adsorption technology to obtain high-purity hydrogen and by-product gas.

[0048] The present invention provides an underground in-situ hydrogen production method for oil and gas reservoirs based on gas compounding, and discloses the following technical effects:

[0049] 1. Accurately express oil and gas reservoir characteristics: By collecting multi-source raw data such as porosity, permeability, lithology, mineral composition, and pressure, a temperature-pressure-formation seepage correlation model and geochemical simulation are constructed, and spatial partitioning and spatial mapping are performed. This can intelligently partition areas prone to reaction, carbon deposition, and failure, and adjust subsequent operations in a targeted manner to improve hydrogen production efficiency and safety. This greatly enhances the digital expression accuracy of the physical and chemical characteristics of oil and gas reservoirs, and can also foresee the risk of failure or carbon deposition in advance. This is the basic guarantee for subsequent gas injection and reaction optimization, and improves the scientific nature and refinement of the overall process.

[0050] 2. Predictive management of underground reaction processes: Through the multi-physics field CFD simulation grid automatically constructed based on the three-dimensional model, multiple variables such as temperature field, seepage field, hydrogen production rate, and carbon deposition risk can be simulated simultaneously, making complex underground reaction processes controllable and visual, realizing predictive management, breaking the traditional static process, realizing dynamic closed-loop control, and improving the accuracy of intelligent gas injection decisions.

[0051] 3. Optimal gas ratio: By combining simulation results, periodically inverting and correcting the optimal gas ratio and process parameters, traditional manual or empirical gas injection is avoided, reaction efficiency and resource utilization are improved, the gas source ratio is precisely controlled, and negative factors such as carbon accumulation and energy consumption are effectively controlled. This greatly reduces operational difficulty and human errors, ensures long-term, stable, and efficient operation of the hydrogen production process, and improves the intelligent level of hydrogen production.

[0052] 4. Automatic injection of compound gases: The main control system automatically splits, issues, and executes control instructions, allowing each gas source to be independently pressurized, stored, mixed, and closed-loop flow regulated. This not only achieves a high degree of automation and refinement in the gas injection process, improving process safety and controllability, but also ensures uniform mixing of gas sources, efficient and stable reactions, avoids local oxygen or carbon enrichment, and ensures the accuracy and stability of the entire reaction chain.

[0053] 5. Real-time monitoring of gas compounding: By deploying optical fibers, MEMS, and micro-gas analyzers in the reaction module, multi-channel component and temperature and pressure online monitoring can be achieved. This can quickly and accurately capture dynamic fluctuations in the reaction, improve safety and hydrogen purity, proactively prevent process failures such as carbon deposition and blockage, dynamically improve optimal energy efficiency and reaction efficiency, and achieve a truly intelligent process closed loop.

[0054] 6. Effective hydrogen purification: High-purity hydrogen is obtained through multi-stage condensation and impurity removal in the ground separation module, combined with temperature and pressure swing adsorption. This not only ensures high hydrogen purity and maximizes the utilization of by-product resources, but also is environmentally friendly and greatly improves efficiency and economic benefits, and reduces resource waste and emissions.

[0055] The technical solution of the present invention is further described in detail below through the accompanying drawings and embodiments. BRIEF DESCRIPTION OF THE DRAWINGS

[0056] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0057] Figure 1 A schematic diagram of a method flow chart provided in an embodiment of the present invention;

[0058] Figure 2 A schematic diagram of the system architecture of an on-site gas injection system provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0059] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0060] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, the present invention is further described in detail below with reference to the accompanying drawings and specific embodiments.

[0061] like Figure 1 As shown, the present invention provides an underground in-situ hydrogen production method for oil and gas reservoirs based on gas compounding, comprising:

[0062] 1. Collect relevant data of the target oil and gas reservoir area to obtain multi-source raw data, perform intelligent processing and spatial mapping based on the multi-source raw data, and obtain a three-dimensional geological-thermal digital model; specifically, the following steps are involved:

[0063] 1.1 Based on the target oil and gas reservoir area, collect porosity, permeability, lithology and mineral composition, in-situ pressure, temperature field distribution, fracture development characteristics and fault development characteristics to obtain multi-source raw data.

[0064] 1.2 Unify the coordinate system and numerical format of the multi-source raw data, use machine learning algorithms to denoise the multi-source raw data and fill in abnormal and missing values, and then use GIS spatial management to dynamically superimpose the physical properties, temperature, pressure and stress fields in the multi-source raw data to obtain well logging geological data.

[0065] 1.3 Based on modeling software such as GOCAD and Petrel, variable-scale grid units are divided, and the porosity, permeability, oil saturation, mineral composition, in-situ temperature, pressure, stress, etc. in the logging geological data are assigned grid by grid. Then, combined with the formation stress-thermal coupling effect, a temperature-pressure-formation seepage correlation model is constructed.

[0066] 1.4 Based on the temperature-pressure-stratum seepage correlation model, according to the mineral distribution and in situ temperature changes, a geochemical simulation algorithm is used to obtain the reaction activity, CO2 storage capacity and heat transfer efficiency of each layer. The spatial partitions are then encoded to dynamically set the target heat input, gas flow rate and monitoring point priority for different areas, completing the spatial mapping of areas prone to reaction, areas prone to carbon deposition, and areas prone to failure.

[0067] 1.5 Mapping the prone-to-reaction area, the prone-to-carbon-deposition area, and the prone-to-failure area into the temperature-pressure-formation seepage correlation model to obtain a three-dimensional geological-thermal digital model.

[0068] 2. Based on the three-dimensional geological-thermal digital model and the target oil and gas reservoir regional structure, a thermal-mass multiphase CFD simulation grid is constructed. The thermal-mass multiphase CFD simulation grid is then used for cloud deployment and real-time coupled simulation to obtain spatial-temporal distribution prediction variables; specifically, the following steps are included:

[0069] 2.1 Based on the three-dimensional geological-thermal digital model, standardized multidimensional spatial distribution parameters are extracted through spatial resampling and automatic conversion of boundary conditions; the standardized multidimensional spatial distribution parameters include porosity, permeability, mineral distribution, temperature and pressure.

[0070] 2.2 Based on the target oil and gas reservoir regional structure, a physical grid is constructed and a multi-physics sub-model is embedded in the physical grid. The multi-physics sub-model includes:

[0071] Heat conduction and convection model: reflects distributed heating - natural heat transfer - formation conduction;

[0072] Multiphase fluid seepage model: full coupling of gas / liquid / solid states and channel evolution;

[0073] Gas composite component transport model: dynamic distribution of multiple gases such as O2, CO2, H2O, CH2 over time.

[0074] The geochemical reaction database is then combined with the standardized multidimensional spatial distribution parameters to obtain dynamic reflection properties; the dynamic reflection properties include reaction rate, product distribution and thermal effect.

[0075] 2.3 Assign the dynamic reflection attributes to the physical grid to obtain a heat-mass multiphase CFD simulation grid, and deploy the heat-mass multiphase CFD simulation grid to the cloud for high-performance calculations to ensure that a 30-minute heat-mass-reaction process prediction can be performed in less than 1 minute to complete the real-time coupled simulation and obtain the space-time distribution prediction variables.

[0076] The space-time distribution prediction variables include: temperature field (10 cm resolution, focusing on identifying thermal field islands and cold end high-risk areas), gas seepage field (distribution and content trend of each gas), hydrogen production rate and spatial distribution, carbon deposition hotspot zoning and mineralized crust risk zoning.

[0077] 2.4 On-site gas injection system

[0078] like Figure 2 As shown, the on-site gas injection system includes a gas phase injection subsystem for injecting air, oxygen and CO2 and a liquid phase injection subsystem for injecting steam; the gas phase injection subsystem includes an air compressor, an oxygen cylinder, a CO2 cylinder, an air booster pump, an oxygen booster pump, a CO2 booster pump, an air storage tank, an oxygen storage tank, a CO2 storage tank and a gas flow meter; the liquid phase injection subsystem includes a high-pressure plunger pump, a water container and a steam generator.

[0079] Step 3: Based on the set optimization objectives and physical boundaries, the optimal gas ratio and process parameters are periodically inverted using the space-time distribution prediction variables to output control instructions and automatically adjust the on-site gas injection system; specifically, the steps include:

[0080] 3.1 Combined with the space-time distribution prediction variables, the optimization objective function and constraints are set, and a multi-objective model predictive control algorithm is constructed.

[0081] Optimize the objective function and combine the spatial-temporal distribution prediction variables output by the digital model, that is, set the optimization target based on the temperature field, gas seepage field, and hydrogen production rate:

[0082] Maximize hydrogen production per unit time;

[0083] Control the temperature of the reaction zone within a safe range (e.g. 750-850°C);

[0084] The purity of hydrogen in the product gas should be higher than the set value (e.g. >90%);

[0085] Avoid carbon accumulation / coking and safety risks in the reaction zone;

[0086] The energy consumption is the lowest and CO2 can be stored on site.

[0087] Constraints: upper limit of gas injection pressure, maximum available amount of each gas, and wellbore pressure / temperature limits.

[0088] The calculation expression of the optimization objective function is:

[0089]

[0090] in, is the product gas hydrogen gas integral fraction predicted for the new cycle, is the preset target hydrogen production purity, is the average temperature of the reaction zone, T opt is the theoretical optimal reaction temperature, Δ C is the carbon accumulation and coking factor, E inj To inject energy consumption indicators, ω1, ω2, ω3, and ω4 are all dynamic weight parameters.

[0091] 3.2 Combine the target model predictive control algorithm and the sequence optimization algorithm (such as genetic algorithm, L-BFGS) to automatically search for the optimal gas ratio and injection flow pattern that meet the target oil and gas reservoir area, and use the space-time distribution prediction variables to dynamically correct the optimal gas ratio and injection flow pattern to obtain the optimal solution, and then output the optimal solution as a control instruction.

[0092] 3.3 The control instructions are sent to the on-site gas injection system in a standardized format, such as json, to perform high-pressure injection of multiple gases; the control instructions include the ratio of five gas sources, the total gas injection flow rate and real-time adjustment parameters. The five gas sources include air, oxygen, CO2 and steam.

[0093] Step 4: Using the main control system, the control instructions are disassembled and distributed to control the pressurized injection, storage, mixing and real-time monitoring of each gas source to obtain a mixed gas, and then the mixed gas is injected into a sealed oil and gas reservoir for reaction to obtain a product gas including hydrogen; specifically, the steps include:

[0094] 4.1 Using the main control system, the control instructions are decomposed to obtain the quality set value, flow set value and response time of each gas source in the current cycle.

[0095] 4.2 Use a booster pump to pressurize each gas source to the target pressure. After the pressurization is completed, the gas from each gas source enters its own high-pressure buffer storage tank, and the mass flow meter and high-frequency proportional valve are used to close the loop to adjust the injection flow of each gas source to complete the pressurized injection and storage of the gas source.

[0096] High-Frequency Proportional Valve: Gas flows through a high-frequency electronic proportional control valve with a response time of ≤50ms and a pressure resistance exceeding 40MPa. The main control system collects valve opening and actual flow in real time. If the deviation from the control command exceeds 0.5%, the self-regulation logic is automatically triggered to optimize the valve's dynamic response process, achieving rapid stabilization in less than 300ms.

[0097] 4.3 The gases in each high-pressure buffer storage tank are mixed evenly through a high-pressure pipeline to obtain a mixed gas, and a component analyzer or infrared sensor is configured at the outlet of the high-pressure pipeline to monitor and feedback the individual proportions, pressure and temperature of the mixed gas in real time.

[0098] 4.4 Injecting the mixed gas into a sealed oil and gas reservoir for reaction to obtain product gas including hydrogen.

[0099] 5. Deploy multiple monitoring sensors inside the sealed oil and gas reservoir to monitor the temperature, pressure, and produced gas composition of the reaction process, and transmit the monitoring data back to the main control system in real time to optimize and adjust the control instructions; specifically including:

[0100] 5.1 Deploy optical fiber sensors, MEMS sensor arrays, and micro gas analyzers inside the sealed oil and gas reservoir to monitor the temperature, pressure, and produced gas components during the reaction process and obtain multi-channel data.

[0101] Fiber optic sensors:

[0102] Distributed Temperature Sensing (DTS): monitors temperature changes and enables real-time 3D thermal imaging. Distributed Acoustic Sensing (DAS): captures sound velocity dynamics to assist in determining fluid flow, gas penetration, and reaction states, such as bubbling and coking sounds.

[0103] MEMS sensor array: Pressure sensors are deployed in each segment or key injection and production point to capture pressure fluctuations in the reaction area in real time and promptly detect micro-leakage, gas escape, crossflow and reaction anomalies.

[0104] Micro gas analyzer: localized in-situ real-time collection of local reaction product concentration, component evolution, escape, breakthrough and other probability events.

[0105] 5.2 Based on the multi-channel data, the AI ​​model is used to automatically analyze temperature mutations, pressure mutations and component mutations, and to detect in real time fatal process hazards such as reaction hotspots, cooling areas, carbon deposition / coking initiation, gas breakthrough, and isolation failure, and obtain abnormal detection results, which are then transmitted back to the main control system in real time.

[0106] 5.3 Using the main control system, determine whether the abnormal monitoring results indicate hidden dangers. If so, automatically issue an alarm and optimize and adjust the control instructions based on the abnormal detection results through the optimal matching strategy. Specifically,

[0107] 5.3.1 Based on the abnormal monitoring results, extract monitoring indicators; the monitoring indicators include the maximum value of the temperature segment, the minimum value of the temperature segment, the temperature gradient, the hydrogen purity and the carbon deposit blockage risk (indirectly determined by CO / CO2 changes and sonic velocity response).

[0108] 5.3.2 Preset the optimal matching strategy and optimize and adjust the control instructions based on the monitoring indicators; the calculation expression of the optimal matching strategy is:

[0109]

[0110] Among them, x1, x2, x3, and x4 are the proportions of oxygen, carbon dioxide, steam, and methane in the mixed gas, respectively. inj is the total injection flow, L seg is the gas injection length of each segment, is the product gas hydrogen gas integral fraction predicted for the new cycle, is the preset target hydrogen purity, T peak is the maximum value of the temperature segment, T opt is the theoretical optimal reaction temperature, φ C The carbon deposition and blockage risk predicted for the new cycle, E inj is the injection energy consumption index, θ1, θ2, θ3, and θ4 are all weight factors.

[0111] Step 6: The product gas is directed to a ground separation module for multi-stage condensation, impurity removal, and purification to obtain high-purity hydrogen, and the by-product gas is returned to the sealed oil and gas reservoir for resource recycling. Specifically, it includes:

[0112] 6.1 The product gas is directed to a ground separation module for primary condensation, secondary condensation, and tertiary condensation to remove impurities from the product gas and obtain an initial product.

[0113] Primary condensation: Crude dehydration or light hydrocarbon condensation. The product gas is first condensed in a high-efficiency heat exchanger, where the temperature drops to 40-60°C. Most of the condensed water vapor and C5+ heavy hydrocarbon condensate are automatically diverted and discharged. Online water and hydrocarbon content monitoring is used to prevent carryover contamination of subsequent systems.

[0114] Secondary condensation: deep removal of water or light hydrocarbons. Entering low-temperature condensation at 0-5°C, further removes entrained water and C1-C4 light hydrocarbons.

[0115] Three-stage condensation: Enhanced pre-separation of extreme hydrocarbons or CO2. For high CO2 / high hydrocarbon conditions, low-temperature condensation of -30 to -60°C is used to enhance the removal of CO2 and polar components.

[0116] 6.2 The initial product is purified using temperature swing adsorption technology and pressure swing adsorption technology to obtain high-purity hydrogen and by-product gas.

[0117] Temperature Swing Adsorption (TSA) technology: Pretreatment. Gas flows into the TSA adsorption towers, where the temperature swing adsorption process prioritizes the removal of residual water, some CO2 / CO, and light hydrocarbons. Multiple towers are switched in a time-sharing manner, and regenerated gas is directly recovered for internal heating or wellhead injection, maximizing energy recovery.

[0118] Pressure Swing Adsorption (PSA) technology: Efficient fractional hydrogen enrichment. After dehydration and crude purification, the gas enters the PSA sequence, where a molecular sieve / activated carbon / polymer composite adsorbent is used to remove CO, CO2, CH4, and some N2 in stages. This achieves a hydrogen output of 85-95 vol% in the main gas stream. Optional subsequent single-tower purification (such as a temperature swing / vacuum PSA or membrane separation module) can be used to achieve 99.99%+ high-purity hydrogen.

[0119] Therefore, the present invention provides an underground in-situ hydrogen production method in oil and gas reservoirs based on gas compounding, which can realize in-situ intelligent hydrogen production in oil and gas reservoirs, significantly improve hydrogen production efficiency and purity, reduce energy consumption and carbon emissions, make the entire process more digital, intelligent and sustainable, and further improve the safety of hydrogen production.

[0120] The various embodiments in this specification are described in a progressive manner, and each embodiment focuses on the differences from other embodiments. The same or similar parts between the various embodiments can be referenced to each other.

[0121] This document uses specific examples to illustrate the principles and implementation methods of the present invention. The above examples are only intended to help understand the method and core concept of the present invention. At the same time, those skilled in the art will find that the specific implementation methods and application scopes may vary based on the concept of the present invention. In summary, the contents of this specification should not be construed as limiting the present invention.

Claims

1. A method for producing hydrogen underground in situ in oil and gas reservoirs based on gas compounding, characterized in that: include: Collect relevant data of the target oil and gas reservoir area to obtain multi-source raw data, and perform intelligent processing and spatial mapping based on the multi-source raw data to obtain a three-dimensional geological-thermal digital model; Based on the three-dimensional geological-thermal digital model and the target oil and gas reservoir regional structure, a thermal-mass multiphase CFD simulation grid is constructed, and then the thermal-mass multiphase CFD simulation grid is used for cloud deployment and real-time coupled simulation to obtain spatial-temporal distribution prediction variables; According to the set optimization objectives and physical boundaries, the optimal gas ratio and process parameters are periodically inverted using the space-time distribution prediction variables to output control instructions and automatically adjust the on-site gas injection system; The control instructions are disassembled and distributed using a main control system to control the pressurized injection, storage, mixing, and real-time monitoring of each gas source to obtain a mixed gas, which is then injected into a sealed oil and gas reservoir for reaction to obtain a product gas including hydrogen; Deploy a variety of monitoring sensors inside the sealed oil and gas reservoir to monitor the temperature, pressure and output gas components of the reaction process, and transmit the monitoring data back to the main control system in real time to optimize and adjust the control instructions; The product gas is directed to a ground separation module for multi-stage condensation, impurity removal and purification to obtain high-purity hydrogen.

2. The method for producing hydrogen underground in situ in oil and gas reservoirs based on gas compounding according to claim 1, characterized in that: Collect relevant data of the target oil and gas reservoir area to obtain multi-source raw data. Based on the multi-source raw data, intelligent processing and spatial mapping are performed to obtain a three-dimensional geological-thermal digital model, including: Based on the target oil and gas reservoir area, porosity, permeability, lithology and mineral composition, in-situ pressure, temperature field distribution, fracture development characteristics and fault development characteristics are collected to obtain multi-source raw data; Unifying the coordinate system and numerical format of the multi-source raw data, using a machine learning algorithm to denoise the multi-source raw data and fill in abnormal and missing values, and then using GIS spatial management to dynamically superimpose the physical properties, temperature, pressure and stress fields in the multi-source raw data to obtain well logging geological data; Based on the modeling software, the grid cells are divided, and the logging geological data are assigned values ​​grid by grid, and then the temperature-pressure-formation seepage correlation model is constructed by combining the formation stress-thermal coupling effect; Based on the temperature-pressure-formation seepage correlation model, and according to mineral distribution and in-situ temperature changes, a geochemical simulation algorithm is used to obtain the reaction activity, CO2 storage capacity, and heat transfer efficiency of each layer. The spatial partitions are then coded to dynamically set target heat input, gas flow, and monitoring point priorities for different regions, completing the spatial mapping of areas prone to reaction, carbon deposition, and failure. The easily reactive areas, the easily carbonized areas and the easily failed areas are mapped to the temperature-pressure-formation seepage correlation model to obtain a three-dimensional geological-thermal digital model.

3. The method for producing hydrogen underground in situ in oil and gas reservoirs based on gas compounding according to claim 2, characterized in that: Based on the three-dimensional geological-thermal digital model and the target oil and gas reservoir regional structure, a thermal-mass multiphase CFD simulation grid is constructed. The thermal-mass multiphase CFD simulation grid is then used for cloud deployment and real-time coupled simulation to obtain spatial-temporal distribution prediction variables, including: Based on the three-dimensional geological-thermal digital model, standardized multi-dimensional spatial distribution parameters are extracted through spatial resampling and automatic conversion of boundary conditions; the standardized multi-dimensional spatial distribution parameters include porosity, permeability, mineral distribution, temperature and pressure; Based on the regional structure of the target oil and gas reservoir, a physical grid is constructed and a multi-physics sub-model is embedded in the physical grid. Then, a geochemical reaction database is combined with the standardized multi-dimensional spatial distribution parameters to obtain dynamic reflection properties. The multi-physics sub-model includes a heat conduction and convection model, a multiphase fluid seepage model, and a gas composite component transport model. The dynamic reflection properties include reaction rate, product distribution, and thermal effect. The dynamic reflection attributes are assigned to the physical grid to obtain a heat-mass multiphase CFD simulation grid, and the heat-mass multiphase CFD simulation grid is deployed to the cloud for calculation. A 30-minute heat-mass-reaction process prediction is performed in less than 1 minute to complete the real-time coupled simulation and obtain space-time distribution prediction variables; the space-time distribution prediction variables include temperature field, gas seepage field, hydrogen production rate and spatial distribution, carbon deposition hotspot zoning, and mineralized crust risk zoning.

4. The method for producing hydrogen underground in situ in oil and gas reservoirs based on gas compounding according to claim 3, characterized in that: The on-site gas injection system includes a gas phase injection subsystem for injecting air, oxygen and CO2 and a liquid phase injection subsystem for injecting steam; The gas phase injection subsystem includes an air compressor, an oxygen cylinder, a CO2 cylinder, an air booster pump, an oxygen booster pump, a CO2 booster pump, an air storage tank, an oxygen storage tank, a CO2 storage tank and a gas flow meter; The liquid phase injection subsystem includes a high-pressure plunger pump, a water container and a steam generator.

5. The method for producing hydrogen underground in situ in oil and gas reservoirs based on gas compounding according to claim 4, characterized in that: Based on the set optimization objectives and physical boundaries, the optimal gas ratio and process parameters are periodically inverted using the space-time distribution prediction variables to output control instructions and automatically adjust the on-site gas injection system, including: Combined with the space-time distribution prediction variables, the optimization objective function and constraints are set, and a multi-objective model predictive control algorithm is constructed; the calculation expression of the optimization objective function is: in, is the product gas hydrogen gas integral fraction predicted for the new cycle, is the preset target hydrogen production purity, is the average temperature of the reaction zone, T opt is the theoretical optimal reaction temperature, Δ C is the carbon accumulation and coking factor, E inj is the injection energy consumption index, ω1, ω2, ω3, and ω4 are all dynamic weight parameters; Combining the target model predictive control algorithm and the sequence optimization algorithm, automatically searching for the optimal gas ratio and injection flow pattern that meet the target oil and gas reservoir area, and dynamically correcting the optimal gas ratio and injection flow pattern using the space-time distribution prediction variables to obtain the optimal solution, and then outputting the optimal solution as a control instruction; The control instructions are sent to the on-site gas injection system in a standardized format for high-pressure injection of multiple gases; the control instructions include the ratio of five gas sources, the total gas injection flow rate and real-time adjustment parameters. The five gas sources include air, oxygen, CO2 and steam.

6. The method for producing hydrogen underground in situ in oil and gas reservoirs based on gas compounding according to claim 5, characterized in that: The control instructions are disassembled and distributed by the main control system to control the pressurized injection, storage, mixing and real-time monitoring of each gas source to obtain a mixed gas, which is then injected into a sealed oil and gas reservoir for reaction to obtain a product gas including hydrogen, including: Using the main control system, the control instructions are decomposed to obtain the quality set value, flow set value and response time of each gas source in the current cycle; Use a booster pump to boost the pressure of each gas source to the target pressure. After the boosting is completed, the gas from each gas source enters its own high-pressure buffer storage tank. The mass flow meter and high-frequency proportional valve are used to adjust the injection flow of each gas source in a closed loop to complete the boosted injection and storage of the gas source. The gases in the high-pressure buffer storage tanks are mixed evenly through a high-pressure pipeline to obtain a mixed gas, and a component analyzer or infrared sensor is arranged at the outlet of the high-pressure pipeline to monitor and feedback the individual proportions, pressure and temperature of the mixed gas in real time; The mixed gas is injected into a sealed oil and gas reservoir for reaction to obtain product gas including hydrogen.

7. The method for producing hydrogen underground in situ in oil and gas reservoirs based on gas compounding according to claim 6, characterized in that: A variety of monitoring sensors are deployed inside the sealed oil and gas reservoir to monitor the temperature, pressure and output gas components of the reaction process, and the monitoring data are transmitted back to the main control system in real time to optimize and adjust the control instructions, including: Deploying optical fiber sensors, MEMS sensor arrays, and micro gas analyzers inside the sealed oil and gas reservoir to monitor the temperature, pressure, and produced gas components of the reaction process and obtain multi-channel data; Based on the multi-channel data, an AI model is used to automatically analyze temperature mutations, pressure mutations, and component mutations to obtain anomaly detection results, and the anomaly detection results are transmitted back to the main control system in real time; The main control system is used to determine whether the abnormal monitoring results have hidden dangers. If so, an automatic alarm is issued, and the control instructions are optimized and adjusted according to the abnormal detection results through the optimal matching strategy.

8. The method for producing hydrogen underground in situ in oil and gas reservoirs based on gas compounding according to claim 7, characterized in that: The main control system is used to determine whether the abnormal monitoring results indicate hidden dangers. If so, an automatic alarm is issued, and the control instructions are optimized and adjusted according to the abnormal detection results through the optimal matching strategy, including: Extract monitoring indicators based on the abnormal monitoring results; the monitoring indicators include the maximum value of the temperature section, the minimum value of the temperature section, the temperature gradient, the hydrogen purity and the carbon deposition blockage risk; The optimal matching strategy is preset, and the control instructions are optimized and adjusted according to the monitoring indicators; the calculation expression of the optimal matching strategy is: Among them, x1, x2, x3, and x4 are the proportions of oxygen, carbon dioxide, steam, and methane in the mixed gas, respectively. inj is the total injection flow, L seg is the gas injection length of each segment, is the product gas hydrogen gas integral fraction predicted for the new cycle, is the preset target hydrogen purity, T peak is the maximum value of the temperature segment, T opt is the theoretical optimal reaction temperature, φ C The carbon deposition and blockage risk predicted for the new cycle, E inj is the injection energy consumption index, θ1, θ2, θ3, and θ4 are all weight factors.

9. The method for producing hydrogen underground in situ in oil and gas reservoirs based on gas compounding according to claim 8, characterized in that: The product gas is directed to a ground separation module for multi-stage condensation, impurity removal and purification to obtain high-purity hydrogen, including: The product gas is directed to a surface separation module for primary condensation, secondary condensation, and tertiary condensation to remove impurities from the product gas and obtain an initial product; the primary condensation includes crude dehydration or light hydrocarbon condensation, the secondary condensation includes water removal or deep removal of light hydrocarbons, and the tertiary condensation includes enhanced pre-separation of extreme hydrocarbons or CO2; The initial product is purified by using temperature swing adsorption technology and pressure swing adsorption technology to obtain high-purity hydrogen and by-product gas.

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