Shaft and pipe network production allocation risk assessment system for sulfur-containing gas reservoir
By constructing a wellbore and pipeline production risk assessment system, the risks under different production schemes are simulated, which solves the problem of insufficient consideration of wellbore and pipeline constraints in existing technologies and improves the safety and feasibility of gas field production schemes.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- PETROCHINA CO LTD
- Filing Date
- 2024-11-21
- Publication Date
- 2026-05-22
AI Technical Summary
When formulating gas field production plans, existing technologies have failed to effectively consider the constraints of wellbore and pipeline networks, resulting in ambiguity of potential risks. In particular, the risks of gas erosion of the wellbore, sulfur deposition after the throttle valve, and hydrates have not been fully assessed.
A risk assessment system for wellbore and pipeline production allocation in sulfur-bearing gas reservoirs is constructed, including a gas reservoir model, a wellbore model, and a pipeline model. The integrated model simulates the risks under different production allocation schemes, the configuration module inputs parameters, the storage module saves the results, the early warning judgment module performs risk assessment, and the display module displays the results.
It enables risk simulation and prediction for different production allocation schemes, improves the safety and feasibility of production allocation schemes, enhances on-site safety management, and clarifies the operational risks of wellbore and pipeline network.
Smart Images
Figure CN122072884A_ABST
Abstract
Description
Technical Field
[0001] This invention relates to the field of data analysis technology, specifically to a wellbore and pipeline production risk assessment system for sulfur-containing gas reservoirs. Background Technology
[0002] Currently, when gas field developers formulate production allocation plans, they mainly consider the production capacity of a single well, while giving less consideration to the constraints of the wellbore and pipeline network. They are also relatively vague about the potential risks after the implementation of production allocation, such as the risk of gas erosion of the wellbore, the risk of sulfur deposition and hydrates after the throttle valve, and the risk of liquid accumulation in the gas gathering branch. Therefore, it is urgent to use intelligent means to simulate and predict the potential risks of different production allocation plans, so that the production allocation plans can be more comprehensive and reasonable. Summary of the Invention
[0003] This invention provides a wellbore and pipeline production risk assessment system for sulfur-containing gas reservoirs to address the aforementioned problems.
[0004] This invention is achieved through the following technical solution:
[0005] A wellbore and pipeline production risk assessment system for sulfur-bearing gas reservoirs includes:
[0006] An integrated model is provided, comprising a gas reservoir model, a wellbore model, and a pipeline network model. The gas reservoir model is used to predict gas reservoir production based on gas reservoir data; the wellbore model is used to calculate critical production conditions for gas wells and determine their production capacity based on wellbore data; and the pipeline network model is used to analyze bottlenecks in the gathering and transportation of the Dukouhe surface production system and optimize the efficiency of production equipment based on pipeline network data.
[0007] The configuration module is used to configure the configuration parameters of the integrated model and input the simulated production value parameters of each production node;
[0008] The storage module is used to store the production allocation results output by the integrated model and the simulated production allocation value parameters of each production node;
[0009] An extraction module is used to extract the production matching results of the integrated model stored in the storage module;
[0010] The early warning judgment module is used to compare the production allocation result with the configuration parameters to determine whether an early warning is needed.
[0011] The sulfur deposition judgment module is used to calculate the sulfur solubility based on the production allocation results and compare the sulfur solubility with the actual sulfur content to determine whether to issue a warning.
[0012] The display module is used to display the configuration results, the judgment results of the early warning judgment module and the sulfur deposition judgment module.
[0013] As an optimization, the gas reservoir data includes the basic geological characteristics of the gas reservoir, changes in gas reservoir pressure during development, and historical actual gas reservoir production.
[0014] As an optimization, the gas reservoir model is constructed based on the reservoir mass balance model of the MBAL software. The specific construction process is as follows:
[0015] The basic geological characteristics of the gas reservoir and the changes in gas reservoir pressure during development are input into the reservoir mass balance model to obtain the predicted gas reservoir production. Then, the predicted gas reservoir production of the reservoir mass balance model is best matched with the historical actual gas reservoir production through nonlinear regression method, and finally the gas reservoir model is obtained.
[0016] As an optimization, the gas reservoir model is divided into a conventional model, a water-drive model, and a high-temperature and high-pressure model based on basic geological characteristics.
[0017] As an optimization, the wellbore model is built based on PROSPER software, and the specific construction process is as follows:
[0018] Construct a PTV property model;
[0019] The PROSPER module in IPM software is used to construct a VLP vertical pipe flow model of the wellbore based on the wellbore data, which is used to calculate the critical production conditions of the gas well and determine the production capacity of the gas well.
[0020] A dynamic characteristic model of IPR inflow in a single well is constructed to obtain the IPR curve of the gas well;
[0021] The model parameters of the wellbore VLP vertical pipe flow model and the single-well IPR inflow dynamic characteristic model are adjusted so that the predicted gas well production capacity output by the wellbore VLP vertical pipe flow model fits the actual gas well production capacity best, and at the same time, the predicted IPR curve output by the single-well IPR inflow dynamic characteristic model fits the actual IPR curve best.
[0022] As an optimization, the pipeline data includes production node parameters, pipeline parameters, and equipment parameters.
[0023] As an optimization, the pipeline data is input into GPA software to build the pipeline model.
[0024] As an optimization, the configuration parameters include production allocation configuration data and early warning threshold configuration data. The production allocation configuration data includes single-well production allocation data, surface pipeline network, wellbore boundary conditions, simulation step size, and simulation start and end time. The single-well production allocation data includes gas reservoir data, wellbore data, and pipeline network data. The early warning threshold configuration data includes single-well wellhead pressure, pipeline operating pressure, separator throughput, and site operating pressure.
[0025] As an optimization, the specific process for calculating sulfur solubility based on the production allocation results is as follows:
[0026] A1. Extract the production allocation results for each production node, including the simulated pressure and simulated temperature of the wellhead, throttle valve, and pipeline.
[0027] A2. Compare the pressure ratios P0 between the simulated pressures and critical pressures at the wellhead, choke valve, and pipeline of the single well, respectively. r And the temperature ratio T between the simulated temperature and the critical temperature of the wellhead, choke valve, and pipeline of the single well. r ;
[0028] A3, Based on the pressure ratio P r The ratio of temperature T r The density ρ of natural gas was calculated.
[0029] A4. Calculate the sulfur solubility C based on the natural gas density ρ. s .
[0030] As an optimization, the temperature ratio T r The specific formula is: T r = (a+273) / 234.5, where a represents the simulated temperature;
[0031] The pressure ratio P r The specific formula is: P r = b / 8.52, where b represents the simulated pressure.
[0032] As an optimization, based on the pressure ratio P r The ratio of temperature T r The specific process for calculating the density ρ of natural gas is as follows:
[0033] Calculate the deviation coefficient Z value of natural gas:
[0034]
[0035] Calculate the density of natural gas:
[0036]
[0037] Where P is the pressure at each production node, in MPa; M a It is the relative molecular mass of air, with a size of 29; γ g ρ is the relative density of natural gas, with a value of 0.73; Z is the deviation coefficient Z value; T is the simulated temperature of the node, i.e. (a+273); R is a constant 0.0083.
[0038] As an optimization, the actual sulfur content is 0.002 g / m³.3 .
[0039] As an optimization, the data flow interaction between the integrated model, configuration module, storage module, extraction module, early warning judgment module, sulfur deposition judgment module, and display module is built based on DIM software.
[0040] Compared with the prior art, the present invention has the following advantages and beneficial effects:
[0041] 1. By inputting different production allocation values through the system page, the integrated model can be driven to run, simulating the risks that may occur in wellbore, pipeline network and site under different production levels. It can also compare key indicators of multiple schemes, screen and optimize production allocation schemes, and improve the safety and feasibility of production allocation schemes.
[0042] 2. By reasonably setting the safety threshold for wellbore pipeline operation, the operational risks of the system under different production schemes can be predicted. This effectively solves the problem of risk ambiguity caused by the complexity and variability of the wellbore pipeline system in the past, coupled with inaccurate and lagging simulation data, and improves the level of on-site safety management. Attached Figure Description
[0043] The accompanying drawings, which are included to provide a further understanding of embodiments of the invention and form part of this application, do not constitute a limitation thereof. In the drawings:
[0044] Figure 1 This is a module connection diagram of a wellbore and pipeline production risk assessment system for sulfur-containing gas reservoirs as described in this invention.
[0045] Figure 2 A schematic diagram illustrating the steps involved in creating a gas reservoir model;
[0046] Figure 3 Technical flowcharts created for pipeline network models;
[0047] Figure 4 This is a flowchart of the data flow interaction process;
[0048] Figure 5 A schematic diagram of the configuration interface for the production allocation plan;
[0049] Figure 6 One of the schematic diagrams for configuring the warning threshold;
[0050] Figure 7 Another schematic diagram of the interface for configuring warning thresholds;
[0051] Figure 8 A schematic diagram of the interface for writing simulated configuration data;
[0052] Figure 9 A schematic diagram of the interface for setting the separator inlet pressure;
[0053] Figure 10 A schematic diagram of the interface for extracting flow protection risks for a single well;
[0054] Figure 11 A schematic diagram of the interface for risk extraction to ensure pipeline flow;
[0055] Figure 12 A schematic diagram of the interface for wellhead pressure extraction;
[0056] Figure 13 A schematic diagram of the interface for pipeline pressure extraction;
[0057] Figure 14 A schematic diagram of the interface for extracting the separator's throughput;
[0058] Figure 15 A schematic diagram of the interface for extracting pressure from the inlet of the heating furnace;
[0059] Figure 16 A schematic diagram of the interface for extracting wellhead temperature and pressure values;
[0060] Figure 17 A schematic diagram of the interface for extracting temperature and pressure values from a throttle valve;
[0061] Figure 18 A schematic diagram of the interface for extracting pipeline temperature and pressure values;
[0062] Figure 19 A topology diagram of the risk assessment data flow;
[0063] Figure 20 A schematic diagram of the interface for production node risk statistics results;
[0064] Figure 21 A schematic diagram showing the interface for comparing the risks of different solutions;
[0065] Figure 22 Flowchart for production risk assessment;
[0066] Figure 23 This is a diagram showing the formation of hydrates. Detailed Implementation
[0067] To make the objectives, technical solutions, and advantages of the present invention clearer, the present invention will be further described in detail below with reference to the embodiments and accompanying drawings. The illustrative embodiments and descriptions of the present invention are only used to explain the present invention and are not intended to limit the present invention.
[0068] This embodiment 1 provides a wellbore and pipeline production risk assessment system for sulfur-containing gas reservoirs, such as... Figure 1 As shown, it includes:
[0069] An integrated model is provided, comprising a gas reservoir model, a wellbore model, and a pipeline network model. The gas reservoir model is used to predict gas reservoir production based on gas reservoir data; the wellbore model is used to calculate critical production conditions for gas wells and determine their production capacity based on wellbore data; and the pipeline network model is used to analyze bottlenecks in the gathering and transportation of the Dukouhe surface production system and optimize the efficiency of production equipment based on pipeline network data.
[0070] In some embodiments, the gas reservoir data includes the basic geological characteristics of the gas reservoir, changes in reservoir pressure during development, and historical actual gas reservoir production.
[0071] In some embodiments, the gas reservoir model is classified into a conventional model, a water-drive model, and a high-temperature and high-pressure model based on basic geological characteristics.
[0072] The geological characteristics of a gas reservoir refer to: the original pressure, temperature, porosity, bound water saturation, rock compressibility, and relative permeability of gas and water.
[0073] The conventional model needs to be described according to the above parameters;
[0074] Water drive models require a description of the water volume size based on conventional models;
[0075] The high-temperature and high-pressure model includes the high-temperature model and the high-pressure model.
[0076] High-temperature model: Reservoir temperatures are typically higher than those of conventional gas reservoirs, potentially reaching over 100 degrees Celsius or even higher. High-temperature environments place higher demands on the temperature resistance of extraction equipment and materials.
[0077] High-pressure model: The reservoir pressure is much higher than that of conventional gas reservoirs, possibly reaching hundreds of atmospheres.
[0078] More specifically, the gas reservoir data includes:
[0079] 1. Static data: PVT, rock compressibility coefficient, reserves, bound water saturation, water permeability, inner diameter, outer diameter, water thickness, relative permeability data, etc.
[0080] 2. Dynamic data: water production, oil production, historical production data, cumulative water production, cumulative oil production, reservoir average pressure data, etc.
[0081] 3. Test data: pressure test data, well test data, etc.;
[0082] 4. Fluid parameters specifically include: natural gas gas composition data, specifically:
[0083] Methane, ethane, propane, isobutane, n-butane, isopentane, n-pentane, hexane, hydrogen sulfide, carbon dioxide, nitrogen, helium, hydrogen, carbon dioxide content, hydrogen sulfide content, total sulfur, water dew point, compressibility factor, higher heating value, relative density, critical temperature, critical pressure, etc.
[0084] In some embodiments, wellbore data includes:
[0085] 1. Static data: well foundation information, PVT, geothermal gradient, well structure, well inclination data, etc.;
[0086] 2. Dynamic data: daily data, daily oil production pressure, daily water production pressure, wellhead temperature, wellhead oil pressure, etc.;
[0087] 3. Test data: Flow pressure test data, well test data, etc.;
[0088] In some embodiments, pipeline data includes:
[0089] 1. Static data:
[0090] 1.1 The node information of the pipeline entity specifically includes:
[0091] Structural data of the gas gathering pipeline: including the length of each pipeline, the inner diameter of each pipeline, the wall thickness of each pipeline, the pipe wall structure of each pipeline, and the roughness of each pipeline.
[0092] Pipe wall structure: The composition of the pipe and its ancillary structures (such as 3PE anti-corrosion layer or insulation layer) from the inside out;
[0093] Pipeline routing data includes horizontal mileage data (originally obtained as geodetic coordinate data, which is then converted into latitude and longitude data, and the horizontal mileage data is calculated from the latitude and longitude data) and elevation data for each pipeline.
[0094] Pipeline heat transfer properties: the thickness and thermal conductivity of each layer of material in the pipeline.
[0095] 1.2 For buried pipelines, the following also need to be collected:
[0096] The average soil temperature at the depth of the external pipeline during summer and winter;
[0097] Average burial depth of the pipeline;
[0098] Soil type (e.g., clay, gravel, limestone or sandstone) or soil thermal conductivity.
[0099] 1.3 For exposed pipes, the following also needs to be collected:
[0100] The local average temperature in summer and winter;
[0101] Local average wind speed in summer and winter;
[0102] The local annual average atmospheric pressure.
[0103] 2. Dynamic production operation data includes: gas production, temperature and pressure at various pipeline nodes;
[0104] 3. Fluid parameters specifically include: natural gas gas composition data for each platform, specifically including:
[0105] 4. Methane, ethane, propane, isobutane, n-butane, isopentane, n-pentane, hexane, hydrogen sulfide, carbon dioxide, nitrogen, helium, hydrogen, carbon dioxide content, hydrogen sulfide content, total sulfur, water dew point, compressibility factor, higher heating value, relative density, critical temperature, critical pressure.
[0106] Next, we will introduce the process of building the integrated model.
[0107] In some embodiments, the gas reservoir model is constructed based on the reservoir mass balance model of the MBAL software, and the specific construction process is as follows:
[0108] The basic geological characteristics of the gas reservoir and the changes in gas reservoir pressure during development are input into the reservoir mass balance model to obtain the predicted gas reservoir production. Then, the predicted gas reservoir production of the reservoir mass balance model is best matched with the historical actual gas reservoir production through nonlinear regression method, and finally the gas reservoir model is obtained.
[0109] The gas reservoir model was built using the reservoir mass balance model in the MBAL software. Starting from the basic geological characteristics of the gas reservoir and incorporating changes in reservoir pressure during development and historical production data, the MBAL reservoir mass balance model uses both analytical and graphical methods to evaluate the dynamic reserves, edge water energy, and edge water intrusion dynamics of the gas reservoir. It forms the basis for subsequent dynamic analysis and integrated research. The gas reservoir model creation process is detailed in the appendix. Figure 2 .
[0110] Based on the geological characteristics of gas reservoirs, three types of reservoir dynamic characteristic models were established: conventional model, water drive model, and high temperature and high pressure model.
[0111] Technical process:
[0112] 1) Data preparation, including PVT, production data, reservoir mean pressure data, and all available reservoir and water body data.
[0113] 2) Input Data. Check the accuracy and consistency of the data at each step. This is crucial for building a good model. If selecting one well at a time to input production data, ensure all wells belong to the same reservoir.
[0114] 3) Use nonlinear regression (analytical method) to make the model fit the production data best.
[0115] 4) Use a graphical method to verify the fitting quality and correctness of the analytical method.
[0116] 5) Run a simulation once to test the model's fit.
[0117] 6) Conduct production forecasting.
[0118] In some embodiments, the wellbore model is constructed based on PROSPER software. A single-well production dynamic characteristic model is established using PROSPER software to calculate the critical production conditions of the gas well and determine its production capacity. The required static data mainly includes tubing data, well trajectory data, PVT data, and reservoir pressure and temperature data (these are the static data in the wellbore data, such as well foundation information, PVT, geothermal gradient, well structure, and well inclination data). Dynamic production data includes daily single-well reports, historical flow temperature and pressure tests, pressure measurement data, and production testing data (i.e., daily data, daily oil production pressure, daily water production pressure, wellhead temperature, and wellhead oil pressure). The construction of the established wellbore model includes six main parts: well information, equipment parameters, high-pressure fluid properties, multiphase pipe flow, inflow dynamics, and node analysis. After completing the data acquisition and pipeline association for each corresponding part, the model is finally corrected based on measured IPR and pressure profile data.
[0119] Wellbore structure refers to the size of the pipelines used in the wellbore and the combination of different pipelines; geothermal gradient: the temperature variation at different depths.
[0120] A wellbore is a vertical pipeline connecting the surface and the formation. To describe the temperature and pressure changes of this pipeline, a VLP model is needed, so a wellbore model needs to be constructed.
[0121] The specific construction process is as follows:
[0122] 1) Comprehensive setup and construction of PTV property model;
[0123] The PVT (Property Value Model) is a mathematical model used in petroleum engineering to describe the physical properties of fluids (including oil, gas, and water) in oil and gas reservoirs under different pressure, volume, and temperature conditions. It can be used to predict the changes in parameters such as density, viscosity, and deviation coefficient of natural gas under different temperature and pressure conditions.
[0124] 2) Input of downhole technical condition parameters
[0125] A detailed wellbore string model is established based on the measured well trajectory data (the wellbore string model includes information such as the wellbore string, the inflow IPR curve and the outflow VLP curve of the fluid): Combined with the actual downhole tools, detailed downhole string structure and wellbore temperature field data (corresponding to the well structure and geothermal gradient respectively) are input in sequence to establish a complete wellbore flow environment.
[0126] 3) Selection and Fitting of Vertical Pipe Flow Model in Wellbore VLP
[0127] The PROSPER module in IPM software is used to construct a VLP vertical pipe flow model of the wellbore based on the wellbore data, which is used to calculate the critical production conditions of the gas well and determine the production capacity of the gas well.
[0128] The dynamic characteristics of single-well production are modeled using the PROSPER module in IPM software to calculate critical production conditions and determine the production capacity of gas wells. The required static data mainly includes tubing data, well trajectory data, PVT data, and reservoir pressure and temperature data. Dynamic production data includes daily single-well reports, historical flow-temperature-flow-pressure tests, pressure measurement data, and production testing data.
[0129] 4) Dynamic characteristic model of IPR inflow in a single well
[0130] A dynamic characteristic model of IPR inflow in a single well is constructed to obtain the IPR curve of the gas well;
[0131] The IPR curve of a gas well reflects the gas production capacity of a single well and is one of the most important parameters in the entire gas well production process. Accurate IPR data plays a crucial role in the rational allocation of gas well production. Based on the actual situation, different methods are used to calculate IPR curves for wells with and without production capacity testing.
[0132] IPR describes the relationship between bottom hole pressure and production under specific conditions. Building an IPR model typically involves the following steps: collecting and processing test data, selecting an algorithm based on the test data, and determining the algorithm to build the model.
[0133] Reference book: *Petroleum Production Engineering, A Computer-Assisted Approach* by Boyun Guo
[0134] This book provides fundamental knowledge of oil production engineering, including the construction and application of IPR models.
[0135] "Reservoir Engineering Handbook" by Tarek Ahmed
[0136] This handbook is a classic reference book in the field of reservoir engineering, covering the theory and practice of the IPR model.
[0137] "Well Test Analysis" by JJAziz and MRSettari
[0138] This book provides a detailed introduction to well testing analysis, including the construction and application of the IPR model.
[0139] 5) Single-well model verification
[0140] The model parameters of the wellbore VLP vertical pipe flow model and the single-well IPR inflow dynamic characteristic model are adjusted so that the predicted gas well production capacity output by the wellbore VLP vertical pipe flow model fits the actual gas well production capacity best, and at the same time, the predicted IPR curve output by the single-well IPR inflow dynamic characteristic model fits the actual IPR curve best.
[0141] To ensure the accuracy of single-well models, historical formation pressure and water-gas ratios were fitted: Based on previous IPR production models and VLP pipe flow models, conductivity, water volume, water intrusion, and relative permeability curves were adjusted to fit the formation pressure and water-gas ratio of each zone model, thus verifying the model's accuracy. Simultaneously, node analysis was applied to calculate coordinated production, which was then compared with actual production and pressure values.
[0142] Pipeline model:
[0143] The ground model (pipeline model) is built using GAP software. This model is used for bottleneck analysis of the Dukouhe surface production system and for optimizing the efficiency of production equipment. It mainly includes establishing a topology diagram of the entire system's flow relationships for nodes (well points, stations, etc.), pipelines (well points to manifolds, manifolds to gas gathering stations, etc.), and equipment (separators / absorption towers, etc.); establishing fluid PVT property calculation models, pressure calculation models, and temperature calculation models; and correcting and updating the ground pipeline model. The model is corrected based on measured data and updated continuously as the pipeline's operating conditions change to ensure its real-time performance and effectiveness.
[0144] The process of building the PVT model includes:
[0145] (1) Data collection: Collect oil and gas samples that represent the fluid composition of the oil and gas reservoir.
[0146] (2) Laboratory testing: PVT analysis is performed, including testing parameters such as density, viscosity, compressibility, solubility, volume index, and saturation pressure of oil and gas. A PVT analyzer is used to test oil and gas samples to determine their physical properties under different conditions.
[0147] (3) Data processing and analysis: Process and analyze laboratory test data to identify trends and patterns in the data.
[0148] Use statistical methods or data analysis software to process the data in order to extract useful information.
[0149] (4) Model selection: Select the appropriate PVT model type based on the reservoir type and fluid characteristics. Common PVT models include black oil models, component models, etc.
[0150] (5) Parameter estimation: Estimate model parameters using statistical methods or optimization algorithms. This typically involves minimizing the difference between the actual data and the model predictions.
[0151] The initial parameters required for establishing a pipeline network model mainly include well point, pipeline, and equipment parameters, such as dynamic data of each individual well, pipeline inner diameter, length, and elevation parameters, pipeline heat tracing and insulation parameters, and inlet pressure parameters of gathering and transmission stations. The overall approach to establishing the pipeline network model starts from the inlet and iterates stepwise along the pipeline according to the infinitesimal segment step size. If a manifold is encountered, the parameters of each pipeline in the manifold are calculated first, summarized, and then the iteration continues downstream until the outlet.
[0152] In the specific calculation process, the flow rate and temperature distribution of the pipeline are calculated first, starting from each individual well and ending at the gathering and transportation station. During the pressure calculation, the fluid property model and multiphase horizontal pipe flow algorithm are called.
[0153] The technical process for constructing the pipeline network model is shown in the appendix. Figure 3 .
[0154] The configuration module is used to configure the configuration parameters of the integrated model and input the simulated production value parameters of each production node;
[0155] Here, production nodes refer to the gas reservoir, well bottom, wellhead, before and after the throttle valve, and upstream and downstream of the pipeline.
[0156] The storage module is used to store the production allocation results output by the integrated model and the simulated production allocation value parameters of each production node;
[0157] The simulated production parameters mainly include wellhead pressure, pipeline pressure, and separator throughput.
[0158] An extraction module is used to extract the production matching results of the integrated model stored in the storage module;
[0159] The early warning judgment module is used to compare the production allocation result with the configuration parameters to determine whether an early warning is needed.
[0160] In some embodiments, the configuration parameters include production allocation configuration data and early warning threshold configuration data. The production allocation configuration data includes single-well production allocation data, surface pipeline network, wellbore boundary conditions, simulation step size, and simulation start and end time. The single-well production allocation data includes gas reservoir data, wellbore data, and pipeline network data. The early warning threshold configuration data includes single-well wellhead pressure, pipeline operating pressure, separator throughput, and site operating pressure.
[0161] The boundary conditions of the wellbore include the inlet pressure, the pipeline ambient temperature, and the separator's processing capacity.
[0162] A sulfur deposition detection module is used to calculate sulfur solubility based on the production allocation results and compare the sulfur solubility with the actual sulfur content to determine whether an early warning is needed; in some embodiments, the actual sulfur content is 0.002 g / m³. 3 .
[0163] The specific process for calculating sulfur solubility based on the aforementioned production results is as follows:
[0164] A1. Extract the production allocation results for each production node, including the simulated pressure and simulated temperature of the wellhead, throttle valve, and pipeline.
[0165] The simulated pressure and temperature at the wellhead are obtained through a wellbore model, while the simulated pressure and temperature of the throttle valve and pipeline are obtained through a surface pipeline network model.
[0166] A2. Compare the pressure ratios P0 between the simulated pressures and critical pressures at the wellhead, choke valve, and pipeline of the single well, respectively. r And the temperature ratio T between the simulated temperature and the critical temperature of the wellhead, choke valve, and pipeline of the single well. r ;
[0167] A3, Based on the pressure ratio P r The ratio of temperature T r The density ρ of natural gas was calculated.
[0168] A4. Calculate the sulfur solubility C based on the natural gas density ρ. s .
[0169] In some embodiments, the temperature ratio T r The specific formula is: T r = (a+273) / 234.5, where a represents the simulated temperature;
[0170] The pressure ratio P r The specific formula is: P r = b / 8.52, where b represents the simulated pressure.
[0171] In some embodiments, based on the pressure ratio P r The ratio of temperature T r The specific process for calculating the density ρ of natural gas is as follows:
[0172] Calculate the deviation coefficient Z value of natural gas:
[0173]
[0174] Calculate the density of natural gas:
[0175]
[0176] Where P is the pressure at each production node, in MPa; M a It is the relative molecular mass of air, with a size of 29; γ g ρ is the relative density of natural gas, with a value of 0.73; Z is the deviation coefficient Z value; T is the simulated temperature of the node, i.e. (a+273); R is a constant 0.0083.
[0177] The display module is used to display the configuration results, the judgment results of the early warning judgment module and the sulfur deposition judgment module.
[0178] In some embodiments, the data flow interaction between the integrated model, configuration module, storage module, extraction module, early warning judgment module, sulfur deposition judgment module, and display module is built based on DIM software.
[0179] However, before setting up the data flow interaction, a unified model running data interaction specification must first be written. The main purpose of this step is to provide documentation for developing the production allocation data flow engine. The data flow interaction process is shown in the attached document. Figure 4 Next, a detailed description will be given in conjunction with the user interface of the system (which has been developed) of the present invention.
[0180] Step S10: Production allocation configuration and early warning threshold configuration:
[0181] (1) Production Allocation Scheme Configuration: Production allocation personnel input single-well production allocation data, surface pipeline network and wellbore boundary conditions, simulation step size and simulation start and end times at the front end, and then read these data into the business database (data repository) through data stream, as shown in the attached figure. Figure 5 .
[0182] (2) Early Warning Configuration: Early warning thresholds are set for the simulation results of each production node, including single-wellhead pressure, pipeline operating pressure, and separator throughput, providing a basis for subsequent risk warnings. (See Appendix) Figure 6 .
[0183] Step S20: Write the single-well production data read from the front end into the model. The path is: well|input|schedule, see appendix. Figure 6 .
[0184] Step S30: Click Prediction:
[0185] Click on Prediction | Run Prediction (see appendix) Figure 7 .
[0186] Step S40: Set simulation parameters (from the front end)
[0187] Next, we need to set the simulation time, step size, and time type in GAP. This data comes from the front-end page; see attached file. Figure 8 .
[0188] Write the prediction start time, prediction end time and time step into the corresponding boxes in the table, and then select year, month, week or day according to the step type input from the front end.
[0189] Click "next" twice, then enter the separator inlet pressure in the separator field (see attached). Figure 9 .
[0190] Click "next", select "optimize with all constriants", and then click "calculate".
[0191] Step 350: Store the results:
[0192] The output data is stored in the database (storage module): The data from single wells, pipelines, and separators are extracted and stored in the database. The data retrieval path for single wells is well|result|prediction, the path for pipelines is pipeline|result|prediction, and the path for separators is Separator|result|prediction.
[0193] Step S60: Extract the liquidity protection risk identifier and time (read from the production allocation results)
[0194] 1) Extraction of risk indicators for single-well flow protection:
[0195] First, extract the risk indicators for single-well flow assurance, see attached. Figure 10 :
[0196] First, select the well in label 1 of the diagram. In label 2 "result", find label 4 "status" in label 3 "prediction". Right-click the table in label 4 and select openserver. You can see various warning labels in label 5. Extract the warning labels in the red box and extract the corresponding "date". Record the start and end dates of the warning and calculate the duration to provide data statistics results for the front-end page.
[0197] 2) Extraction of pipeline flow assurance risk indicators:
[0198] Pipeline risk assessment is the same as single-well risk assessment, as shown in the attached document. Figure 11 The extraction process is consistent with the risk identification of the extracted single well.
[0199] Step S70: Early Warning Judgment and Statistics (Comparison of IPM Simulation Results with Early Warning Values):
[0200] 1) Single-well early warning and results statistics (see appendix) Figure 12 )
[0201] Extract WH_PRESSURE(kpa.a) from the table, divide it by 1000 to convert it to MPa, and compare it with the "Production Risk Assessment" > "Scheme Configuration" > "Early Warning Settings" > "Single Well" > "Maximum Wellhead Pressure" on the front-end page. If it is greater than "Maximum Wellhead Pressure", it is considered an early warning. Record the start date and end date of the early warning, calculate the duration, and provide data statistics results for the front-end page.
[0202] 2) Pipeline early warning and results statistics (see appendix) Figure 13 )
[0203] Extract the Max line pressure from the table (in kPa.a), divide it by 1000 to convert it to MPa, and compare it with "Production Risk Assessment" > "Scheme Configuration" > "Early Warning Settings" > "Pipeline" > "Maximum Operating Pressure". If it is greater than this value, it is considered a pressure warning. Record the start and end dates of the warning, calculate the duration, and provide data statistics results for the front-end page.
[0204] 3) Separator early warning and result statistics (see appendix) Figure 14 )
[0205] Early warning of separator processing capacity at well sites: The system judges and issues warnings based on the predicted gas production of a single well and the processing capacity of the separator.
[0206] The gas rate of a single well is taken from the graph below. The unit is 1000 m3 / d (thousand cubic meters / day). To convert it to 10000 m3 / d (ten thousand cubic meters / day), divide it by 10. Compare this value with the "Production Risk Assessment" > "Scheme Configuration" > "Early Warning Settings" > "Separator" > "Maximum Processing Capacity" on the front-end page (see the table below for the correspondence between single wells and separators). If it exceeds the separator's processing capacity, an early warning is issued. Record the start and end dates of the warning and calculate the duration to provide statistical data for the front-end page.
[0207] 4) Station early warning and result statistics
[0208] The operating pressure of the station is set at the inlet pressure of the heating furnace (see appendix). Figure 15 Take the pressure value from the table (in kPa.a), divide it by 1000 to convert it to MPa, and compare it with "Production Risk Assessment" > "Scheme Configuration" > "Early Warning Settings" > "Site Early Warning" > "Maximum Operating Pressure". If it is greater than the value, it is considered an early warning. Record the start and end dates of the early warning, calculate the duration, and provide data statistics results for the front-end page.
[0209] The external output operating pressure is set as the manifold node pressure. The pressure is taken from the table, and the unit is (kPa.a). Divide it by 1000 to convert it to MPa. This value is compared with "Production Risk Assessment" > "Scheme Configuration" > "Early Warning Settings" > "Station Early Warning" > "External Output Allowable Pressure". If it is greater than this value, it is considered an early warning. The start date and end date of the early warning are recorded, and the duration is calculated to provide data statistics results for the front-end page.
[0210] Step S80: Sulfur Deposition Assessment:
[0211] Extract the simulation parameters for each production node, including:
[0212] 1) Wellhead, choke valve, pipeline pressure and temperature
[0213] ① Extract wellhead temperature and pressure (attached) Figure 16 )
[0214] ② Extract the temperature and pressure of choke1 and choke2 throttle valves, see appendix. Figure 17
[0215] ③ Extract the pressure and temperature of the pipeline, see appendix. Figure 18
[0216] 2) Calculate the ratio of simulated temperature and pressure to critical pressure.
[0217] The temperature value is a℃, converted to absolute temperature: (a+273), and compared with the critical temperature, the value is Tr: Tr=(a+273) / 234.5. The pressure value is b MPa, and compared with the critical pressure: Pr=b / 8.52.
[0218] ① Calculate the deviation coefficient Z value of natural gas.
[0219]
[0220] ② Calculate the density of natural gas
[0221] The Z-value calculated in the previous step is substituted into the following formula to calculate the density of natural gas:
[0222]
[0223] Where P is the pressure (MPa) at each node;
[0224] Ma is the relative molecular mass of air, 29;
[0225] γg: Relative density of natural gas 0.73;
[0226] Z: Calculated in the previous step;
[0227] T: is the temperature of the node, i.e., the temperature calculated in step 2 (a+273);
[0228] R: constant 0.0083;
[0229] ③ Calculate sulfur solubility
[0230] Substituting the density ρ calculated in the previous step into the following formula:
[0231]
[0232] ④ Warning
[0233] Based on the sulfur solubility calculated in the above formula, a comparison is made with the actual sulfur content in the produced natural gas. If the solubility value is greater than the sulfur content value, no warning is issued; if it is less than the actual sulfur content, a warning is issued. The actual sulfur content is 0.002 g / m³. 3 (Threshold), that is:
[0234] No warning will be issued if Cs > 0.002;
[0235] A warning is issued when Cs < 0.002.
[0236] Next, we will introduce the process of setting up the data flow:
[0237] Note: The interactive data flow is built based on DIM (Data Interaction for Model) software. This step involves developing the data flow according to the data flow specification to enable data interaction between the front-end page and the model, achieving automatic model execution and automatic output of results. The data flow interaction tool is developed using Python. Model operations are performed using the software's built-in OpenServer, with key commands including `doset` (setting parameters), `doget` (reading parameters), and `cmd` (executing commands, including running and closing the model).
[0238] Step T10: Use DIM common components to build a topology diagram of the production risk assessment data flow and name each component.
[0239] Based on the data flow specification and using the common components provided by the DIM software, a risk assessment data flow topology diagram was constructed using drag-and-drop functionality. (See attached diagram) Figure 19 .
[0240] The main added functional modules and drinking functions include:
[0241] 1) Open the GAP model;
[0242] 2) Read the component names of the GAP model and convert them into a one-dimensional array;
[0243] 3) Set the unit, select Canada units;
[0244] 4) Input the production allocation data into the corresponding single well using the loop command;
[0245] 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.
[0246] 6) Write the predicted single-well production data, which comes from the front-end business input.
[0247] 7) Forecast and run: Make production forecasts based on the input forecast deadline and output.
[0248] 8) Read the prediction results, store them in the database, and use relevant algorithms to issue early warnings.
[0249] Step T20: Configure the functions of each module:
[0250] 1) Parameter configuration: The parameter configuration allows you to configure the parameters that need to be used in the current component, and you can perform operations such as adding, modifying, and deleting.
[0251] Click the "Add" button to add a parameter. In the Add Parameter window, enter the parameter name, display name, data type, IO type, default value, and unit in sequence, then click the "OK" button to save the parameter, close the Add Parameter window, and refresh the parameter list.
[0252] Data Relationships: This allows configuration of parameter passing relationships within components. Parameters in the current component are displayed on the left, while parameters from preceding and following components are displayed on the right. Arrows connect parameters between components to configure their passing relationships. (Note: When a 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.)
[0253] Production risk is displayed on the front-end page via data stream (see attached). Figure 20 and attached Figure 21 The code is displayed according to production nodes. The front-end uses Vue3, TypeScript, and Ant-Design-Vue as the main implementation technologies, and the project's business modules are built step-by-step using a functional block model. A set of industry-standard code management tools, including Prettier, ESLint, lint-staged, and commitlint, is used to support this. Automatic code formatting is implemented, resolving code conflicts in multi-user collaborations, and code formatting and quality checks are provided.
[0254] Taking a gas field of PetroChina as an example, business personnel configure plans at the front end, inputting different production plans. After running the data stream, they can obtain the risk distribution of the wellbore, pipeline network, and separator under different plans. After comparing the risks of different plans and assessing them, the business personnel select the optimal production allocation plan based on actual production (see...). Figure 21 ).
[0255] In summary, the system of the present invention mainly includes three aspects:
[0256] (1) One-click generation of production risk assessment plan: Automatic data interaction between the front end, back end and model is realized through the data flow engine, as well as automatic push and display of results;
[0257] (2) Risk assessment method: Combine the mechanism model and risk assessment calculation formula to judge the risk generation situation.
[0258] Production allocation risk visualization technology: It statistically analyzes and displays the results of risk assessment, making it easier for business personnel to view the potential risks of production allocation plans and compare and select the best plan from different options.
[0259] The construction process of the system of this invention is shown in the appendix. Figure 22 .
[0260] Figure 22 In this context, erosion risk is directly assessed using the results of the integrated model (the integrated model can directly determine erosion risk; the result is simply an identifier that can be extracted directly from the model). Hydrate risk is assessed based on a hydrate formation chart. The temperature and pressure calculated by the model are overlaid on the hydrate formation chart, and the potential for hydrate formation is determined based on the safe / dangerous zones indicated by the temperature and pressure. Figure 23 As shown.
[0261] 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 wellbore and pipeline production risk assessment system for sulfur-bearing gas reservoirs, characterized in that, include: An integrated model is provided, comprising a gas reservoir model, a wellbore model, and a pipeline network model. The gas reservoir model is used to predict gas reservoir production based on gas reservoir data; the wellbore model is used to calculate critical production conditions for gas wells and determine their production capacity based on wellbore data; and the pipeline network model is used to analyze bottlenecks in the gathering and transportation of the Dukouhe surface production system and optimize the efficiency of production equipment based on pipeline network data. The configuration module is used to configure the configuration parameters of the integrated model and input the simulated production value parameters of each production node; The storage module is used to store the production allocation results output by the integrated model and the simulated production allocation value parameters of each production node; An extraction module is used to extract the production matching results of the integrated model stored in the storage module; The early warning judgment module is used to compare the production allocation result with the configuration parameters to determine whether an early warning is needed. The sulfur deposition judgment module is used to calculate the sulfur solubility based on the production allocation results and compare the sulfur solubility with the actual sulfur content to determine whether to issue a warning. The display module is used to display the configuration results, the judgment results of the early warning judgment module and the sulfur deposition judgment module.
2. The wellbore and pipeline production risk assessment system for sulfur-bearing gas reservoirs according to claim 1, characterized in that, The gas reservoir data includes the basic geological characteristics of the gas reservoir, changes in gas reservoir pressure during development, and historical actual gas reservoir production.
3. The wellbore and pipeline production risk assessment system for sulfur-bearing gas reservoirs according to claim 2, characterized in that, The gas reservoir model is constructed based on the reservoir mass balance model in MBAL software. The specific construction process is as follows: The basic geological characteristics of the gas reservoir and the changes in gas reservoir pressure during development are input into the reservoir mass balance model to obtain the predicted gas reservoir production. Then, the predicted gas reservoir production of the reservoir mass balance model is best matched with the historical actual gas reservoir production through nonlinear regression method, and finally the gas reservoir model is obtained.
4. The wellbore and pipeline production risk assessment system for sulfur-bearing gas reservoirs according to claim 3, characterized in that, The gas reservoir models are divided into conventional models, water-drive models, and high-temperature and high-pressure models based on basic geological characteristics.
5. The wellbore and pipeline production risk assessment system for sulfur-bearing gas reservoirs according to claim 1, characterized in that, The wellbore model was built using PROSPER software, and the specific construction process is as follows: Construct a PTV property model; The PROSPER module in IPM software is used to construct a VLP vertical pipe flow model of the wellbore based on the wellbore data, which is used to calculate the critical production conditions of the gas well and determine the production capacity of the gas well. A dynamic characteristic model of IPR inflow in a single well is constructed to obtain the IPR curve of the gas well; The model parameters of the wellbore VLP vertical pipe flow model and the single-well IPR inflow dynamic characteristic model are adjusted so that the predicted gas well production capacity output by the wellbore VLP vertical pipe flow model fits the actual gas well production capacity best, and at the same time, the predicted IPR curve output by the single-well IPR inflow dynamic characteristic model fits the actual IPR curve best.
6. The wellbore and pipeline production risk assessment system for sulfur-bearing gas reservoirs according to claim 1, characterized in that, The pipeline network data includes production node parameters, pipeline parameters, and equipment parameters.
7. The wellbore and pipeline production risk assessment system for sulfur-bearing gas reservoirs according to claim 6, characterized in that, The pipeline data is input into GPA software to build the pipeline model.
8. The wellbore and pipeline production risk assessment system for sulfur-bearing gas reservoirs according to claim 1, characterized in that, The configuration parameters include production allocation configuration data and early warning threshold configuration data. The production allocation configuration data includes single-well production allocation data, surface pipeline network, wellbore boundary conditions, simulation step size, and simulation start and end time. The single-well production allocation data includes gas reservoir data, wellbore data, and pipeline network data. The early warning threshold configuration data includes single-well wellhead pressure, pipeline operating pressure, separator throughput, and site operating pressure.
9. The wellbore and pipeline production risk assessment system for sulfur-bearing gas reservoirs according to claim 1, characterized in that, The specific process for calculating sulfur solubility based on the aforementioned production results is as follows: A1. Extract the production allocation results for each production node, including the simulated pressure and simulated temperature of the wellhead, throttle valve, and pipeline. A2. Compare the pressure ratios P0 between the simulated pressures and critical pressures at the wellhead, choke valve, and pipeline of the single well, respectively. r And the temperature ratio T between the simulated temperature and the critical temperature of the wellhead, choke valve, and pipeline of the single well. r ; A3, Based on the pressure ratio P r The ratio of temperature T r The density ρ of natural gas was calculated. A4. Calculate the sulfur solubility C based on the natural gas density ρ. s .
10. The wellbore and pipeline production risk assessment system for sulfur-bearing gas reservoirs according to claim 9, characterized in that, The temperature ratio T r The specific formula is: T r = (a+273) / 234.5, where a represents the simulated temperature; The pressure ratio P r The specific formula is: P r = b / 8.52, where b represents the simulated pressure.
11. A wellbore and pipeline production risk assessment system for sulfur-bearing gas reservoirs according to claim 9 or 10, characterized in that, Based on the pressure ratio P r The ratio of temperature T r The specific process for calculating the density ρ of natural gas is as follows: Calculate the deviation coefficient Z value of natural gas: Calculate the density of natural gas: Where P is the pressure at each production node, in MPa; M a It is the relative molecular mass of air, with a size of 29; γ g ρ is the relative density of natural gas, with a value of 0.73; Z is the deviation coefficient Z value; T is the simulated temperature of the node, i.e. (a+273); R is a constant 0.0083.
12. The wellbore and pipeline production risk assessment system for sulfur-bearing gas reservoirs according to claim 1, characterized in that, The actual sulfur content is 0.002 g / m³. 3 .
13. The wellbore and pipeline production risk assessment system for sulfur-bearing gas reservoirs according to claim 1, characterized in that, The data flow interaction between the integrated model, configuration module, storage module, extraction module, early warning judgment module, sulfur deposition judgment module, and display module is built based on DIM software.