Fracture parameter joint inversion method based on regional multi-well constraint

Abstract

The invention provides a crack parameter joint inversion method based on regional multi-well constraint. According to the method, on the basis of well test response of multiple wells in a target block, an inter-well weight relation is constructed by utilizing inter-well space distance and structure consistency information, and a joint inversion model containing a single well fitting item, a region consistency constraint item and a prior constraint item is established on the basis. Through a double-layer iteration structure formed by inner layer single well parameter optimization and outer layer area coordination updating, while single well test fitting precision is ensured, area consistency constraint is applied to key crack parameters, inter-well parameter interpretation differences are gradually weakened, and a stable and convergent multi-well collaborative inversion result is obtained. Compared with a traditional single well independent interpretation method, the method has the advantages that discreteness and multiplicity of an inversion result can be reduced, continuous and coordinated expression of fracture parameters on the regional scale is formed, and the method can be used for multi-well test comprehensive interpretation, fracturing effect evaluation and regional fracture parameter characterization under the complex reservoir condition.

Description

A Joint Inversion Method for Fracture Parameters Based on Regional Multi-Well Constraints Technical Field

[0001] This invention relates to the fields of oil and gas reservoir engineering and well test interpretation technology, and in particular to a joint inversion method for fracture parameters based on regional multi-well constraints, which is used to solve the problems of inconsistent interpretation results of multi-well well tests, unstable inversion parameters of single wells, and difficulty in regionalized continuous description of fracturing fracture parameters. Background Technology

[0002] Existing well test interpretation techniques typically rely on single-well pressure curves, employing appropriate flow models for fitting calculations to invert key parameters such as fracture half-length, fracture conductivity, and wellbore skin coefficient. However, in practical oil and gas reservoir development, this single-well-based interpretation model still has certain limitations: Firstly, due to differences in testing regimes, well location structural conditions, reservoir heterogeneity spatial variation characteristics, and fracturing parameters among wells, the fracture parameters obtained from single-well interpretation exhibit strong dispersion at the block scale, making it difficult to obtain interpretation results with spatial continuity and mutual coordination at the block scale. For well groups with the same structural unit and consistent target stratigraphic interval, inversion results are prone to inconsistencies in lateral comparisons between wells when inter-well correlation constraints are lacking. Secondly, the identifiability of fracture parameters during well test inversion is insufficient, and the constraint ability of single-well test information on fracture parameters is limited, easily leading to multiple solutions and sensitivity to initial assumptions, thus affecting the stability and engineering usability of the inversion results. Thirdly, traditional interpretation methods mostly output discrete parameters on a wellpoint basis, making it difficult to further construct a continuous spatial distribution expression of parameters such as fracture half-length and conductivity, thereby limiting their promotion and deepening in engineering applications such as fracturing effect evaluation, regional reservoir characterization, well network optimization design, and development scheme adjustment. Summary of the Invention

[0003] Technical problems to be solved

[0004] This invention aims to solve the following technical problems:

[0005] The interpretation results of multiple well tests are inconsistent, and the overall results are scattered and lack regional regularity.

[0006] Problems include instability in single-well inversion, sensitivity to noise, and non-unique parameters;

[0007] The existing technology cannot provide a regionalized and continuous description of crack parameters.

[0008] Technical solution

[0009] According to one aspect of the present invention, a joint inversion method for fracture parameters based on regional multi-well constraints is provided. The method uses the well test responses of multiple wells within a target block as a basis, and achieves the collaborative inversion of fracture parameters of multiple wells at a regional scale by introducing inter-well spatial relationship constraints and a regional consistency regularization mechanism. The method is characterized by comprising:

[0010] Acquire well test data, well location spatial information, and reservoir prior parameters from multiple wells within the target block; wherein, the well test data is used to characterize the pressure response characteristics of each reservoir well within a preset well test period, the well location spatial information is used to describe the spatial distribution relationship of multiple wells, and the reservoir prior parameters are used to limit the reasonable range of fracture parameters in terms of engineering and geological significance, thereby providing basic constraints for subsequent joint inversion;

[0011] Based on the well test data, a single-well well test characteristic curve is constructed for each reservoir well to characterize the seepage response characteristics of each well at different time scales; and further, based on the spatial relationship of well locations and structural consistency information, an inter-well weight relationship is constructed to quantitatively describe the correlation strength between different reservoir wells in terms of spatial distribution and structural conditions, so that the inversion process between multiple wells can introduce spatial coupling constraints through the inter-well weight relationship.

[0012] A fracture parameter vector is defined for joint inversion to uniformly characterize the fracture geometry and permeability of each reservoir well; and key fracture parameters for regional consistency constraints are selected from the fracture parameter vector so that constraints are applied to the fracture parameter components that have a dominant influence on regional continuity during the regional joint inversion process.

[0013] A multi-well joint inversion objective function is constructed, which includes a single-well test fitting error term, a regional consistency constraint term, and a priori constraint term. The single-well test fitting error term is used to ensure the fitting accuracy of the test response of each reservoir well. The regional consistency constraint term is used to limit unreasonable abrupt changes in the key fracture parameters of adjacent wells in space. The priori constraint term is used to suppress parameter solutions that do not conform to engineering and geological understanding, thereby establishing a unified optimization framework between single-well fitting accuracy and regional spatial continuity.

[0014] The objective function of the multi-well joint inversion is solved by a two-layer iterative optimization method. The inner layer iteration is used to locally optimize and update the fracture parameters of a single well under the condition of fixed parameters of neighboring wells, so as to gradually reduce the well test fitting error of the corresponding well. The outer layer iteration is used to coordinate the distribution of parameters of multiple wells at the regional scale after completing a round of single-well parameter updates, and to determine whether the preset convergence conditions are met based on the changes in the objective function or parameter changes, thereby realizing the synergistic optimization of single-well fine fitting and regional consistency control.

[0015] After the joint inversion process meets the convergence condition, the fracture parameter inversion results of multiple wells in the target block are output, and a regional fracture parameter distribution field is constructed based on the inversion results, so that the fracture parameters are extended from the discrete well point scale to the continuous regional scale, thereby realizing the consistent interpretation of the fracture parameters of multiple wells at the regional scale.

[0016] The technical solutions of the embodiments of the present invention are further described below.

[0017] In this embodiment of the invention, by introducing an inter-well spatial relationship and regional consistency constraint mechanism during well test interpretation, the fracture parameter inversion problem of multiple wells within the target block is uniformly incorporated into the same optimization framework for solution. Unlike traditional well test interpretation methods that treat each well as an independent object for separate inversion, this invention constructs an inter-well weight relationship, enabling adjacent wells to mutually constrain each other during fracture parameter inversion, thereby achieving collaborative updating of fracture parameters at the regional scale.

[0018] In this technical solution, the single-well test fitting error term ensures that the test response of each well is reasonably fitted, while the regional consistency constraint term limits unreasonable abrupt changes in the spatial distribution of fracture parameters, enabling the inversion results to meet the single-well fitting requirements while possessing good regional continuity. Through a two-layer iterative optimization mechanism, the fine fitting at the single-well level and the overall coordination at the regional level are modeled and optimized in a unified manner, avoiding the parameter dispersion problem that easily arises from independent single-well inversion.

[0019] Therefore, compared with the traditional single-well independent inversion method, the technical solution of the present invention can improve the coordination and consistency of fracture parameters at the regional scale while ensuring the fitting accuracy of single-well test, so that the well test interpretation results are more consistent with the actual reservoir structure characteristics, thereby improving the reliability and applicability of well test interpretation results in engineering applications such as regional evaluation, fracturing effect analysis and geological modeling.

[0020] Beneficial effects

[0021] Compared with the prior art, the present invention has the following beneficial effects:

[0022] A joint inversion technology framework based on regional multi-well constraints was constructed: This invention introduces a regional multi-well constraint mechanism into the well test interpretation process, transforming the fracture parameter inversion from the traditional single-well independent fitting to a joint inversion process at the regional scale. This enables the fracture parameters of multiple wells to be solved collaboratively within a unified mathematical framework, thus solving the problem of strong parameter non-uniqueness in single-well well test interpretation from a technical structural perspective.

[0023] Introducing the spatial relationship between wells into the inversion calculation in the form of a defined weight matrix: This invention constructs an inter-well relationship diagram and forms an inter-well weight matrix, transforming the spatial distance between well locations and structural consistency into mathematical quantities that can directly participate in the optimization calculation. This allows the spatial relationship between multiple wells to be clearly constrained during the inversion process, thereby reducing the uncertainty caused by subjective judgment and manual adjustment during the regional interpretation process.

[0024] By unifying and coordinating single-well fitting and regional consistency constraints through a joint objective function: This invention constructs a joint objective function that includes data fitting terms, regional regularization terms, and prior constraint terms. Under a unified optimization system, it simultaneously constrains the matching of single-well test responses and the consistency of fracture parameters at the regional scale, so that the inversion process has a clear optimization objective and a stable constraint structure.

[0025] The invention employs a two-layer iterative solution structure to achieve local updates and overall convergence control: By setting up a two-layer iterative structure that combines inner layer single-well parameter updates with outer layer regional convergence determination, the fracture parameter inversion can simultaneously meet the local well test fitting requirements and the regional overall coordination requirements during the calculation process, thereby enhancing the stability and controllability of the multi-well joint inversion process.

[0026] By defining the dimension of regional constraints through regional key parameter mapping, this invention introduces only parameters that have a major controlling effect on well test response, such as fracture half-length and fracture conductivity, into the regional regularization constraint, avoiding the application of unnecessary spatial smoothing to local sensitive parameters, and technically ensuring a reasonable balance between regional constraints and single-well response.

[0027] Forming a regional fracture parameter expression form directly determined by inversion results: After completing the joint inversion of fracture parameters from multiple wells, this invention constructs a spatially continuous distribution expression of regional fracture parameters based on the discrete well point parameters obtained by inversion, realizing the transformation of fracture parameters from well point results to regional continuous expression, and providing a unified data foundation for fracture development characteristic analysis and engineering evaluation. Attached Figure Description

[0028] The technical solution of the present invention is illustrated by way of example with reference to the accompanying drawings.

[0029] Figure 1 is a schematic diagram of the overall process of the fracture parameter joint inversion method based on regional multi-well constraints of the present invention.

[0030] As shown in the figure, the method includes the following steps in sequence: acquiring multi-well test data and constructing test characteristic curves (step S101), constructing inter-well relationships and weight matrices (step S102), defining fracture parameter vectors (step S103), constructing the objective function for multi-well joint inversion (step S104), solving the dual-layer iterative joint inversion (step S105), performing seepage forward modeling and mapping of regional key parameters (step S106), and constructing the regional fracture parameter distribution field and outputting the results (step S107), thereby completing the regional consistency inversion and interpretation of fracture parameters of multiple wells within the target block.

[0031] Figure 2 is a schematic diagram of the two-layer iterative joint inversion principle of the fracture parameter joint inversion method based on regional multi-well constraints of the present invention.

[0032] As shown in the figure, the well test characteristic curves of multiple wells in the target block and their corresponding fracture parameter vectors are used as inputs. A joint inversion objective function is constructed, which includes single-well data fitting terms, regional consistency regularization terms, and prior constraint terms. During the inversion process, the inner iteration is used to update the fracture parameters of a single well under the condition of fixed neighboring well parameters, and to optimize the single-well parameters through seepage forward modeling and fitting error calculation. The outer iteration is used to perform overall evaluation and regional coordinated updating of the parameters of multiple wells, and to determine whether the inversion process meets the convergence conditions through regional convergence diagnosis, thereby realizing the collaborative inversion of fracture parameters of multiple wells at the regional scale.

[0033] Figure 3 shows the fracture half-length of the fracture parameter joint inversion method based on regional multi-well constraints of the present invention. A diagram showing the comparison of regional inversion results.

[0034] As shown in the figure, the figure contains two sub-figures: the left sub-figure shows the fracture half-length obtained by inverting the pressure-derivative curves of the same set of well tests in the target block without introducing regional multi-well constraints. Spatial distribution; the right subplot shows the fracture half-length obtained using the joint inversion method of this invention on the same initial well test data under the condition of introducing regional multi-well constraints. Spatial Distribution. In the figure, black dots and labels W1–W8 represent the well locations within the target block, while contour fills and contour lines represent the continuous distribution characteristics of the inversion parameters in the planar coordinate system. It can be seen that without the introduction of regional multi-well constraints, localized high or low value areas easily appear near the well points, and the contour lines exhibit local abrupt changes or irregular closures, reflecting inconsistencies in inter-well correlation and insufficient spatial continuity; however, after introducing regional multi-well constraints… The spatial distribution is smoother and more continuous, the contour transition is more stable, and the changes in inter-well parameters show a gradual characteristic that is more in line with the regional structure and connectivity scale. This shows that the present invention can improve the consistency and coordination of the regional scale while maintaining the interpretability of a single well. Detailed Implementation

[0035] S101, Acquisition and Feature Construction of Multi-Well Test Data: Acquire test data, well location spatial information, and reservoir engineering and geological prior parameters of multiple wells within the target block. Preprocess the test data and perform dimensionless processing to construct single-well test characteristic curves such as pressure and pressure derivative.

[0036] In this embodiment, before performing joint inversion of fracture parameters constrained by multiple wells in the target block, it is first necessary to obtain basic data that can be used for well test interpretation from multiple wells within the target block. The original records from different wells and under different testing regimes are then uniformly converted into comparable single-well well test characteristic curves to ensure that subsequent construction of inter-well relationships (S102) and construction of the multi-well joint objective function (S104) are carried out under the same data. The well test data is used to characterize the pressure response characteristics of each well within a preset well test period, and preferably includes: shut-in and open-out event times, pressure sampling sequences, production (or injection) sequences, wellhead and bottom hole pressure type identifiers, temperature records, and pressure gauge calibration information; wherein, the pressure sampling sequence can be the bottom hole pressure... or wellhead pressure When the wellhead pressure is used, the equivalent bottom hole pressure is calculated by combining the wellbore temperature and pressure profile with the multiphase flow pressure drop model to reduce the impact of wellbore pressure drop on formation response interpretation. The well location spatial information preferably includes wellhead coordinates, well trajectory inclination data, coordinates of the completion section (perforation) center point, and the calculation caliber of the well distance matrix (wellhead distance / completion section distance / nearest distance). The reservoir engineering and geological prior parameters are used to define the reasonable engineering range of fracture parameters and provide a scale dimension for dimensionless processing, preferably including effective thickness. Porosity Overall compression ratio Fluid viscosity Volume index , wellbore radius Information such as stratigraphic boundaries and initial range of reservoir permeability values.

[0037] After data acquisition, this embodiment performs unified quality control and preprocessing on the well test data to reduce the interference of "non-formation response" on the characteristic curves and inversion objective function. Specifically, firstly, the original time axis of each well is standardized, and the sampling time is converted into a time series relative to the shut-in (or relative variable flow event) time. Furthermore, data from different sampling intervals are resampled and aligned to ensure that data from multiple wells can participate in subsequent error calculations with the same time resolution. Secondly, the pressure sequence is processed for drift and jump points. Pressure drift can be corrected based on the pressure gauge zero-point calibration record or the fitting result of the stabilizing segment at the end of the shut-in period. Pressure jump points can be identified and eliminated based on the criterion of "short-term drastic changes that cannot continue to adjacent points." Further, this embodiment identifies and standardizes the test regime: when the well test is a single shut-in recovery, the shut-in start point is directly used as... When the well test involves multiple variable flow rates or multiple shut-in stages (e.g., DST or multi-stage shut-in recovery), it is preferable to divide each event segment into an independent interpretation segment and record the start and end times, equivalent flow rate, and pressure baseline of each segment to avoid misjudgment of curve inflection points caused by the superposition of different event segments.

[0038] After completing data quality control, this embodiment constructs single-well test characteristic curves. These single-well test characteristic curves include at least a pressure recovery curve. With pressure derivative curve The pressure derivative curve is used to enhance the identification capability of flow pattern sections and improve the sensitivity of crack parameters to the objective function. To improve the robustness of the derivative curve, this embodiment preferably uses the logarithmic time derivative (i.e., The model is constructed, and the derivative is calculated using a "local logarithmic window regression" method in the numerical implementation: a coverage area is selected on the double logarithmic time axis. A local window for each sampling point, for A linear fit is performed, and the slope of the fit is used as the estimated derivative value at that point, thereby reducing the noise amplification effect caused by discrete differences while preserving the location of key inflection points. Optionally, when the early data is significantly affected by the wellbore energy storage effect, this embodiment marks the early segment and reduces its weight or delays the start time in the derivative calculation to avoid the wellbore effect dominating the early derivative platform and affecting fracture parameter identification; when there is a significant skin effect or wellbore fluid column disturbance, the pressure curve can be equivalently corrected by combining prior parameters or the disturbed segment can be removed from the fitting error term.

[0039] Furthermore, to achieve comparability under regional multi-well constraints, this embodiment performs dimensionless processing on the characteristic curves of each well to construct a dimensionless pressure... Dimensionless time With dimensionless pressure derivative The unified representation ensures that the differences in curves between different wells are primarily determined by fracture geometry and permeability parameters, rather than by differences in production levels, physical property dimensions, or testing duration. The scale parameters required for dimensionlessness are provided by reservoir engineering and geological priors, combined with the equivalent flow rate during the pre-shutdown stable phase. (Or equivalent flow rate in the variable flow range) Determine the pressure and time scales; wherein, the equivalent flow rate is preferably taken from the average value of the stable production period (or injection period) before well shut-in, and short-term fluctuations are smoothed to avoid introducing production system noise into the dimensionless scale. Optionally, for cases where only wellhead production or daily production data is provided, the equivalent bottomhole flow rate before well shut-in can be obtained through steady-state node analysis or production capacity formula conversion, so that the dimensionless scale is consistent with the physical meaning of pressure response.

[0040] After constructing the single-well characteristic curves, this embodiment generates a "single-well input package" for joint inversion. The single-well input package includes at least: the original pressure-time series (including event markers), the preprocessed pressure series, the pressure derivative series, dimensionless pressure, the time / derivative curve, the equivalent flow rate before shut-in and its statistical interval, the set of prior parameters for the wellbore and formation, and an effective fitting segment mask for subsequent error calculation. The effective fitting segment mask explicitly indicates the "time period involved in the calculation of the data fitting term," thereby allowing the data fitting term in the subsequent joint inversion objective function to exclude wellbore disturbance segments, pressure replenishment segments, or instrument anomaly segments, improving the objective function's targeting of fracture parameters and the stability of the solution.

[0041] For example, selecting a target block A joint inversion well group was formed, consisting of eight wells located in the same structural zone with consistent completion strata. Each well recorded 8–24 hours of shut-in recovery pressure data and data on the stable production period before shut-in. For example, the system first reads its pressure gauge records and the time of the shut-in event, and converts the data into a relative shut-in time. The pressure sequence below Based on the abnormal jump point identification rules, short-term abrupt changes caused by wellbore operations were eliminated; then, the stable period in the 30 minutes before well shut-in was identified and the equivalent flow rate was calculated. ,based on The logarithmic time derivative of the local window regression calculation is obtained. And the early stage dominated by wellbore energy storage is marked as a low-weight stage; then combined with Equal prior parameters , , Perform dimensionless transformation to obtain Characteristic curve. For After the above processing, a set of multi-well characteristic curves and corresponding single-well input packages are obtained, providing a standardized and traceable data foundation for subsequent steps S102 to construct inter-well weight relationships, S104 to construct multi-well joint objective functions, and S105 to solve the two-layer iterative problem.

[0042] S102, Based on the well location spatial information and structural consistency information, construct an inter-well weight relationship to characterize the spatial correlation strength of multiple wells within the target block, and form an inter-well relationship structure for regional multi-well constraints.

[0043] In this embodiment, after constructing the characteristic curves of each well's individual well test, it is necessary to further characterize the degree of correlation between different wells in terms of space and geological structure, so as to apply "regional consistency constraints" to key fracture parameters in the subsequent joint inversion process. The well location spatial information describes the spatial distribution relationship of multiple wells in the planar and vertical directions, and may include wellhead coordinates, well bottom trajectory coordinates, midpoint coordinates of the target layer, and well spacing matrix. The structural consistency information describes whether multiple wells are located in the same structural unit, the same sublayer, or the same sedimentary facies zone, and may further include fault segmentation relationships, structural line trends, stratigraphic correlation results, and sand body connectivity evaluation indicators. It should be noted that this embodiment does not require all of the above information to be available. When some geological information is missing, a combination of well spacing and stratigraphic similarity can be preferred to construct the weights. When richer structural interpretation data is available, factors such as fault shielding and facies zone differences can be introduced to correct the weights, thereby improving the engineering rationality of the inter-well constraints.

[0044] Specifically, in this embodiment, the basic distance between wells is first calculated based on the well location spatial information. The preferred distance is the "target layer equivalent distance," which means selecting a representative point for each well within the target layer depth range (e.g., the midpoint of the target layer, the center of the test layer, or the midpoint of the perforation section consistent with the fracture control layer). Based on this, the distance between any two wells is calculated. and spatial distance When the well is a directional or horizontal well, considering the influence of the well trajectory on the location of the bedding plane, this embodiment may optionally use the three-dimensional distance between the intersection of the well trajectory and the target bedding plane, or the weighted distance of the planar distance projected along the bedding plane and the vertical bedding plane difference, thereby avoiding the error caused by using only the wellhead distance. For example, the equivalent distance can be defined as: ,in and The difference between the coordinates of the representative points of the target layer in the plane coordinate system. This refers to the vertical depth difference or stratigraphic difference between representative points in the target stratigraphic segment. This is a vertical scale conversion factor used to reflect the impact of vertical heterogeneity on connectivity. It is obtained through the above method. It can better align with the engineering intuition of "connectivity within the same layer and priority constraints for adjacent wells" and provide basic quantitative indicators for subsequent weight construction.

[0045] After obtaining the well-to-well distance, this embodiment further introduces a structural consistency factor to correct the inter-well correlation strength. The structural consistency factor can be used to reflect the actual situation of "close but not connected" or "far apart but connected to the same sand body," avoiding unreasonable constraints imposed solely on well distance. Specifically, the structural consistency factor can be composed of multiple sub-factors, such as: same structural unit factor, same stratigraphic factor, fault segmentation factor, and facies similarity factor. For example, when two wells are located in the same fault block and within the same target layer, the structural consistency factor takes a higher value; when there is a fault between the two wells and the fault distance is greater than the effective thickness of the target layer, the structural consistency factor takes a lower value or even zero, thus directly "disconnecting" the edge connection in the well-to-well relationship structure. Furthermore, when sand body connectivity or facies interpretation results are available, facies consistency can be mapped to continuous weights, for example, taking 1 for the same facies, 0.6 for different but adjacent facies, and 0.2 for significantly different facies to reflect the engineering principle that "the more consistent the facies, the stronger the constraint."

[0046] After integrating information on inter-well distance and structural consistency, this embodiment constructs an inter-well weighting relationship. Used for quantitative characterization of wells With well The spatial correlation strength between wells. Preferably, the inter-well weights adopt a function form that decays with distance and are scaled and corrected by a construction consistency factor to form a joint criterion of "the closer the distance, the greater the weight; the more consistent the construction, the greater the weight." For example, a Gaussian kernel decay form can be used: ,in To construct a consistency factor, This is a distance attenuation scale parameter used to control the effective adjacent well range; when Exceeding the preset neighborhood radius At that time, it can be Setting the values ​​directly to zero limits computational complexity and avoids unreasonable influence of distant wells on the inversion results of the current well. and The spacing between wells in the block can be set empirically based on the scale of reservoir connectivity. For example, when the well spacing between wells in the block is 300–800 m and the sand body distribution scale is around 500 m, the spacing can be set accordingly. Set it to 1000m, and The distance is set to 400-600m to ensure that each well has a limited number of effective neighboring wells participating in the constraint, while maintaining a smooth decrease in weight with distance. To avoid excessive constraint due to too many neighboring wells for a single well, this embodiment can optionally retain only the top-weighted wells for each well. Each neighboring well is considered as an effective neighborhood (e.g.) The retained weights are then normalized to ensure that... This ensures that the total amount of constraints for different wells is comparable in subsequent consistency constraints.

[0047] After establishing the inter-well weight relationships, this embodiment further constructs an inter-well relationship structure for regional multi-well constraints. Preferably, reservoir wells are used as graph nodes, and the inter-well weight relationships are used as edge weights to construct the inter-well relationship graph. ,in For well collection, For the set of effective adjacency relations, This is the corresponding weight matrix. It should be noted that the inter-well relationship structure can be used for regularization calculations in subsequent regional consistency constraint terms, and also for dynamically adjusting the neighborhood structure during the outer regional coordinated update process (e.g., gradually strengthening or weakening the constraint strength of certain edges with each inversion iteration), thereby improving the continuity and coordination of the regional parameter field while ensuring the fitting accuracy of individual wells. For example, in cases where there are significant fault divisions within a block, this embodiment can explicitly establish a "fault shielding rule" in the inter-well relationship diagram, where the edge weights between nodes on both sides of a fault are reduced or set to zero, so that the joint inversion naturally follows the geological division boundary in structure; while in continuous structural zones with strong sand body connectivity, higher weight edges are retained to enhance the regional consistency constraint effect.

[0048] For example, multiple wells in the target block (e.g.) ~ When constructing the inter-well relationship structure, the system first extracts the coordinates of the midpoint of the target layer and calculates the equivalent distance matrix between wells. Then, it combines the fault interpretation results to determine whether there is fault cutting between the well pairs, and calculates the stratigraphic consistency factor based on the stratigraphic correlation results, finally forming a weight matrix. For well pairs less than 600m apart and located in the same fault block and sublayer, the weights are maintained at a high level; for well pairs that are close but separated by faults, the weights are significantly reduced or set to zero. The resulting well relationship structure can impose spatial continuity constraints on key fracture parameters of adjacent wells in subsequent steps, avoiding the problem of "reasonable interpretation of a single well but discrete regional distribution," and providing a stable and reliable structural foundation for regional multi-well joint inversion.

[0049] S103, define a fracture parameter vector for joint inversion for each reservoir well, and select at least one key fracture parameter from the fracture parameter vector as a coupling parameter for regional consistency constraints.

[0050] In this embodiment, after obtaining the well test characteristic curves (such as pressure curves and their derivative curves) of each well in step S101, and constructing the inter-well relationship structure reflecting "inter-layer connectivity, structural consistency, and distance attenuation" in step S102, it is still necessary to clarify "what are the parameter objects" to be solved by the joint inversion, and which parameters need to maintain spatial continuity between adjacent wells, so as to truly reduce "multi-well coupling" to the level of computable variables. To this end, this embodiment first constructs a fracture parameter vector (also known as the parameter vector to be inverted) for each well to simultaneously characterize the fracture geometry and seepage capacity characteristics; then, parameters that are representative of the regional fracture development law and have identifiable well test responses are selected from this parameter vector as key fracture parameters for cross-well coupling in the subsequent regional consistency constraint term.

[0051] Specifically, targeting the first [unit / block] within the target block Wellhead (e.g.) (any well in the example), this embodiment defines a fracture parameter vector. Preferably, It should include at least two categories: fracture geometry parameters and seepage capacity parameters. The fracture geometry parameters may include the fracture half-length. Crack width With the number of cracks (Or equivalent to the parameter "equivalent crack area / equivalent crack volume"); seepage capacity parameters may include crack conductivity. (or dimensionless flow-guiding capability) ), reservoir permeability Wellbore skin coefficient With wellbore energy storage coefficient In engineering implementation, to avoid numerical instability in the optimization process due to different units of measurement, this embodiment adopts a parameter representation method of "logarithmic transformation + standardization". For example, for positive parameters ( Logarithmic mapping (etc.) is performed: , , and compare it with dimensionless parameters (such as...) , They are concatenated together to form a unified optimization variable vector. This process can significantly reduce the "gradient bias" problem caused by differences in parameter scales, making subsequent iterative solutions more stable and convergence more reliable.

[0052] Furthermore, to ensure the inversion results meet engineering and geological rationality requirements, this embodiment uses the reservoir engineering and geological prior parameters obtained in S101 (such as target layer thickness, porosity, gas saturation, fracturing scale, perforation length of the well section, consistency of fracturing sections in adjacent wells, and historical interpretation experience values) as the source of parameter boundary constraints, and provides an allowable value range for each parameter. For example, fracture half-length... The permeability can be constrained within the range calculated from the fracturing scale and the effective stimulation radius. Upper and lower limits can be given based on core / logging interpretation or historical block statistics, wellbore skin coefficient. A reasonable range can be set according to the well completion method and flowback situation for wellbore energy storage. An empirical range can be given based on wellbore diameter, well fluid properties, and testing procedures. By explicitly incorporating this prior information into parameter definitions and constraints, this embodiment can avoid spurious optimal solutions in the inversion process where "the curve fits very well, but the parameters do not conform to common sense in engineering," thereby improving the usability of the interpretation results.

[0053] After defining the parameter vector for each well, this embodiment further... At least one key fracture parameter is selected as the coupling parameter for regional consistency constraints. It should be noted that more key fracture parameters are not necessarily better: selecting too many parameters to impose spatial continuity constraints may lead to overly strong regional constraints, suppressing the true differences in individual wells and thus reducing the fitting accuracy of some wells; selecting too few parameters will result in limited improvement in regional continuity. To achieve a balance between "single-well identifiability" and "regional regularity," this embodiment preferably adopts a joint screening principle of "response sensitivity + geological representativeness": on the one hand, the key parameter should have a significant impact on the well test characteristic curve (high identifiability); on the other hand, the key parameter should be able to represent the spatial variation law of fracture development and fracturing effect at the block scale (strong geological / engineering representativeness).

[0054] For example, this embodiment can construct a single-well forward modeling response based on the pressure derivative curve obtained in S101 and a selected seepage physics model (such as radial flow, fracture linear flow, dual-medium or finite conductivity fracture model, etc.). And calculate the parameter sensitivity (Jacobi) matrix:

[0055]

[0056] in The parameter dimension is defined by the derivative curve. Combining the physical meaning of the derivative curve at different flow stages (e.g., early wellbore storage section, transition section, fracture linear flow section, and late radial flow section), this embodiment can determine which parameters have a stronger influence and lower parameter correlation in key flow stages, thus making them more suitable as regional coupling objects. Typically, the fracture half-length... With fracture conductivity (or dimensionless flow-guiding capability) The pressure derivative morphology of the linear flow section and transition section of the fracture has a strong controlling effect and can directly reflect the scale of fracturing and the effectiveness of the fracture. Therefore, it is preferred as a key fracture parameter for regional consistency constraints; while wellbore energy storage With wellbore skin coefficient It is more susceptible to testing regimes, wellbore conditions and near-well contamination, and spatial continuity is not necessarily established. Therefore, it may not be the first choice of coupling parameter when there is a lack of strong geological evidence, or only a weaker regional constraint strength may be applied.

[0057] Based on the inter-well relationship structure in step S102, this embodiment achieves consistent coupling of key fracture parameters at the block scale: for example, by... As a set of key crack parameters And constrain adjacent wells in the regional consistency constraint term of the subsequent objective function. of and Differences with well weight Weighted convergence is used to achieve the engineering-coupling logic of "strong near-wellbore constraints, weak far-wellbore constraints, and fault shielding disconnection constraints." For example, for... Well groups, if and If they are located in the same fault block and the wells are close together, then their and In joint inversion, they will be more strongly pulled toward consistency; if and Although they are close in distance, they are cut by a fault and their weights have been reduced in S102, so the coupling strength of their key parameters increases with... Natural weakening or even failure can avoid erroneous assumptions about continuity across faults.

[0058] Through the above methods, this embodiment, based on the principle of "independent interpretability for each well," further clarifies the parameter organization and coupling objects of multi-well joint inversion, enabling the subsequent construction of the joint inversion objective function in S104 to simultaneously consider: single-well fitting (from the full parameter vector) Support) and regional continuity (composed of key parameter subsets) (Support). This not only improves the computability and convergence stability of the joint inversion framework, but also provides a clear and consistent physical meaning and engineering interpretation basis for the final output regional fracture parameter distribution field.

[0059] S104. Under the constraints of the inter-well relationship structure, a regional multi-well joint inversion objective function is constructed. The joint inversion objective function includes at least a data fitting term to characterize the fitting accuracy of single-well well testing, a regional consistency constraint term to constrain the spatial continuity of key fracture parameters of adjacent wells, and a priori constraint term to limit the range of fracture parameter values.

[0060] In this embodiment, the processing and derivative calculation of the well test data of multiple wells in the target block have been completed in step S101, resulting in the single-well test characteristic curves (e.g., pressure recovery curves) of each well. With Bourdet derivative curve In step S102, the inter-well weight relationship has been established. And form a well-to-well relationship diagram In step S103, a fracture parameter vector has been defined for each well. And preferably select the half-length of the crack. With fracture conductivity As a key coupling parameter of regional consistency constraints Based on this, the core of this step is to unify "single-well curve matching" and "regional spatial coordination" into a single solvable optimization objective, so that the joint inversion can not only ensure the well test fitting accuracy of each well, but also significantly improve the continuity and coordination of fracture parameters at the block scale.

[0061] Specifically, this embodiment first defines a single-well data fitting term to measure the deviation between the well test characteristic curve and the theoretical forward modeling response for each well. For the first... For a well, the theoretical pressure response can be calculated based on its selected flow physics model (e.g., a combination of a finite conductivity fracture model / fracture linear flow model and a radial flow model). and the corresponding theoretical derivative response Considering that well test data is often analyzed on a logarithmic time scale, and that the magnitudes of pressure and derivatives may differ, to avoid one type of curve numerically "overwhelmingly dominating," this embodiment preferably constructs errors in the logarithmic domain and simultaneously introduces joint fitting of pressure and derivative curves to enhance parameter identifiability. For example, the fitting error for a single well can be written as:

[0062]

[0063] in, and These are the observation pressure and observation derivative obtained from S101 (e.g., the derivative points calculated on the logarithmic time axis using the Bourdet algorithm). For the first The number of effective sampling points in the wellbore used for fitting; This is a noise scale for the pressure and derivative of the well (which can be estimated by instrument accuracy, consistency of repeated tests, or dispersion of the derivative scatter plot). and Weighting coefficients for the two types of curves (e.g.) (This makes the derivative more sensitive to the identification of flow stages). A robust loss function (e.g., Huber loss or Tukey loss) is used to suppress the interference of individual outliers and residual wellbore effects during the initial shut-in period on the optimization, thereby improving the robustness of the inversion process. It should be noted that the significance of introducing the derivative curve into the fitting term is that when using only the pressure curve, different parameter combinations may result in a non-unique "equally good fit," while the derivative curve can more clearly characterize the inflection points and slope changes of the flow stages, thus enhancing the fit. , The ability to identify parameters such as...

[0064] In addition to the single-well fitting term, this embodiment further constructs a regional consistency constraint term, which is used to incorporate the inter-well weight relationship obtained in step S102. This is transformed into a computable "adjacent well traction" mechanism, thereby constraining the spatially continuous variation of key fracture parameters. Preferably, the regional consistency constraint term applies only to key coupling parameters. Apply constraints, but do not force all parameters to be consistent across wells, to avoid overly strong constraints that could smooth out the true differences between individual wells. For example, a regional consistency constraint can be written as:

[0065]

[0066] in, For an effective set of adjacent well pairs, The normalized edge weights are obtained from S102. The physical meaning of the square norm used here is: the greater the difference in critical fracture parameters between adjacent wells, the greater the penalty; the larger the weight (closer distance, more consistent structure), the stronger the penalty. Because... use , In formal terms, this constraint is numerically equivalent to ensuring that the "relative change in fracture size and conductivity between adjacent wells" remains smooth, which is more in line with the engineering principle that fracturing stimulation usually exhibits a gradual distribution at the block scale.

[0067] To demonstrate that this embodiment does indeed use a specific block example, this embodiment uses the target block. Taking the well group as an example, the construction method of the above-mentioned regional consistency constraint term is explained as follows: In step S102, the system uses the midpoint of the target layer as the representative point and sets the neighborhood radius. Distance attenuation scale Furthermore, a fault shielding rule is introduced. The resulting inter-well weight relationships can be exemplified as follows: and Located in the same fault block and the same sub-layer, with an equivalent distance of approximately Constructing a consistency factor Therefore, edge weight At a relatively high level (e.g., after normalization approximately) ); and Distance approximately And the stratigraphic comparison is consistent but the phase bands are slightly different (e.g. After normalization, the weights are at a moderate level (e.g., approximately). );and and Although the planar distance is relatively short (e.g.) However, a fault exists between the two wells, and the fault displacement is greater than the effective thickness of the target layer. Therefore, according to the shielding rules, [the following is taken]. ,therefore In the well-to-well relationship diagram, this is equivalent to "breaking the edge," thus avoiding the erroneous continuity assumption across faults from entering the joint inversion. Therefore, it can be seen that... It is not simply a matter of "constraining whoever is closer," but rather the geological structure segmentation and stratigraphic connectivity are explicitly encoded into the optimization structure, making the constraints more in line with engineering practice and more convincing.

[0068] Meanwhile, this embodiment also constructs prior constraint terms to transform the engineering and geological prior parameters obtained in S101 into reasonable restrictions on the inversion parameters, avoiding physically unreliable solutions. Prior constraints can adopt either "hard boundaries" (directly restricting the parameter search space) or "soft penalties" (adding over-boundary penalties to the objective function). Preferably, to ensure differentiability and more stable convergence of the optimization process, this embodiment adopts a soft penalty approach, for example:

[0069]

[0070] in, , These are the lower and upper limits, respectively, determined by prior engineering and geological considerations. (The last part, "still based on...", seems unrelated and likely refers to a separate set of rules.) Taking the well group as an example: Based on the scale of fracturing operations and historical interpretation experience in this block, the fracture half-length can be constrained to within... To constrain the flow conductivity of the fracture within (or equivalent dimensionless conductivity range), constraining reservoir permeability within The skin coefficient of the wellbore is constrained to within This constrains the wellbore storage coefficient to a reasonable range estimated by the well diameter and wellbore fluid. Thus, when a well attempts to achieve a local fit, it can... Pushing to abnormally high values ​​or When pushed to an impossible scale, This will rapidly increase and suppress updates in that direction, thereby improving the engineering reliability of the inversion results.

[0071] Based on the above three categories of sub-items, the final objective function for regional multi-well joint inversion in this embodiment is as follows:

[0072]

[0073] in, This represents the regional consistency constraint strength coefficient. These are the prior constraint strength coefficients. It should be noted that... and Not fixed: In the two-layer iterative optimization framework of this embodiment, the outer-layer regional coordinated update process can be adjusted based on the "single-well fitting residual level" and the "regional parameter dispersion level". Make adaptive adjustments, for example, in the initial stage, prioritize ensuring that a single well can be fitted ( (relatively small), gradually enhance regional coordination after the curve fitting of each well enters the stable range (smaller), (gradually increase), thereby avoiding excessively strong regional constraints from the beginning that could lead to local optima or convergence difficulties; It can be used to suppress inconsistencies and stabilize the iterative process, especially in wells with poor data quality or highly discrete derivative scatter points in the wellbore energy storage section. Prior constraints can significantly improve inversion stability.

[0074] Using the above-described method for constructing the objective function, this embodiment will... The well test fitting problem for multiple wells is uniformly incorporated into the same optimization framework: the data fitting term ensures the matching accuracy of the pressure and derivative curves for each well, and the regional consistency term is weighted... By applying the principles of "same fault block, same layer connectivity, and strong near-well constraint" to the coupling of key fracture parameters, and ensuring that the inversion parameters remain within an engineering-acceptable range through prior constraints, this approach not only solves the problems of discrete results and difficulty in providing a unified regional interpretation in traditional single-well inversion but also provides a clear, differentiable, and engineering-significant optimization objective for the subsequent two-layer iterative solution in step S105.

[0075] S105, the joint inversion objective function is solved using a two-layer iterative optimization method to obtain the fracture parameter inversion results of multiple wells in the target block.

[0076] In this embodiment, the regional multi-well joint inversion objective function has been constructed in step S104.

[0077]

[0078] in Used to ensure the matching accuracy between the characteristic curve of a single well test and the theoretical response. Based on the inter-well weight relationship in step S102 For key crack parameters (e.g.) Apply regional consistency constraints. The engineering and geological priors are mapped to computable value constraints. The purpose of this step is to ensure that the well test response of each well has a good match with the forward model and meets the preset fitting accuracy requirements, while gradually suppressing non-physical abrupt changes in the interpretation of parameters between wells through a regional coordination mechanism, so that the inversion results present continuous, coordinated, and spatially distributed characteristics that conform to geological engineering understanding at the block scale.

[0079] Specifically, this embodiment adopts a "two-layer iterative optimization" framework: the inner layer iteration uses "single well" as the basic update unit, and performs local optimal updates of the current well parameters under the condition of fixed key parameters of neighboring wells; the outer layer iteration uses "region" as the basic coordination unit, and after completing a round of inner layer updates for all wells, evaluates the regional consistency constraint strength, the effectiveness of inter-well weights, and the overall dispersion of the parameter field, and adjusts the constraint environment of the next round of inner layer updates accordingly until the preset convergence conditions are met. Compared to the method of splicing together the parameter vectors of multiple wells and then performing a one-time global solution, the two-layer iterative structure adopted in this invention can effectively reduce the parameter dimensionality and coupling strength of a single optimization, and reduce the risks of numerical instability and convergence difficulties introduced by high-dimensional, highly nonlinear well test forward modeling. At the same time, this structure organizes "single-well fitting" and "regional coordination" in stages, making the inversion process more in line with the engineering interpretation process. That is, first, pressure response matching and parameter availability are ensured at the single-well scale, and then key fracture parameters are uniformly constrained and overall consistency is corrected at the regional scale, thereby improving the continuity and coordination of the block-scale parameter system while ensuring the interpretation accuracy of single wells.

[0080] In the process of optimizing parameters for a single well in the inner layer, for any well Let its crack parameter vector be denoted as ,in and These are key crack parameters. , , These are auxiliary parameters such as permeability, wellbore skin coefficient, and wellbore storage coefficient. During inner-layer iteration, key parameters from adjacent wells are maintained. Fixed (of which) (For the effective neighboring well set formed in step S102), and construct the local objective function for the current well:

[0081]

[0082] in The objective function consists of three terms: the first is the fitting error term of the well pressure versus derivative curve (preferably including the derivative curve to enhance flow stage identification); the second is the neighboring well consistency penalty term; and the third is the prior soft constraint term. The neighboring well consistency penalty term measures and constrains the spatial differences in key fracture parameters between adjacent wells. As the differences in parameters between adjacent wells increase, this term contributes more to the objective function, thus suppressing non-physical abrupt changes in parameters between wells and enhancing regional continuity. The prior soft constraint term introduces engineering and geological prior information to apply flexible constraints to the range or reference interval of fracture parameters. When parameters deviate from a preset reasonable range, a penalty mechanism is used to increase the objective function value, thereby preventing the inversion results from falling into unreasonable parameter domains and improving the stability and engineering usability of the solution. Therefore, the inner layer update is not an independent and isolated fitting of single-well data. Instead, based on meeting the fitting accuracy requirements of the well test response of this well, the key fracture parameters of the adjacent wells are introduced into the same optimization process as the reference quantity for regional constraints. This guides and constrains the update direction and update magnitude of the key parameters of this well, thereby suppressing the non-physical jump of parameters between wells. This allows the inversion results of this well to maintain statistical consistency with those of the adjacent wells, while further showing a continuous and coordinated parameter change trend at the block scale.

[0083] Taking the specific case well group in step S102 as an example For example: Suppose Effective neighboring wells are And the normalized weights are respectively , , Then update in the inner layer. When, if the key parameters of the adjacent wells in the current iteration round are estimated as follows: , , , The system will tend to put of Updated to the optimal solution under the combined effect of the single-well data fitting term and the adjacent well consistency constraint term, so that it satisfies While ensuring accurate pressure-derivative curve matching, it also suppresses unreasonable parameter abrupt changes between the well and its effective neighboring wells; and for well groups shielded by faults (e.g. and In step S102 Even if the two wells are close in plane, the inter-well consistency constraint term does not generate coupling constraints on the well group, thereby blocking the cross-fault constraint transmission at the level of inter-well relationship structure and avoiding the introduction of incorrect spatial continuity assumptions into the inversion process.

[0084] In terms of specific solution strategies, the optimization of parameters in inner single wells can adopt a two-stage update strategy of "global exploration first, followed by local refinement": first, a global coarse search is performed to avoid getting trapped in local optima (e.g., for...). and Within the prior interval, Latin hypercube sampling or coarse grid scanning is performed to quickly screen several sets of candidate initial values; subsequently, local optimization methods, trust region methods, and quasi-Newton methods are used to iteratively update the parameters near the candidate initial values. Since well test forward modeling is typically nonlinear and some parameters are correlated, this embodiment preferably updates key fracture parameters and auxiliary parameters in groups: first, the auxiliary parameters are fixed, and then convergence is prioritized. , To identify the main controlling features of the crack; then update the parameters after the crack parameters have stabilized. , , This refines the matching between the wellbore storage section and the radial flow section. This reduces oscillations caused by parameter coupling and improves the inner-layer convergence stability. The termination condition for inner-layer iteration can be determined jointly based on the decrease in the local objective function of a single well and the magnitude of parameter changes. For example, when continuous... The next update satisfies and The well was determined to have convergent layers, among which... and Desirable Magnitude.

[0085] After completing an inner-layer update for all wells in one round, the outer-layer regional coordinated update process begins. The main function of the outer layer is to assess the "dispersion" and "single-well fit" of the current regional parameter field, and adjust the constraint environment for the next round of inner-layer updates accordingly, especially the regional consistency constraint strength. And the effectiveness of (optional) inter-well weighting relationships. To this end, this embodiment preferably defines a regional dispersion index to quantify the overall discontinuity of key fracture parameters on the adjacency map, for example:

[0086]

[0087] In the process of coordinated updating of the outer region, to facilitate the quantitative evaluation of the overall fit of multiple wells, this embodiment further defines a regional data fit index: in, Indicates the first Wellhead in fracture parameter vector The single-well data fitting cost is used to characterize the matching deviation between the well test characteristic curve and the single-well forward modeling response; This represents the total cost of data fitting for all wells within the target block.

[0088] When the outer layer is updated, the system performs synchronous comparisons. Regional Consistency Indicators The changing trend of the regional constraint strength parameters is used to achieve the desired regional constraint strength parameters. Adaptive adjustment: when When the level remains high and the rate of decline is slow, it indicates that the single-well fitting has not yet fully converged. To avoid premature intervention of regional consistency constraints and affecting the quality of single-well fitting, it is preferable to maintain... No change or appropriate reduction ;when It has stabilized within the preset acceptable range, and If the value is still too large, it indicates that the parameter dispersion at the regional scale is still relatively strong. In this case, it is preferable to gradually increase the value. To enhance regional coordination and suppress unreasonable fluctuations in inter-well parameters:

[0089] For example, a segmented update strategy can be used to update the data. Adjustments were made: Initially, adjustments were made using a smaller amount of... Initiate to prioritize ensuring convergence of single-well data fitting; in Once a preset threshold is reached or its relative rate of change falls below the preset threshold, the regional coordination phase begins, and the changes are gradually increased according to a set step size or proportion. until and Simultaneously satisfying the convergence criterion, a unified balance is achieved between "single-well fitting reliability" and "regional consistency".

[0090] when relative decline rate and relative decline rate season ;

[0091] when When a rebound or significant deterioration occurs (e.g., the increase exceeds a threshold), let ;in , To update the multiplier (e.g., 1.2 vs. 0.8). , The constraint strength is within an engineering-acceptable range. This outer-layer coordinated update mechanism adjusts the regional constraint strength in stages and adaptively, ensuring that the joint inversion prioritizes meeting the fitting convergence requirements of the single-well test response in the early stages of iteration. After the single-well fitting reaches the preset accuracy or stability criterion, the regional consistency constraint is gradually strengthened, thus forming an optimization path that progresses from "single-well convergence" to "regional consistency convergence." This avoids undue influence on the single-well inversion due to excessively strong regional constraints in the early iteration stages, thereby reducing the risk of fracture parameter interpretation deviating from the true physical response.

[0092] Furthermore, the outer layer update can optionally perform "reliability correction" on the inter-well relationship structure. For example, if the well test data quality of a certain well is significantly low, the derivative curve has high noise, or the flow stage is incomplete, forcing it to participate in regional coupling may propagate unreliable information to neighboring wells. Therefore, this embodiment can introduce a single-well data quality coefficient. (This can be estimated using indicators such as the dispersion of the derivative curve in S101, the effective flow section length, and the stability of the pressure gauge), and the edge weights in the outer layer are corrected accordingly.

[0093]

[0094] This reduces the impact of low-quality wells on regional consistency constraints and improves the overall robustness of joint inversion without altering the inter-well topology. Taking well groups as an example, if The derivative curve is significantly affected by the wellbore effect during the initial shut-in period; therefore, the quality coefficient of the outer layer can be set to a lower value (e.g., If the weight of the edge connected to it is reduced after normalization, the weight of the adjacent well will be reduced accordingly, so that the adjacent well will not be overly affected. The uncertainty of the explanation is traction.

[0095] Regarding the convergence criterion, this embodiment preferably adopts a triple criterion of "objective function + parameter change + regional dispersion": when two consecutive rounds of outer layer iterations satisfy... , And regional dispersion When the value drops to a preset threshold or the rate of decrease is below the threshold, the joint inversion process is considered to have converged and the iteration ends. Desirable The system is designed to balance computational efficiency with interpretability. If the maximum number of outer iterations is reached but the convergence condition is still not met, the system can output the current optimal solution and mark the reason for non-convergence (e.g., insufficient data from a single well, fault segmentation leading to an overly sparse connectivity graph), so that engineers can manually review or supplement the data.

[0096] Through the above-described two-layer iterative optimization method, this embodiment can achieve... In multi-well cases, the following effects are achieved: Firstly, the fitting error of the pressure-derivative curves of each well is gradually reduced, avoiding the situation where "the region is very smooth but the fitting of a single well is very poor"; secondly, key fracture parameters... and The fracture parameters exhibit a smooth transition within the connected region of the same fault block, and naturally abrupt parameter changes occur at the fault segmentation points. This results in fracture parameter inversion that satisfies both the well test response and the geological structural segmentation rules, providing reliable input for the parameter field construction in the subsequent step S106.

[0097] S106 After the joint inversion process meets the convergence condition, the fracture parameter inversion results of multiple wells in the target block are output, and a regional fracture parameter distribution field is constructed based on the inversion results to achieve a consistent interpretation of the fracture parameters of multiple wells at the regional scale.

[0098] In this embodiment, once the two-level iterative optimization in step S105 meets the preset convergence condition, the system can obtain multiple wells within the target block (e.g., a well group used throughout steps S101 to S105). The final inversion parameter set under the same optimization framework The inversion results for each well include not only fracture geometry parameters (such as fracture half-length) but also... Crack width Number of cracks (etc.), and also includes parameters related to seepage capacity and wellbore effects (such as fracture conductivity). reservoir permeability Wellbore skin coefficient Wellbore energy storage coefficient (etc.). It should be noted that the above parameters can be selected and tailored according to the seepage physics model used and the completeness of the well test data: when the well test only includes the typical shut-in recovery section and the flow stage is clearly identified, inversion can be performed simultaneously. When the proportion of the energy storage section in the wellbore is too large or the effective segment of the derivative curve is insufficient, the optimal choice can be made for... A priori fixed or weak constraint method is adopted to ensure the stability of the inversion of key crack parameters.

[0099] While outputting the single-well inversion results, this embodiment further generates "single-well interpretation quality indicators" as engineering information output along with the parameters, used to support the reliability control of subsequent regional parameter field construction. For example, the quality indicators include at least: single-well goodness-of-fit indicators (such as the weighted error between the pressure curve and the derivative curve). ), key parameter uncertainty indicators (such as confidence intervals based on local curvature of the objective function or Monte Carlo perturbation estimation), and ), and regional consistency residual indicators (such as ). (Based on well groups) For example, the system can organize the final output of each well into a structured record of "well number - key fracture parameters - auxiliary parameters - quality indicators" and mark the information of the fault block / sub-layer to which it belongs, so that engineers can quickly check abnormal wells (such as wells with low fitting error but large regional consistency residuals or high uncertainty of key parameters).

[0100] When constructing the regional fracture parameter distribution field, this embodiment treats the "inversion results of each well" as discrete sampling points of the regional-scale parameter field and maps them to the spatial coordinate system of the study block to form a set of points with attributes. Specifically, for directional or horizontal wells, to avoid spatial deviations caused by "wellhead coordinates representing the entire well," this embodiment preferably uses representative points of the target layer as spatial markers of the well, such as the midpoint of the perforated section of the target layer, the center point of the tested section, or the coordinates of the midpoint of the fracture-controlling layer. If the block modeling adopts a two-dimensional layer unfolding method, representative points can be projected onto the top / bottom interface of the target layer to form two-dimensional coordinates. Therefore, the well group Key crack parameter estimates , They can be respectively mapped to spatial point sets and This provides input for subsequent interpolation, mapping, and field construction.

[0101] Furthermore, this embodiment preferably constructs at least two types of regional crack parameter distribution fields: crack half-length distribution field. With the distribution field of fracture conductivity .in, Used to reflect the spatial variation of crack geometry within the block. These two metrics reflect the spatial differences in the effective seepage capacity of fractures; in engineering applications, they correspond to the evaluation dimensions of "whether the fracture scale has fully expanded" and "whether the fracture has effective conductivity," respectively. If further support is needed for a comprehensive evaluation of fracturing effectiveness, a comprehensive index field can be generated, such as a fracture equivalent conductivity length field. Or the cracks can be effectively modified to transform the volume of the proxy field, etc., but this embodiment is not limited to this.

[0102] In terms of specific field construction algorithms, this embodiment may employ spatial interpolation and parameter mapping. Spatial interpolation methods may include inverse distance weighting (IDW), ordinary kriging (OK), or constrained co-kriging; parameter mapping methods may include partitioning assignment and smooth transition based on structural units / facies zones. To ensure the regional field is both continuous and conforms to structural segmentation rules, this embodiment preferably introduces a "structural shielding and anisotropy" mechanism during the interpolation process: the fault shielding relationship identified in step S102 when constructing the inter-well relationship structure can be transformed into a shielding rule for the interpolation neighborhood in S106—well points on both sides of the fault do not participate in each other's weight calculation; simultaneously, if the block has a clear sand body distribution direction or structural orientation, anisotropic distance metrics can be used to set a larger influence radius along the sand body orientation and a smaller vertical or lateral influence radius, thereby making the generated parameter field more consistent with sedimentary and structural control rules. For example, for well groups... In the given block, if the main distribution direction of the sedimentary sand bodies is NE–SW, the system can set anisotropic distances:

[0103]

[0104] in and These represent the distance components in the parallel and perpendicular directions relative to the sand body's strike. This indicates that the influence extends further along the direction; it can be used in IDW interpolation. The weighting is performed in a certain form to avoid generating an unreasonable field shape of "circular diffusion".

[0105] To improve the reliability of the field construction results, this embodiment further introduces the single-well quality index output in step S105 to reweight the interpolation contribution. Specifically, when the key parameters of a well have high uncertainty or large fitting error, even if its spatial location is at the center of a local neighborhood, it should not be given excessive interpolation weight; conversely, for wells with reliable fitting and low uncertainty, their contribution to the regional field should be increased. For example, a well point reliability coefficient can be defined. And introduce it into arbitrary interpolation or mapping weights:

[0106]

[0107] in It can be determined by combining the fitting error of a single well with the width of the confidence interval, for example... , This indicates the relative width of the confidence interval for key parameters. This confidence correction helps suppress the "contamination" of results by low-quality wells during the regional field construction stage, making the final parameter field more robust and engineering-ready.

[0108] After obtaining the regional fracture parameter distribution field, this embodiment further outputs "field-level consistent interpretation results" to achieve the "consistent interpretation of multi-well fracture parameters at the regional scale" as described in the claims. Specifically, the system can output at least the following: First, continuous parameter field raster / grid data (e.g., covered with a 50m×50m grid resolution). The research block and Secondly, spatial gradients or partition statistics of key parameters (e.g., within different blocks). and The mean, variance, and coefficient of variation are used to determine the regional differences in fracturing effects; thirdly, the consistency verification results between well points and the parameter field (for example, for each well, the field values ​​are...). With inversion value Compare the results, output the deviation, and mark abnormal well points. (Still using...) For example, if of If the field value is significantly higher than its surrounding values ​​and the well's fitting error is large, the system can mark it as a candidate well that "may have overfitting or data anomalies," prompting engineers to review the well test data processing or well condition interpretation; if the overall value within a fault block is significantly higher than the surrounding values ​​and the well's fitting error is large, the system can mark it as a candidate well that "may have overfitting or data anomalies," prompting engineers to review the well test data processing or well condition interpretation; If the field value is low and consistent with the insufficient proppant application shown in the fracturing operation curve, it can be used as direct evidence for evaluating the fracturing effect and input into subsequent decision-making.

[0109] Through the output of S106 and the construction of the regional parameter field, this embodiment achieves a closed loop from "multi-well joint inversion" to "regional continuous interpretation": on the one hand, The fracture parameter inversion results of each well are obtained under the same constraint framework, which is comparable and consistent. On the other hand, the regional fracture parameter distribution field can transform the discrete well point interpretation results into continuous spatial information that can be used for geological modeling, fracturing effect evaluation and well network optimization, thereby avoiding the problem of regional scale parameter dispersion and difficulty in extrapolation caused by traditional single-well independent interpretation, and significantly improving the reliability and usability of well test interpretation results in engineering applications.

[0110] S107. Based on the regional fracture parameter distribution field, generate block-scale engineering interpretation and application output, and perform consistency verification and application validation on the joint inversion results to support fracturing effect evaluation, well network optimization design and geological modeling analysis.

[0111] In this embodiment, the regional crack parameter distribution field (e.g., crack half-length distribution field) is obtained in step S106. With the distribution field of fracture conductivity Following this, the "parameter field" needs to be further transformed into interpretive conclusions and outputs that can be directly used for engineering decision-making. It should be noted that while the regional parameter field itself is a "continuous spatial representation," engineering applications often require more explicit zoning, indicators, and conclusion expressions, such as "which fault block has insufficient fracture scale," "which strike has a zone of reduced conductivity," and "which well interpretation results still need verification." Therefore, the core of this step is: without compromising the regional consistency established in S106, to engineer and summarize the continuous parameter field, forming actionable application recommendations, and to improve the overall persuasiveness and reliability through verification mechanisms.

[0112] Specifically, this embodiment first performs "block-based statistical analysis and anomaly identification" on the regional fracture parameter distribution field. Preferably, the system analyzes the structural partitions (fault blocks, sub-layers, facies zones) to... and Perform zonal statistics and output the mean, variance, coefficient of variation, and spatial gradient characteristics of each zone to characterize the "strength and amplitude of fracture development." For example, for well groups... In the study area, the system can divide the study area into two parts, fault block A and fault block B, according to the fault segmentation results: if statistics show that within fault block A... The average is high but A large coefficient of variation can be interpreted as "the overall crack size is sufficient, but the conductivity is significantly affected by support conditions or closure sensitivity"; if within block B... The overall low level, exhibiting a gradual decreasing trend along the sand body's strike, can be interpreted as a "directional decrease in fracture conductivity zone," suggesting potential issues such as phase zonation, stress differences, or insufficient construction support. Simultaneously, the system can also identify anomalous gradient regions in the parameter field (e.g., (For areas that are too large), and designate them as "key areas for verification", prompting engineers to further confirm their causes by combining construction data with geological interpretation.

[0113] After completing the zonal statistics, this embodiment further constructs a "fracturing effect evaluation index system" to... and This is mapped to a more comprehensive index that aligns with engineering semantics, used to support the comparison of fracturing effectiveness. For example, a fracture modification effectiveness index can be defined. This is used to simultaneously characterize the combined contribution of fracture size and conductivity, for example:

[0114]

[0115] in and Use it as a reference scale (the median of the block or the design target value can be taken). This is a weighting coefficient (which can be set according to the project's priorities; for example, if more emphasis is placed on the flow-guiding capacity, then [the weighting coefficient] is used). This index allows the system to further transform the continuous parameter field into a spatial distribution map of "high-reformed zones, medium-reformed zones, and low-reformed zones," thus providing a more intuitive answer to "which areas have better fracturing effects and which areas require reinforcement." (Based on well groups...) For example, if and The surrounding area forms a continuous high Bring, and If the surrounding area has been in a low-value zone for a long time and coincides with the boundary of the fault, it can be interpreted as "the fault segmentation makes it difficult for the transformation effect to extend laterally, and the area needs to optimize fracturing parameters or adjust well location deployment".

[0116] Furthermore, to meet the needs of well network optimization and scheme design, this embodiment outputs "well location and measure suggestions" based on the regional index field, realizing a closed loop from interpretation to decision-making. Specifically, the system can, based on... Based on the spatial distribution and gradient characteristics, three typical recommendations are given: The first is "reinforcement recommendations," which, for areas with low-reformation zones and strong connectivity between adjacent wells, suggest improving the situation by increasing proppant strength, optimizing displacement, or increasing cluster spacing. The second recommendation is "densification," which suggests placing densification wells along the gradient direction in areas with significant spatial gradients at the edges of high-efficiency redevelopment zones to utilize the extension potential of the high-efficiency redevelopment zone. The third recommendation is "avoidance," which addresses areas with significant fault obstruction and... In areas with persistently low values ​​and dramatic gradients, it is recommended to avoid these areas when deploying wells or to adopt a segmented design across fault blocks. (Still based on...) For example, if a high-conductivity corridor along the NE–SW direction is identified within block A ( (High-value zone), the system can suggest that subsequent well locations should be prioritized along the direction of the corridor, and a transition well should be set up near the boundary of the corridor to verify the location of the high-value zone boundary; if there is a low-value zone caused by fault shielding in the B fault block, it is recommended that the fracturing section design avoid the fracture crossing the shielding boundary or reduce the uncertainty risk through segmented isolation.

[0117] To enhance the persuasiveness and traceability of the results, this embodiment also performs "consistency verification and application validation" on the joint inversion and regional parameter field construction results. Preferably, the system performs at least the following two types of validation: The first type is internal consistency verification, which checks whether the "well point inversion value" and the "value of the regional field at the well point location" are consistent, and quantifies and attributes the deviation. For example, for each well... Calculate well point deviation:

[0118]

[0119] The deviation is assessed by combining single-well quality indicators (fitting error and uncertainty): if the deviation is large and the well's fitting error is also large, it is more likely a data or model stage identification problem; if the deviation is large but the well's fitting quality is high, it suggests that the regional interpolation neighborhood setting or fault shielding rules may need adjustment. The second category is external application verification, which involves comparing key fracture parameters or comprehensive indices with the available engineering responses. For example, consistency comparisons are made with fracturing operation curve characteristics (proppant strength, displacement, net pressure change trends), production dynamic indicators (initial production capacity, pressure drop rate, decline characteristics), or external data such as microseismic / tracer data. This is done by well group. For example, if of This aligns with its actual post-production dynamic characteristics of "high early yield but rapid decline" (high flow capacity but potentially sensitive to closure), and its surrounding parameter field also exhibits high... However, a high gradient can serve as evidence of "interpretation-dynamic consistency," enhancing the reliability of the conclusion; if a well is interpreted as having a high gradient... However, the low yield over a long period suggests the existence of non-crack factors (such as water content, damage, and effective thickness deviation) that need to be further incorporated into the prior constraints or model selection.

[0120] Furthermore, this embodiment optionally employs a "leave-one-out cross-validation" method to test the generalization ability of the regional parameter field, in order to avoid overfitting of the field formed solely by fitting all well points. Specifically, the system can perform well group... Perform the "leave one" operation one by one: remove one well at a time. The inversion values ​​are used to construct the parameter field using only the remaining wells. , Then use it in The predicted value and By comparing and calculating the prediction error, and statistically analyzing the mean square error or average relative error, the stability of the regional field is quantified. Through this mechanism, the system not only outputs "what the result is," but also "whether the result remains stable after removing some information," making the regional interpretation of this invention more engineering-convincing.

[0121] Through the engineering output, verification, and validation of S107 above, this embodiment further transforms the joint inversion results of steps S101 to S106 into "interpretable, usable, and verifiable" block-scale conclusions: it can not only provide the continuous distribution law of multi-well fracture parameters at the regional scale, but also implement them as direct inputs for fracturing effect evaluation, well network deployment, and geological modeling. Furthermore, it reduces the uncertainty and controversy of the results through consistency verification and cross-validation mechanisms, thereby significantly improving the reliability and application value of well test interpretation results in block evaluation and development decision-making.

[0122] Other implementation methods

[0123] Based on the specific embodiments described above, the fracture parameter joint inversion method based on regional multi-well constraints proposed in this invention can also have various implementation forms. As long as the implementation form still follows the overall technical route of: S101 acquiring multi-well test data and prior information, S102 constructing an inter-well weight relationship structure, S103 defining fracture parameter vectors and coupling parameters, S104 constructing a joint objective function including fitting terms, consistency terms, and prior terms, S105 using two-layer iterative optimization to solve, S106 outputting multi-well parameter inversion results, and S107 constructing a regional fracture parameter distribution field, then any adjustments made to some data conditions, execution methods, or parameter composition are equivalent substitutions for this invention and should fall within the protection scope of this invention.

[0124] Other implementations of S101: Equivalent introduction of multi-source, multi-stage well test characteristic data. In another implementation, the well test data, in addition to instantaneous bottomhole flowing pressure data, may further include pressure recovery stage data, segmented well test data, and multi-condition well test data (such as different production regimes, different shut-in times, or multiple retest data). In this implementation, the construction of single-well well test characteristic curves in S101 still revolves around the "pressure-time response and its derivative curves," but the dimensionless pressure curves and pressure derivative curves obtained under different conditions can be uniformly mapped to the same feature space as inputs for the single-well forward modeling function and data fitting terms, thereby improving the adaptability to complex well conditions. It should be noted that this extension only changes the data coverage of S101 and the sample segment selection method required for single-well characteristic curve construction, and does not change the construction of inter-well constraints, objective function structure, and two-layer iterative solution framework in S102–S107.

[0125] In another embodiment, when some wells lack complete well test curves, alternative "equivalent well test characteristic data" can be used to supplement them. For example, key interpretation quantities (wellbore storage coefficient, wellbore skin coefficient, boundary effect parameters, identified flow section characteristic points, etc.) from the already interpreted well test results report, as well as production system statistics consistent with the well test section, can be used to construct characteristic constraints equivalent to the standard well test derivative curve. This method still satisfies the requirement of "constructing single-well well test characteristic curves" in S101, and the data fitting term in S104 can be equivalently fitted by a combination of "curve error + characteristic point error," thus still reproducing the joint inversion process of this invention.

[0126] Other implementations of S102: Extended construction and dynamic adjustment of inter-well weight relationships. In another implementation, in addition to constructing the inter-well relationship structure based on inter-well spatial distance and structural consistency factors, inter-well injection-production relationships, fracturing operation similarity, or formation zoning information can be introduced as weight correction factors to adjust the inter-well weight coefficients. For example, when two wells are in the same fracturing operation batch, and the fracture orientation and stimulation scale are similar, their weights can be increased; when two wells are located in different formation zoning zones or have obvious dividing boundaries, their weights can be decreased or set to zero. This implementation only extends the set of weight calculation factors in S102 and does not change the way the "inter-well consistency constraint term" calls the weight matrix in the subsequent S104, nor does it change the two-layer iterative solution structure of S105.

[0127] In another implementation, the inter-well weight relationship can employ a dynamic update mechanism. Specifically, during the coordinated update process in the outer region of S105, the inter-well weights are adaptively adjusted based on the differences in key fracture parameters obtained in the current iteration. When the differences in key parameters between adjacent wells are excessively large over a long period and cannot be improved through single-well fitting, the corresponding edge weights can be reduced to avoid unreasonable traction. When the parameters of adjacent wells gradually become consistent and the fitting error is controllable, the weights can be increased to improve regional continuity. Essentially, this approach allows for equivalent updates in the outer iteration of S105, based on the initial inter-well relationship structure constructed in S102, without altering the overall form and solution logic of the joint inversion objective function.

[0128] Other implementations of S103: Simplification, expansion, and selection of coupling parameters for fracture parameter vectors. In another implementation, the fracture parameter vector can be simplified based on reservoir type and data completeness. For example, under conditions of limited data or focused interpretation objectives, only fracture half-length and fracture conductivity are selected as the main inversion parameters and used as key coupling parameters for regional consistency constraints. Other parameters (such as wellbore storage coefficient and wellbore skin coefficient) can be fixed as prior values ​​or used as weak constraint parameters in the solution. This approach still satisfies the parameter vector definition and key coupling parameter selection requirements of S103 and maintains the mathematical structure of consistency constraint terms unchanged in S104.

[0129] In another implementation, when studying the development of natural fractures or complex fracture networks in the study block, the fracture parameter vector can be expanded to include equivalent fracture density, fracture network anisotropy indices, or equivalent permeability tensor parameters to characterize the differences in the directionality and connectivity of the fracture system. In this case, the key coupling parameters in S103 can be selected as indices more representative of the regional fracture development characteristics (such as equivalent fracture density or dominant orientation conductivity) to ensure that regional consistency constraints reflect the dominant laws at the block scale. Regardless of the parameter dimensions, their processing in the joint objective function of S104 still follows a unified framework of "single-well fitting + regional consistency + prior constraints".

[0130] Other implementations of S104: Equivalent replacement and robustness of the joint objective function term. In another implementation, to improve robustness to outliers or noise interference, the single-well test fitting error term in S104 can be replaced by the squared error form with Huber loss, Cauchy loss, or weighted residual form; the regional consistency constraint term can also be replaced by L2 regularization with L1 regularization or elastic net regularization to enhance adaptability to local abrupt changes (such as parameter abrupt changes near faults). This method only changes the error metric function or regularization norm form, but still maintains the structure of the joint objective function consisting of "fitting term + consistency term + prior term" unchanged, and does not affect the two-level iterative optimization process of S105.

[0131] In another implementation, when a known block has a clearly defined structural boundary, the regional consistency constraint term in S104 can introduce a "boundary shielding weight" or a "partition consistency constraint strategy" to significantly reduce or eliminate the constraint strength on both sides of the fault, while maintaining strong continuity constraints within the same partition. This approach represents an engineering implementation of the weight structure in the objective function of S102, without altering the overall form and solution framework of the joint objective function.

[0132] Other implementations of S105: Equivalent implementation of the two-layer iterative solution strategy. In another implementation, the inner layer single-well parameter optimization of S105 can be implemented using different numerical optimization algorithms, including but not limited to Levenberg-Marquardt, quasi-Newton methods, conjugate gradient methods, or iterative solutions based on stochastic gradients; the outer layer regional coordination update can be implemented using the alternating direction multiplier method (ADMM) or a split solution strategy based on Lagrange multipliers. The above substitution only changes the solver implementation method and does not change the two-layer iterative structure of "updating single-well parameters in the inner layer under fixed neighboring well conditions, and coordinating the regional scale parameter distribution and determining convergence in the outer layer".

[0133] In another implementation, to reduce computational load, the outer-layer iteration can employ a partitioned parallel update strategy. This involves dividing the well-to-well relationship graph into structural units or connected subgraphs, performing inner-layer updates in parallel on each subgraph, and then coordinating global consistency at the outer layer. This approach still follows the two-layer iteration concept of S105, and its convergence criterion can still use the magnitude of the objective function decrease, the magnitude of changes in key parameters, or the change in the residual of the consistency term as a unified criterion, thereby achieving an equivalent reproduction of the steps of this invention.

[0134] Other implementations of S106–S107: Extensions to parameter output and regional parameter field construction methods. In one additional implementation, in addition to fracture geometry and conductivity parameters, the multi-well fracture parameter results output by S106 can also simultaneously output parameter uncertainty indices (such as confidence intervals, posterior variance, or sensitivity coefficients) to characterize the reliability of the inversion results; these uncertainty indices can be obtained by comprehensively evaluating the inner layer fitting residuals and the outer layer consistency term residuals. This extension does not change the basic content and form of the multi-well parameter output, but only adds an expression of the results' reliability.

[0135] In another embodiment, the regional fracture parameter distribution field of S107 can be constructed using different spatial mapping methods, including but not limited to Kriging interpolation, radial basis function interpolation, inverse distance weighted interpolation, or parameter mapping based on regular grids. When the well network in the block is dense and has obvious directionality, anisotropic interpolation or a partitioned interpolation strategy along the structural strike can also be used. It should be noted that, regardless of the spatial mapping method used, the input is derived from the multi-well fracture parameter results after joint inversion convergence. Therefore, this step is an equivalent replacement of the parameter field construction tool and does not change the basic technical idea of ​​the present invention to achieve consistent interpretation through regional multi-well constraints.

[0136] In summary, the other embodiments described above are all equivalent modifications or engineering extensions of the data sources, weighting factors, parameter dimensions, error metrics, solver implementations, and spatial mapping methods under the overall framework of "regional multi-well constrained joint inversion" proposed in this invention. Their common feature is that they still follow the processing chain of S101–S107, and integrate the multi-well fracture parameter inversion into the same optimization framework for solving through the inter-well relationship structure and regional consistency constraint mechanism. They do not change the core idea of ​​this invention to achieve multi-well fracture parameter collaborative inversion and regional scale consistent interpretation, and should all be considered to fall within the protection scope of this invention.

Claims

1. A joint inversion method for fracture parameters based on regional multi-well constraints, characterized in that, include: Acquire well test data, well location spatial information, and reservoir engineering and geological prior parameters of multiple reservoir wells within the target block, and construct single-well well test characteristic curves for each reservoir well based on the well test data to characterize the pressure response behavior of each reservoir well during the well test process; Based on the well location spatial information and structural consistency information, an inter-well weight relationship is constructed to quantitatively describe the spatial correlation strength of multiple reservoir wells. The reservoir wells are used as nodes, and the inter-well weight relationship is used as the connection weight to form an inter-well relationship structure for regional multi-well constraints. A fracture parameter vector for joint inversion is defined for each reservoir well, and at least one key fracture parameter that is representative of the regional fracture development characteristics is selected from the fracture parameter vector as a constraint parameter for regional consistency coupling under the constraints of the inter-well relationship structure. Under the constraints of the inter-well relationship structure, a regional multi-well joint inversion objective function is constructed. This joint inversion objective function includes at least: a data fitting term characterizing the matching degree between the single-well test characteristic curve and the theoretical seepage response; a regional consistency constraint term constraining the spatial continuity of key fracture parameters in adjacent wells through the inter-well relationship structure; and engineering and geological prior constraints limiting the range of fracture parameter values. A two-layer iterative optimization method, including an inner-layer single-well parameter optimization process and an outer-layer regional coordinated update process, is used to solve the regional multi-well joint inversion objective function. Wherein: the inner layer... The single-well parameter optimization process is used to locally optimize and update the fracture parameter vector of the current reservoir well under the condition of fixed key fracture parameters of adjacent wells; the outer-layer regional coordinated update process is used to coordinate the fracture parameter distribution at the regional scale after the parameter updates of multiple reservoir wells are completed, and to determine whether the joint inversion process meets the preset convergence conditions; after the joint inversion process meets the convergence conditions, the fracture parameter inversion results of multiple reservoir wells are output, and a regional fracture parameter distribution field is constructed based on the inversion results to achieve collaborative inversion and consistent interpretation of fracture parameters of multiple wells at the regional scale.

2. The joint inversion method for fracture parameters based on regional multi-well constraints according to claim 1, characterized in that: The fracture parameter vector includes at least fracture geometric parameters and permeability parameters. The fracture geometric parameters include fracture half-length, fracture width, and number of fractures. The permeability parameters include fracture conductivity, reservoir permeability, skin coefficient, and wellbore energy storage coefficient. At least one parameter representative of the regional fracture development characteristics is selected from the fracture parameter vector as a key fracture parameter for regional consistency constraints, which is used for parameter coupling and constraints in the regional multi-well joint inversion process.

3. The joint inversion method for fracture parameters based on regional multi-well constraints according to claim 1 or 2, characterized in that: The inter-well weight relationship is determined based on the inter-well spatial distance and structural consistency factor, and is used to characterize the spatial correlation strength between multiple reservoir wells. The reservoir wells are used as graph nodes and the inter-well weight relationship is used as the edge weight to construct an inter-well relationship graph for regional multi-well joint inversion. The inter-well relationship graph is used to apply regional consistency constraints to the key fracture parameters of adjacent wells during the joint inversion process.

4. The joint inversion method for fracture parameters based on regional multi-well constraints according to claim 3, characterized in that: The objective function for the multi-well joint inversion in the region includes a single-well test fitting error term, an inter-well consistency regularization term, and a priori constraint term. The single-well test fitting error term is used to characterize the degree of matching between the single-well test characteristic curve and the theoretical seepage response. The inter-well consistency regularization term is used to constrain the continuous spatial variation of key fracture parameters in adjacent wells. The priori constraint term is used to limit the values ​​of fracture parameters within a reasonable range for engineering and geology.

5. The joint inversion method for fracture parameters based on regional multi-well constraints according to claim 4, characterized in that: The inner layer single-well parameter optimization in the dual-layer iterative optimization method is carried out under the condition of fixed fracture parameters of adjacent wells. By iteratively optimizing and solving the local objective function of a single well, the fracture parameter vector of the corresponding reservoir well is updated, thereby reducing the deviation between the well test characteristic curve and the theoretical seepage response.

6. The joint inversion method for fracture parameters based on regional multi-well constraints according to claim 5, characterized in that: The outer region update in the dual-layer iterative optimization method is performed after the inner layer parameter optimization of multiple reservoir wells is completed. The joint inversion objective function at the regional scale is evaluated as a whole, the weight relationship between wells and the regional consistency constraint strength are updated in a coordinated manner, and the preset convergence conditions are judged based on the change of the objective function or the change of fracture parameters.

7. The joint inversion method for fracture parameters based on regional multi-well constraints according to claim 1, characterized in that: The single-well forward modeling process matches the corresponding seepage physics model according to the reservoir properties, fracture morphology and boundary conditions. The seepage physics model includes one or more of the following: radial flow model, fracture linear flow model, dual-medium seepage model and finite conductivity fracture model, which are used to generate the corresponding theoretical pressure response.

8. The joint inversion method for fracture parameters based on regional multi-well constraints according to claim 1, characterized in that: The regional fracture parameter distribution field is constructed by spatial interpolation or parameter mapping of the fracture parameter inversion results of multiple reservoir wells. The regional fracture parameter distribution field includes at least a fracture half-length distribution field and a fracture conductivity distribution field, which are used to characterize the spatial continuity of fracture parameters at the regional scale.

9. The joint inversion method for fracture parameters based on regional multi-well constraints according to claim 8, characterized in that: The well test interpretation output includes fracture geometry parameters, fracture conductivity parameters, and reservoir seepage parameters of multiple reservoir wells, and further outputs a continuous fracture parameter distribution field covering the entire study block, which is used for fracturing effect evaluation, well network optimization design, and geological modeling analysis.

10. The joint inversion method for fracture parameters based on regional multi-well constraints according to claim 1, characterized in that: The well test interpretation output includes, but is not limited to, fracture geometry parameters, fracture conductivity parameters, and reservoir permeability parameters for each well, and further outputs a continuous fracture parameter distribution field covering the entire study block for fracturing effect evaluation, well network optimization, and geological modeling.

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