Gas reservoir dynamic visualization method and system

Abstract

The invention discloses a gas reservoir dynamic visualization method and system, and relates to the technical field of gas data analysis. A gas reservoir dynamic visualization method comprises the following steps: S1, building a material balance partition gas reservoir model based on oil reservoir and water body data; s2, reading configuration data, and updating the material balance partition gas reservoir model; s3, constructing a gas reservoir model interaction data flow topological structure based on the updated material balance partition gas reservoir model; s4, reading oil reservoir and water body data, and performing interpolation; and S5, saving the interpolation result as an SVG graph, and outputting the SVG graph to a front-end page for display. According to the method, dimension raising visual display of various oil and gas reservoir dynamic attribute data can be realized, business personnel can conveniently check the oil and gas reservoir dynamic attributes, and related work of dynamic analysis is assisted.

Description

Technical Field

[0001] This invention relates to the field of gas data analysis technology, specifically to a method and system for dynamic visualization of gas reservoirs. Background Technology

[0002] Currently, the mainstream software used for reservoir dynamic simulation includes Schlumberger's Eclipse, Intersect, and PETEX's MBAL.

[0003] The Eclipse software supports the construction of models for all types of oil and gas reservoirs, including black oil, composition, thermal recovery, and streamline models. It integrates knowledge from multiple disciplines such as geophysics, geology, reservoir engineering, drilling engineering, production engineering, and economic evaluation to predict reservoir development dynamics under different conditions. The Intersect software is a technical means to achieve detailed numerical simulation of large-scale heterogeneous oil and gas reservoirs. It provides technical means for the detailed study of oil and gas reservoirs with complex structures, high heterogeneity, and complex completion methods. The MBAL module integrates a large number of classic oil and gas reservoir dynamic analysis methods, including: material balance method, multi-layer syndicated production splitting, Monte Carlo simulation, decline curve analysis, water drive front method, and typical gas reservoir curve method.

[0004] The above numerical simulation software has three problems when performing simulations and outputting result maps:

[0005] First, the simulation configuration conditions must be set within the software; they cannot be configured or used for model calculations through the front-end interface. This is because the interfaces of mathematical modeling software are not sufficiently open, preventing the full automation of model calculations and data extraction; the extracted data cannot be automatically plotted.

[0006] Secondly, the simulation results maps are all generated offline and cannot be displayed online; secondly, some software cannot perform interpolation to generate maps and has no visualization function, so the prediction results can only be displayed in the form of data, such as the prediction results of oil and gas reservoir material balance zoning in IPM. Summary of the Invention

[0007] The technical problem to be solved by the present invention is that existing numerical simulation software has the following problems when performing simulation operation and outputting result maps: it cannot be configured and the model is calculated on the front page, the simulation result maps cannot be displayed online, and there is no visualization function. The purpose is to provide a gas reservoir dynamic visualization method and system to solve the above problems.

[0008] This invention is achieved through the following technical solution:

[0009] A method for dynamic visualization of gas reservoirs includes the following steps:

[0010] S1. Based on reservoir and water data, build a material balance zoned gas reservoir model;

[0011] S2. Read configuration data and update the material balance zone gas reservoir model;

[0012] S3. Construct the topology of the gas reservoir model interaction data flow based on the updated material balance partition gas reservoir model;

[0013] S4. Read reservoir and water data and perform interpolation;

[0014] S5. Save the interpolation result as an SVG image (scalable vector graphics) and output it to the front-end page for display.

[0015] As one possible design, step S1 above includes the following steps:

[0016] S11. Collect reservoir and water body data to form a basic analysis dataset;

[0017] S12. Construct a material balance zoned gas reservoir model;

[0018] S13. Construct a gas reservoir topology diagram based on the connectivity of the gas reservoirs;

[0019] S14. Input the basic analysis dataset into the material balance zone gas reservoir model, and use the nonlinear regression method to fit the material balance zone gas reservoir model with the basic analysis data.

[0020] S15. Use a graphical method to verify the fitting quality and correctness of the nonlinear regression method;

[0021] S16. Predict oil and gas well production value using a material balance zoned gas reservoir model.

[0022] As one possible design, the aforementioned reservoir and water body data include relevant data on gas and water. The relevant gas data includes PVT experimental data (high pressure physical property experimental data), gas reservoir production data, gas reservoir porosity, bound water saturation, gas-water two-phase permeability curves, and rock compressibility. The relevant water body data includes water body size, water intrusion angle, and permeability.

[0023] As one possible design, step S2 above includes the following steps:

[0024] S21. Read the daily configuration data of each oil and gas well in the same reservoir area and write it into the material balance zoning gas reservoir model to perform historical data fitting.

[0025] S22. Input the simulation time period into the material balance zone gas reservoir model to predict production.

[0026] As one possible design, the above configuration data includes predicted output, simulation step size, and simulation start and end times.

[0027] As one possible design, S3 above includes the following steps:

[0028] S31. Building an interactive data stream for gas reservoir models based on DIM software;

[0029] S32. Use the common components of DIM software to add functional modules, and build a gas reservoir simulation data flow topology diagram through the functional modules.

[0030] S33. Configure the functions of each functional module.

[0031] As one possible design, step S4 above includes the following steps:

[0032] S41. Read reservoir and water body data;

[0033] S42. Based on the maximum and minimum coordinate values ​​in the read data, form a two-dimensional array, and then form a rectangular boundary based on the two-dimensional array;

[0034] S43. Draw vectorized inner boundary lines based on a two-dimensional array of rectangular boundaries;

[0035] S44. Perform interpolation calculations based on the database data to obtain a data curve graph.

[0036] As one possible design, the database data includes well point coordinates and attribute values ​​output by mbal; the interpolation method includes Kriging interpolation, Lagrange interpolation, Newton interpolation, shortest path first, or cubic spline interpolation.

[0037] As one possible design, step S5 above includes the following steps:

[0038] S51. Push the interpolation results to the front end for display;

[0039] S52. Process the interpolated image and then convert it into an SVG format image.

[0040] S53. Store the SVG format graphics in the database in binary data format;

[0041] S54. Retrieve the binary SVG format graphics from the database and output them to the front-end page for display.

[0042] A dynamic visualization system for gas reservoirs, including

[0043] Modules for building gas reservoirs: used to extract reservoir and water data to construct mass balance zoning models;

[0044] Update module: Used to read configuration data to update the material balance zone gas reservoir model;

[0045] Topology module: Used to construct the interactive data flow topology of the gas reservoir model based on the updated mass balance partition gas reservoir model;

[0046] Interpolation module: Used to estimate database data and perform interpolation processing;

[0047] Visualization and Interaction Module: This module is used to process the interpolation results into graphs, convert them into SVG graphs, and then display them visually.

[0048] Compared with the prior art, the present invention has the following advantages and beneficial effects:

[0049] This invention enhances the human-computer interaction capabilities of oil and gas reservoir simulation software through data flow interaction, making it easier for business personnel to configure simulations online, drive model calculations, and display the simulated dynamic attribute results of oil and gas reservoirs online. At the same time, it automatically generates graphs from the model simulation results, facilitating real-time viewing of simulation results by business personnel.

[0050] This invention requires only that business personnel configure the simulated boundary conditions on the front-end page, then call the data interaction data stream to automatically write the simulation configuration conditions into the corresponding module of the professional software, drive the model to perform calculations, and then read the simulation results data. This realizes the automatic configuration and operation of the software and the automatic extraction of data. According to the built-in algorithm, the dynamic simulation data is interpolated and output to the page for display, realizing the enhanced visualization of dynamic attribute data of various oil and gas reservoirs. It also realizes the automatic generation of data maps, making it convenient for business personnel to view the dynamic attributes of oil and gas reservoirs and assisting in dynamic analysis-related work. Attached Figure Description

[0051] To more clearly illustrate the technical solutions of the exemplary embodiments of the present invention, the accompanying drawings used in the embodiments will be briefly described below. It should be understood that the following drawings only show some embodiments of the present invention and should not be considered as a limitation of the scope. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort. In the drawings:

[0052] Figure 1 This is a visual flowchart of the present invention;

[0053] Figure 2 The flowchart for constructing a material balance zoned gas reservoir model according to the present invention is shown below;

[0054] Figure 3 This is the configuration interface for the gas reservoir simulation front-end of the present invention;

[0055] Figure 4 This is the historical data fitting path for the present invention;

[0056] Figure 5 This refers to the historical fitting calculation results of this invention;

[0057] Figure 6 Configuration for the prediction time period of this invention;

[0058] Figure 7 These are the predictions and limitations of the present invention;

[0059] Figure 8 This is the prediction result of the present invention;

[0060] Figure 9 This is a topology diagram of the gas reservoir simulation data stream of the present invention;

[0061] Figure 10 This is one of the module configuration-parameter configuration interface diagrams of the present invention;

[0062] Figure 11 This is the second diagram of the module configuration-parameter configuration interface of the present invention;

[0063] Figure 12 A new interface diagram has been added for the module configuration parameters of this invention;

[0064] Figure 13 This is a diagram of the module configuration and data relationship interface of the present invention;

[0065] Figure 14 A graph of the pressure difference for this invention;

[0066] Figure 15 This is a flowchart of the automatic dimensional upgrade technology for gas reservoir attributes of the present invention;

[0067] Figure 16 This is a pressure distribution map of a gas reservoir (4 million cubic meters) according to Embodiment 1 of the present invention;

[0068] Figure 17 This is a pressure distribution map of a gas reservoir (5 million cubic meters) according to Embodiment 1 of the present invention. Detailed Implementation

[0069] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to embodiments and accompanying drawings. The illustrative embodiments and descriptions of this invention are for explanation only and are not intended to limit the invention. Unless otherwise specified in the embodiments, conventional conditions or conditions recommended by the manufacturer shall apply. Reagents or instruments whose manufacturers are not specified are all commercially available conventional products.

[0070] A method for dynamic visualization of gas reservoirs, referring to Figure 1 It includes the following steps:

[0071] S1. Based on reservoir and water data, build a material balance zoned gas reservoir model;

[0072] S2. Read configuration data and update the material balance zone gas reservoir model;

[0073] S3. Construct the topology of the gas reservoir model interaction data flow based on the updated material balance partition gas reservoir model;

[0074] S4. Read reservoir and water data and perform interpolation;

[0075] S5. Save the interpolation result as an SVG image (scalable vector graphics) and output it to the front-end page for display.

[0076] Step S3, the gas reservoir model interactive data stream, is built based on the DIM (Data Interaction for Model) software. The purpose of this step is to develop the data stream according to the data stream specification, enabling data interaction between the front-end page and the model, and achieving automatic model operation and automatic output of results data. The data stream interaction tool is developed using Python, and model operations are performed using the software's built-in OpenServer. Its main commands include `doset` (setting parameters), `doget` (reading parameters), and `cmd` (executing commands, including running and closing the model).

[0077] Step S4 uses interpolation to create a graph, thus achieving a higher-dimensional display of the results data.

[0078] The above-mentioned material balance zoning gas reservoir model was built using the professional software MBAL. For the specific modeling process, please refer to [link / reference needed]. Figure 2 .

[0079] As one possible design, step S1 above includes the following steps:

[0080] S11. Collect reservoir and water body data to form a basic analysis dataset;

[0081] The aforementioned reservoir and water body data include relevant data on gas and water. The relevant data on gas includes PVT experimental data, gas reservoir production data (including gas production, water production, and pressure change data), gas reservoir porosity, bound water saturation, gas-water two-phase permeability curves, and rock compressibility. The relevant data on water includes water body size, water intrusion angle, and permeability.

[0082] S12. Reference Figure 2 Constructing a material balance zoned gas reservoir model:

[0083] S121. Data preparation, including PVT, production data, reservoir mean pressure data, and all available reservoir and water body data;

[0084] S122. Input data. Check the correctness and consistency of the data in each operation step. This is important for building a good model. If you choose to input production data for one well at a time, ensure that all wells belong to the same reservoir.

[0085] S123. Use nonlinear regression (analytical method) to make the model fit the production data best;

[0086] S124. Use a graphical method to verify the fitting quality and correctness of the analytical method;

[0087] S125. Run a simulation test to check the model's fitting accuracy.

[0088] S126. Conduct production forecasting.

[0089] S13. Based on the connectivity of gas reservoirs (specifically, the connectivity between gas reservoirs and between gas reservoirs and water bodies), construct a topological structure diagram of the gas reservoirs.

[0090] S14. Input the basic analysis dataset into the material balance zone gas reservoir model, and use the nonlinear regression method to fit the material balance zone gas reservoir model with the basic analysis data.

[0091] The aforementioned basic analysis datasets all originate from the same oil and gas reservoir.

[0092] S15. Use a graphical method to verify the fitting quality and correctness of the nonlinear regression method;

[0093] S16. Predict oil and gas well production value using a material balance zoned gas reservoir model.

[0094] As one possible design, step S2 above includes the following steps:

[0095] S21, Reference Figure 3 The configuration data of each oil and gas well in the same reservoir area is read daily and written into the material balance zoning gas reservoir model for historical data fitting.

[0096] As one possible design, the above configuration data includes predicted output, simulation step size, and simulation start and end times.

[0097] S22. Input the simulation time period into the material balance zone gas reservoir model to predict production.

[0098] Step S22 specifically includes:

[0099] S221. Update cumulative production data: The daily production data needs to be accumulated first and then written into the model.

[0100] S222, Historical Data Fitting: Model Path: History Matching | Run Simulation, refer to... Figure 4 Then select Calc on this page to run the calculation, such as... Figure 5 ;

[0101] S223, Model Prediction:

[0102] (1) Write the simulation time period, model path: production prediction|prediction setup, set the prediction time;

[0103] Write the simulated start and end times (read from the front end), see attached. Figure 6 :

[0104] Write the simulation start time (Prediction start): Select "user defined", and write the time, such as 2023 / 07 / 01.

[0105] Write the simulated deadline (Prediction end): Select "user defined", and write the time, such as 2023 / 12 / 01;

[0106] (2) Write the predicted output

[0107] Model path: Production prediction | Production and constraints

[0108] Write the average monthly production data into the corresponding table, see attachment ( Figure 7 );

[0109] (3) Click prediction: Path Production prediction | Run prediction

[0110] (4) Data Acquisition: Extract data from the operation results and store it in the database. The extracted data includes pressure, gas saturation, and water saturation. Other data can also be extracted as needed. See Appendix. Figure 8 .

[0111] Preferably, S2 above also includes writing a gas reservoir model operation data interaction manual to provide documentation for the development of gas reservoir model operation data streams.

[0112] As one possible design, S3 above includes the following steps:

[0113] S31. Build an interactive data flow for the gas reservoir model based on DIM software, and name each functional module. Specifically, according to the data interaction instructions in step S2, and in conjunction with the common components provided by the DIM software, build the gas reservoir simulation data flow topology diagram by dragging and dropping. (Refer to the attached diagram.) Figure 9 ;

[0114] S32. Use the common components of DIM software to add functional modules, and build a gas reservoir simulation data flow topology diagram through the functional modules.

[0115] Preferably, the functions corresponding to the above-mentioned added functional modules include:

[0116] 1) Open the mbal model;

[0117] 2) Read the names of the tanks in the mbal model and convert them into a one-dimensional array;

[0118] 3) Set the unit, select Canada units;

[0119] 4) Use a loop command to input the daily production data into the corresponding partition module in a cumulative production manner;

[0120] 5) Read the prediction parameters, including the start and end times of the prediction, and write them to the prediction module. This data comes from the front-end business input.

[0121] 6) Write the predicted single-well production data, which comes from the front-end business input;

[0122] 7) Forecast and Run: Make production forecasts based on the input forecast deadlines and output levels;

[0123] 8) Read the prediction results and store them in the database for use in the next step of interpolation and mapping.

[0124] S33. Configure the functions of each module. By configuring the functions, data can be automatically written into the model, the model can be run, and data can be extracted after the model has finished running.

[0125] The above S33 includes

[0126] (1) Parameter Configuration: The parameter configuration allows you to configure the parameters required for the current component. You can add, modify, and delete parameters. The interface is shown in the attached image. Figures 10-13 ;

[0127] (2) Click the [Add] button to add parameters. In the Add Parameter window, enter the parameter name, display name, data type, IO type, default value and unit in sequence, and then click the [OK] button to save the parameters and close the Add Parameter window and refresh the parameter list;

[0128] (3) Data Relationships: The parameter passing relationships within components can be configured. Parameters in the current component are displayed on the left, and parameters in preceding and following components are displayed on the right. Arrows connect the parameters to configure their passing relationships between components. Specifically, when the parameter's IO type is input, it only accepts parameters passed from other components. When the IO type is output, it only accepts parameters passed from the current component. When the IO type is input-output, it can be used as either input or output.

[0129] As one possible design, step S4 above includes the following steps:

[0130] S41. Read reservoir and water body data;

[0131] As one possible design, the database data mentioned above includes well point coordinates and attribute values ​​output by mbal.

[0132] S42. Based on the maximum and minimum values ​​of the read coordinates, form a two-dimensional array, and then form a rectangular boundary based on the two-dimensional array; and according to business needs, use the meshgrid function in MATLAB to divide the grid size. The essence of the meshgrid function is to determine the value of each position on the x and y coordinate axes. This is very important when drawing 3D plots, because a 3D plot is actually drawn based on a specific z corresponding to each position on the x and y plane.

[0133] S43. Based on a two-dimensional array of rectangular boundaries, draw vectorized inner boundary lines according to the characteristics of the flow unit / well area boundary;

[0134] S44. Perform interpolation calculations based on the database data to obtain a data curve graph.

[0135] The interpolation methods mentioned above include Kriging interpolation, Lagrange interpolation, Newton interpolation, shortest path first interpolation, or cubic spline interpolation.

[0136] Preferably, the above interpolation method is cubic spline interpolation. Cubic spline interpolation is an interpolation method that constructs a smooth curve using data points within a local region. Specifically, it divides the data points to be interpolated into segments, and uses a cubic polynomial function to fit the data points in each segment, ensuring that the fitted function has continuous first and second derivatives at the endpoints, thus obtaining a continuous and smooth curve.

[0137] Preferably, the above cubic spline interpolation includes the following steps:

[0138] (1) First, it is necessary to calculate the coefficients of the cubic polynomial within each subinterval. For a subinterval [x... i-1 ,x i We can assume that its cubic polynomial is S. i (x), then we have: S i (x)=a i +b i (xx i-1 )+c i (xx i-1 ) 2 +d i (xx i-1 ) 3 , where a i ,b i ,c i ,d i It is an unknown coefficient and needs to be solved using the following conditions.

[0139] (2) Let the interpolation function S(x) satisfy the following condition at each node xi:

[0140] ·S i (x i )=f(x i That is, the interpolation function must pass through the data point exactly at the node;

[0141] ·S i+1 (x i ) = S i ( xi This means that the cubic functions of adjacent intervals have the same function value at the connection point;

[0142] ·S' i (x i )=S' i+1 (x i That is, the first derivative of the function is equal (continuous) at the connection point of adjacent intervals;

[0143] ·S” i (x i ) = S” i+1 (x i That is, the second derivative of the function is equal (continuous) at the connection point of adjacent intervals;

[0144] Where, f(x) i ) represents the given data points.

[0145] (3) Based on the above conditions, a system of linear equations can be set up for each small interval, and the unknown coefficient a can be solved from this system. i,b i ,c i ,d i .

[0146] (4) For (x) i-1 ,x i Interpolation function S within the interval i (x), whose first and second derivatives are:

[0147] S' i (x)=b i +2c i (xx i-1 )+3d i (xx i-1 ) 2

[0148] S i "(x)=2c i +6d i (xx i-1 )

[0149] (5) For any point x in the interval [a, b], the corresponding interval [x] can be found based on the position of x. i-1 ,x i Then, the value of the interpolation function y = S is calculated using the cubic polynomial Si(x) within that interval. i (x);

[0150] Determine the interpolation interval, and divide the data points to be interpolated into segments according to the horizontal coordinate, with 4 data points in each segment;

[0151] Within each interval, a cubic function is calculated and fitted to the data points;

[0152] The interpolation function for the entire dataset is constructed by using cubic functions within multiple intervals.

[0153] Solve the interpolation function to obtain the corresponding contour values.

[0154] As one possible design, step S5 above includes the following steps:

[0155] S51. Push the interpolation results to the front end for display;

[0156] S52. After interpolating the image, convert it into an SVG format image.

[0157] S53. Store the SVG format graphics in the database in binary data format;

[0158] S54. Retrieve binary SVG format graphics from the database and output them to the front-end page for display by business personnel.

[0159] Step S5 allows business personnel to intuitively understand the simulation results, solving the need for higher-dimensional display of simulation results. It transforms the simulated numerical results into various attribute graphs and displays them dynamically, making it convenient for business personnel to view the results and assisting them in development and adjustments, thereby improving the efficiency of solution formulation.

[0160] A dynamic visualization system for gas reservoirs, including

[0161] Modules for building gas reservoirs: used to extract reservoir and water data to construct mass balance zoning models;

[0162] Update module: Used to read configuration data to update the material balance zone gas reservoir model;

[0163] Topology module: Used to construct the interactive data flow topology of the gas reservoir model based on the updated mass balance partition gas reservoir model;

[0164] Interpolation module: Used to estimate database data and perform interpolation processing;

[0165] Visualization and Interaction Module: This module is used to process the interpolation results into graphs, convert them into SVG graphs, and then display them visually.

[0166] As one possible design, step S2 above

[0167] As one possible design, the above S3

[0168] As one possible design, step S4 above includes the following steps:

[0169] S41. Read reservoir and water body data;

[0170] As one possible design, the database data mentioned above includes well point coordinates and attribute values ​​output by mbal.

[0171] S42. Based on the maximum and minimum values ​​of the read coordinates, form a two-dimensional array, and then form a rectangular boundary based on the two-dimensional array; and according to business needs, use the meshgrid function in MATLAB to divide the grid size. The essence of the meshgrid function is to determine the value of each position on the x and y coordinate axes. This is very important when drawing 3D plots, because a 3D plot is actually drawn based on a specific z corresponding to each position on the x and y plane.

[0172] S43. Based on a two-dimensional array of rectangular boundaries, draw vectorized inner boundary lines according to the characteristics of the flow unit / well area boundary;

[0173] S44. Perform interpolation calculations based on the database data to obtain a data curve graph.

[0174] The interpolation methods mentioned above include Kriging interpolation, Lagrange interpolation, Newton interpolation, shortest path first interpolation, or cubic spline interpolation.

[0175] Preferably, the above interpolation method is cubic spline interpolation. Cubic spline interpolation is an interpolation method that constructs a smooth curve using data points within a local region. Specifically, it divides the data points to be interpolated into segments, and uses a cubic polynomial function to fit the data points in each segment, ensuring that the fitted function has continuous first and second derivatives at the endpoints, thus obtaining a continuous and smooth curve.

[0176] Preferably, the above cubic spline interpolation includes the following steps:

[0177] (1) First, it is necessary to calculate the coefficients of the cubic polynomial within each subinterval. For a subinterval [x... i-1 ,x i We can assume that its cubic polynomial is S. i (x), then we have: S i (x)=a i +b i (xx i-1 )+c i (xx i-1 ) 2 +d i (xx i-1 ) 3 , where a i ,b i ,c i ,d i It is an unknown coefficient and needs to be solved using the following conditions.

[0178] (2) Let the interpolation function S(x) satisfy the following condition at each node xi:

[0179] ·S i (x i )=f(x i That is, the interpolation function must pass through the data point exactly at the node;

[0180] ·S i+1 (x i ) = S i ( xi This means that the cubic functions of adjacent intervals have the same function value at the connection point;

[0181] ·S' i (xi )=S' i+1 (x i That is, the first derivative of the function is equal (continuous) at the connection point of adjacent intervals;

[0182] ·S” i (x i ) = S” i+1 (x i That is, the second derivative of the function is equal (continuous) at the connection point of adjacent intervals;

[0183] Where, f(x) i ) represents the given data points.

[0184] (3) Based on the above conditions, a system of linear equations can be set up for each small interval, and the unknown coefficient a can be solved from this system. i ,b i ,c i ,d i .

[0185] (4) For (x) i-1 ,x i Interpolation function S within the interval i (x), whose first and second derivatives are:

[0186] S' i (x)=b i +2c i (xx i-1 )+3d i (xx i-1 ) 2

[0187] S i "(x)=2c i +6d i (xx i-1 )

[0188] (5) For any point x in the interval [a, b], the corresponding interval [x] can be found based on the position of x. i-1 ,x i Then, the value of the interpolation function y = S is calculated using the cubic polynomial Si(x) within that interval. i (x);

[0189] Determine the interpolation interval, and divide the data points to be interpolated into segments according to the horizontal coordinate, with 4 data points in each segment;

[0190] Within each interval, a cubic function is calculated and fitted to the data points;

[0191] The interpolation function for the entire dataset is constructed by using cubic functions within multiple intervals.

[0192] Solve the interpolation function to obtain the corresponding contour values.

[0193] Example 1

[0194] Taking a gas field of PetroChina as an example Figure 15 Business personnel configure the scheme at the front end, input different production schemes, and after running the data stream, they can obtain the distribution of gas reservoir attributes such as pressure, water saturation, and gas saturation under different schemes, providing business personnel with an intuitive comparison for optimizing development schemes.

[0195] In this embodiment, the input data includes: the gas reservoir's production output, the predicted time period, and the time step; the output data includes: the gas reservoir's pressure, water saturation, gas saturation, and other data under different scenarios.

[0196] like Figure 16 and Figure 17 The study compared the changes in reservoir pressure one year after production volumes of 4 million cubic meters and 5 million cubic meters.

[0197] A method for dynamic visualization of gas reservoirs, referring to Figure 1 It includes the following steps:

[0198] S1. Based on reservoir and water data, construct a material balance zoning gas reservoir model, which includes the following steps:

[0199] S11. Collect reservoir and water body data to form a basic analysis dataset;

[0200] The aforementioned reservoir and water body data include relevant data on gas and water. The relevant gas data includes PVT experimental data, production data, reservoir mean pressure data, gas reservoir porosity, bound water saturation, gas-water two-phase permeability curves, and rock compressibility. The relevant water body data includes water body size, water intrusion angle, and permeability.

[0201] S12. Reference Figure 2 Build a material balance zoned gas reservoir model;

[0202] S13. Construct a gas reservoir topology diagram based on the connectivity of the gas reservoirs;

[0203] S14. Input the basic analysis dataset into the material balance zone gas reservoir model, and use the nonlinear regression method to fit the material balance zone gas reservoir model with the basic analysis data.

[0204] S15. Use a graphical method to verify the fitting quality and correctness of the nonlinear regression method;

[0205] S16. Predict oil and gas well production value using a material balance zoned gas reservoir model.

[0206] S2. Read the configuration data (predicted production, simulation step size, and simulation start and end times), and update the mass balance zoning gas reservoir model. This includes the following steps:

[0207] S21. Read the daily configuration data of each oil and gas well in the same reservoir area and write it into the material balance zoning gas reservoir model to perform historical data fitting.

[0208] S22. Input the simulation time period into the material balance zone gas reservoir model to predict production; Step S22 specifically includes:

[0209] S221. Update cumulative production data: The daily production data needs to be accumulated first and then written into the model.

[0210] S222, Historical Data Fitting: Model Path: History Matching | Run Simulation, refer to... Figure 4 Then select Calc on this page to run the calculation;

[0211] S223, Model Prediction:

[0212] (1) Write the simulation time period, model path: production prediction|prediction setup, set the prediction time;

[0213] Write the simulated start and end times (read from the front end), see attached. Figure 6 :

[0214] Write the simulation start time (Prediction start): Select "user defined", and write the time, such as 2023 / 07 / 01.

[0215] Write the simulated deadline (Prediction end): Select "user defined", and write the time, such as 2023 / 12 / 01;

[0216] (2) Write the predicted output

[0217] Model path: Production prediction | Production and constraints

[0218] Write the average monthly production data into the corresponding table, see attachment ( Figure 7 );

[0219] (3) Click prediction: Path Production prediction | Run prediction

[0220] (4) Data acquisition: Extract the data from the operation results and store it in the database. The extracted data includes pressure, gas saturation and water saturation. Other data can also be extracted as needed for business operations.

[0221] S3. Construct the interactive data flow topology of the gas reservoir model based on the updated material balance partition gas reservoir model, specifically including the following steps:

[0222] S31. Build a gas reservoir model interactive data stream based on DIM software and name each functional module.

[0223] S32. Use the common components of DIM software to add functional modules, and build a gas reservoir simulation data flow topology diagram through the functional modules.

[0224] S33. Configure the functions of each functional module. S33 includes...

[0225] (1) Parameter Configuration: The parameter configuration allows you to configure the parameters required for the current component. You can add, modify, and delete parameters. The interface is shown in the attached image. Figures 10-13 ;

[0226] (2) Click the [Add] button to add parameters. In the Add Parameter window, enter the parameter name, display name, data type, IO type, default value and unit in sequence, and then click the [OK] button to save the parameters and close the Add Parameter window and refresh the parameter list;

[0227] (3) Data Relationships: The parameter passing relationships within components can be configured. Parameters in the current component are displayed on the left, and parameters in preceding and following components are displayed on the right. Arrows connect the parameters to configure their passing relationships between components. Specifically, when the parameter's IO type is input, it only accepts parameters passed from other components. When the IO type is output, it only accepts parameters passed from the current component. When the IO type is input-output, it can be used as either input or output.

[0228] S4. Read reservoir and water data and perform interpolation;

[0229] S5. Save the interpolation result as an SVG image and output it to the front-end page for display. The specific steps include the following:

[0230] S51. Push the interpolation results to the front end for display;

[0231] S52. After interpolating the image, convert it into an SVG format image.

[0232] S53. Store the SVG format graphics in the database in binary data format;

[0233] S54: Use the JAVA language to read binary SVG format graphics from the database and output them to the front-end page for display by business personnel (e.g., ...). Figure 14 ).

[0234] The specific embodiments described above further illustrate the purpose, technical solution, and beneficial effects of the present invention. It should be understood that the above description is only a specific embodiment of the present invention and is not intended to limit the scope of protection of the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A method for dynamic visualization of gas reservoirs, characterized in that, Includes the following steps: S1. Based on reservoir and water data, build a material balance zoned gas reservoir model; S2. Read configuration data and update the material balance zone gas reservoir model; S3. Construct the topology of the gas reservoir model interaction data flow based on the updated material balance partition gas reservoir model; S4. Read reservoir and water data and perform interpolation; S5. Save the interpolation result as a scalable vector graphic and output it to the front-end page for display.

2. The gas reservoir dynamic visualization method according to claim 1, characterized in that, Step S1 includes the following steps: S11. Collect reservoir and water body data to form a basic analysis dataset; S12. Construct a material balance zoned gas reservoir model; S13. Construct a gas reservoir topology diagram based on the connectivity of the gas reservoirs; S14. Input the basic analysis dataset into the material balance zone gas reservoir model, and use the nonlinear regression method to fit the material balance zone gas reservoir model with the basic analysis data. S15. Use a graphical method to verify the fitting quality and correctness of the nonlinear regression method; S16. Predict oil and gas well production value using a material balance zoned gas reservoir model.

3. A method for dynamic visualization of gas reservoirs according to claim 1 or 2, characterized in that, The reservoir and water body data include relevant data on gases and water bodies. The relevant data on gases include high-pressure physical property test data, gas reservoir production data, gas reservoir porosity, bound water saturation, gas-water two-phase permeability curves, and rock compressibility. The relevant data on water bodies include water body size, water intrusion angle, and permeability.

4. The gas reservoir dynamic visualization method according to claim 1, characterized in that, Step S2 includes the following steps: S21. Read the daily configuration data of each oil and gas well in the same reservoir area and write it into the material balance zoning gas reservoir model to perform historical data fitting. S22. Input the simulation time period into the material balance zone gas reservoir model to predict production.

5. A method for dynamic visualization of gas reservoirs according to claim 1 or 4, characterized in that, The configuration data includes predicted output, simulation step size, and simulation start and end times.

6. The gas reservoir dynamic visualization method according to claim 1, characterized in that, S3 includes the following steps: S31. Building an interactive data stream for gas reservoir models based on DIM software; S32. Use the common components of DIM software to add functional modules, and build a gas reservoir simulation data flow topology diagram through the functional modules. S33. Configure the functions of each functional module.

7. The method for dynamic visualization of gas reservoirs according to claim 1, characterized in that, Step S4 includes the following steps: S41. Read reservoir and water body data; S42. Based on the maximum and minimum coordinate values ​​in the read data, form a two-dimensional array, and then form a rectangular boundary based on the two-dimensional array; S43. Draw vectorized inner boundary lines based on a two-dimensional array of rectangular boundaries; S44. Perform interpolation calculations based on the database data to obtain a data curve graph.

8. The gas reservoir dynamic visualization method according to claim 7, characterized in that, The database data includes well point coordinates and attribute values ​​output by mbal; the interpolation methods include Kriging interpolation, Lagrange interpolation, Newton interpolation, shortest path first, or cubic spline interpolation.

9. The gas reservoir dynamic visualization method according to claim 7, characterized in that, Step S5 includes the following steps: S51. Push the interpolation results to the front end for display; S52. Process the interpolated image and then convert it into a scalable vector graphics file; S53. Store the scalable vector graphics file in a binary data format in the database; S54. Read the binary scalable vector graphics file from the database and output it to the front-end page for display.

10. A dynamic visualization system for gas reservoirs, characterized in that, include Modules for building gas reservoirs: used to extract reservoir and water data to construct mass balance zoning models; Update module: Used to read configuration data to update the material balance zone gas reservoir model; Topology module: Used to construct the interactive data flow topology of the gas reservoir model based on the updated mass balance partition gas reservoir model; Interpolation module: Used to estimate database data and perform interpolation processing; Visualization and Interaction Module: This module is used to process the interpolation results into graphs, convert them into scalable vector graphics, and then display them visually.

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