Method for determining optimal flow pressure and drainage and mining opportunity of gas well based on numerical reservoir simulation technology

Abstract

The invention discloses a method for determining optimal flow pressure and discharge and mining time of a gas well based on numerical reservoir simulation, which belongs to the technical field of oil and gas exploitation, and is characterized by comprising the following steps: a, in the numerical reservoir simulation process, calculating a gas phase flow capability index GFI representing near-well gas phase flow efficiency; b, calculating the ratio R (liquid-carrying coefficient) of the current actual gas production rate to the critical liquid-carrying gas production rate; c, analyzing the change trend of the GFI and the absolute value of the R value; and d, outputting a decision instruction for flow pressure control or manual lifting intervention of the gas well based on a collaborative analysis result of the GFI trend and the R value. According to the method, the gas phase flow efficiency of the near wellbore zone is quantified by defining the gas phase flow capacity index (GFI), a dual-signal collaborative decision-making mechanism is constructed in combination with the wellbore liquid carrying coefficient (R), and flow pressure optimization and prospective judgment of drainage and mining opportunities are achieved.

Description

Technical Field

[0001] This invention belongs to the field of intelligent development of oil and gas fields and numerical simulation of reservoirs. Specifically, it relates to a method for coordinated optimization and intelligent decision-making of the flow pressure of high water-cut gas reservoirs that integrates near-well gas phase flow efficiency diagnosis and wellbore liquid carrying critical criteria. Background Technology

[0002] In the mid-to-late stages of high water-cut gas reservoir development, gas well production management faces the core contradiction of "reducing pressure and increasing production" versus "controlling water flooding." Existing gas well flowing pressure optimization and drainage timing determination technologies have significant defects, mainly manifested in the dual separation of reservoir-side analysis and wellbore-side criteria, and the lack of foresight and coordination in the decision-making mechanism.

[0003] Traditional reservoir numerical simulation techniques only focus on predicting macroscopic production and pressure, lacking quantitative diagnostic indicators for near-wellbore gas-phase flow efficiency. In particular, they cannot accurately characterize the impact of water intrusion on near-wellbore flow relative permeability changes, leading to an inability to identify trends of deteriorating formation flow efficiency in advance. Critical fluid-carrying capacity determination on the wellbore side relies on Turner or Li Min models to calculate the critical fluid-carrying flow rate (q). crit This method can only guide gas wells at a level not lower than q. crit Production is carried out under the condition that the actual output (q) is produced, and only if the actual output (q) is produced. actual (Below q) crit It is only after this period that the occurrence of fluid accumulation can be determined, which is a retrospective judgment and misses the best opportunity for preventive drainage intervention.

[0004] Overall, the core shortcomings of existing technologies are the disconnect between reservoir simulation and wellbore analysis, delayed early warning signals, reliance on single and fixed-threshold indicators for decision-making, inability to respond to the dynamically changing reservoir-wellbore coupling relationship during development, and difficulty in achieving precise optimization of gas well flow pressure and forward-looking determination of drainage timing. Summary of the Invention

[0005] In order to overcome the shortcomings of the prior art, this invention provides a method that can realize integrated diagnosis of reservoir and wellbore within reservoir numerical simulation and provide forward-looking and collaborative decision signals to solve the gas well management problems in the middle and late stages of high water-cut gas reservoir development.

[0006] This invention is achieved through the following technical solution:

[0007] A method for determining the optimal flowing pressure and drainage timing of gas wells based on reservoir numerical simulation is characterized by the following steps:

[0008] a. Define a gas flowability index (GFI) that can comprehensively reflect the degree of control over near-wellbore gas flow.

[0009] b. Establish a system based on the dynamic trend of GFI and the liquid carrying capacity (q) actual / q crit A dual-signal collaborative decision-making mechanism based on relative relationships;

[0010] c. An intelligent algorithm based on a dual-signal collaborative decision-making mechanism, which runs automatically within a numerical simulator and is used to optimize flow pressure and determine the timing of drainage intervention.

[0011] Step a specifically involves creating a Gas Phase Flow Capacity Index (GFI). The GFI aims to more comprehensively quantify the effective gas phase fluid flow capacity in the near-wellbore zone, and its calculation formula is shown in Equation 1:

[0012]

[0013] In the formula, k is the absolute permeability of the well grid, in μm², characterizing the basic conductivity of the reservoir; k rg The gas-phase relative permeability of the well grid is dimensionless and is derived from the current water saturation (S). w According to the table, this parameter is the core variable reflecting the negative effects of water intrusion, and its value varies with (S). w ) rises and then falls sharply; μ g Let be the gas viscosity under bottom hole conditions, in mPa·s. Gas viscosity varies significantly with temperature and pressure, directly affecting gas flow resistance. A PVT model is needed to determine the viscosity based on the current bottom hole pressure P. wf Calculated with temperature T; ρ represents the pressure gradient amplitude at the well grid, in MPa / m, reflecting the driving energy provided by the current production pressure differential and directly reflecting the positive effect of reducing flowing pressure; g , ρ w ρ represents the gas and liquid densities under bottom hole conditions, in kg / m³; WOR is the water-to-gas ratio, dimensionless, characterizing the water content of the produced fluid; α is an empirical index (usually taken as 0.5~1.0), dimensionless, used to adjust the contribution weight of the gas phase density term to the index, which can be determined by fitting historical data; the fractional term in the formula is the gas phase effectiveness coefficient, reflecting the mass proportion of the gas phase in the well fluid, and the smaller the value, the heavier the liquid load.

[0014] The core decision-making logic for step b is as follows:

[0015] 1. GFI Peak Point: When GFI reaches its peak, it signifies both the "positive effect" (increased production pressure differential, enhanced driving energy) and the "negative effect" (exacerbated water intrusion leading to k) of reduced flow pressure. rg The optimal equilibrium is reached when the flow rate (decreases and changes in gas properties) reaches its peak. The bottomhole flowing pressure corresponding to this peak point is the theoretical optimal flowing pressure P for the gas well. opt ;

[0016] 2. GFI Trend Warning: A decline in GFI from its peak value is an early warning indicator that near-wellbore flow efficiency in the formation is beginning to deteriorate, and can identify the trend of water flooding and reduced flow efficiency in gas wells in advance.

[0017] 3. Liquid carrying coefficient risk confirmation: When the liquid carrying coefficient R approaches or is less than 1, it is a confirmation indicator of the risk of liquid accumulation in the wellbore flow stability. R<1 indicates that liquid accumulation has occurred in the gas well.

[0018] 4. Dual-signal coordinated judgment: When GFI begins to decline (efficiency warning) and R value approaches the critical value of 1 (risk confirmation), the strongest joint intervention signal is issued. This judgment method is more reliable and forward-looking than using R<1 (post-judgment) or GFI decline alone.

[0019] Step c specifically involves embedding the entire process of GFI calculation, trend identification, critical fluid carrying capacity calculation, dual-signal collaborative analysis, and decision output into the reservoir numerical simulator, forming a closed-loop workflow of "simulation-diagnosis-decision-control," and automatically performing the following operations at each time step of the numerical simulator:

[0020] 1. Data Extraction: Extract the well grid parameters (P, S) of each production well from the simulator memory. w k), production data (q) g q w ) and PVT attributes (ρ g ρ w μ g (Z);

[0021] 2. Calculation of Indicators: Calculate the GFI value and critical liquid carrying flow rate q at the current time step. crit And solve for the liquid carrying coefficient R;

[0022] 3. Trend Analysis: Update the historical sequences of GFI and R, identify the peak point of GFI, calculate the cumulative relative decrease of GFI since the peak, and determine the trend of GFI change;

[0023] 4. Collaborative Decision Making: Based on the relative relationship between GFI trends and R values, corresponding flow pressure control and drainage intervention decision commands are output through the decision matrix;

[0024] 5. Command Execution: Based on the decision command, automatically adjust the production constraints of the gas well in subsequent simulation time steps (such as changing from fixed production to fixed flow pressure, modifying the target value of flow pressure), or trigger the artificial lift model.

[0025] The innovative aspects of this invention include:

[0026] 1. For the first time, a gas flow capacity index (GFI) was defined based on the results of reservoir numerical simulation calculations. This index comprehensively considers multiple factors such as reservoir permeability, relative gas permeability, gas viscosity, pressure gradient, and water-gas ratio.

[0027] 2. A creative reservoir-wellbore dual-signal collaborative decision-making mechanism was proposed, which combines the dynamic trend of GFI reflecting formation supply capacity with the fluid carrying coefficient R reflecting wellbore lifting capacity to establish a multi-level early warning decision matrix, realizing a leap from "single-point threshold alarm" to "multi-dimensional trend collaborative decision-making".

[0028] 3. An embedded intelligent decision-making workflow has been implemented, which integrates the entire process of GFI calculation, trend analysis, dual signal judgment, and decision execution into the reservoir numerical simulator, forming a closed-loop intelligent system of "simulation-diagnosis-decision-control". It can perform online diagnosis and automatically output flow pressure optimization suggestions and drainage process intervention instructions during the simulation process.

[0029] The beneficial effects of this invention are mainly reflected in the following aspects:

[0030] 1. Significantly improved forward-looking decision-making for drainage: By using GFI trend changes to provide early warning of near-wellbore flow efficiency deterioration, the timing of drainage decisions can be advanced by several weeks to several months, transforming the traditional "drain after liquid is seen" post-event treatment into "early warning and preventive drainage", effectively avoiding the aggravation of water flooding in gas wells;

[0031] 2. Significantly enhanced decision reliability: A dual-signal collaborative decision-making mechanism based on GFI and fluid carrying coefficient R is constructed, which comprehensively considers the dynamic changes in formation supply capacity and wellbore lifting capacity, avoiding the misjudgment problem of single indicator judgment, and making the decision basis more comprehensive and more in line with the actual situation on site.

[0032] 3. More complete physical mechanism: The GFI index integrates multiple parameters such as reservoir, fluid, and production dynamics. At the same time, it introduces relative permeability and gas viscosity in the gas phase, accurately captures the impact of water intrusion and temperature and pressure changes on near-well gas phase flow, and is more sensitive to changes in flow capacity, which is consistent with the physical reality of gas phase flow.

[0033] 4. Achieve automation and intelligence in gas well management: The entire process is embedded in the reservoir numerical simulator, realizing online diagnosis, automatic decision-making, and closed-loop control during the simulation process. It can output flow pressure optimization and drainage intervention commands without manual intervention, which is a key technology for the digital and intelligent development of oil and gas fields.

[0034] 5. High engineering applicability: The output results of this invention directly correspond to specific operational problems in on-site production, such as "when to adjust the flowing pressure", "to what flowing pressure", "when to intervene in drainage", and "what drainage process to use", forming directly operable production instructions that can be directly applied by on-site engineers, greatly reducing the technical threshold for gas well management. Attached Figure Description

[0035] The present invention will now be further described in detail with reference to the accompanying drawings and specific embodiments:

[0036] Figure 1 is a flowchart of the present invention;

[0037] This flowchart illustrates the entire process from the numerical simulation time step, through data extraction, GFI calculation, critical liquid carrying coefficient calculation, GFI trend identification, dual-signal collaborative analysis, decision judgment, and finally output of control commands and feedback to the numerical simulation, fully presenting the closed-loop workflow of "simulation-diagnosis-decision-control".

[0038] Figure 1 Explanation:

[0039] 1. Start Node: The numerical simulation time step begins, and the nonlinear iteration converges;

[0040] 2. Data Layer: The data acquisition module extracts well grid parameters, production data, and PVT attributes;

[0041] 3. Calculation layer: The GFI calculation engine calculates GFI values ​​and updates historical sequences, and the critical liquid-carrying analyzer calculates qcrit and R values;

[0042] 4. Analysis layer: Identify GFI peak points, calculate the cumulative relative decrease of GFI, and determine the trend of GFI changes;

[0043] 5. Decision-making layer: The intelligent decision-making engine performs dual-signal collaborative analysis and outputs decision codes;

[0044] 6. Execution layer: The control command executor adjusts the gas well production parameters or triggers the artificial lift model based on the decision code;

[0045] 7. Feedback Node: Completes the decision for the current time step, proceeds to the next numerical simulation time step, and executes in a loop. Detailed Implementation

[0046] System module configuration based on numerical simulator

[0047] To implement the method of this invention, the following functional modules are added / enhanced in the reservoir numerical simulator. These modules work together to automate the flow pressure optimization and drainage timing determination:

[0048] 1. Data Acquisition Module: Extracts the well grid parameters of each production well at each iteration step from the simulator memory. P, S w , k ), well production data ( q g , q w ) and PVT attributes ( r g , r w , m g , Z );

[0049] 2. GFI Calculation Engine: Calculates GFI according to Equation 1 and maintains its historical sequence;

[0050] 3. Critical Fluid Carrying Analyzer: Integrates Turner, Li Min, and other models to calculate (q) based on current bottom hole conditions. crit ), and calculate the liquid carrying capacity (R);

[0051] 4. Intelligent Decision Engine: Executes the dual-signal collaborative analysis logic shown in Figure 1 and outputs decision codes (e.g., 0-Continue to reduce pressure, 1-Early warning preparation, 2-Intervention and screening, 3-Emergency measures).

[0052] e. Control command executor: Based on the decision code, automatically adjust the well's constraints in subsequent simulation time steps (such as changing fixed production to fixed flow pressure, or modifying the target flow pressure value), or trigger an artificial lift model.

[0053] Specific algorithm implementation steps

[0054] The method of this invention is automatically executed at each time step after the convergence of each nonlinear iteration in the reservoir numerical simulator. For each monitored gas well, it sequentially completes four steps: calculation and monitoring, feature point identification, collaborative decision-making, and command execution, as detailed below:

[0055] Step 1: Calculation and Monitoring

[0056] For each monitoring well, extract the parameters of the current time step, calculate the current GFI value GFI(t) and fluid carrying coefficient R(t); update the historical data arrays of GFI and R, and use the moving average method to calculate the changing trend of GFI (such as the GFI slope of the previous 3 time steps) to achieve real-time monitoring of GFI and R.

[0057] Step 2: Feature Point Recognition

[0058] 1. GFI Peak Identification: Compare the current GFI(t) with all GFI values ​​in the historical sequence. If GFI(t) is the historical maximum value, then mark the current time step as the peak time t.peak Record the peak GFI value. max The bottom hole pressure corresponding to this time step is determined as the optimal flow pressure Popt.

[0059] 2. GFI Trend Judgment: If GFI has passed its peak, calculate the cumulative relative decrease Δ of GFI since the peak. The calculation formula is shown in Equation 2:

[0060] Δ=(GFI max −GFI(t)) / GFI max Formula 2

[0061] The magnitude of Δ quantifies the degree of GFI decline.

[0062] Step 3: Dual-signal collaborative judgment based on decision matrix

[0063] Based on the changing trend of GFI, the cumulative relative decrease Δ, and the magnitude of the liquid carrying coefficient R, production operation decision instructions are output through a preset decision matrix. The core pseudocode logic is as follows:

[0064] If (GFI is on an upward trend) and (R > 1.2): decision = “CONTINUE_OPTIMIZE” # Continue to optimize and reduce bottom hole flowing pressure to approach the optimal flowing pressure.

[0065] elseif (GFI first declines from peak, Δ in the range of 5%~15%) and (R > 1.0): decision = "WARNING_PREPARE" # Issue an early warning and prepare for intervention in the drainage process.

[0066] elseif (GFI continues to decline, Δ > 15%) and (R rapidly approaches 1.0): decision = "ACTIVATE_ARTIFICIAL_LIFT" # Immediately activate artificial lift and implement drainage.

[0067] elseif (GFI drops significantly, Δ > 30%) and (R < 0.8): decision = “EMERGENCY_INTERVENTION” # Take emergency drainage measures to control flooding

[0068] else: decision = “MAINTAIN_CURRENT” # Maintain the current production schedule and continue monitoring

[0069] Step 4: Command Execution and Feedback

[0070] The control instruction executor automatically modifies the production operation parameters of the corresponding gas well in the simulator according to the decision instruction output in step 3 to achieve closed-loop control:

[0071] 1. If the decision is "CONTINUE_OPTIMIZE", gradually reduce the bottom-hole flowing pressure to approach the optimal flowing pressure Popt;

[0072] 2. If the decision is "WARNING_PREPARE", output a warning signal to the site and complete the preparation work for drainage and production processes such as foam drainage and gas lift;

[0073] 3. If the decision is "ACTIVATE_ARTIFICIAL_LIFT", switch the gas well production mode from a conventional producer to a gas lift / pump pumping producer and load the preset lifting process parameters;

[0074] 4. If the decision is "EMERGENCY_INTERVENTION", start the emergency strong drainage mode, greatly increase the drainage intensity, and inhibit the intensification of water invasion;

[0075] 5. If the decision is "MAINTAIN_CURRENT", maintain the current flowing pressure and production regime, and continuously monitor the changes in GFI and R.

[0076] Embodiment

[0077] Taking the gas well B in an active edge water and gas reservoir as the research object, applying the method of the present invention for flowing pressure optimization and drainage timing determination to verify the effectiveness of the method. The specific results are as follows:

[0078] 1. Historical fitting and warning effect: Replay and simulate the historical production data of gas well B using the method of the present invention. The system identifies that GFI reaches the peak in January 2020 and then starts to continuously decline. At this time, the system immediately issues a drainage warning; while the traditional method based on the determination method of qactual < qcrit identifies the occurrence of liquid accumulation and issues a signal in April 2k20. The warning time of the method of the present invention is 3 months earlier than that of the traditional method, achieving preventive intervention in drainage and production;

[0079] 2. Prediction optimization and plan formulation: In the future production prediction of gas well B, set the system target as "avoid entering the water flooding intervention area". The simulator automatically generates a flowing pressure control and drainage intervention plan through the method of the present invention: Gradually reduce the bottom-hole flowing pressure to 20 MPa by the end of 2023 to increase the GFI value and enhance the near-well gas phase flow efficiency; when the prediction shows that GFI will start to decline in March 2024 and the R value will drop to 1.1, the system automatically instructs to introduce the foam drainage process in February 2024 in advance to achieve forward-looking intervention in drainage and production;

[0080] 3. Implementation Results: Compared with the traditional "draining only after liquid is found" production strategy, the production plan guided by the method of this invention increases the predicted cumulative gas production of gas well B by 12%. At the same time, due to the preventive drainage in advance, the high-intensity drainage after liquid accumulation is avoided, and the drainage energy consumption is reduced by 25%, realizing efficient and energy-saving production of gas wells.

Claims

1. A method for determining the optimal flowing pressure and drainage timing of gas wells based on reservoir numerical simulation, characterized in that, Includes the following steps: a. At each time step after the convergence of each nonlinear iteration in the reservoir numerical simulation, extract the well grid parameters, production dynamics data, and fluid PVT properties of the gas well; b. Based on the extracted parameters, calculate the gas flow capacity index GFI, which characterizes the near-wellbore gas flow efficiency, and simultaneously calculate the critical liquid carrying capacity q. crit And the ratio of actual gas production to critical liquid carrying flow rate, i.e., liquid carrying coefficient R; c. Analyze the dynamic trend of GFI, identify the peak point of GFI, and determine the optimal flowing pressure P of the gas well corresponding to the peak point. opt And calculate the cumulative relative decrease Δ of GFI since its peak; d. Based on the changing trend of GFI, the relative relationship between the cumulative relative decrease Δ and the liquid carrying coefficient R, the decision instructions for gas well flow pressure control or artificial lift drainage intervention are output through the dual-signal collaborative decision matrix. e. Based on the decision instructions, the production constraints of the gas well are automatically adjusted in subsequent numerical simulation time steps, or an artificial lift model is triggered to form a closed-loop control.

2. The method according to claim 1, characterized in that, The formula for calculating the gas phase flowability index (GFI) in step ② is as follows: In the formula, k is the absolute permeability of the well grid, in μm²; k rg μ represents the relative gas-phase permeability of the well grid, dimensionless. g Here, represents the gas viscosity under bottom-hole conditions, in mPa·s; ρ represents the pressure gradient magnitude at the well grid, in MPa / m. g , ρ w ρ represents the gas and liquid densities under bottom-hole conditions, in kg / m³; WOR represents the water-to-gas ratio, dimensionless; α represents an empirical index (usually taken as 0.5~1.0), dimensionless.

3. The method according to claim 2, characterized in that, The k rg The water saturation S of the current well grid w Obtained by looking up a table; the μ g ρ g ρ w The PVT model is based on the current bottom hole pressure P. wf The WOR is calculated based on the temperature T; the WOR is derived from the actual gas production q of the gas well. g and water production q w The α value is calculated; it is determined by fitting historical production data from the gas field.

4. The method according to claim 1, characterized in that, The method for identifying the GFI peak point in step ③ is as follows: compare the GFI value GFI(t) at the current time step with all GFI values ​​in the historical sequence. If GFI(t) is the historical maximum value, then mark the current time step as the GFI peak time, and the bottom hole flowing pressure corresponding to this time step is the optimal flowing pressure P. opt The formula for calculating the cumulative relative decrease Δ of GFI after its peak is as follows: Δ=(GFI max −GFI(t)) / GFI max Among them, GFI max This is the peak value of GFI.

5. The method according to claim 1, characterized in that, The core logic of the dual-signal collaborative decision matrix described in step 4) is as follows: a. When GFI is on an upward trend and R>1.2, output the command to continue optimizing and reducing the bottom hole flowing pressure; b. When GFI first drops from its peak and the relative decrease Δ is between 5% and 15% and R > 1.0, output the early warning for drainage and prepare for manual lifting; c. When GFI continues to decrease and R rapidly approaches 1.0, output the instruction to immediately activate manual lifting and implement drainage. d. When GFI drops significantly and R < 0.8, output an instruction to take emergency forced drainage measures; e. In other cases, output instructions to maintain the current production system and continue monitoring.

6. The method according to claim 1, characterized in that, The critical liquid carrying flow rate q mentioned in step ② crit The liquid carrying coefficient R is obtained by integrating the Turner model or the Li Min model; the formula for calculating the liquid carrying coefficient R is: R=q actual / q crit , where q actual This represents the actual gas production of the gas well.

7. The method according to claim 1, characterized in that, The adjustment of production constraints for the gas well in step ⑤ includes: changing fixed production to fixed flow pressure production and modifying the target value of bottom hole flow pressure; the artificial lift model includes a gas lift model and a pumping model, and after triggering, the preset lift process parameters are loaded.

8. A non-volatile computer-readable storage medium storing a computer program, characterized in that, When the computer program is executed by the processor, it implements the method for determining the optimal flowing pressure and drainage timing of a gas well based on reservoir numerical simulation as described in any one of claims 1 to 7.

9. A smart decision-making system for gas well drainage based on reservoir numerical simulation, characterized in that, The system includes a processor and a memory, the memory storing a computer program. When the processor executes the computer program, it implements the method as described in any one of claims 1 to 7. The system is integrated into a reservoir numerical simulator and includes a data acquisition module, a GFI calculation engine, a critical liquid-carrying analyzer, an intelligent decision engine, and a control command executor. Each module works together to determine the optimal flowing pressure of the gas well and the timing of drainage, and outputs decision commands to the numerical simulator or the field production control system.

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