Integrated supercharging parameter optimization method

Abstract

An integrated pressurization parameter optimization method relates to the technical field of oil and gas development, and comprises the following steps: collecting geological and seismic data of a target well area, establishing a three-dimensional geological attribute model containing a mechanical model, performing hydraulic fracture propagation simulation on the three-dimensional geological attribute model, and respectively establishing a gas reservoir model and a shaft flow model. Combining to obtain a gas reservoir-shaft-ground integrated model, carrying out production history fitting on a target well on the basis of the integrated model, judging a matching condition, applying the integrated model to pressurization and yield increase simulation, and sequentially carrying out pressurization opportunity and pressurization system optimization on the target well with a poor result to obtain an optimized EUR prediction curve; according to the method, the gas reservoir-shaft-ground integrated model is established to perform production dynamic analysis on the gas reservoir, geologic features, dynamic changes and opportunity requirements in the production process are comprehensively considered, the pressurization opportunity and the pressurization system are preferably selected for different gas wells, and therefore the gas well yield and economic benefits are improved to the maximum extent.

Description

Technical Field

[0001] The present invention relates to the technical field of oil and gas development, and specifically to an integrated pressurization parameter optimization method. Background Art

[0002] When the wellhead pressure of a gas well approaches the pressure of the platform gathering system, due to the continuous reduction of the production pressure difference, the daily gas production will show a continuous decreasing trend. Once the daily gas production drops below the economic limit production of the gas well, the conventional production method will no longer be economically viable, and it is necessary to realize the exploitation of the remaining reserves through pressurized production. The pressurization process is to establish a wellhead node pressure difference with a booster, reduce the bottom-hole flowing pressure while increasing the export pressure, and then increase the production pressure difference of the reservoir. However, engineering practice shows that the pressurization parameters have a decisive impact on the development effect, and the matching degree of the equipment working conditions and the adjustment rhythm of the pressure gradient directly determine the desorption efficiency of the reservoir and the production capacity maintenance ability. In this context, optimizing the pressurization timing and system has become a key link to improve the economic benefits of gas well development.

[0003] At present, there are many deficiencies in determining the pressurization parameters of gas wells. The decision-making of the pressurization timing of gas wells mainly relies on static empirical thresholds or the extrapolation method of single-well production curves, which fails to fully consider the multi-scale seepage mechanism of gas reservoirs and the dynamic evolution law of reservoir physical properties. At the same time, the optimization of the pressurization system mostly adopts a fixed pressurization ratio or a staged jump adjustment, lacking a systematic consideration of the heterogeneity of the fracture network and the well interference effect. In addition, although a large amount of downhole pressure monitoring, microseismic monitoring and production dynamic data have been accumulated, the existing methods have not established an adaptive model driven by multi-source data, resulting in decision-making lagging behind the dynamic changes of the reservoir. These problems indicate that there are obvious deficiencies in the existing technology in optimizing the pressurization timing and system of gas wells, and it is difficult to meet the actual needs of efficient gas reservoir development. Summary of the Invention

[0004] In view of this, the present invention provides an integrated pressurization parameter optimization method. By coupling the gas reservoir, the gas wellbore and the fracture network, an integrated gas reservoir - wellbore - surface model is established, and the pressurization timing and system are optimized through the production dynamic analysis of the gas reservoir. This method can comprehensively consider the geological characteristics, dynamic changes of the gas reservoir and the timing requirements during the production process, and provide a scientific basis for the efficient development of gas wells.

[0005] The technical solution of the present invention is an integrated pressurization parameter optimization method, including the following steps:

[0006] Step S1: Collect the geological and seismic data of the target well area and establish a three-dimensional geological attribute model including a mechanical model;

[0007] Step S2: Perform hydraulic fracture propagation simulation on the three-dimensional geological property model, and on this basis, establish a gas reservoir model and a vertical tubular flow wellbore flow model for the target well respectively, and combine them to obtain a gas reservoir - wellbore - surface integrated model;

[0008] Step S3: Perform production history matching on the target well based on the integrated model, compare the matched result with the actual production result, if the two match, execute the subsequent steps, if they do not match, adjust the relevant parameters until they match;

[0009] Step S4: Use the integrated model for the simulation of pressure increase and production enhancement of the target well, and classify the target well according to the pressure increase and production enhancement effect obtained from the simulation;

[0010] Step S5: Optimize the pressure increase timing and pressure increase regime for the target wells with relatively poor results in the classification in turn to obtain the optimized EUR prediction curve of the target well.

[0011] The technical effect of the present invention is:

[0012] The present invention effectively overcomes the limitations of the existing booster parameter optimization, comprehensively considers the comprehensive influence of the gas reservoir, wellbore flow, and ground on natural gas production, establishes a gas reservoir - wellbore - surface integrated model to conduct production dynamic analysis on the gas reservoir, and combines the geological characteristics, dynamic changes of the gas reservoir, and timing requirements during the production process. For different gas well production situations, the pressure increase timing and pressure increase regime are optimized, so as to maximize the gas well production and economic benefits. BRIEF DESCRIPTION OF THE DRAWINGS

[0013] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following will briefly introduce the drawings required for the embodiments.

[0014] Figure 1 It is the overall flow chart of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0015] The following will further elaborate on the present invention in combination with the embodiments and the drawings.

[0016] To make the purpose, technical solutions, and advantages of the embodiments of the present invention clearer, the following will clearly and completely describe the technical solutions in the embodiments of the present invention in combination with the drawings in the embodiments of the present invention. The described embodiments are some but not all of the embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts fall within the scope of protection of the present invention.

[0017] See Figure 1 , an integrated pressure increase parameter optimization method, including the following steps:

[0018] Step S1: Collect the geological and seismic data of the target well area and establish a three-dimensional geological property model including a mechanical model.

[0019] Both the mechanical model and the three-dimensional geological property model are obtained based on the results of well-seismic analysis and interpretation and constructed using Petrel software. The three-dimensional geological property model is established using a facies-controlled property modeling method. The mechanical model can be finely modeled for the three-dimensional in-situ stress field using the finite element method or can be interpolated and modeled using geological mechanical properties.

[0020] The modeling operation can specifically include the following operation steps:

[0021] Step S1-1, data collection and sorting: Obtain formation layering data, planar structure maps, geological physical property parameters, mechanical parameters of the target well area, well trajectory data, completion parameters, log interpretation, fracturing construction records, and production dynamic data of the target well to provide a complete data basis for subsequent modeling;

[0022] The geological physical property parameters of the target well area include porosity, permeability, water saturation, formation pressure, rock density, and acoustic time difference;

[0023] The mechanical parameters of the target well area include Young's modulus, Poisson's ratio, minimum horizontal principal stress, maximum horizontal principal stress, minimum horizontal principal stress direction, vertical stress, and fracture pressure;

[0024] The well trajectory data of the target well include wellhead coordinates, well deviation angle, azimuth, well depth, and vertical depth;

[0025] The completion parameters of the target well include casing inner diameter and tubing inner diameter;

[0026] The log interpretation of the target well includes natural gamma, resistivity, porosity, and permeability;

[0027] The fracturing construction records of the target well include perforation positions, fracturing fluid parameters, and proppant properties;

[0028] The production dynamic data of the target well include production time, casing pressure, tubing pressure, daily gas production, daily water production, cumulative gas production, cumulative water production, and export pressure.

[0029] Step S1-2, well-seismic analysis and interpretation: Import the wellhead coordinates and well trajectory and determine the work area scope; Use the log data of the pilot well or three-dimensional seismic data to carry out basic data interpretation work such as formation series division, structural interpretation, and geological mechanical parameter interpretation, and establish a unified geological mechanical interpretation framework.

[0030] Step S1-3, Construction Model Establishment: The construction model consists of a fault model and a horizon model. An initial fault model is generated using the digitized fault lines on the planar structure map and corrected with the breakpoint data from wells to form the final fault model; a preliminary horizon model is established using the formation stratification data and adjusted based on the seismic inversion results and the single-well encounter situation; the grid step size of the construction model is set to 25m×25m, and the vertical grid step size is set to 0.5m for division to ensure the construction representation accuracy.

[0031] Step S1-4, 3D Geological Attribute Modeling: The well logging data of reservoir parameters is discretized using Petrel software to establish a discretized data set; by analyzing the discretized data and combining well logging, core analysis, and dynamic test data, the distribution characteristics of reservoir parameters are determined and a spatial distribution model is established; a spherical model is used to construct the variogram, and the distribution of inter-well attribute parameters is predicted using interpolation or sequential Gaussian methods to establish a facies-controlled attribute model for parameters such as permeability and porosity.

[0032] Step S1-5, Mechanical Model Construction: The basic framework of the 3D geological model is constructed through grid division and rock mechanics parameter assignment, and then the boundary conditions are set and the finite element analysis module is called to calculate the distribution of the in-situ stress field; based on well logging and seismic data, spatial modeling of mechanical properties is carried out using methods such as Kriging interpolation and corrected in combination with geological structure characteristics to finally generate a complete mechanical model.

[0033] Step S2: Conduct hydraulic fracture propagation simulation on the 3D geological attribute model, and on this basis, establish a gas reservoir model and a vertical pipe flow wellbore flow model for the target well respectively, and combine them to obtain a gas reservoir - wellbore - surface integrated model.

[0034] The main method of hydraulic fracture propagation simulation is to simulate the hydraulic fracture propagation considering the stress interference between segments based on the geological model and actual engineering parameters, and verify the fitting results using microseismic monitoring data and pump injection construction curves, mainly including the following steps:

[0035] Step S2-1.1, Input of Perforation Parameters by Section: According to the fracturing construction report, input the perforation position, depth, section number, and cluster information, where the depth data is in meters and accurate to two decimal places to ensure consistency with geological data and fracturing design requirements, and ensure the accurate matching of perforation parameters with the vertical stratification and mechanical properties of the geological model.

[0036] Step S2-1.2, Design of Fracturing Pump Injection Program: The selected fracturing fluid and proppant are input into the established model, and the construction curve is divided according to the fracturing second point data to realize the digital mapping of the pump injection process;

[0037] The fracturing second-point data refers to the construction pressure, the displacement of the proppant-carrying fluid, and the proppant concentration at key time nodes during the fracturing process.

[0038] The division of the construction curve includes the preflush stage, the proppant-carrying fluid stage, and the displacement fluid stage.

[0039] Preferably, according to the fracturing design report, suitable fracturing fluids and proppants are selected, and the key parameters of the material properties are set.

[0040] The parameters of the fracturing fluid include rheological properties, filtration rate, and flow module.

[0041] The parameters of the proppant include particle size, density, proppant permeability, and Young's modulus.

[0042] Step S2-1.3, fracture propagation simulation: Using the UFM unstructured fracture propagation model, carry out the simulation of the fracture network expansion.

[0043] Preferably, during the simulation, the stress interference between segments and clusters should be considered, and the fracture height and cross-layer situation should be adjusted according to the actual in-situ stress conditions to simulate the hydraulic fracture characteristics during segmented and clustered fracturing.

[0044] The adjustment of the fracture height refers to, based on the collected in-situ stress data, combined with the rock mechanical properties (rock compressive strength, tensile strength, and elastic modulus), calculating the expansion height of the fracture under different in-situ stress conditions through numerical simulation methods, and adjusting the fracture height according to the simulation results until it conforms to the actual geological situation.

[0045] The adjustment of the cross-layer situation refers to, combined with the geological stratification characteristics of the target well (the thickness of the rock formation, lithological changes, and permeability differences), adjusting the cross-layer path and cross-layer depth of the fracture by simulating the expansion behavior of the fracture in different rock formations.

[0046] Using the UFM unstructured fracture propagation model, comprehensively considering the competitive propagation mechanism between segments and clusters, constraining the vertical fracture height by the rock tensile strength and controlling the cross-layer behavior by the fracture toughness at the lithological interface, simulate the complex fracture network morphology of synchronous expansion of multiple fractures.

[0047] Step S2-1.4, stress interference quantification: Based on the simulation results, calculate the fracture propagation situation of each fracturing stage of each well in the order of fracturing construction.

[0048] Preferably, when calculating, the stress shadow effects between wells, between segments, and between clusters should be considered.

[0049] Step S2-1.5, Data Fitting and Verification: Fit the fracture propagation situation obtained from the simulation calculation with the fracturing construction pumping program, compare the actual construction pressure and the simulated calculation pressure, verify the accuracy of the simulation results, ensure that the simulation results truly reflect the fracturing transformation effect, control the fracture propagation prediction error within the engineering allowable range, and provide a highly reliable decision-making basis for fracturing parameter optimization, section-cluster differential design, and development plan adjustment.

[0050] The method for establishing the gas reservoir model mainly uses the Petrel RE module to encrypt the reservoir near the target well, couple the artificial fracture network, and generate an unstructured grid. Locally encrypt the grid around the fracture, and perform unstructured grid division according to the geological attribute model and the fracturing propagation simulation results. Embed different relative permeability curves into it, perform zoning treatment on the fractured and unfractured areas, and set the stress sensitivity curves for the fracture area and the matrix area, which mainly includes the following steps:

[0051] Step S2-2.1, Attribute Assignment: Use the Petrel RE module to encrypt the reservoir near the target well, couple the artificial fracture network, generate an unstructured grid, and assign rock mechanics properties (elastic modulus, Poisson's ratio, compressive strength), reservoir fluid properties (porosity, permeability, fluid viscosity), fracture properties (fracture conductivity, fracture length, fracture height), proppant properties (proppant particle size, density, strength), and in-situ stress properties (maximum horizontal principal stress, minimum horizontal principal stress) to the grid cells to ensure accurate simulation.

[0052] Step S2-2.2, Grid Processing: Locally encrypt the grid around the fracture using a quadrilateral unstructured grid, complete the unstructured grid division, and establish a numerical simulation grid model;

[0053] Preferably, perform equivalent treatment on the fracture, and set the equivalent fracture grid size to 1m.

[0054] Step S2-2.3, Reservoir Zoning Setting: According to the porosity, permeability characteristics, and relative permeability (phase permeability) relationships corresponding to different reservoir types, combined with the significant differences in phase permeability between the fractured and unfractured areas, use the Petrel software to perform zoning settings on the fractured and unfractured areas respectively;

[0055] Preferably, for the unfractured area, since it is difficult to obtain the matrix phase permeability through experimental or simulation means, usually an arbitrary set of phase permeability curves is given first in the numerical simulation, and through the history matching process, the matrix phase permeability curve is adjusted to match the actual production data.

[0056] Preferably, for the fracture transformation area, according to the characteristics of the fracture network (fracture conductivity, fracture width), the fracture relative permeability curve is input. The fracture relative permeability curve can be obtained from experimental data or adjusted according to the simulation results of the fracture network.

[0057] Preferably, the fracture relative permeability curve usually has a relatively high permeability, and the influence of the existence of fractures on fluid flow should be considered.

[0058] Considering the influence of fractures on fluid flow requires characterizing the fracture network, calculating the conductivity, coupling between fractures and matrix, changes in flow paths, dynamic effects of pressure and saturation, and adjusting the relative permeability relationship.

[0059] Step S2-2.4, stress sensitivity curve setting: Through on-site monitoring data (pressure changes during the fracturing construction process, production dynamic data), combined with numerical simulation inversion technology, obtain the preliminary stress sensitivity curves of the fracture area and the matrix area. Apply the preliminary stress sensitivity curves to the established model for numerical simulation. Compare the simulation results with the actual production data, including production rate and pressure changes. Optimize the stress sensitivity curves by repeatedly adjusting the above parameters and comparing with the actual production data.

[0060] The parameters for adjusting the stress sensitivity curve include fracture conductivity, fracture closure pressure, fracture elastic modulus, matrix permeability, pore elastic modulus, and Poisson's ratio.

[0061] The vertical flow performance (VFP) wellbore flow model is mainly obtained by fitting the relationship between pressure and gas production and water production, and mainly includes the following steps:

[0062] Step S2-3.1, data input and model initialization: Load wellbore structure data (casing / tubing diameter), import production history data (gas production, water production, wellhead pressure, bottom-hole flowing pressure), input PVT data (densities, viscosities, dissolved gas-oil ratios of oil, gas, and water), and set the wellbore temperature gradient or temperature profile.

[0063] Preferably, input the possible ranges of water production and gas production, and set the discretization step size.

[0064] Preferably, specify the wellhead pressure or bottom-hole flowing pressure as a historical condition (usually, in gas reservoir exploitation, the wellhead pressure is often used as the input).

[0065] Step S2-3.2, initial VFP model selection: According to the fluid type (gas-water two-phase, oil-gas-water three-phase) and flow pattern, select the built-in multi-phase flow model. If a custom model is needed, it can be extended through scripts (Python) or third-party plugins.

[0066] Step S2-3.3, Pressure gradient solution and VFP table construction: Call the built-in solver in Petrel to calculate the pressure gradient under different flow rate combinations according to the segmented wellbore (integrating from the wellhead to the bottomhole or vice versa). The output VFP table format is usually a three-dimensional table (water production, gas production, wellhead / bottomhole pressure).

[0067] Preferably, after plotting the VFP curve in Petrel, it is necessary to check whether the pressure-flow relationship conforms to physical laws. If not, recheck the above steps until the requirements are met.

[0068] Step S2-3.4, Model verification and optimization: Verify the results of the initially generated VFP table, and further adjust the model parameters or optimize the model structure until the fitting error between the model prediction results and the actual data is within an acceptable range.

[0069] The adjusted model parameters include fluid density, fluid viscosity, fluid phase fraction, friction coefficient, gas production, water production, relative permeability, and temperature.

[0070] Preferably, the acceptable range of the fitting error between the prediction results and the actual data is about 4.5%. If the prediction error exceeds the acceptable range, key parameters such as density, viscosity, phase fraction, and friction factor will be mainly adjusted, and the model structure will be optimized until a satisfactory fitting effect is obtained. This iterative optimization process ensures the high precision and reliability of the model.

[0071] Step S3: Perform production history matching on the target well based on the integrated model, compare the results after matching with the actual production results, and if the two match, perform the subsequent steps; if they do not match, adjust the relevant parameters until they match.

[0072] Based on the integrated model obtained by coupling the corrected wellbore and the established gas reservoir model above, use the Intersect unstructured network gas reservoir numerical simulation module to perform production history matching on the target well to determine the historical gas production to fit the wellhead pressure and water production. Compare the results after matching with the actual well production data. If the situation basically matches, it proves that the established model conforms to the actual situation of the research block. The main steps of the production history matching are as follows:

[0073] Step S3-1, Fracture stimulation area range and permeability adjustment: Calibrate the range of the fracture stimulation area with the actual fracturing design, and adjust the permeability of the fracture area to be between 10 times and 1000 times the matrix permeability. Simulate and calculate the wellhead pressure to make its change trend consistent with the actual wellhead pressure.

[0074] Step S3-2, Adjustment of water saturation in the fracture area: Adjust the water saturation in the fracture area within the range of 10% to 50% to make the pressure change trend in the early liquid drainage stage consistent;

[0075] Step S3-3, Adjustment of relative permeability curves: Adjust the relative permeability to make the early water production and gas production consistent with the production dynamics. Generally, the adjustment range of the relative permeability curve in the fracture area is 2 to 10 times that of the matrix relative permeability curve, and the adjustment range of the relative permeability curve in the matrix area is ±20% of the production data;

[0076] Step S3-4, Adjustment of stress sensitivity curves: Adjust the stress sensitivity curves based on the deviation degree between the later wellhead pressure and the actual curve. The adjustment range of the stress sensitivity curve in the fracture area is ±50% of the initial conductivity, and the adjustment range of the stress sensitivity curve in the matrix area is ±30% of the initial permeability;

[0077] Step S3-5, Comprehensive verification and optimization: Conduct a comprehensive verification of the adjusted model, and compare the fitting results of the wellhead pressure, gas production, and water production with the actual production data. If the deviation between the fitting result and the actual data exceeds ±10%, repeat steps S3-1 to S3-4 to further optimize the model parameters until the data deviation meets the range. Through multiple iterative adjustments, ensure that the model can comprehensively reflect the actual situation of the research block.

[0078] Step S4: Use the integrated model for the simulation of pressure boosting and production increase of the target well, and classify the target well according to the simulated pressure boosting and production increase effect.

[0079] Based on the gas reservoir - wellbore - surface integrated model after history matching of production, conduct a simulation of the pressure boosting effect on multiple target wells in the target block. Set the boundary condition for the start of the booster as the daily gas production drops to a certain threshold value. According to the production increase effect after pressure boosting, classify the target wells into four categories, and set the boundaries of the production increase effect as 20%, 10%, and 5% respectively. The target wells with a production increase effect higher than 20% are defined as having a good production increase effect category, the target wells with a production increase effect in the range of 10% - 20% are defined as having a relatively good production increase effect category, the target wells with a production increase effect in the range of 5% - 10% are defined as having an average production increase effect category, and the target wells with a production increase effect lower than 5% are defined as having a poor production increase effect category. Determine the production increase effect classification of each well through the simulation results.

[0080] Step S5: Optimize the pressure boosting timing and pressure boosting system for the target wells with poor results in the classification in turn to obtain the optimized EUR prediction curve of the target wells.

[0081] Based on the integrated gas reservoir - wellbore - surface model after production history matching, with the boosting timing and boosting regime set as the optimization objectives, select the relatively poor target wells with a boosting production increase effect lower than 5% as the optimization objects, and conduct numerical simulation optimization. Among them, the main steps for optimizing the boosting timing are as follows:

[0082] Step S5-1.1, prediction of gas well production dynamics and EUR curve: Based on the integrated gas reservoir - wellbore - surface model after production history matching, input the actual production data of the target well, simulate the gas production volume and wellhead pressure at each time node during the future production process, and accumulate the gas production volume through time integration to generate EUR prediction curves under different production cycles;

[0083] The production data includes gas production volume, water production volume, casing pressure, and tubing head pressure;

[0084] Step S5-1.2, optimization of boosting timing: Based on the EUR prediction in Step S5-1.1, set different intervention time points of the booster and the corresponding wellhead pressure control thresholds, and simulate the instantaneous flow rate of the gas well under different boosting schemes through the model to generate multiple differentiated EUR curves. The wellhead pressure control threshold refers to the target wellhead pressure value set by the booster, which is used to control the start and stop of the boosting operation.

[0085] Since the wellhead pressure changes during the production process, and in order to ensure the normal transmission pressure, the tubing head pressure after the booster intervenes and regulates should not be lower than the surface gathering pressure of the current gas well platform. Therefore, a certain number of time nodes corresponding to the pressure values of the wellhead pressure control thresholds not lower than the surface gathering pressure during the production process can be randomly selected for the booster intervention and regulation test. With the maximization of the cumulative gas production volume as the optimization objective, compare the cumulative gas volume values at the end points of each intervention and regulation scheme, and then determine the boosting pressure threshold and its corresponding time node that can obtain the maximum cumulative gas production volume as the optimal boosting timing of the integrated model;

[0086] The optimization of the boosting regime is carried out by setting different boosting speeds on the basis of determining the optimal boosting timing. The main steps are as follows:

[0087] Step S5-2.1, obtain the booster power and maximum boosting range: Obtain the power of the booster used for the gas well, and determine the maximum boosting range of the booster considering the platform gathering pressure and the performance of the booster under different working conditions;

[0088] Step S5-2.2, prediction of production dynamics of the boosting regime: On the basis of determining the optimal boosting timing through Step S5-2.1, change the boosting speed of the booster until the maximum boosting range of the booster is reached and then stop. The boosting strategies include four boosting strategies: average gradient boosting, accelerated gradient boosting, decelerated gradient boosting, and single - stage boosting.

[0089] The average stepwise pressurization is carried out with the optimal pressurization timing of the supercharger as the initial condition. When the actual production is lower than the economic limit of the well area, the efficiency of the supercharger is gradually and evenly increased. For example, when the power of the supercharger is sufficient and the gas well is in production, the wellhead pressure drops from the high pressure at the start of production to 6 MPa. The average stepwise pressurization can be carried out in the way of 6 MPa - 5 MPa - 4 MPa, with each change being an average change. Similarly, it can also be 5 MPa - 4.5 MPa - 4 MPa, and each change is also an average change.

[0090] After each adjustment, the daily gas production will increase accordingly. If the minimum production requirement of the well area still cannot be met after the adjustment, the pressurization efficiency will continue to increase in the same amplitude until the maximum working range of the supercharger is reached.

[0091] The accelerating stepwise pressurization is based on the optimal pressurization timing, and the pressurization efficiency of the supercharger is increased slowly first and then quickly. In the initial stage, the increase amplitude of the pressurization efficiency is small, and the adjustment speed is gradually accelerated with the change of production demand. When the minimum daily production acceptable to the well area is reached again after each adjustment, the pressurization efficiency of the supercharger is changed.

[0092] The decelerating stepwise pressurization is based on the optimal pressurization timing, and the efficiency of the supercharger is adjusted from fast to slow. The pressurization efficiency is greatly increased in the initial stage to increase production quickly, and then the adjustment amplitude is gradually reduced. When the minimum daily production acceptable to the well area is reached again after each adjustment, the pressurization efficiency of the supercharger is changed until the maximum pressurization range is reached.

[0093] The range of accelerating or decelerating stepwise pressurization is the same as that of average stepwise pressurization, and the difference lies in the pressurization amplitude. For example, the supercharger can be pumped up to 4 MPa at most, the gas well is in production, and the wellhead pressure drops from the high pressure at the start of production to 6 MPa. The accelerating stepwise pressurization is 6 MPa - 5.8 MPa - 5 MPa - 4 MPa, and the pressure changes are 0.2 MPa, 0.8 MPa, 1 MPa respectively, and each change is larger than the previous one. The decelerating stepwise pressurization is the same as the accelerating stepwise pressurization. The supercharger can be pumped up to 4 MPa at most, the gas well is in production, and the wellhead pressure drops from the high pressure at the start of production to 6 MPa. The decelerating stepwise pressurization is 6 MPa - 5 MPa - 4.2 MPa - 4 MPa, and the pressure changes are 1 MPa, 0.8 MPa, 0.2 MPa respectively, and each change is smaller than the previous one.

[0094] The one-time pressurization means that when the daily production is insufficient to meet the production requirement of the well area at the power of the optimal pressurization timing, the power of the supercharger is immediately increased to the maximum efficiency. When the minimum daily production limit is reached again when adjusted to the maximum pressurization range, the production stops.

[0095] Step S5-2.3, Optimization selection of the boosting regime: On the gas reservoir - wellbore - surface integrated model with the optimal boosting timing already determined, set and input into the model the strategies of five different boosting regimes, namely not installing a booster, average gradient boosting, accelerating gradient boosting, decelerating gradient boosting, and single-stage boosting, to obtain five different EUR curves. By comparing the five different EUR curves, determine the optimal boosting regime after optimization.

[0096] Preferably, when selecting the optimal boosting regime, if the EUR value increase of a certain strategy is significantly higher than that of other strategies (the deviation compared with other strategies > 1%), directly select this strategy. If the EUR value increases of multiple strategies are close (the deviation compared with other strategies ≤ 1%), preferentially select the plan with low equipment energy consumption.

[0097] Example:

[0098] According to the actual data of an oilfield, use the steps described above to construct an integrated model, and then simulate the boosting effect of 11 wells in the target block. Set the daily gas production to drop to 3800 m 3 As the boundary condition for starting the booster, according to the production increase effect after boosting, divide the target wells into four categories, and set the boundaries of the production increase effect to 20%, 10%, and 5% respectively. The target wells with a production increase effect higher than 20% are defined as having a good production increase effect. The target wells with a production increase effect in the range of 10% - 20% are defined as having a relatively good production increase effect. The target wells with a production increase effect in the range of 5% - 10% are defined as having an average production increase effect. The target wells with a production increase effect lower than 5% are defined as having a poor production increase effect. The comparison of the production increase effects of each well before and after boosting is shown in Table 1:

[0099] Table 1 Comparison of production increase effects before and after boosting

[0100]

[0101]

[0102] According to the comparison of the production increase effects before and after boosting in Table 1, select the poor W5 well with a production increase effect lower than 5% as the target well, and use the integrated model for numerical simulation to optimize the boosting timing.

[0103] Considering the actual situation on site, the wellhead pressure cannot be lower than 4 MPa (surface transmission pressure 4 MPa). Select to install a booster when the wellhead pressure is 10 MPa, 8 MPa, 6 MPa, and 4 MPa respectively, and at the same time introduce the results without installing a booster for comparison to generate five different EUR curves. The simulation results of the EUR curves are shown in Table 2:

[0104] Table 2 EUR simulation results corresponding to different boosting timings

[0105]

[0106] Taking the maximization of cumulative gas production as the optimization goal, comparing the cumulative gas volume values at the end points of each scheme, and selecting the time node of the boost pressure threshold corresponding to the maximum cumulative gas production as the boosting timing, it is determined that the optimal boosting timing is 4 MPa.

[0107] The optimization of the boosting system is carried out by setting different boosting speeds on the basis of determining the optimal boosting timing. A DTY220 type booster (the maximum can be pumped to 0.5 MPa) is used, and 5 boosting systems are simulated:

[0108] 1) Average stepped boosting (4 MPa → 3.125 MPa → 2.25 MPa → 1.375 MPa → 0.5 MPa);

[0109] 2) Accelerated stepped boosting (4 MPa → 3.6 MPa → 3 MPa → 2.3 MPa → 1.5 MPa → 0.5 MPa);

[0110] 3) Decelerated stepped boosting (4 MPa → 2.5 MPa → 1.6 MPa → 1 MPa → 0.5 MPa);

[0111] 4) Single - stage boosting (4 MPa → 0.5 MPa);

[0112] 5) Without booster (normal production).

[0113] The specific simulation results are shown in Table 3:

[0114] Table 3 EUR simulation results corresponding to different boosting systems

[0115]

[0116]

[0117] As can be seen from Table 3, on the integrated gas reservoir - wellbore - surface model with the optimal boosting timing determined, the strategies of five different boosting systems, namely without booster, average stepped boosting, accelerated stepped boosting, decelerated stepped boosting, and single - stage boosting, are respectively input into the model to obtain 5 different EUR curves. By comparing the 5 different EUR curves and examining the cumulative gas production growth rate after the measures, it can be seen that the increase rate of the EUR value under the accelerated stepped boosting strategy can exceed that of other strategies by more than 1%. Therefore, it is determined that the best optimized boosting system is the accelerated stepped boosting.

[0118] It can be seen that the pressurization optimization method of the present invention has significant advantages compared with the prior art. In the prior art, the decision-making of pressurization timing mostly relies on static empirical thresholds or the extrapolation method of single-well production curves, lacking the consideration of the multi-scale seepage mechanism of gas reservoirs and the dynamic changes of reservoir physical properties. However, the present invention comprehensively couples all production links of gas wells by establishing an integrated model of gas reservoir - wellbore - surface, and can comprehensively analyze the geological characteristics, dynamic changes of gas reservoirs and the timing requirements during the production process.

[0119] In terms of predicting the pressurization effect, the present invention not only considers the static geological parameters of the gas reservoir, but also combines dynamic production data and wellbore flow characteristics, making the prediction of the pressurization effect more accurate and reliable. In addition, in the prior art, the optimization of pressurization systems mostly adopts fixed pressurization ratios or stage-by-stage jump adjustments, lacking systematicness and flexibility. The present invention determines the optimal pressurization system by simulating the production dynamics under different pressurization systems and comparing the EUR curves under multiple systems, thus realizing the refined optimization of pressurization parameters.

[0120] In summary, the present invention provides an integrated pressurization parameter optimization method. After establishing and validating the integrated model, the pressurization timing and pressurization system are selected as parameters, and the parameters of target wells with poor pressurization effects are optimized through sub-steps such as predicting the production dynamics of gas wells, predicting the production dynamics of different pressurization systems and optimizing the selection, so as to increase the gas well production and economic benefits, thereby guiding the gas well production to achieve the optimal balance between economic benefits and production effects.

[0121] The above is only the preferred specific implementation manner of the present invention, but the protection scope of the present invention is not limited thereto. Any changes or substitutions that can be easily thought of by those skilled in the art within the technical scope disclosed in the embodiments of the present invention should be covered by the protection scope of the present invention. Therefore, the protection scope of the present invention should be subject to the protection scope of the claims.

Claims

1. An integrated supercharging parameter optimization method, characterized in that: The following steps are involved: Step S1: Collect geological and seismic data of the target well area and establish a three-dimensional geological attribute model including a mechanical model; Step S2: Conduct hydraulic fracture extension simulation on the three-dimensional geological attribute model, and on this basis, establish a gas reservoir model and a vertical pipe flow wellbore flow model of the target well, and combine them to obtain a gas reservoir-wellbore-ground integrated model; Step S3: Perform production history matching on the target well based on the integrated model, and compare the fitting results with the actual production results. If the two are consistent, the subsequent steps are executed; if the two are inconsistent, the relevant parameters are adjusted until they are consistent; Step S4: using the integrated model for pressure-increasing and production-increasing simulation of target wells, and classifying the target wells according to the pressure-increasing and production-increasing effects obtained by simulation; Step S5: Optimize the pressurization timing and pressurization system for the target wells with poor classification results in turn to obtain an optimized EUR prediction curve for the target wells.

2. The integrated supercharging parameter optimization method according to claim 1, characterized in that: The geological data in step S1 include stratigraphic layering data, planar structural maps, geological physical parameters of the target well area, mechanical parameters, and well trajectory data of the target well, completion parameters, well logging interpretation, fracturing construction records, and production dynamic data; Among them, the geological physical parameters of the target well area include porosity, permeability, water saturation, formation pressure, rock density, and acoustic time difference; The mechanical parameters of the target well area include Young's modulus, Poisson's ratio, minimum horizontal principal stress, maximum horizontal principal stress, direction of minimum horizontal principal stress, vertical stress, and fracture pressure; The well trajectory data of the target well includes wellhead coordinates, well inclination, azimuth well depth, and vertical depth; The completion parameters of the target well include the inner diameter of casing and tubing; Well logging interpretation of target wells includes natural gamma, resistivity, porosity, and permeability; The target well fracturing operation records include perforation locations, fracturing fluid parameters, and proppant properties; The production dynamic data of the target well include production time, casing pressure, oil pressure, daily gas production, daily water production, cumulative gas production, cumulative water production, and external transmission pressure.

3. The integrated supercharging parameter optimization method according to claim 1, characterized in that: The method for constructing the mechanical model in step S1 includes fine modeling of the three-dimensional geostress field based on the finite element method and interpolation modeling based on geomechanical properties.

4. The integrated supercharging parameter optimization method according to claim 1, characterized in that: The method for simulating hydraulic fracture extension in step S2 comprises the following steps: Step 1: Enter segmented perforation parameters into the three-dimensional geological attribute model, wherein the segmented perforation parameters include entering perforation position, depth, segment number and cluster information; Step 2: Set up the fracturing pumping program in the 3D geological attribute model and divide the construction curve according to the fracturing second point data; Step 3: Substitute the non-structural crack propagation model, carry out the simulation of the hydraulic fracture network expansion, calculate the expansion and verify the results.

5. The integrated supercharging parameter optimization method according to claim 1, characterized in that: The method for establishing the gas reservoir model of the target well in step S2 comprises the following steps: Step 1: Densify the reservoir near the target well, couple the artificial fracture network, and generate an unstructured grid; Step 2: Perform local encryption on the mesh around the fracture and perform unstructured meshing based on the geological attribute model and fracturing extension simulation results; Step 3: Embed different phase permeability curves, partition the fracture reformed area and the unreformed area, and set the stress sensitivity curves of the fracture area and the matrix area.

6. The integrated supercharging parameter optimization method according to claim 1, characterized in that: The method for establishing the vertical pipe flow wellbore flow model of the target well in step S2 comprises the following steps: Input data and initialize the model, select the model category, solve the relationship between pressure gradient and flow combination, and draw the vertical pipe flow model curve.

7. The integrated supercharging parameter optimization method according to claim 1, characterized in that: The production history matching method in step S3 comprises the following steps: Step 1: Calibrate the scope of the fracture stimulation area with the actual fracturing design, adjust the permeability of the fracture area to be between 10 and 1000 times the matrix permeability, and simulate and calculate the wellhead pressure to make it consistent with the change trend of the actual wellhead pressure; Step 2: Adjust the water saturation in the fracture area from 10% to 50% to make the pressure change trend consistent with that in the previous drainage stage; Step 3: Adjust the phase permeability to make the early water production and gas production consistent with the production performance. The adjustment range of the phase permeability curve in the fracture area is 2 to 10 times that of the matrix phase permeability curve, and the adjustment range of the phase permeability curve in the matrix area is ±20% of the production data. Step 4: Use the deviation between the later wellhead pressure and the actual curve to adjust the stress sensitivity curve. The adjustment range of the stress sensitivity curve in the fracture area is ±50% of the initial conductivity, and the adjustment range of the stress sensitivity curve in the matrix area is ±30% of the initial permeability. The comparative judgment method comprises the following steps: The fitting results of wellhead pressure, gas production and water production are compared with the actual production data. If the deviation between the fitting results and the actual data exceeds ±10%, the production history fitting is repeated until the data deviation is within the range.

8. The integrated supercharging parameter optimization method according to claim 1, characterized in that: The categories of the pressurization and production increase effects described in step S4 are defined as follows: the target wells with a pressurization and production increase effect higher than 20% are defined as good, the target wells with a pressurization and production increase effect in the range of 10% to 20% are defined as relatively good, the target wells with a pressurization and production increase effect in the range of 5% to 10% are defined as average, and the target wells with a pressurization and production increase effect lower than 5% are defined as poor.

9. The integrated supercharging parameter optimization method according to claim 8, characterized in that: The method for optimizing the boost timing in step S5 comprises the following steps: Step 1: Input the production data of the target well into the integrated model to generate EUR prediction curves under different production cycles; Step 2: Set the wellhead pressure threshold and bring it into the EUR prediction curve, and take the time point corresponding to the wellhead pressure threshold with the largest cumulative gas production in the EUR prediction curve as the optimal boosting time; The production data include gas production, water production, casing pressure, oil pressure, and the threshold of the wellhead pressure is not less than the surface gathering pressure.

10. The integrated supercharging parameter optimization method according to claim 1, characterized in that: The method for optimizing the boosting system in step S5 comprises the following steps: based on the integrated model for determining the optimal boosting timing, five boosting strategies, namely, no boosting, average step boosting, accelerated step boosting, decelerated step boosting, and single boosting, are introduced to obtain five different boosting EUR prediction curves, and the five curves are compared to select the optimal boosting system; Among them, the method for selecting the optimal boosting system is as follows: if the EUR value increase of a certain boosting EUR prediction curve is higher than that of other strategies, and the deviation is greater than 1%, then the boosting strategy corresponding to the boosting EUR prediction curve is determined as the optimal boosting system; if the deviation of the EUR value increase of all boosting EUR prediction curves is ≤1%, then the solution with the lowest equipment energy consumption is determined as the optimal boosting system.

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