An integrated supercharging parameter optimization method

Abstract

An integrated boosting 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 including a mechanical model, simulating hydraulic fracture expansion on the three-dimensional geological attribute model, and separately establishing a gas reservoir model and a wellbore flow model to obtain a combined gas reservoir-wellbore-surface integrated model; performing production history matching on the target well based on the integrated model to determine the matching status; applying the integrated model to boosting and production simulation; optimizing the boosting timing and system for target wells with poor results to obtain an optimized EUR prediction curve; and establishing a gas reservoir-wellbore-surface integrated model to perform production dynamic analysis on the gas reservoir, comprehensively considering geological characteristics, dynamic changes, and timing requirements during production, and optimizing the boosting timing and system for different gas wells, thereby maximizing gas well production and economic benefits.

Description

Technical Field

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

[0002] When the oil pressure in a gas well approaches the pressure of the platform's gathering and transportation system, the daily gas production will show a continuous downward trend due to the shrinking production pressure difference. Once the daily gas production falls below the economic limit of the gas well, conventional production methods are no longer economically viable, and supercharging must be used to mobilize the remaining reserves. The supercharging process uses a booster to establish a pressure difference at the wellhead node, increasing the external transmission pressure while reducing the bottomhole flow pressure, thereby increasing the reservoir production pressure difference. However, engineering practice has shown that supercharging parameters have a decisive influence on development results. The matching degree of equipment operating conditions and the rhythm of pressure gradient adjustment directly determine the reservoir desorption efficiency and production capacity maintenance capacity. In this context, optimizing the timing and system of supercharging has become a key link in improving the economic benefits of gas well development.

[0003] Currently, the determination of gas well pressurization parameters faces numerous deficiencies. Decisions on the timing of gas well pressurization primarily rely on static empirical thresholds or extrapolation of single-well production curves, methods that fail to fully consider the multi-scale flow mechanisms of gas reservoirs and the dynamic evolution of reservoir physical properties. Furthermore, the optimization of pressurization schedules often employs fixed pressurization ratios or staged jump adjustments, lacking systematic consideration of fracture network heterogeneity and inter-well interference effects. Furthermore, despite the accumulation of extensive downhole pressure monitoring, microseismic monitoring, and production performance data, existing methods have failed to establish adaptive models driven by multi-source data, resulting in decisions lagging behind reservoir dynamics. These issues demonstrate that existing technologies have significant deficiencies in optimizing gas well pressurization timing and schedules, making it difficult to meet the actual needs of efficient gas reservoir development. Summary of the Invention

[0004] In light of this, the present invention provides an integrated boosting parameter optimization method. By coupling the gas reservoir, gas wellbore, and fracture network, an integrated reservoir-wellbore-surface model is established. Furthermore, through a production performance analysis of the gas reservoir, the timing and schedule of boosting are optimized. This method comprehensively considers the geological characteristics and dynamic changes of the gas reservoir, as well as the timing requirements during production, providing a scientific basis for the efficient development of gas wells.

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

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

[0007] Step S2: Conduct hydraulic fracture propagation 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-surface integrated model;

[0008] Step S3: Based on the integrated model, the production history of the target well is matched, and the matching results are compared 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;

[0009] Step S4: applying the integrated model to the target wells for pressurization and production stimulation simulation, and classifying the target wells according to the simulated pressurization and production stimulation effects;

[0010] Step S5: Optimize the pressurization timing and pressurization system for the target wells with poor classification results in sequence to obtain an optimized EUR prediction curve for the target wells.

[0011] The technical effects of the present invention are:

[0012] The present invention effectively overcomes the limitations of existing booster parameter optimization, comprehensively considers the combined impact of gas reservoirs, wellbore flow and the ground on natural gas extraction, establishes a gas reservoir-wellbore-ground integrated model to conduct production dynamic analysis of the gas reservoir, and comprehensively considers the geological characteristics, dynamic changes and timing requirements of the gas reservoir during the production process. According to the production conditions of different gas wells, the boosting timing and boosting system are optimized, thereby maximizing 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 briefly introduces the drawings required for use in the embodiments.

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

[0015] The present invention will be further described in detail below with reference to the embodiments and accompanying drawings.

[0016] To make the purpose, technical solutions, and advantages of the embodiments of the present invention more clear, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. The described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts shall fall within the scope of protection of the present invention.

[0017] See also Figure 1 , an integrated supercharging parameter optimization method, comprising the following steps:

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

[0019] Both the mechanical model and the three-dimensional geological attribute model are constructed using Petrel software based on the results of well-seismic analysis and interpretation. The three-dimensional geological attribute model is established using the phase-controlled attribute modeling method. The mechanical model can use the finite element method to perform detailed modeling of the three-dimensional stress field, or it can use geomechanical attributes for interpolation modeling.

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

[0021] Step S1-1, data collection and collation: Acquire stratigraphic data, planar structural maps, geological and physical parameters of the target well area, mechanical parameters, target well trajectory data, completion parameters, logging interpretation, fracturing operation records, and production performance data to provide a complete data foundation for subsequent modeling;

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

[0023] 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;

[0024] The well trajectory data of the target well includes wellhead coordinates, well inclination, azimuth well depth, and vertical depth;

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

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

[0027] The target well fracturing operation record includes perforation locations, fracturing fluid parameters and proppant properties;

[0028] The production dynamic data of the target well includes production time, casing pressure, oil pressure, daily gas production, daily water production, cumulative gas production, cumulative water production and external transmission pressure.

[0029] Step S1-2, well-seismic analysis and interpretation: wellhead coordinates and well trajectory are imported, and the scope of the work area is determined; using the well logging data or 3D seismic data of the pilot well, basic data interpretation work such as stratigraphic division, structural interpretation, and geomechanical parameter interpretation is carried out to establish a unified geomechanical interpretation framework.

[0030] Step S1-3, Structural Model Establishment: The structural model consists of a fault model and a layer model. An initial fault model is generated using digitized fault lines on the planar structural map, and the final fault model is corrected using breakpoint data from wells. A preliminary layer model is established using stratigraphic layer data, and the layer model is adjusted based on seismic inversion results and individual well drilling encounters. The structural model grid is set to a 25m×25m grid step size, with a vertical grid step size of 0.5m to ensure accurate structural characterization.

[0031] Step S1-4, 3D geological attribute modeling: Use Petrel software to discretize reservoir parameter logging data and establish a discretized data set; by analyzing the discretized data and combining it with logging, core analysis, and dynamic testing data, determine the distribution characteristics of reservoir parameters and establish a spatial distribution model; use a spherical model to construct a variogram, use interpolation or sequential Gaussian method to predict the distribution of interwell attribute parameters, and establish a phase-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 mechanical parameter assignment. Boundary conditions are then set and the finite element analysis module is called to calculate the distribution of the geostress field. Based on well logging and seismic data, the mechanical properties are spatially modeled using methods such as Kriging interpolation, and corrections are made based on geological structural characteristics to finally generate a complete mechanical model.

[0033] Step S2: Perform hydraulic fracture propagation 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-surface integrated model.

[0034] The main method for simulating hydraulic fracture propagation is to simulate hydraulic fracture propagation based on geological models and actual engineering parameters, taking into account inter-segment stress interference, and verify the fitting results using microseismic monitoring data and pumping operation curves. The main steps include:

[0035] Step S2-1.1, perforation parameter entry: Based on the fracturing construction report, enter the perforation location, depth, segment number, and cluster information. The depth data is in meters and accurate to two decimal places. Ensure consistency with the geological data and fracturing design requirements, and ensure that the perforation parameters accurately match the vertical stratification and mechanical properties of the geological model.

[0036] Step S2-1.2, fracturing pumping program design: The selected fracturing fluid and proppant are entered into the established model, and the construction curve is divided according to the fracturing second point data, realizing the digital mapping of the pumping process;

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

[0038] The construction curve is divided into a pre-fluid stage, a sand-carrying fluid stage and a displacement fluid stage;

[0039] As a priority, according to the fracturing design report, appropriate fracturing fluid and proppant were selected, and key material performance parameters were set;

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

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

[0042] Step S2-1.3, crack propagation simulation: Use the UFM non-structural crack propagation model to simulate the expansion of the hydraulic fracture network;

[0043] As a preference, the stress interference between segments and clusters should be considered during the simulation process, and the fracture height and penetration conditions should be adjusted according to the actual ground stress conditions to simulate the hydraulic fracture characteristics during segmented and clustered fracturing.

[0044] Adjusting the fracture height refers to calculating the fracture extension height under different fracture stress conditions through numerical simulation based on the collected fracture stress data and combined with rock mechanical properties (rock compressive strength, tensile strength and elastic modulus). Based on the simulation results, the fracture height is adjusted until it conforms to the actual geological conditions.

[0045] The adjustment of the penetration condition refers to adjusting the penetration path and penetration depth of the fracture by simulating the expansion behavior of the fracture in different rock layers in combination with the geological stratification characteristics of the target well (thickness, lithology changes and permeability differences of the rock layer).

[0046] The UFM non-structural crack propagation model is adopted, and the competitive expansion mechanism between segments and clusters is comprehensively considered. The vertical fracture height is constrained by the tensile strength of the rock, and the fracture toughness of the lithologic interface is used to control the penetration behavior, thereby simulating the complex fracture network morphology of multiple cracks extending synchronously.

[0047] Step S2-1.4, stress interference quantification: Based on the simulation results, calculate the fracture propagation of each fracturing section of each well according to the order of fracturing operation;

[0048] As a preference, stress shadow effects between wells, sections, and clusters should be considered during calculations.

[0049] Step S2-1.5, data fitting and verification: Fit the simulated fracture expansion results to the fracturing operation pumping program, compare the actual operation pressure with the simulated pressure, verify the accuracy of the simulation results, and ensure that the simulated structure truly reflects the fracturing effect. This technical system controls the fracture expansion prediction error within the engineering allowable range, providing a highly reliable decision-making basis for fracturing parameter optimization, segment cluster differentiation design, and development plan adjustment.

[0050] The reservoir model was established using the Petrel RE module to densify the reservoir near the target well, coupled with an artificial fracture network, and generate an unstructured grid. The grid surrounding the fractures was locally densified, and the unstructured grid was partitioned based on the geological attribute model and fracture propagation simulation results. Different phase permeability curves were embedded within the model, zoning the fracture-stimulated and unstimulated areas. Stress sensitivity curves were also set for the fracture and matrix regions. The model involved the following steps:

[0051] Step S2-2.1, attribute assignment: The reservoir near the target well is densified using the Petrel RE module, coupled to an artificial fracture network to generate an unstructured grid. Rock mechanical 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 geostress properties (maximum horizontal principal stress, minimum horizontal principal stress) are assigned to the grid cells to ensure accurate simulation.

[0052] Step S2-2.2, mesh processing: Use quadrilateral unstructured mesh to perform local densification on the mesh around the crack, complete the unstructured mesh division and establish the numerical simulation mesh model;

[0053] As a preference, equivalent treatment is performed on the cracks, and the equivalent crack grid size is set to 1m.

[0054] Step S2-2.3, reservoir zoning: Based on the porosity, permeability characteristics, and relative permeability (relative permeability) relationships corresponding to different reservoir types, and the significant difference in relative permeability between fracture-stimulated and unstimulated areas, Petrel software was used to zonate the fracture-stimulated and unstimulated areas.

[0055] Preferably, for unmodified areas, since matrix relative permeability is difficult to obtain through experiments or simulations, a set of relative permeability curves is usually given arbitrarily in numerical simulations, and the matrix relative permeability curves are adjusted through a history fitting process to match the actual production data items;

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

[0057] Preferably, the fracture phase permeability curve generally has a higher permeability, and the effect of the presence of fractures on fluid flow should be considered.

[0058] The consideration of the effect of fractures on fluid flow requires characterization of the fracture network, calculation of conductivity, coupling of fractures with the matrix, changes in flow paths, dynamic effects of pressure and saturation, and adjustment of phase permeability relationships.

[0059] Step S2-2.4, stress sensitivity curve setting: Using field monitoring data (pressure changes during fracturing construction and production dynamics data) combined with numerical simulation inversion technology, preliminary stress sensitivity curves for the fracture region and matrix region are obtained. The preliminary stress sensitivity curves are applied to the established model and numerical simulation is performed. The simulated structure is compared with actual production data, including production and pressure changes. The stress sensitivity curve is optimized by repeatedly adjusting the above parameters and comparing them with 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 pipe flow (VFP) wellbore flow model is mainly obtained by fitting the relationship between pressure and gas 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, bottomhole flowing pressure), input PVT data (density, viscosity, and dissolved gas-oil ratio of oil, gas, and water), and set the wellbore temperature gradient or temperature profile;

[0063] As a preference, input the possible ranges of water production and gas production and set the discretization step size;

[0064] As a preference, the wellhead pressure or bottom hole flowing pressure is set as the historical condition (normally, wellhead pressure is often used as input for gas reservoir production);

[0065] Step S2-3.2, initial VFP model selection: select the built-in multi-phase flow model based on the fluid type (gas-water two-phase, oil-gas-water three-phase) and flow morphology. If a custom model is required, it can be extended through scripts (Python) or third-party plug-ins;

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

[0067] As a preference, after drawing the VFP curve in Petrel, it is necessary to check whether the pressure-flow relationship conforms to the physical laws. If not, recheck the above steps until it meets the requirements;

[0068] Step S2-3.4, model verification and optimization: Verify the results of the initially generated VFP table, 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 adjustment model parameters include fluid density, fluid viscosity, fluid phase fraction, friction coefficient, gas production, water production, relative permeability and temperature;

[0070] The acceptable range of fitting error between the predicted results and the actual data is preferably around 4.5%. If the prediction error exceeds this range, key parameters such as density, viscosity, phase fraction, and friction factor will be adjusted, and the model structure will be optimized until a satisfactory fit is achieved. This iterative optimization process ensures the high accuracy and reliability of the model.

[0071] Step S3: Based on the integrated model, the production history of the target well is matched, and the fitting results are compared 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.

[0072] Based on the integrated model obtained by coupling the calibrated wellbore with the established gas reservoir model, the Intersect unstructured network gas reservoir numerical simulation module is used to perform production history fitting on the target well to determine the historical gas production to fit the wellhead pressure and water production. The fitting results are compared with the actual well production data. If the results are basically consistent, it proves that the established model is consistent with the actual situation of the study block. The main steps of production history fitting are:

[0073] Step S3-1, Fracture Stimulation Zone Scope and Permeability Adjustment: The fracture stimulation zone scope is calibrated with the actual fracturing design, and the permeability of the fracture area is adjusted to be between 10 and 1000 times the matrix permeability. The wellhead pressure is simulated and calculated to align with the actual wellhead pressure variation trend.

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

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

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

[0077] Step S3-5, Comprehensive Verification and Optimization: The adjusted model is fully verified by comparing the fitted results for wellhead pressure, gas production, and water production with actual production data. If the fitted results deviate from the actual data by more than ±10%, steps S3-1 through S3-4 are repeated to further optimize the model parameters until the data deviation is within the specified range. Through multiple iterations, the model is ensured to fully reflect the actual conditions in the study area.

[0078] Step S4: Using the integrated model to simulate the pressure increase and production enhancement of target wells, and classifying the target wells according to the pressure increase and production enhancement effects obtained by the simulation.

[0079] Based on an integrated reservoir-wellbore-surface model constructed after production history fitting, the boosting effect of multiple target wells within the target block was simulated. A threshold value for daily gas production was set as the boundary condition for booster activation. The target wells were divided into four categories based on the boosted production effect, with the thresholds set at 20%, 10%, and 5%, respectively. Wells with a boosted production increase of more than 20% were classified as good, those with a boosted production increase between 10% and 20% were classified as good, those with a boosted production increase between 5% and 10% were classified as fair, and those with a boosted production increase below 5% were classified as poor. The simulation results determined the production increase effect classification for each well.

[0080] Step S5: Optimize the pressurization timing and pressurization system for the target wells with poor classification results in sequence to obtain an optimized EUR prediction curve for the target wells.

[0081] Based on the integrated reservoir-wellbore-surface model after production history matching, the boosting timing and boosting system were set as optimization targets. Poor target wells with boosting production increase effects less than 5% were selected as optimization objects for numerical simulation optimization. The main steps of boosting timing optimization are as follows:

[0082] Step S5-1.1, gas well production performance and EUR curve prediction: Based on the reservoir-wellbore-surface integrated model after production history fitting, the actual production data of the target well is input to simulate the gas production and wellhead pressure at each time point in the future production process. The gas production is accumulated by time integration to generate EUR prediction curves under different production cycles;

[0083] The production data includes gas production, water production, casing pressure, and oil pressure;

[0084] Step S5-1.2, optimization of boosting timing: Based on the EUR prediction in step S5-1.1, set different booster intervention time points and corresponding wellhead pressure control thresholds, simulate the instantaneous flow of the gas well under different boosting schemes through the model, and generate multiple differentiated EUR curves. The wellhead pressure control threshold refers to the wellhead pressure target 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 normal transmission pressure, the oil pressure after the booster intervention control should not be lower than the current ground gathering pressure of the gas well platform, therefore, a certain number of time nodes corresponding to the pressure values ​​where the wellhead pressure control threshold is not lower than the ground gathering pressure during the production process can be selected to conduct booster intervention control tests. With the maximization of cumulative gas production as the optimization goal, the cumulative gas volume values ​​at the end points of each intervention control scheme are compared, and then the boosting pressure threshold with the maximum cumulative gas production and its corresponding time node can be obtained to determine the optimal boosting time for the integrated model.

[0086] The optimization of the boost system is carried out by setting different boost speeds based on the determination of the optimal boost timing. The main steps are:

[0087] Step S5-2.1, obtaining the booster power and maximum boosting range: Obtain the booster power used by the gas well, consider the platform gathering pressure and the performance of the booster under different operating conditions, and determine the maximum boosting range of the booster;

[0088] Step S5-2.2, production dynamic prediction of the boosting system: Based on the optimal boosting timing determined in step S5-2.1, the boosting speed of the supercharger is changed until the maximum boosting range of the supercharger is reached. The boosting strategies include average step-by-step boosting, accelerated step-by-step boosting, decelerated step-by-step boosting and single-step boosting.

[0089] The average step-by-step pressure boosting method uses the optimal boosting timing of the booster as the initial condition for production. When the actual production rate falls below the economic limit of the well area, the booster efficiency is gradually and evenly increased. For example, if the booster power is sufficient and the gas well is producing, and the wellhead pressure is reduced from the initial high pressure to 6MPa, the average step-by-step pressure boosting method can be 6MPa-5MPa-4MPa, with each step being an average increase. Similarly, the method can be 5MPa-4.5MPa-4MPa, with each step being an average increase.

[0090] After each adjustment, the daily gas production will increase accordingly. If the minimum production requirement of the well area cannot be met after adjustment, the boosting efficiency will continue to increase by the same amount until the maximum operating range of the booster is reached.

[0091] The accelerated cascade boosting is based on the optimal boosting timing, and the boosting efficiency of the booster is improved slowly first and then quickly. The boosting efficiency increase is small in the initial stage, and the adjustment speed is gradually accelerated as the production demand changes. After each adjustment, when the minimum daily production acceptable to the well area is reached again, the booster efficiency is changed.

[0092] The deceleration step-by-step boosting is based on the optimal boosting timing, and the booster efficiency is adjusted first faster and then slower. The boosting efficiency is greatly improved in the initial stage to quickly increase production, and then the adjustment range is gradually reduced. After each adjustment, when the minimum daily production acceptable to the well area is reached again, the booster efficiency is changed until the maximum boosting range is reached.

[0093] The range of accelerated or decelerated boosting is the same as the range of average boosting, the difference being the boost amplitude. For example, if the booster can pump up to 4 MPa and the gas well is producing, the wellhead pressure will decrease from the high pressure at the start of the well to 6 MPa. The accelerated boosting will be 6 MPa-5.8 MPa-5 MPa-4 MPa, with pressure changes of 0.2 MPa, 0.8 MPa, and 1 MPa, respectively, with each change being larger than the previous one. The same principle applies to decelerated boosting. If the booster can pump up to 4 MPa and the gas well is producing, the wellhead pressure will decrease from the high pressure at the start of the well to 6 MPa. The decelerated boosting will be 6 MPa-5 MPa-4.2 MPa-4 MPa, with pressure changes of 1 MPa, 0.8 MPa, and 0.2 MPa, respectively, with each change being smaller than the previous one.

[0094] The one-time boosting means that when the daily production is less than the production requirement of the well area, the power of the booster is increased to the maximum efficiency at one time under the power of the optimal boosting time. When the maximum boosting range is adjusted and the minimum limit of daily production is reached again, production is stopped.

[0095] Step S5-2.3, Optimizing the Boosting System: Based on the reservoir-wellbore-surface integrated model with the optimal boosting timing determined, five different boosting system strategies—no booster, average cascade boosting, accelerated cascade boosting, decelerated cascade boosting, and single boosting—are set and input into the model. Five different EUR curves are generated. By comparing these five EUR curves, the optimal boosting system is determined.

[0096] As a preference, when selecting the optimal boosting system, if the EUR value increase of a certain strategy is significantly higher than that of other strategies (the deviation is >1% compared with other strategies), then this strategy will be directly selected. If the EUR value increases of multiple strategies are close (the deviation is ≤1% compared with other strategies), then the solution with low equipment energy consumption will be given priority.

[0097] Example:

[0098] Based on the actual data of an oil field, the integrated model was constructed using the steps described above. Then, the pressurization effect of 11 wells in the target block was simulated, and the daily gas production was set to 3800m3. 3 As the boundary conditions for booster startup, the target wells are divided into four categories according to the production increase effect after boosting, with the production increase effect limits set at 20%, 10%, and 5%, respectively. Target wells with a boosting production increase effect higher than 20% are classified as good, target wells with a boosting production increase effect between 10% and 20% are classified as good, target wells with a boosting production increase effect between 5% and 10% are classified as fair, and target wells with a boosting production increase effect lower than 5% are classified as poor. The comparison of the production increase effect of each well before and after boosting is shown in Table 1:

[0099] Table 1 Comparison of production increase effect before and after pressurization

[0100]

[0101]

[0102] According to the comparison of the production increase effect before and after pressurization in Table 1, Well W5, which has a poor production increase effect of less than 5% due to pressurization, was selected as the target well. Numerical simulation was performed using the integrated model to optimize the pressurization timing.

[0103] Considering the actual situation on site, the wellhead pressure cannot be lower than 4MPa (the surface pressure is 4MPa). The booster is installed when the wellhead pressure is 10MPa, 8MPa, 6MPa, and 4MPa, and the results without the booster are introduced for comparison. Five differentiated EUR curves are generated. The simulation results of the EUR curve are shown in Table 2:

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

[0105]

[0106] Taking the maximization of cumulative gas production as the optimization goal, the cumulative gas volume values ​​at the end points of each scheme are compared, and the time node of the boost pressure threshold corresponding to the maximum cumulative gas production is selected as the boost timing, and the optimal boost timing is determined to be 4MPa.

[0107] The optimization of the boosting system is carried out by setting different boosting speeds based on the determination of the optimal boosting timing. A DTY220 booster (with a maximum pumping pressure of 0.5 MPa) is used for simulation with five boosting systems:

[0108] 1) Average step-by-step pressure increase (4MPa→3.125MPa→2.25MPa→1.375MPa→0.5MPa);

[0109] 2) Accelerated step-by-step pressurization (4MPa→3.6MPa→3MPa→2.3MPa→1.5MPa→0.5MPa);

[0110] 3) Deceleration and step-by-step pressure increase (4MPa→2.5MPa→1.6MPa→1MPa→0.5MPa);

[0111] 4) Primary pressure increase (4MPa→0.5MPa);

[0112] 5) Booster compressor not installed (normal mining).

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

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

[0115]

[0116]

[0117] Table 3 shows that in a reservoir-wellbore-surface integrated model with the optimal boosting timing determined, five different boosting strategies (no booster, average sequential boosting, accelerated sequential boosting, decelerated sequential boosting, and single boosting) were input into the model, resulting in five different EUR curves. By comparing these five different EUR curves and examining the cumulative gas production increase rate after each measure, it can be seen that the EUR value increase under the accelerated sequential boosting strategy exceeds that of the other strategies by more than 1%. Therefore, the optimal boosting strategy after optimization is determined to be accelerated sequential boosting.

[0118] As can be seen, the boosting optimization method of the present invention offers significant advantages over existing technologies. Prior art decisions on boosting timing often rely on static empirical thresholds or extrapolation of single-well production curves, lacking consideration of the multi-scale flow mechanisms within gas reservoirs and the dynamic changes in reservoir properties. However, the present invention, by establishing an integrated reservoir-wellbore-surface model, comprehensively couples all aspects of gas well production, enabling a comprehensive analysis of reservoir geological characteristics, dynamic changes, and timing requirements during production.

[0119] In predicting the boosting effect, this invention not only considers the static geological parameters of the gas reservoir but also incorporates dynamic production data and wellbore flow characteristics, making the prediction of the boosting effect more accurate and reliable. Furthermore, existing technologies often use fixed boosting ratios or step-by-step adjustments to optimize the boosting schedule, which lacks systematicity and flexibility. This invention simulates production dynamics under different boosting schedules and compares EUR curves under various schedules to determine the optimal schedule, achieving refined optimization of boosting parameters.

[0120] In summary, the present invention provides an integrated boosting parameter optimization method. After establishing and verifying the integrated model, the boosting timing and boosting system are selected as parameters. Through sub-steps such as gas well production dynamic prediction, production dynamic prediction of different boosting systems and optimization selection, the parameters of target wells with poor boosting effect are optimized to improve the gas well production and economic benefits, thereby guiding gas well production to achieve the optimal balance between economic benefits and production effects.

[0121] The above description is merely a preferred embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any changes or substitutions that can be easily conceived by a person skilled in the art within the technical scope disclosed in the embodiments of the present invention should be included in the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be based on the scope of protection 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 propagation 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-surface integrated model; Step S3: Based on the integrated model, the production history of the target well is matched, and the matching results are compared 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: applying the integrated model to a pressure-increasing and production-increasing simulation of a target well, and classifying the target well according to the pressure-increasing and production-increasing effect obtained by the simulation, wherein the pressure-increasing and production-increasing effect is classified as follows: a target well with a pressure-increasing and production-increasing effect higher than 20% is classified as a good production-increasing effect; a target well with a pressure-increasing and production-increasing effect between 10% and 20% is classified as a relatively good production-increasing effect; a target well with a pressure-increasing and production-increasing effect between 5% and 10% is classified as a fair production-increasing effect; and a target well with a pressure-increasing and production-increasing effect lower than 5% is classified as a poor production-increasing effect. Step S5: Optimizing the pressurization timing and pressurization system for the target wells with poor classification results in sequence to obtain an optimized EUR prediction curve for the target wells, wherein the method for optimizing the pressurization timing includes 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. The time point corresponding to the wellhead pressure threshold with the maximum cumulative gas production in the EUR prediction curve is taken as the optimal boosting time. The production data includes gas production, water production, casing pressure, and oil pressure, and the threshold of the wellhead pressure is not less than the surface gathering pressure; The method for optimizing the supercharging system includes the following steps: based on an integrated model that determines the optimal supercharging timing, introducing five supercharging strategies, namely, no supercharging, average step supercharging, accelerated step supercharging, decelerated step supercharging, and single supercharging, to obtain five different supercharging EUR prediction curves, comparing the five curves, and selecting the optimal supercharging system; 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.

2. The integrated supercharging parameter optimization method according to claim 1, characterized in that: The geological data in step S1 include stratigraphic layer data, planar structural maps, geological physical parameters and mechanical parameters of the target well area, well trajectory data of the target well, completion parameters, well logging interpretation, fracturing operation records, and production performance data; Among them, the geological and physical parameters of the target well area include porosity, permeability, water saturation, formation pressure, rock density, and acoustic travel time; 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 target well trajectory data includes wellhead coordinates, well inclination, azimuth well depth, and vertical depth; The completion parameters of the target well include casing inner diameter and tubing inner diameter; 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 propagation in step S2 comprises the following steps: Step 1: Enter segmented perforation parameters into the 3D geological attribute model, where the segmented perforation parameters include perforation location, 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 fracture network propagation simulation, calculate the propagation situation 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 densification of the mesh around the fracture and perform unstructured meshing based on the geological attribute model and the fracture propagation 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 fracture stimulation zone to the actual fracturing design, adjust the permeability of the fracture zone to between 10 and 1,000 times the matrix permeability, and simulate and calculate the wellhead pressure to ensure that it is consistent with the actual wellhead pressure change trend; Step 2: Adjust the water saturation in the fracture area within a range of 10% to 50% to ensure that the pressure change trend is consistent with that in the early drainage stage; Step 3: Adjust the relative permeability to make the early water and gas production consistent with the production performance. The relative permeability curve of the fracture area is adjusted to 2 to 10 times the relative permeability of the matrix. The relative permeability curve of the matrix area is adjusted to ±20% of the production data. Step 4: Adjust the stress sensitivity curve based on the deviation 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. 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.

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