Simulation and optimization method and device for co-extraction of gas hydrate reservoirs in sea areas

Abstract

The application discloses a simulation and optimization method and device for double-gas co-mining of a hydrate deposit in a sea area, and relates to the technical field of hydrate mining. The method comprises the following steps: constructing a multi-layer geological model and discretizing the multi-layer geological model according to geological data of a target sea area, determining initial pressure, temperature and initial saturation of each grid of the model. Setting a basic mining pressure, carrying out numerical simulation of pressure reduction mining on the multi-layer geological model and a comparative model, and obtaining spatial distribution data of wellhead gas production rate, water production rate and hydrate saturation. Based on the numerical simulation results, the influence of underlying free gas on gas production energy efficiency and hydrate decomposition is analyzed. Different gradients of mining pressure are set, and numerical simulation is iteratively carried out. After updating the parameters, simulation is continued. A target function is constructed for the simulation results under each mining pressure, and the mining pressure that makes the value of the target function maximum is selected as the optimal mining scheme. The problem of how to improve the mining efficiency of natural gas hydrate while ensuring the gas production energy efficiency is solved.

Description

Technical Field

[0001] This application relates to the field of hydrate extraction technology, and in particular to a simulation and optimization method and apparatus for the co-extraction of two gases from marine hydrate reservoirs. Background Technology

[0002] Natural gas hydrates are ice-like crystalline compounds formed from natural gas and water under high pressure and low temperature conditions, mainly distributed in deep-sea sediments or terrestrial permafrost zones. Marine hydrate development often faces complex geological conditions; most hydrate reservoirs are not single hydrate layers but exhibit multi-layered geological structures, and may contain underlying free gas, meaning a multiphase, layered system where hydrates and free gas coexist. In actual extraction, different extraction pressures have varying impacts on gas production efficiency and hydrate decomposition.

[0003] How to improve the extraction efficiency of natural gas hydrates while ensuring gas production efficiency is a key technical problem currently facing the depressurization extraction of marine hydrates. Summary of the Invention

[0004] In this application embodiment, a simulation and optimization method for the co-production of natural gas hydrates in marine hydrate reservoirs is provided, which solves the problem of how to improve the extraction efficiency of natural gas hydrates while ensuring gas production efficiency.

[0005] In a first aspect, embodiments of this application provide a simulation and optimization method for the co-production of gas and hydrate in marine hydrate reservoirs. The method includes: Step 101, establishing a multi-layer geological model based on geological data of the target marine area, and discretizing the multi-layer geological model using a non-uniform grid partitioning method; Step 102, obtaining the initial pressure and initial temperature of each grid in the multi-layer geological model, and assigning initial saturation to the corresponding hydrate phase and / or water phase and / or free gas phase to each grid; Step 103, setting a basic production pressure, and performing pressure-reducing production numerical simulations on the multi-layer geological model and a comparative model based on the basic production pressure, initial pressure, initial temperature, and initial saturation, respectively, to obtain numerical simulation results; wherein the numerical simulation results include spatial distribution data of wellhead gas production rate, water production rate, and hydrate saturation corresponding to the multi-layer geological model and the comparative model; Step 104, ... Step 104: Based on the numerical simulation results, obtain the analysis results of the impact of underlying free gas on gas production efficiency and hydrate decomposition; Step 105: Set a set of extraction pressures with different gradients. For each set extraction pressure, iteratively execute steps 103 and 104. In each iteration of step 103, use the currently set extraction pressure as the new base extraction pressure, and use the pressure, temperature, and saturation of each grid at the end of the previous numerical simulation as the initial conditions for this numerical simulation to update the parameters. Based on the updated parameters, perform pressure reduction extraction numerical simulations on the multi-layer geological model and the comparative model respectively to obtain the numerical simulation results; Step 106: For the numerical simulation results under each extraction pressure, construct an objective function that considers gas production efficiency and hydrate decomposition, and select the extraction pressure that maximizes the objective function value as the optimal extraction scheme.

[0006] In one possible implementation, a multi-layer geological model is established based on geological data of the target sea area, and a non-uniform grid partitioning method is used to discretize the multi-layer geological model. This includes: the multi-layer geological model includes an upper overburden layer, a first hydrate layer, a second hydrate layer, an underlying free gas layer, and an lower overburden layer; in the radial direction, with the center of the wellbore as the origin, the radial thickness of the first grid around the wellbore is 0.05 meters, and it increases outward with a growth factor of 1.1 until it reaches the boundary of the multi-layer geological model; in the depth direction, each layer is uniformly partitioned, and the grid height is determined based on the stratum thickness.

[0007] In one possible implementation, based on Obtain the initial pressure for each grid in the multi-layer geological model; where, for The initial pressure at the location, Due to the pressure on the seabed, The density of the formation pore fluid. It is the acceleration due to gravity. The depth of the current grid. For the depth of the seabed; based on Obtain the initial temperature of each grid in the multi-layer geological model; where, for The initial temperature at that location, The temperature of the seabed, This represents the geothermal gradient.

[0008] In one possible implementation, the comparative model is a simplified geological model based on a multi-layered geological model that does not include the underlying free gas layer.

[0009] In one possible implementation, the step of obtaining the analysis results of the impact of underlying free gas on gas production efficiency and hydrate decomposition based on numerical simulation results includes: obtaining the hydrate decomposition rate and gas-liquid ratio of each of the multi-layer geological model and the comparative model based on the numerical simulation results; wherein, the gas-liquid ratio is the ratio of gas production to water production, and the gas-liquid ratio is directly proportional to the cumulative gas production; by comparing the hydrate decomposition rates of the multi-layer geological model and the comparative model, the inhibitory effect of underlying free gas on hydrate decomposition is analyzed and determined; by comparing the cumulative gas production of the multi-layer geological model and the comparative model, the impact of underlying free gas on gas production efficiency is analyzed and determined.

[0010] In one possible implementation, based on The hydrate decomposition rates of the multi-layer geological model and the comparative model were obtained; among them, for hydrate decomposition rate at any time The initial hydrate mass, The density of the hydrate. For the volume of a single grid, for Hydrate saturation at any given time.

[0011] In one possible implementation, the objective function is expressed as: ;in, The objective function value, This represents the cumulative gas production of a multi-layered geological model. This represents the cumulative water production of a multi-layered geological model. This is the weighting factor for cumulative gas production. This is the weighting coefficient for cumulative water production.

[0012] Secondly, embodiments of this application provide a simulation and optimization device for the co-production of gas and hydrate in marine hydrate reservoirs. The device includes: a modeling module for establishing a multi-layer geological model based on geological data of the target marine area, and discretizing the multi-layer geological model using a non-uniform grid partitioning method; an assignment module for acquiring the initial pressure and initial temperature of each grid in the multi-layer geological model, and assigning initial saturation to the corresponding hydrate phase and / or water phase and / or free gas phase to each grid; and a simulation module for setting a base production pressure, and performing pressure-reducing production numerical simulations on the multi-layer geological model and a comparative model based on the base production pressure, initial pressure, initial temperature, and initial saturation, respectively, to obtain numerical simulation results; wherein the numerical simulation results include spatial distribution data of wellhead gas production rate, water production rate, and hydrate saturation corresponding to the multi-layer geological model and the comparative model; and acquiring... The results module is used to obtain the analysis results of the impact of underlying free gas on gas production efficiency and hydrate decomposition based on the numerical simulation results; the iteration module is used to set a set of extraction pressures with different gradients, and for each set extraction pressure, iteratively execute steps 103 and 104. In each iteration of step 103, the currently set extraction pressure is used as the new base extraction pressure, and the pressure, temperature and saturation of each grid at the end of the previous numerical simulation are used as the initial conditions for this numerical simulation to update the parameters. Based on the updated parameters, pressure reduction extraction numerical simulations are performed on the multi-layer geological model and the comparative model respectively to obtain the numerical simulation results; the construction module is used to construct an objective function considering gas production efficiency and hydrate decomposition for the numerical simulation results under each extraction pressure, and select the extraction pressure that maximizes the objective function value as the optimal extraction scheme.

[0013] One or more technical solutions provided in this application embodiment have at least the following technical effects: This application embodiment provides a simulation and optimization method for the co-production of gas and hydrate in marine hydrate reservoirs. The method first constructs and discretizes a multi-layer geological model based on geological data of the target marine area. Then, it determines the initial pressure, temperature, and initial saturation of each phase in each grid of the model. A base production pressure is set, and numerical simulations of pressure-reducing production are conducted on the multi-layer geological model and the comparative model to obtain spatial distribution data of wellhead gas production rate, water production rate, and hydrate saturation. Based on the numerical simulation results, the influence of underlying free gas on gas production efficiency and hydrate decomposition is analyzed. Different gradient production pressures are set, and iterative numerical simulations are performed, updating parameters and continuing the simulation. An objective function is constructed based on the simulation results under each production pressure, and the production pressure that maximizes the objective function value is selected as the optimal production scheme to achieve the simulation and optimization of co-production of gas and hydrate in marine hydrate reservoirs. This solves the problem of how to improve the extraction efficiency of natural gas hydrates while ensuring gas production efficiency. Attached Figure Description

[0014] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the description of the embodiments of this application or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0015] Figure 1 A flowchart illustrating a simulation and optimization method for dual gas extraction from marine hydrate reservoirs, provided as an embodiment of this application;

[0016] Figure 2 A schematic diagram of a multi-layer geological model provided in the embodiments of this application;

[0017] Figure 3 A comparison diagram of the cumulative water production of the multi-layer geological model and the comparative model provided in the embodiments of this application;

[0018] Figure 4 The saturation spatial distribution cloud map of the multi-layer geological model provided in this application embodiment at 60 days of mining;

[0019] Figure 5 The saturation spatial distribution cloud map of the multi-layer geological model provided in this application embodiment after one year of mining;

[0020] Figure 6 The saturation spatial distribution cloud map of the multi-layer geological model provided in this application embodiment after 5 years of mining;

[0021] Figure 7 The saturation spatial distribution cloud map of the multi-layer geological model provided in this application embodiment after 10 years of mining;

[0022] Figure 8 The spatial distribution cloud map of saturation of the comparative model provided in the embodiments of this application after 60 days of mining;

[0023] Figure 9 The spatial distribution cloud map of saturation of the comparative model provided in the embodiments of this application after one year of mining;

[0024] Figure 10 Spatial distribution cloud map of saturation of the comparative model provided in the embodiments of this application after 5 years of mining;

[0025] Figure 11 The spatial distribution cloud map of saturation of the comparative model provided in this application embodiment is shown after 10 years of mining.

[0026] Figure 12 A comparison chart of hydrate decomposition rates between the multi-layer geological model and the comparative model provided in the embodiments of this application;

[0027] Figure 13 A comparison diagram of the cumulative gas production of the multi-layer geological model and the comparative model provided in the embodiments of this application;

[0028] Figure 14 A schematic diagram of a simulation and optimization device for dual gas extraction from marine hydrate reservoirs provided in an embodiment of this application;

[0029] Figure 15 This is a schematic diagram of a simulation and optimization server for dual gas extraction from marine hydrate reservoirs, provided as an embodiment of this application. Detailed Implementation

[0030] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of this application. All other embodiments obtained by those skilled in the art based on the embodiments of this application without creative effort are within the scope of protection of this application.

[0031] The following description of some technologies involved in the embodiments of this application is provided to aid understanding and should be considered merely exemplary. Therefore, those skilled in the art should recognize that various changes and modifications can be made to the embodiments described herein without departing from the scope and spirit of this application. Similarly, for clarity and brevity, some descriptions of well-known functions and structures are omitted in the following description.

[0032] This application provides a simulation and optimization method for the co-production of gases from marine hydrate reservoirs, such as... Figure 1 As shown, the method includes steps S101 to S106. Wherein, Figure 1 This is merely one execution order shown in the embodiments of this application, and does not represent the only execution order for a simulation and optimization method of dual gas extraction from marine hydrate reservoirs. The execution order can be adjusted to achieve the desired final result. Figure 1 The steps shown can be performed in parallel or in reverse order.

[0033] Step 101: Based on the geological data of the target sea area, establish a multi-layer geological model and discretize the multi-layer geological model using a non-uniform grid partitioning method.

[0034] Based on geological data of the target sea area, a multi-layer geological model was established, and the multi-layer geological model was discretized using a non-uniform grid partitioning method, including the following content.

[0035] The multi-layered geological model includes an upper overburden layer, a first hydrate layer, a second hydrate layer, an underlying free gas layer, and an lower overburden layer.

[0036] In the radial direction, with the center of the wellbore as the origin, the radial thickness of the first grid around the wellbore is 0.05 meters, and it increases outward with a growth factor of 1.1 until it reaches the boundary of the multi-layer geological model.

[0037] Specifically, this method of division allows for a finer grid near the wellbore, thus better capturing the complex physical changes in the area.

[0038] In the depth direction, each layer is uniformly divided, and the grid height is determined based on the formation thickness.

[0039] Figure 2 This is a schematic diagram of a multi-layer geological model provided in this application embodiment. The multi-layer geological model of this application is a physical model of hydrate reservoirs at station W17 in the Shenhu sea area. The uppermost and lowermost layers of the multi-layer geological model are the upper overburden layer and the lower overburden layer, respectively, both with a thickness of 20 meters. Their porosity and permeability parameters are consistent with those of adjacent layers. The first hydrate layer has a thickness of 35 meters, a porosity of 0.35, an average hydrate saturation of 0.34, and a permeability of 2.9 mD. The second hydrate layer has a thickness of 15 meters, a porosity of 0.33, an average hydrate saturation of 0.31, and also exhibits an average gas saturation of 0.078, with a permeability of 1.5 mD. The underlying free gas layer has a thickness of 27 meters, a porosity of 0.32, an average gas saturation of 0.078, and a permeability of 7.4 mD. Regarding formation parameters, the seafloor temperature was determined to be 3.7℃, the gas composition was 100% methane, the geothermal gradient was 0.0443℃ / m, the porosity and salinity were 3.05wt%, and the actual sediment density was taken as 2200 kg / m³. This simulation focused on a cylindrical system with a range of 0 m ≤ R ≤ 150 m and -117 m ≤ z ≤ 0 m. Here, R represents the radial distance within the cylindrical system, i.e., the horizontal distance from the wellbore center, and z represents the depth of the current grid. In the numerical simulation, the formation and decomposition of hydrates were treated as phase equilibrium reactions. Figure 2 In The radius of the vertical well is 0.1m. This refers to the depth of the seabed. The point is located at the center of the bottom boundary of the production section.

[0040] Step 102: Obtain the initial pressure and initial temperature of each grid in the multi-layer geological model, and assign the initial saturation of the corresponding hydrate phase and / or aqueous phase and / or free gas phase to each grid.

[0041] based on Obtain the initial pressure for each grid in the multi-layer geological model. Among them, for The initial pressure at the location, Due to the pressure on the seabed, The density of the formation pore fluid. It is the acceleration due to gravity. The depth of the current grid. This refers to the depth of the seabed.

[0042] based on Obtain the initial temperature of each grid in the multi-layer geological model. Among them, for The initial temperature at that location, The temperature of the seabed, This represents the geothermal gradient.

[0043] Specifically, when assigning initial saturation, it is necessary to collect well logging data and core analysis data from station W17 in the Shenhu sea area. These data contain information on the content of hydrates, water, and free gas in each stratum. The collected data are then processed and analyzed to extract saturation-related information for the hydrate, water, and free gas phases in the formation corresponding to each grid. Based on the stratigraphic position of each grid in the multi-layer geological model, and combined with well logging interpretation results, the initial saturation of the hydrate, water, and free gas phases in the grid is determined.

[0044] Step 103: Set the base extraction pressure. Based on the base extraction pressure, initial pressure, initial temperature, and initial saturation, perform pressure reduction extraction numerical simulations on the multi-layer geological model and the comparative model respectively, and obtain the numerical simulation results. The numerical simulation results include the spatial distribution data of wellhead gas production rate, water production rate, and hydrate saturation corresponding to the multi-layer geological model and the comparative model.

[0045] The comparative model is a simplified geological model based on a multi-layered geological model, excluding the underlying free gas layer.

[0046] Specifically, taking into account the actual conditions of the study area, previous research results, and the feasibility of actual mining operations, a basic mining pressure was set. In this application, 4.5 MPa was selected as the basic mining pressure. This pressure value was selected based on a comprehensive consideration of various factors, including the geological characteristics of the hydrate reservoir in the Shenhu sea area, the stability conditions of the hydrates, and past trial mining experience, aiming to simulate a relatively reasonable mining scenario with practical reference value.

[0047] The comparative model is a simplified version of an existing multi-layered geological model. Specifically, the multi-layered geological model includes an overlying layer, a first hydrate layer, a second hydrate layer (containing free gas), an underlying free gas layer, and an underlying overlying layer. In constructing the comparative model, the free gas components in the second hydrate layer and the underlying free gas layer are removed, retaining only the hydrate and water two-phase system. The structure and parameters of other strata remain unchanged. The purpose of constructing the comparative model is to highlight the impact of underlying free gas on the depressurization and exploitation process of hydrate reservoirs. By comparing and analyzing the simulation results with those of the complete multi-layered geological model, the mechanism of action of underlying free gas can be revealed more clearly.

[0048] This application utilizes the numerical simulation software TOUGH+HYDRATE to conduct numerical simulations of depressurization mining on multi-layer geological models and comparative models. The following operating procedures and parameter settings were followed during the numerical simulation: A base mining pressure of 4.5 MPa was input into the numerical simulation software as a key control parameter during the mining process. Simultaneously, other physical parameters in the software, such as formation permeability, porosity, hydrate density, and gas composition, were ensured to be consistent with the values ​​set during model initialization to guarantee the accuracy and reliability of the simulation results. The total simulation duration was set to 10 years to fully observe and analyze the dynamic changes of hydrate deposits during long-term depressurization mining. The simulation time step was adjusted appropriately based on the stability of the simulation process and computational efficiency. In the early stages of mining, due to drastic changes in hydrate decomposition and fluid flow, a smaller time step (e.g., 1 day) was used for detailed simulation; as mining progressed and the system gradually stabilized, the time step was appropriately increased (e.g., 10 days) to improve computational efficiency. During the simulation, the boundary conditions set during model establishment were strictly followed. The outermost boundary of the model is defined as the flow boundary, which is permeable to both the upper and lower overburden layers. The temperature and pressure at the boundary remain constant to ensure the simulated environment matches actual formation conditions. During the numerical simulation, key data are monitored and recorded in real time. After the simulation, the following important numerical simulation results are extracted: 1. Gas production rate. Using the post-processing function of the numerical simulation software, wellhead gas production rate data for each time step during the production period of the multi-layer geological model and the comparative model are obtained. These data are then organized into a time series to analyze the trend of gas production rate over time. 2. Water production rate. Similarly, using the post-processing function, wellhead water production rate data for the multi-layer geological model and the comparative model during the production process are obtained. Figure 3 A comparison chart of the cumulative water yield of the multi-layer geological model and the comparative model provided in the embodiments of this application. Figure 3The cumulative water production and production time can be used to obtain the water production rate, thereby analyzing the dynamic changes in the water production rate and its relationship with the gas production rate. 3. Spatial distribution data of hydrate saturation. After the simulation, hydrate saturation data of each grid in the multi-layer geological model and the comparison model are extracted at different mining time points (such as 60 days, 1 year, 5 years, 10 years of mining).

[0049] Figure 4 The saturation spatial distribution cloud map of the multi-layer geological model provided in this application embodiment at 60 days of mining. Figure 5 The spatial distribution cloud map of saturation of the multi-layer geological model provided in this application embodiment after one year of mining. Figure 6 The spatial distribution cloud map of saturation of the multi-layer geological model provided in this application embodiment after 5 years of mining. Figure 7 The spatial distribution cloud map of saturation of the multi-layer geological model provided in this application embodiment after 10 years of mining. Figures 4 to 7 In this context, A represents a multi-layered geological model.

[0050] Figure 8 The spatial distribution cloud map of saturation of the comparative model provided in the embodiments of this application after 60 days of mining. Figure 9 The spatial distribution cloud map of saturation of the comparative model provided in the embodiments of this application after one year of mining. Figure 10 The spatial distribution cloud map of saturation of the comparative model provided in the embodiments of this application after 5 years of mining. Figure 11 This is a spatial distribution cloud map of hydrate saturation after 10 years of mining, provided as an embodiment of this application. By comparing the spatial distribution cloud maps of hydrate saturation from the two models, the influence of underlying free gas on hydrate decomposition and distribution is analyzed in depth. Figures 8 to 11 In this context, B represents the contrast model.

[0051] Step 104: Based on the numerical simulation results, obtain the analysis results of the impact of underlying free gas on gas production efficiency and hydrate decomposition.

[0052] Based on numerical simulation results, the impact of underlying free gas on gas production efficiency and hydrate decomposition is analyzed, including the following:

[0053] Based on the numerical simulation results, the hydrate decomposition rate and gas-liquid ratio were obtained for both the multi-layer geological model and the comparative model. The gas-liquid ratio is the ratio of gas production to water production, and it is directly proportional to the cumulative gas production.

[0054] Specifically, during the simulation, the gas and water production at each moment are recorded according to a set time step, and the corresponding gas-liquid ratio is calculated accordingly. Furthermore, since the gas-liquid ratio is directly proportional to the cumulative gas production, its changing trend also reflects the changes in cumulative gas production. By calculating the gas-liquid ratio at different extraction moments, the dynamic changes in the proportion of gas and liquid production during the extraction process can be intuitively understood.

[0055] based on The hydrate decomposition rates of the multi-layer geological model and the comparative model were obtained. for hydrate decomposition rate at any time The initial hydrate mass, The density of the hydrate. For the volume of a single grid, for Hydrate saturation at any given time.

[0056] Specifically, during the calculation process, all grids in the model are traversed and summed to calculate... Hydrate decomposition rate at different times. The above calculations were performed on the multi-layer geological model and the comparative model respectively to obtain the hydrate decomposition rate of the two models at different mining times.

[0057] By comparing the hydrate decomposition rates of multi-layer geological models and comparative models, the inhibitory effect of underlying free gas on hydrate decomposition was analyzed and determined.

[0058] By comparing the cumulative gas production of multi-layer geological models and comparative models, the impact of underlying free gas on gas production efficiency was analyzed and determined.

[0059] Figure 12 A comparison chart of the hydrate decomposition rates of the multi-layer geological model and the comparative model provided in the embodiments of this application. Figure 13A comparison chart of the cumulative gas production of the multi-layer geological model and the comparative model provided in this application embodiment is shown. The comparison clearly shows that the hydrate decomposition rate of the multi-layer geological model is lower than that of the comparative model. The presence of underlying free gas alters the pressure propagation and heat transfer processes within the sedimentary layer. In the multi-layer geological model, the upward movement of underlying free gas hinders the contact between the decomposition front of the lower end of the second hydrate layer and nearby hot fluids to some extent, weakening the heat conduction between them. This results in insufficient heat supply for hydrate decomposition, thus inhibiting hydrate decomposition. Furthermore, the possibility of free gas combining with water to form hydrates in situ also interferes with the normal decomposition process of hydrates. Based on comprehensive data comparison and physical process analysis, it can be determined that underlying free gas has an inhibitory effect on the effective decomposition of hydrates within the reservoir. The curves clearly show that the cumulative gas production of the multi-layer geological model is consistently higher than that of the comparative model throughout the entire extraction process. Gas production efficiency reflects the efficiency of converting hydrate resources into usable gas during extraction. The presence of initial free gas in the multi-layer geological model, due to the disturbance caused by free gas in the early stages of extraction, promotes hydrate decomposition, resulting in more hydrates being decomposed into gas and produced. Meanwhile, although the underlying free gas has a certain inhibitory effect on hydrate decomposition, it does not significantly reduce gas production overall. On the contrary, due to the production of free gas and the complexity of the hydrate decomposition process, the cumulative gas production of the multi-layer geological model is higher than that of the control model. Therefore, by comparing the cumulative gas production of the two models, it can be determined that the presence of underlying free gas is beneficial to improving wellhead gas production efficiency.

[0060] Step 105: Set a set of mining pressures with different gradients. For each set mining pressure, iteratively execute steps 103 and 104. In each iteration of step 103, use the currently set mining pressure as the new base mining pressure. Use the pressure, temperature, and saturation of each grid at the end of the previous numerical simulation as the initial conditions for this numerical simulation to update the parameters. Based on the updated parameters, perform depressurization mining numerical simulations on the multi-layer geological model and the comparative model respectively to obtain the numerical simulation results.

[0061] Specifically, based on the pressure ranges commonly encountered in actual mining operations, a set of mining pressures with different gradients is set. For example, mining pressures are set at 3.0 MPa, 3.5 MPa, 4.0 MPa, 4.5 MPa, 5.0 MPa, 5.5 MPa, and 6.0 MPa. The selection of these pressure values ​​aims to comprehensively cover the pressure range that may affect the mining effectiveness of hydrate reservoirs.

[0062] Step 106: For the numerical simulation results under each extraction pressure, construct an objective function that considers gas production efficiency and hydrate decomposition, and select the extraction pressure that maximizes the objective function value as the optimal extraction scheme.

[0063] The expression for the objective function is: .in, The objective function value, This represents the cumulative gas production of a multi-layered geological model. This represents the cumulative water production of a multi-layered geological model. This is the weighting factor for cumulative gas production. This is the weighting coefficient for cumulative water production. Weighting coefficient for cumulative gas production The value can range from 0.6 to 0.8.

[0064] This application also provides a simulation and optimization device 1400 for the co-production of gas from marine hydrate reservoirs, such as... Figure 14 As shown, the device includes: a setup module 1401, an assignment module 1402, a simulation module 1403, a result acquisition module 1404, an iteration module 1405, and a construction module 1406.

[0065] Module 1401 is used to establish a multi-layer geological model based on geological data of the target sea area, and to discretize the multi-layer geological model using a non-uniform grid partitioning method.

[0066] The module 1402 is used to obtain the initial pressure and initial temperature of each grid in the multi-layer geological model, and to assign the initial saturation of the hydrate phase and / or aqueous phase and / or free gas phase to each grid.

[0067] The simulation module 1403 is used to set the basic mining pressure. Based on the basic mining pressure, initial pressure, initial temperature, and initial saturation, it performs pressure reduction mining numerical simulations on the multi-layer geological model and the comparative model, respectively, to obtain numerical simulation results. The numerical simulation results include the spatial distribution data of wellhead gas production rate, water production rate, and hydrate saturation corresponding to the multi-layer geological model and the comparative model.

[0068] The results acquisition module 1404 is used to obtain the analysis results of the impact of underlying free gas on gas production efficiency and hydrate decomposition based on numerical simulation results.

[0069] The iteration module 1405 is used to set a set of mining pressures with different gradients. For each set mining pressure, steps 103 and 104 are executed iteratively. When step 103 is executed in each iteration, the currently set mining pressure is used as the new base mining pressure. The pressure, temperature and saturation of each grid at the end of the previous numerical simulation are used as the initial conditions for this numerical simulation to update the parameters. Based on the updated parameters, the multi-layer geological model and the comparison model are subjected to pressure reduction mining numerical simulation to obtain the numerical simulation results.

[0070] Module 1406 is used to construct an objective function that considers gas production efficiency and hydrate decomposition for the numerical simulation results under each extraction pressure, and selects the extraction pressure that maximizes the objective function value as the optimal extraction scheme.

[0071] Some modules in the apparatus described in this application can be described in the general context of computer-executable instructions that are executed by a computer, such as program modules. Generally, program modules include routines, programs, objects, components, data structures, classes, etc., that perform a specific task or implement a specific abstract data type. This application can also be practiced in distributed computing environments where tasks are performed by remote processing devices connected via a communication network. In distributed computing environments, program modules can reside in local and remote computer storage media, including storage devices.

[0072] The apparatus or module described in the above embodiments can be implemented by a computer chip or physical entity, or by a product with a certain function. For ease of description, the above apparatus is described by dividing it into various modules according to their functions. When implementing the embodiments of this application, the functions of each module can be implemented in one or more software and / or hardware. Of course, a module that implements a certain function can also be implemented by combining multiple sub-modules or sub-units.

[0073] The methods, apparatus, or modules described in this application can be implemented in a computer-readable program code manner. The controller can be implemented in any suitable manner, for example, as a microprocessor or processor and a computer-readable medium storing computer-readable program code (e.g., software or firmware) executable by the (micro)processor, logic gates, switches, application-specific integrated circuits (ASICs), programmable logic controllers, and embedded microcontrollers. Examples of controllers include, but are not limited to, the following microcontrollers: ARC 625D, Atmel AT91SAM, Microchip PIC18F26K20, and Silicon Labs C8051F320. A memory controller can also be implemented as part of the control logic of a memory. Those skilled in the art will also recognize that, in addition to implementing the controller in purely computer-readable program code manner, the same functionality can be achieved by logically programming the method steps to make the controller take the form of logic gates, switches, application-specific integrated circuits, programmable logic controllers, and embedded microcontrollers. Therefore, such a controller can be considered a hardware component, and the means included within it for implementing various functions can also be considered as structures within the hardware component. Alternatively, the device used to implement various functions can be viewed as either a software module that implements the method or a structure within a hardware component.

[0074] like Figure 15 As shown in the figure, this application embodiment also provides a simulation and optimization server for the co-production of two gases in marine hydrate reservoirs, including a memory 1501 and a processor 1502; the memory 1501 is used to store computer-executable instructions; the processor 1502 is used to execute computer-executable instructions to implement the simulation and optimization method for the co-production of two gases in marine hydrate reservoirs described above in this application embodiment.

[0075] This application also provides a computer-readable storage medium storing executable instructions. When a computer executes the executable instructions, it can implement the simulation and optimization method for dual gas extraction from marine hydrate reservoirs described above in this application.

[0076] As can be seen from the above description of the embodiments, those skilled in the art can clearly understand that this application can be implemented by means of software plus necessary hardware. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, can be embodied in the form of a software product, or it can be embodied in the process of data migration. The computer software product can be stored in a storage medium, such as ROM / RAM, magnetic disk, optical disk, etc., and includes several instructions to cause a computer device (which may be a personal computer, mobile terminal, server, or network device, etc.) to execute the methods described in the embodiments of this application.

[0077] The various embodiments described in this specification are presented in a progressive manner. Similar or identical parts between embodiments can be referred to interchangeably. Each embodiment focuses on its differences from other embodiments. All or part of this application can be used in numerous general-purpose or special-purpose computer system environments or configurations.

[0078] The above embodiments are only used to illustrate the technical solutions of this application, and are not intended to limit this application. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some or all of the technical features therein. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of this application.

Claims

1. A method of simulation and optimization of dual gas co-production from a marine hydrate reservoir, characterized in that, include: Step 101: Based on the geological data of the target sea area, establish a multi-layer geological model and discretize the multi-layer geological model using a non-uniform grid partitioning method. Step 102: Obtain the initial pressure and initial temperature of each grid in the multi-layer geological model, and assign the initial saturation of the corresponding hydrate phase and / or aqueous phase and / or free gas phase to each grid. Step 103: Set the basic mining pressure. Based on the basic mining pressure, initial pressure, initial temperature, and initial saturation, perform pressure reduction mining numerical simulations on the multi-layer geological model and the comparative model respectively to obtain numerical simulation results. The numerical simulation results include the spatial distribution data of wellhead gas production rate, water production rate, and hydrate saturation corresponding to the multi-layer geological model and the comparative model. Step 104: Based on the numerical simulation results, obtain the analysis results of the impact of underlying free gas on gas production efficiency and hydrate decomposition; Step 105: Set a set of mining pressures with different gradients. For each set mining pressure, iteratively execute steps 103 and 104. In each iteration of step 103, use the currently set mining pressure as the new base mining pressure. Use the pressure, temperature, and saturation of each grid at the end of the previous numerical simulation as the initial conditions for this numerical simulation to update the parameters. Based on the updated parameters, perform depressurization mining numerical simulations on the multi-layer geological model and the comparison model respectively to obtain the numerical simulation results. Step 106: For the numerical simulation results under each extraction pressure, construct an objective function that considers gas production efficiency and hydrate decomposition, and select the extraction pressure that maximizes the objective function value as the optimal extraction scheme. The comparison model is a simplified geological model based on a multi-layered geological model, excluding the underlying free gas layer; The analysis of the impact of underlying free gas on gas production efficiency and hydrate decomposition based on numerical simulation results includes: obtaining the hydrate decomposition rate and gas-liquid ratio of the multi-layer geological model and the comparative model, respectively, based on the numerical simulation results; wherein, the gas-liquid ratio is the ratio of gas production to water production, and the gas-liquid ratio is directly proportional to the cumulative gas production; by comparing the hydrate decomposition rates of the multi-layer geological model and the comparative model, the inhibitory effect of underlying free gas on hydrate decomposition is analyzed and determined; by comparing the cumulative gas production of the multi-layer geological model and the comparative model, the impact of underlying free gas on gas production efficiency is analyzed and determined. based on The hydrate decomposition rates of the multi-layer geological model and the comparative model were obtained; among them, for hydrate decomposition rate at any time The initial hydrate mass, The density of the hydrate. For the volume of a single grid, for hydrate saturation at any given time; The expression for the objective function is: ;in, The objective function value, This represents the cumulative gas production of a multi-layered geological model. This represents the cumulative water production of a multi-layered geological model. This is the weighting factor for cumulative gas production. This is the weighting coefficient for cumulative water production.

2. The simulation and optimization method of dual gas co-production from a marine water-hydrate reservoir according to claim 1, characterized in that, The process of establishing a multi-layer geological model based on geological data of the target sea area, and discretizing the multi-layer geological model using a non-uniform grid partitioning method, includes: The multi-layered geological model includes an upper overburden layer, a first hydrate layer, a second hydrate layer, an underlying free gas layer, and an lower overburden layer; In the radial direction, with the center of the well as the origin, the radial thickness of the first grid around the well is 0.05 meters, and it increases outward with a growth factor of 1.1 until it reaches the boundary of the multi-layer geological model. In the depth direction, each layer is uniformly divided, and the grid height is determined based on the formation thickness.

3. The simulation and optimization method for dual gas extraction from marine hydrate reservoirs according to claim 1, characterized in that, based on Obtain the initial pressure for each grid in the multi-layer geological model; where, for The initial pressure at the location, Due to the pressure on the seabed, The density of the formation pore fluid. It is the acceleration due to gravity. The depth of the current grid. The depth of the seabed; based on Obtain the initial temperature of each grid in the multi-layer geological model; where, for The initial temperature at that location, The temperature of the seabed, This represents the geothermal gradient.

4. A device for simulation and optimization of dual gas co-production from a marine hydrate reservoir, characterized in that, The device performs the method as described in any one of claims 1 to 3, including: A module is established to build a multi-layer geological model based on geological data of the target sea area, and a non-uniform grid partitioning method is used to discretize the multi-layer geological model. The module is used to obtain the initial pressure and initial temperature of each grid in the multi-layer geological model, and to assign the initial saturation of the corresponding hydrate phase and / or aqueous phase and / or free gas phase to each grid. The simulation module is used to set the basic mining pressure. Based on the basic mining pressure, initial pressure, initial temperature, and initial saturation, it performs pressure reduction mining numerical simulations on the multi-layer geological model and the comparative model, respectively, and obtains numerical simulation results. The numerical simulation results include the spatial distribution data of wellhead gas production rate, water production rate, and hydrate saturation corresponding to the multi-layer geological model and the comparative model. The results acquisition module is used to obtain the analysis results of the impact of underlying free gas on gas production efficiency and hydrate decomposition based on numerical simulation results; The iterative module is used to set a set of mining pressures with different gradients. For each set mining pressure, steps 103 and 104 are executed iteratively. When step 103 is executed in each iteration, the currently set mining pressure is used as the new base mining pressure. The pressure, temperature and saturation of each grid at the end of the previous numerical simulation are used as the initial conditions for this numerical simulation to update the parameters. Based on the updated parameters, pressure reduction mining numerical simulations are performed on the multi-layer geological model and the comparison model respectively to obtain the numerical simulation results. The module is used to construct an objective function that considers gas production efficiency and hydrate decomposition for the numerical simulation results under each extraction pressure, and select the extraction pressure that maximizes the objective function value as the optimal extraction scheme. The comparison model is a simplified geological model based on a multi-layered geological model, excluding the underlying free gas layer; The analysis of the impact of underlying free gas on gas production efficiency and hydrate decomposition based on numerical simulation results includes: obtaining the hydrate decomposition rate and gas-liquid ratio of the multi-layer geological model and the comparative model, respectively, based on the numerical simulation results; wherein, the gas-liquid ratio is the ratio of gas production to water production, and the gas-liquid ratio is directly proportional to the cumulative gas production; by comparing the hydrate decomposition rates of the multi-layer geological model and the comparative model, the inhibitory effect of underlying free gas on hydrate decomposition is analyzed and determined; by comparing the cumulative gas production of the multi-layer geological model and the comparative model, the impact of underlying free gas on gas production efficiency is analyzed and determined. based on The hydrate decomposition rates of the multi-layer geological model and the comparative model were obtained; among them, for hydrate decomposition rate at any time The initial hydrate mass, The density of the hydrate. For the volume of a single grid, for hydrate saturation at any given time; The expression for the objective function is: ;in, The objective function value, This represents the cumulative gas production of a multi-layered geological model. This represents the cumulative water production of a multi-layered geological model. This is the weighting factor for cumulative gas production. This is the weighting coefficient for cumulative water production.

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