Well interference early warning threshold determination method, device, equipment and storage medium

Abstract

This application provides a method, apparatus, equipment, and storage medium for determining the early warning threshold for crosstalk between adjacent wells. The method includes: acquiring geological and geostress parameters of the target reservoir and its surrounding rock formations, as well as historical fracturing engineering parameters and production capacity data of the target fracturing well; generating multiple sets of simulated parameters for the wells to be fractured; based on the acquired parameters and the multiple sets of simulated parameters, generating a first parameter, a second parameter, and a third parameter corresponding to each set of simulated parameters. The first parameter indicates the degree of overlap between the inter-well fracture and reservoir stimulation volumes; the second parameter indicates the degree of influence of fracturing fluid volume on the crosstalk intensity; the third parameter indicates the degree of reduction in the production capacity of the target fracturing well due to crosstalk; determining a target third parameter greater than a preset threshold; and determining the threshold range of the first and second parameters based on the first and second parameters associated with the target third parameter. This method provides a low-cost, rapid, and objective early warning means for crosstalk.

Description

Technical Field

[0001] This application relates to the field of oil and gas field development engineering, and in particular to a method, apparatus, equipment and storage medium for determining the early warning threshold of adjacent well crosstalk. Background Technology

[0002] Crosstalk refers to the phenomenon in oil and gas field development, particularly in deep coal and gas reservoirs employing dense horizontal well deployment and volumetric fracturing techniques. During fracturing operations in new wells, the expansion of fractures or the extension of the reservoir stimulation volume (SRV) causes the fracture network or SRV region of the new well to overlap or connect with that of adjacent wells. This crosstalk can cause fracturing fluid or proppant from the new well to infiltrate the control zone of older wells, leading to abnormal increases in water production and decreases in gas production. In severe cases, it can even cause water flooding, significantly impacting development efficiency and economic benefits. Crosstalk is particularly prominent in deep coal and gas reservoirs because the fracture propagation patterns in coal and gas differ fundamentally from those in conventional shale gas, and the risk is even higher due to geological heterogeneity and the superposition of engineering parameters.

[0003] In existing technologies, early warning of crosstalk mainly relies on two types of methods: one is prediction based on numerical simulation, which uses a geomechanical model to simulate the fracture propagation process and combines stress evolution analysis to predict the possibility of crosstalk; the other is based on field monitoring technologies, such as microseismic monitoring, which uses microseismic signals collected during the fracturing process to invert the spatial distribution of fractures and directly identify the overlap between the fracture extension range and the SRV of adjacent wells. Numerical simulation methods usually involve multi-field coupled calculations to theoretically assess the risk; while microseismic monitoring provides empirical data, but requires the deployment of expensive surface or in-well sensor arrays.

[0004] However, the aforementioned existing technologies have the following problems: First, due to the interaction of complex geological structures (such as the development of natural fractures and variable stress fields) and engineering factors (such as fracturing scale and well spacing), traditional numerical simulation methods simplify actual conditions, resulting in significant deviations between predicted results and actual mine conditions, making it difficult to accurately reflect the dynamic process of crosstalk. Second, although microseismic monitoring can provide direct evidence, it is costly and complex to deploy, making it impossible to apply in every well, thus limiting its widespread use and economic efficiency. More importantly, crosstalk effects are random and cumulative, and existing methods cannot reflect the extent to which crosstalk damages production capacity, resulting in a lack of basis for fracturing scale design. Summary of the Invention

[0005] This application provides a method, device, equipment, and storage medium for determining the early warning threshold of crosstalk between adjacent wells, in order to solve the following problems of the prior art: 1. Due to the complexity of geological and engineering factors, traditional numerical simulation methods often deviate significantly from the actual situation, and microseismic monitoring is costly and cannot be widely applied; 2. Crosstalk effects are random and cumulative, and existing methods are difficult to reflect the degree of damage to production capacity caused by crosstalk, resulting in a lack of basis for fracturing scale design.

[0006] In a first aspect, this application provides a method for determining an early warning threshold for crosstalk between adjacent wells, the method comprising:

[0007] Obtain geological parameters and geostress parameters of the target reservoir and the surrounding rock formations, as well as historical fracturing engineering parameters and historical production data of the target fracturing well in the target reservoir;

[0008] Multiple sets of simulation parameters for wells to be fractured are generated, including the well distance between the well to be fractured and the target fractured well, and the fracture engineering parameters of the well to be fractured.

[0009] Based on the geological parameters, the geostress parameters, the historical fracturing engineering parameters, the historical production capacity data, and multiple sets of simulation parameters, a first parameter, a second parameter, and a third parameter are generated for each set of simulation parameters. The first parameter indicates the degree of overlap between the inter-well fracture and reservoir stimulation volumes of the target fracturing well and the well to be fractured. The second parameter indicates the influence of the fracturing fluid volume of the target fracturing well and the well to be fractured on the crosstalk intensity. The third parameter indicates the degree to which the crosstalk from the well to the target fracturing well reduces the production capacity of the target fracturing well, and the value of the third parameter is positively correlated with the degree of reduction.

[0010] Among the third parameters, a target third parameter greater than a preset threshold is determined;

[0011] Based on the first parameter and the second parameter associated with the target third parameter, a first threshold range for the first parameter and a second threshold range for the second parameter are determined.

[0012] In one possible implementation, the step of generating a first parameter, a second parameter, and a third parameter corresponding to each set of simulation parameters based on the geological parameters, the geostress parameters, the historical fracturing engineering parameters, the historical production capacity data, and multiple sets of simulation parameters includes:

[0013] Based on the geological parameters, the geostress parameters, the historical fracturing engineering parameters, and the simulation parameters, the average fracture half-length of the target fracturing well and the well to be fractured on the crosstalk side, the projected area of ​​the reservoir stimulation volume of the target fracturing well and the well to be fractured on the plane, and the inter-well control area of ​​the target fracturing well and the well to be fractured are determined.

[0014] The first parameter is determined based on the average fracture half-length, the well spacing, the projected area, and the inter-well control area.

[0015] In one possible implementation, the step of generating a first parameter, a second parameter, and a third parameter corresponding to each set of simulation parameters based on the geological parameters, the geostress parameters, the historical fracturing engineering parameters, the historical production capacity data, and multiple sets of simulation parameters includes:

[0016] Based on the geological parameters, the geostress parameters, the historical fracturing engineering parameters, the historical production data, and the simulation parameters, the first average fracture half-length on the crosstalk side of the well to be fractured, the second average fracture half-length on the non-crosstalk side of the well to be fractured, the first total fracturing fluid volume of the target fracturing well, the second total fracturing fluid volume of the well to be fractured, the cumulative fluid production of the target fracturing well before the fracturing of the well to be fractured, the average porosity of the coal seam in the target reservoir, and the inter-well coal seam volume between the target fracturing well and the well to be fractured are determined.

[0017] The second parameter is determined based on the first average fracture half-length, the second average fracture half-length, the first total fracturing fluid volume, the second total fracturing fluid volume, the cumulative fluid production volume, the average porosity of the coal seam, and the inter-well coal seam volume.

[0018] In one possible implementation, the step of generating a first parameter, a second parameter, and a third parameter corresponding to each set of simulation parameters based on the geological parameters, the geostress parameters, the historical fracturing engineering parameters, the historical production capacity data, and multiple sets of simulation parameters includes:

[0019] Based on the geological parameters, the geostress parameters, the historical fracturing engineering parameters, the historical production capacity data, and the simulation parameters, the average daily water production and average daily gas production of the target fracturing well are determined after the fracturing of the well to be fractured is completed.

[0020] The third parameter is determined based on the average daily water production and the average daily gas production.

[0021] In one possible implementation, determining the third parameter based on the average daily water production and the average daily gas production includes:

[0022] The third parameter is determined based on the ratio of the average daily water production to the average daily gas production.

[0023] In one possible implementation, after determining a first threshold range for the first parameter and a second threshold range for the second parameter based on the first parameter and the second parameter associated with the target third parameter, the method further includes:

[0024] Based on the first threshold range, the horizontal coordinate range of the high-risk area in the target two-dimensional coordinate system is determined, and based on the second threshold range, the vertical coordinate range of the high-risk area is determined.

[0025] The target two-dimensional coordinate system is displayed on the target interactive interface, and the high-risk area is displayed on the target interactive interface according to the range of the horizontal coordinate and the range of the vertical coordinate.

[0026] In one possible implementation, determining the first threshold range of the first parameter and the second threshold range of the second parameter based on the first parameter and the second parameter associated with the target third parameter includes:

[0027] Obtain the first statistical feature value of the first parameter associated with the target third parameter, and the second statistical feature value of the second parameter associated with the target third parameter;

[0028] The first threshold range is determined based on the first statistical feature value, and the second threshold range is determined based on the second statistical feature value.

[0029] Secondly, this application provides a device for determining the early warning threshold of adjacent well crosstalk, the device comprising:

[0030] The acquisition module is used to acquire geological parameters and geostress parameters of the target reservoir and the surrounding rock strata, as well as historical fracturing engineering parameters and historical production data of the target fracturing well in the target reservoir.

[0031] The simulation module is used to generate multiple sets of simulation parameters for wells to be fractured, including the well distance between the well to be fractured and the target fractured well, and the fracture engineering parameters of the well to be fractured.

[0032] Based on the geological parameters, the geostress parameters, the historical fracturing engineering parameters, the historical production capacity data, and multiple sets of simulation parameters, a first parameter, a second parameter, and a third parameter are generated for each set of simulation parameters. The first parameter indicates the degree of overlap between the inter-well fracture and reservoir stimulation volumes of the target fracturing well and the well to be fractured. The second parameter indicates the influence of the fracturing fluid volume of the target fracturing well and the well to be fractured on the crosstalk intensity. The third parameter indicates the degree to which the crosstalk from the well to the target fracturing well reduces the production capacity of the target fracturing well, and the value of the third parameter is positively correlated with the degree of reduction.

[0033] A threshold determination module is used to determine a target third parameter that is greater than a preset threshold among the third parameters;

[0034] Based on the first parameter and the second parameter associated with the target third parameter, a first threshold range for the first parameter and a second threshold range for the second parameter are determined.

[0035] Thirdly, this application provides an electronic device, including: a processor, and a memory communicatively connected to the processor;

[0036] The memory stores computer-executed instructions;

[0037] The processor executes computer execution instructions stored in the memory to implement the method as described in the first aspect.

[0038] Fourthly, this application provides a computer-readable storage medium storing computer-executable instructions, which, when executed by a processor, are used to implement the method described in the first aspect.

[0039] The method, apparatus, equipment, and storage medium for determining the early warning threshold for crosstalk between adjacent wells provided in this application have the following technical advantages:

[0040] 1. This method, based on the actual geological parameters and geostress parameters of the target reservoir and historical data of the target fractured wells, sets multiple sets of simulation parameters (such as changes in well spacing and fracturing engineering parameters) and conducts batch numerical experiments to simulate the uncertainties in geology and engineering in real-world scenarios. This is equivalent to reproducing multiple possible operating conditions in a computer, thereby reducing random errors in single simulations. By generating first and second parameters, the complex fracture propagation problem is transformed into a calculable dimensionless index, avoiding direct reliance on high-cost microseismic monitoring. Because the first and second parameters act as surrogate parameters, they can indirectly reflect crosstalk risk, and the calculations are based on existing geological and engineering data, resulting in low cost.

[0041] 2. The larger the value of the third parameter, the more severe the damage to the target fracturing well's production capacity due to crosstalk. This method, based on historical production data and simulation calculations, transforms the abstract degree of damage into a concrete numerical value, eliminating the arbitrariness of subjective judgment. This method determines the first parameter and its threshold range by using a third parameter exceeding a preset threshold (i.e., crosstalk has a severe impact on production capacity). The threshold range defines a high-risk area for crosstalk: when the first and second parameters corresponding to the fracturing design of the well are expected to fall within this range, crosstalk may cause a significant decrease in the target fracturing well's production capacity. In field applications, only the estimated values ​​of the first and second parameters of the well to be fractured need to be calculated to quickly determine the crosstalk risk and avoid over-fracturing. Attached Figure Description

[0042] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this application and, together with the description, serve to explain the principles of this application.

[0043] Figure 1 A flowchart illustrating the method for determining the early warning threshold for crosstalk between adjacent wells provided in this application embodiment. Figure 1 ;

[0044] Figure 2 A flowchart illustrating the method for determining the early warning threshold for crosstalk between adjacent wells provided in this application embodiment. Figure 2 ;

[0045] Figure 3 Three-dimensional geological model diagram provided for embodiments of this application;

[0046] Figure 4 A wellbore structure model diagram provided for an embodiment of this application;

[0047] Figure 5 This is a schematic diagram of a fracturing model provided in an embodiment of this application;

[0048] Figure 6 Schematic diagrams of fracture networks under different geological conditions and fracturing conditions provided for embodiments of this application;

[0049] Figure 7 A schematic diagram of the capacity simulation numerical model SRV provided in the embodiments of this application;

[0050] Figure 8 A schematic diagram of a model generated by the history matching module of numerical simulation software to complete a history fitting study, provided for an embodiment of this application;

[0051] Figure 9 A schematic diagram of the initial calculation provided for an embodiment of this application;

[0052] Figure 10 A schematic diagram illustrating the selection of critical parameters provided in the embodiments of this application;

[0053] Figure 11 A schematic diagram of the adjacent well crosstalk early warning threshold determination device provided in this application;

[0054] Figure 12 A schematic diagram of the structure of the electronic device provided in this application.

[0055] The accompanying drawings illustrate specific embodiments of this application, which will be described in more detail below. These drawings and descriptions are not intended to limit the scope of the concept in any way, but rather to illustrate the concepts of this application to those skilled in the art through reference to particular embodiments. Detailed Implementation

[0056] Exemplary embodiments will now be described in detail, examples of which are illustrated in the accompanying drawings. When the following description relates to the drawings, unless otherwise indicated, the same numbers in different drawings denote the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments matching this application. Rather, they are merely examples of apparatuses and methods matching some aspects of this application as detailed in the appended claims.

[0057] To address the following problems in existing technologies: 1. Due to the complexity of geological and engineering factors, traditional numerical simulation methods often deviate significantly from actual conditions, and microseismic monitoring is costly and cannot be widely applied; 2. Crosstalk effects are random and cumulative, and existing methods are unable to reflect the degree of damage to production capacity caused by crosstalk, resulting in a lack of basis for fracturing scale design. The inventors of this application have considered that geological parameters and geostress parameters of the target reservoir and its surrounding rock strata can be collected, as well as historical fracturing engineering parameters and historical production capacity data of the target fracturing well (old well). Then, multiple sets of simulation parameters for the new well to be fractured can be generated, such as the well distance and fracturing engineering parameters with the old well. Using the acquired data and simulation parameters, the first parameter (representing the degree of overlap between the inter-well fracture and reservoir stimulation volume of the new well and the old well), the second parameter (the degree of influence of fracturing fluid volume on crosstalk intensity), and the third parameter (the degree of reduction in the production capacity of the target fracturing well due to crosstalk from the new well to the target fracturing well) can be calculated. At this time, multiple combinations of the first to third parameters can be obtained. Then we can filter out combinations with relatively large third parameter values ​​(the larger the third parameter, the greater the impact of crosstalk on production capacity). Based on the first and second parameters associated with these larger third parameters, we can determine the threshold ranges for the first and second parameters. These threshold ranges indicate the range within which the first and second parameters significantly impact the production capacity of the old well. Therefore, when fracturing a new well, we only need to obtain the first and second parameters of the new well and compare them with the previously simulated threshold ranges to determine whether the fracturing design of the new well will have a significant impact on the production capacity of the old well. In this way, by utilizing the simulated threshold ranges for the first and second parameters, engineers can understand the degree of impact of the current first and second parameters of the new well on the production capacity of the old well, providing a basis for the fracturing design of the new well. Furthermore, this threshold calculation method only requires the acquisition of geological engineering data, which is far less costly than using microseismic methods.

[0058] The technical solution of this application and how the technical solution of this application solves the above-mentioned technical problems are described in detail below with specific embodiments. These specific embodiments can be combined with each other, and the same or similar concepts or processes may not be described again in some embodiments. The embodiments of this application will be described below with reference to the accompanying drawings.

[0059] Example 1

[0060] Figure 1 A flowchart illustrating the method for determining the early warning threshold for crosstalk between adjacent wells provided in this application embodiment. Figure 1 This method can be executed by the server, such as... Figure 1 As shown, the method includes:

[0061] S101. Obtain the geological parameters and geostress parameters of the target reservoir and the surrounding rock formations, as well as the historical fracturing engineering parameters and historical production data of the target fracturing well in the target reservoir.

[0062] In this step, the server can use the API interface to make batch calls to external databases to obtain the above data. These external databases include: geological databases, geostress databases, and engineering history databases.

[0063] Geological parameters of the rock formations surrounding the target reservoir are obtained from the geological database. These geological parameters may include parameters such as porosity, permeability, adsorbed gas content, and reservoir thickness of the top and bottom plates of the target reservoir. These data can be obtained from well logging interpretation systems and seismic data platforms.

[0064] The geostress parameters of the rock formations surrounding the target reservoir are obtained from the geostress database. These geostress parameters may include, for example, the maximum / minimum horizontal principal stress, Young's modulus, and Poisson's ratio. These data may come from rock mechanics testing systems or field monitoring equipment (such as downhole sensors).

[0065] The fracturing engineering parameters (such as fluid volume, discharge rate, and pump pressure) and production capacity data (such as cumulative fluid production and gas production) of the target fracturing well (old well) are obtained from the engineering history database. These data come from the oilfield production management system.

[0066] The server can standardize the data format, clean the data, and handle missing and outlier values.

[0067] S102. Generate multiple sets of simulation parameters for wells to be fractured. The simulation parameters include the well distance between the well to be fractured and the target fractured well, and the fracture engineering parameters of the well to be fractured.

[0068] In this step, the server can generate multiple sets of simulation parameters for the wells to be fractured, based on the physical constraints of the target reservoir and its surrounding rock formations (such as well spacing range of 300-500m and fracturing fluid volume range of 500-1500m³), using the Latin hypercube sampling algorithm. The number of sets can be, for example, 1000 sets. The simulation parameters include:

[0069] Well spacing: The distance between the new well to be fractured and the target fractured well (old well), such as 300 meters, 320 meters, 500 meters, etc.

[0070] Fracturing engineering parameters include the total amount of fracturing fluid, the flow rate, and the pump pressure of the new well to be fracturing. These parameters can be set based on the fluctuation range of historical data from old wells. For example, if the old well has a fluid volume of 1000 cubic meters, the new well's fluid volume can be generated as a value between 800 and 1200 cubic meters, calculated by ±20%.

[0071] By utilizing the Latin hypercube sampling algorithm, the randomness of the simulated parameter combination can be ensured. At the same time, the parameter values ​​generated by the Latin hypercube sampling algorithm are uniformly distributed, so that the generated simulated parameter combinations cover a comprehensive range of production scenarios.

[0072] Furthermore, the server can assign a unique ID to each set of simulation parameters and store it in the simulation parameter table.

[0073] S103. Based on geological parameters, geostress parameters, historical fracturing engineering parameters, historical production data, and multiple sets of simulation parameters, generate a first parameter, a second parameter, and a third parameter corresponding to each set of simulation parameters. The first parameter indicates the degree of overlap between the inter-well fracture and reservoir stimulation volume of the target fracturing well and the well to be fractured; the second parameter indicates the degree of influence of the fracturing fluid volume of the target fracturing well and the well to be fractured on the crosstalk intensity; the third parameter indicates the degree of reduction in the production capacity of the target fracturing well due to crosstalk from the well to be fractured, and the value of the third parameter is positively correlated with the degree of reduction.

[0074] In this step, the server can use a pre-trained prediction model to quickly estimate the first, second, and third parameters corresponding to each set of simulated parameters. The prediction model could be, for example:

[0075] Random forest model: used to predict the first parameter. Input data may include well spacing, fracturing fluid volume, geostress difference, and reservoir thickness; the output is the first parameter.

[0076] Gradient boosting tree model: used to predict the second parameter. Input data may include, for example, the ratio of new to old well fluid volume, cumulative fluid production, and porosity; the output is the second parameter.

[0077] Neural network model: used to predict the third parameter. The input data can be, for example, the first parameter and the second parameter, and the output is the third parameter. The larger the value of the third parameter, the more severe the reduction in production capacity.

[0078] S104. In the third parameter, determine the target third parameter that is greater than the preset threshold;

[0079] In this step, a preset threshold can be determined based on historical experience. For example, the preset threshold could be 1.2, meaning that when the third parameter is greater than this value, the production capacity of the old well drops significantly. The server will then determine the third parameter that is greater than 1.2 as the target third parameter.

[0080] S105. Based on the first parameter and the second parameter associated with the target third parameter, determine the first threshold range of the first parameter and the second threshold range of the second parameter.

[0081] In this step, the server can obtain the first and second parameters associated with the target third parameter, and then calculate the percentiles (e.g., 90th percentile) of the first and second parameters as the first threshold range and the second threshold range.

[0082] When designing new wells, the first and second parameters of the new well can be calculated to determine whether they exceed the above threshold range. If they do, it means that the fracturing design of the new well has a serious impact on the production capacity of the old well and needs to be adjusted.

[0083] Example 2

[0084] Figure 2 A flowchart illustrating the method for determining the early warning threshold for crosstalk between adjacent wells provided in this application embodiment. Figure 2 ,like Figure 2 As shown, the method includes:

[0085] S201. Obtain the geological parameters and geostress parameters of the target reservoir and the surrounding rock formations, as well as the historical fracturing engineering parameters and historical production data of the target fracturing well in the target reservoir.

[0086] First, it should be noted that this embodiment uses the No. 8 deep coal seam in Daningji County as an example.

[0087] The purpose of this step is to collect geological parameters and in-situ stress parameters of the target reservoir (deep No. 8 coal seam) and surrounding rock strata (roof and floor), as well as historical fracturing engineering parameters and historical production data of the target fracturing well (i.e., old well). Among these:

[0088] Geological parameters include: porosity, permeability, gas saturation, adsorbed gas content, and distribution of natural fractures in the coal and its top and bottom plates. These data can be derived from well logging interpretation results (such as sonic logging and density logging), seismic interpretation horizons (such as structural maps), core descriptions (laboratory analysis), and sedimentary facies interpretation results. For example, porosity distribution can be obtained through well logging curve inversion, and permeability can be calibrated using core data.

[0089] In-situ stress parameters include: maximum horizontal principal stress, minimum horizontal principal stress, Young's modulus, Poisson's ratio, and Lamé constant. These data can be derived from well logging stress interpretation (e.g., based on acoustic anisotropy), seismic rock mechanics parameters (e.g., pre-stack inversion), and low-pressure test results (e.g., obtaining fracture pressure through micro-fracturing). Specifically, dynamic mechanical parameters can be calculated using well logging data and then corrected to static values ​​through low-pressure tests.

[0090] Historical fracturing engineering parameters include: for older wells, collecting fracturing operation data such as total fracturing fluid usage, operation flow rate, maximum pump pressure, sand volume, and operation pressure history. This data can be extracted from the fracturing operation report and needs to be matched with the geological model.

[0091] Historical production data: This includes the cumulative liquid production, cumulative gas production, daily water production, and daily gas production of old wells. This data can be obtained from a production database.

[0092] In the specific implementation of this step, the target area first needs to be delineated, for example, taking a certain well area in Daning-Jixian as the center to determine the distribution range of coal seams. Then, multi-source data is integrated to establish the basic database of the three-dimensional geological model. In the data preprocessing stage, this may include unit normalization (e.g., unifying the stress unit to MPa) and gridded interpolation (e.g., using the Kriging method to generate a continuous attribute field). It should be noted that the key to this step is to verify the reliability of the data through historical fitting. For example, the production history data of old wells is input into numerical simulation software (such as CMG or ECLIPSE), and geological parameters are adjusted so that the error between the simulated gas production and the actual data is less than 10%, thereby ensuring that the model can accurately reflect the underground conditions.

[0093] S202. Generate multiple sets of simulation parameters for wells to be fractured. The simulation parameters include the well distance between the well to be fractured and the target fractured well, and the fracture engineering parameters of the well to be fractured.

[0094] The technical approach of this step is to generate multiple sets of simulation parameters for the wells to be fractured (i.e., new wells) based on orthogonal experimental design to cover different operating conditions. The simulation parameters include the well distance between the well to be fractured and the target fractured well, and the fracturing engineering parameters of the well to be fractured (such as fracturing fluid usage, discharge rate, and pump pressure).

[0095] For well spacing, four typical well spacings can be selected based on the density of the well network on site: 300 meters, 350 meters, 400 meters and 450 meters, representing well layout schemes from dense to sparse.

[0096] For fracturing engineering parameters, the design of old wells can be referenced, and the parameters such as fracturing fluid usage, discharge rate, and pump pressure of the well to be fractured can be designed with variability. Specifically, for example, based on the average parameters of old wells, different strength levels can be generated by scaling them at proportions of 85%, 90%, 95%, 100%, 105%, 110%, and 115%. For example, the fracturing fluid usage can vary in a gradient from 500 cubic meters to 1133 cubic meters.

[0097] In generating multiple sets of simulation parameters, the simulation combinations are arranged using orthogonal arrays to reduce computational load. For example, the well spacing (4 levels) is interleaved with fracturing parameters (7 levels) to form 28 basic combinations. Then, considering other variables (such as roof thickness and stress difference), this expands to 80 simulation tests. Each set of parameters corresponds to a numerical simulation case for subsequent fracture propagation and production prediction. These multiple sets of simulation parameters can be generated using scripts or the built-in experimental design module of the simulation software, ensuring the parameter space uniformly covers different operating conditions.

[0098] S203. For each set of simulation parameters, based on geological parameters, geostress parameters, historical fracturing engineering parameters, and simulation parameters, determine the average fracture half-length of the target fracturing well and the well to be fracturing on the crosstalk side, the projected area of ​​the reservoir stimulation volume of the target fracturing well and the well to be fracturing on the plane, and the well control area between the target fracturing well and the well to be fracturing. Then, determine the first parameter based on the average fracture half-length, well spacing, projected area, and well control area.

[0099] In this step, the first parameter (dimensionless inter-well connectivity parameter) is used to quantify the overlap between fractures and reservoir stimulation volume (SRV) between new and old wells. The calculation formula is as follows:

[0100]

[0101] in:

[0102] The first parameter;

[0103] The average fracture half-length (in meters) on the crosstalk side of the target fractured well (old well), where the crosstalk side refers to the direction closer to the old well;

[0104] Mean: Average fracture half-length (in meters) on the crosstalk side of the well to be fractured (new well);

[0105] Well spacing;

[0106] Here: The projected area of ​​the Laojing SRV on the plane (unit: square meters);

[0107] For: the projected area of ​​the Arai SRV on the plane (unit: square meters);

[0108] The area is the overlapping area of ​​the projected area of ​​the old well SRV on the plane and the projected area of ​​the new well SRV on the plane.

[0109] The area controlled between wells (unit: square meters) can be taken as the product of the well spacing and the fracture propagation length, and is defined as the sum of the well spacing and the average fracture half length, reflecting the potential impact area.

[0110] Specifically, fracture propagation simulations can be performed for each set of simulation parameters. Geological parameters, geostress parameters, historical fracturing engineering parameters, and simulation parameters are used, and the simulation software performs the simulation based on geomechanical equations. During the simulation, the half-length of each fracture cluster is calculated using a linear elastic fracture model, and then the average half-length of the crosstalk side is determined. The fracture mesh module of the simulation software generates an SRV (Self-Range Vessel), and its planar projected area is determined using the grid integration method. The overlapping area is calculated using a geometric intersection algorithm; for example, by superimposing the SRV meshes from two wells, the area of ​​the overlapping units is statistically analyzed.

[0111] When calculating the first parameter, a post-processing tool can be used based on the parameters obtained above to calculate the crack term first:

[0112]

[0113] Recalculate the SRV overlap term:

[0114]

[0115] Finally, the maximum value is taken. The larger the value of the first parameter, the higher the risk of interconnection between the old and new wells.

[0116] S204. For each set of simulation parameters, based on geological parameters, geostress parameters, historical fracturing engineering parameters, historical production data, and simulation parameters, determine the first average fracture half-length on the crosstalk side of the well to be fractured, the second average fracture half-length on the non-crosstalk side of the well to be fractured, the first total fracturing fluid volume of the target fracturing well, the second total fracturing fluid volume of the well to be fractured, the cumulative fluid production of the target fracturing well before the construction of the well to be fractured, the average porosity of the coal seam in the target reservoir, and the inter-well coal seam volume between the target fracturing well and the well to be fractured. Based on the first average fracture half-length, the second average fracture half-length, the first total fracturing fluid volume, the second total fracturing fluid volume, the cumulative fluid production, the average porosity of the coal seam, and the inter-well coal seam volume, determine the second parameter.

[0117] The second parameter (dimensionless fracturing fluid volume parameter V) characterizes the influence of fracturing fluid volume on the crosstalk intensity, and the calculation formula is:

[0118]

[0119] Where V is the second parameter, The average fracture half-length (in meters) on the crosstalk side of the new well. The average fracture half-length (in meters) on the non-crossing side of the new well. Total volume of fracturing fluid in a new well (unit: cubic meters); Total volume of fracturing fluid in old wells (unit: cubic meters); The cumulative fluid production of the old well before fracturing the new well (unit: cubic meters); The average porosity of the coal seam (dimensionless); The volume of the coal seam between wells (unit: cubic meters) is calculated using the formula: coal seam thickness × area between wells.

[0120] Of the parameters mentioned above, the fracture half-length is obtained through the aforementioned simulation process, the total fracturing fluid volume of the old well comes from historical fracturing engineering data of the old well, and the total fracturing fluid volume of the new well comes from simulation parameters. The coal seam volume can be calculated based on a geological model, by multiplying the well spacing by the well section length using the area between wells, and the coal seam thickness is obtained from well logging data.

[0121] In the specific implementation of this step, the first step is to read the half-lengths of the fractures on both sides of the new well from the aforementioned simulation output to ensure correct direction identification. Then, input the fracturing fluid volume data and porosity (e.g., the average porosity of the coal seam is 0.03). When calculating the coal seam volume, for example, with a well spacing of 400 meters, a horizontal well section length of 800 meters, and a coal seam thickness of 10 meters, then... =400×800×10=3.2×10⁶ cubic meters. Finally, substitute into the above formula to calculate the value of the second parameter. The higher the value of the second parameter, the greater the liquid volume intensity and the stronger the crosstalk potential.

[0122] S205. For each set of simulation parameters, based on geological parameters, geostress parameters, historical fracturing engineering parameters, historical production capacity data, and simulation parameters, determine the average daily water production and average daily gas production of the target fracturing well after the completion of the fracturing well construction. Based on the average daily water production and average daily gas production, determine the third parameter.

[0123] The third parameter is used to evaluate the degree to which crosstalk reduces the productivity of older wells. The calculation formula is as follows:

[0124]

[0125] in, As the third parameter, The average daily water production of the old well in the first week after the fracturing of the new well (unit: cubic meters / day); This represents the average daily gas production of the old well in the first week after fracturing the new well (unit: cubic meters / day). The third parameter is in cubic meters per thousand cubic meters. The value of the third parameter is positively correlated with the degree of production reduction (i.e., the larger the third parameter, the more water is produced, and the more severe the gas production is inhibited).

[0126] This step involves simulating production capacity using a dual-pore, dual-permeability model, based on geological parameters, geostress parameters, historical fracturing engineering parameters, historical production data, and simulation parameters. The simulation must consider the damage to gas phase permeability caused by fracturing fluid intrusion. It also requires setting the old well to produce at a constant gas rate (e.g., 100,000 cubic meters / day) after fracturing the new well. The simulation outputs the daily water and gas production for the first seven days, generating a production curve, and then calculates the average daily water and gas production. For example, if the average water production for the first week is 5 cubic meters / day and the average gas production is 100,000 cubic meters / day, then the third parameter = 1000 × 5 / 100000 = 0.05 cubic meters / thousand cubic meters.

[0127] S206. Obtain the first parameter, second parameter, and third parameter corresponding to each set of simulation parameters;

[0128] In this step, for 80 sets of simulated cases, the values ​​of the first, second, and third parameters corresponding to each set of simulated parameters are summarized to form a dataset.

[0129] S207. In the third parameter, determine the target third parameter that is greater than the preset threshold;

[0130] In this step, a preset threshold for the third parameter (e.g., 1.2 cubic meters per thousand cubic meters) can be set based on historical experience. This value indicates that crosstalk has led to a significant decrease in production capacity. Target third parameter correlation cases with a third parameter value greater than 1.2 are then selected.

[0131] S208. Obtain the first statistical feature value of the first parameter associated with the third parameter of the target, and the second statistical feature value of the second parameter associated with the third parameter of the target;

[0132] In this step, statistical characteristic values ​​of the C and V values ​​of the target case are obtained. For example, the mean of the first parameter (e.g., 0.15) and the mean of the second parameter (e.g., 0.2) can be calculated, or quantiles (e.g., the 75th percentile) can be used as statistical characteristics.

[0133] S209. Determine a first threshold range based on a first statistical characteristic value, and determine a second threshold range based on a second statistical characteristic value;

[0134] The threshold range is determined based on statistical characteristic values. For example, the mean ± standard deviation of the first parameter can be used as the first threshold range (e.g., 0.12-0.18), and the mean ± standard deviation of the second parameter can be used as the second threshold range (e.g., 0.18-0.22).

[0135] S210. Based on the first threshold range, determine the range of the horizontal coordinates of the high-risk area in the target two-dimensional coordinate system, and based on the second threshold range, determine the range of the vertical coordinates of the high-risk area.

[0136] Specifically, in the target two-dimensional coordinate system, the first parameter is used as the abscissa and the second parameter as the ordinate. The first threshold range is defined as the abscissa range of the high-risk area, and the second threshold range is defined as the ordinate range, thus delineating the upper right quadrant as the high-risk area.

[0137] S211. Display the target's two-dimensional coordinate system on the target interaction interface, and display high-risk areas on the target interaction interface based on the range of the horizontal and vertical coordinates.

[0138] In this step, a two-dimensional scatter plot is displayed on the target interactive interface (such as a professional software GUI or web interface). Each point represents a set of simulation cases, and the point's color or size can be used to map a third parameter value. High-risk areas are marked with red shading, and the point's location can be displayed in real time to provide early warnings when the user enters new well parameters.

[0139] Figure 2 The method shown, by calculating the first parameter, the second parameter, and the third parameter, achieves at least the following technical effects:

[0140] 1. The core technical effect of the first parameter (dimensionless inter-well connectivity parameter) lies in quantifying the geometric connectivity risk of the fracture system and reservoir stimulation volume (SRV) between new and old wells, providing a spatial basis for assessing the likelihood of hydraulic channeling. The first parameter value directly reflects the degree of interweaving of the inter-well fracture network by comprehensively considering the relative relationship between the sum of fracture half-lengths and well spacing, as well as the proportion of SRV overlap area. When the first parameter value is high, it indicates that the fracturing fractures in the new well may extend into the SRV region of the old well, or that there is a significant overlap between the SRVs of the two, thereby increasing the geometric probability of fracturing fluid channeling. This assessment avoids the limitations of relying solely on fracture length or SRV area, ensuring the comprehensiveness of risk identification.

[0141] During the fracturing design phase, the first parameter value can serve as a reference indicator for well spacing optimization and well placement schemes. For example, if the predicted first parameter value is too high, it suggests that the location of new wells or the direction of fracturing should be adjusted to reduce the risk of interconnection. At the same time, the first parameter value, in conjunction with geological parameters (such as stress field distribution), can help identify potential crosstalk channels in areas of high stress difference, improving the targeting of early warnings.

[0142] By using dimensionless processing, the first parameter value eliminates the dimensional influence of well spacing and fracture size, making risk assessments between different blocks comparable and reducing the complexity of on-site decision-making.

[0143] 2. The core technical effect of the second parameter (dimensionless fracturing fluid volume parameter) lies in characterizing the dynamic influence of fracturing fluid volume on crosstalk intensity. It combines construction parameters with reservoir properties to assess the potential pressure of fluid injection on the production capacity of older wells. The second parameter value comprehensively considers factors such as the fluid distribution in new wells, the historical fluid production of older wells, and the coal seam pore volume, reflecting the fluid load per unit reservoir space. A high second parameter value indicates a high fracturing fluid injection intensity in new wells, and that the pressure from previous fluid production in older wells has not been fully released, which may lead to fracturing fluid easily intruding into the fracture network of older wells, causing water flooding.

[0144] Because the second parameter value is directly related to the fracturing scale design, it can provide a basis for setting the limits of construction parameters (such as fluid volume and discharge rate). For example, by controlling the second parameter value within a reasonable range, the production capacity demand of new wells and the protection objectives of old wells can be balanced, avoiding the negative effects caused by excessive fracturing. At the same time, the second parameter value is sensitive to coal seam properties (such as porosity) and can adapt to risk assessment under different reservoir conditions.

[0145] After the second parameter value is dimensionless, it integrates engineering parameters and geological properties, not only providing early warning of cross-flow risks, but also indirectly reflecting the reservoir's capacity to accommodate fracturing fluid, thus enhancing the engineering applicability of this scheme.

[0146] 3. The core technical effect of the third parameter (average water-to-gas ratio in the first week) lies in directly evaluating the actual damage to the production capacity of old wells caused by fracturing fluid crosstalk, transforming the crosstalk effect into an observable dynamic production indicator. Specifically, the third parameter value, through the ratio of water production to gas production in the first week, can capture the phenomenon of increased water production and inhibited gas production caused by fracturing fluid crosstalk. The higher the value of the third parameter, the deeper the fracturing fluid penetrates the fracture network of the old well, and the more severe the damage to production capacity.

[0147] The third parameter value serves as a critical indicator for classifying risk areas and can be linked with the first and second parameters to form a chart. For example, when the third parameter exceeds a certain threshold, it is identified as a high-risk case, thereby recalibrating the threshold ranges of the first and second parameters.

[0148] Below, in conjunction with Figure 3 , 4 Sections 5, 6, 7, and 8 provide supplementary explanations of the simulation process in this embodiment.

[0149] Figure 3 The three-dimensional geological model diagram provided for the embodiments of this application, such as Figure 3As shown, this figure is a three-dimensional geological mechanism model generated using geological modeling software (such as Petrel or GOCAD). The generation process is based on geological parameters collected in step S201, including well logging interpretation results (such as porosity and permeability curves), seismic interpretation horizons (such as structural surfaces), and core data. The software uses a gridding algorithm (such as Kriging interpolation) to transform discrete data into a continuous three-dimensional attribute field. The grid covers the coal seam and its roof and floor (50 meters above the roof and 50 meters below the floor), and uses color gradients to represent parameter changes (e.g., red represents high-stress areas). For example, after inputting data such as a coal seam thickness of 10 meters and a porosity of 0.03, the software generates a grid model and assigns geostress and mechanical parameters. Figure 3 Its functions include:

[0150] 1. As input to steps S202 and S203, provide geological background for fracture propagation and productivity simulation to ensure that the simulation results are consistent with real geological conditions;

[0151] 2. Highlighting the differences in properties between the coal seam and the roof and floor (such as stress barriers) helps analyze the fracture propagation path and explain why pressure fractures may be restricted or overlap.

[0152] 3. Use 3D visualization to check the data coverage and quality, and avoid simulation deviations caused by missing data.

[0153] Figure 4 The wellbore structure model diagram provided in the embodiments of this application is as follows: Figure 4 As shown, this diagram is a wellbore structure model generated using drilling engineering software such as Landmark or Petrel. The generation process begins with inputting actual drilling data for the target fractured well (old well) and the well to be fractured (new well), including well depth, casing procedures (such as the dimensions and depth of surface casing, technical casing, and production casing), wellbore trajectory (such as directional survey data), and completion details (such as perforation cluster locations). Based on these parameters, the software automatically renders a three-dimensional wellbore structure and marks the locations of the coal seam top and bottom plates, ensuring that the model is consistent with the actual well conditions. For example, in the Daning-Jixian case, the old well is a horizontal well extending 800 meters along the coal seam, with each stage of fracturing containing 6 perforation clusters. The software generates a 3D view of the well trajectory using a geometric modeling algorithm. Figure 3 Its functions include:

[0154] 1. By displaying the relative position of the well trajectory and the coal seam, it is ensured that the fracturing fractures are controlled within the target layer and prevented from interfering with non-producing layers;

[0155] 2. As data input for step S201, it helps determine well spacing and fracturing cluster distribution, providing wellbore constraints for subsequent fracture propagation simulation.

[0156] Figure 5 This is a schematic diagram of a fracturing model provided in an embodiment of this application, such as... Figure 5 As shown, this figure illustrates a fracturing model generated using fracture propagation simulation software (such as GOHFER or Kinetix). The generation process is based on the simulation parameters from step S202, inputting geostress distribution, rock mechanics parameters (such as Young's modulus), and fracturing engineering data (such as a flow rate of 16 cubic meters per minute). The software uses the finite element method to solve the fluid-solid coupling equations, calculates the fracture geometry (such as length and width), and renders a three-dimensional fracture network, including main fractures and branch fractures. For example, under conditions of 400 meters well spacing and 1000 cubic meters of fluid flow, the software simulates the fracture propagation trajectory and outputs the SRV boundary. Figure 5 Its functions include providing key data such as average fracture half-length and SRV area for calculating parameters of inter-well connectivity.

[0157] Figure 6 This application provides schematic diagrams of fracture networks under different geological conditions and fracturing scenarios in its embodiments, such as... Figure 6 As shown, this series of figures are schematic diagrams of fracture networks generated using post-processing software (such as ParaView or TechLog) after batch running fracture propagation simulations. The generation process is based on the orthogonal experimental design of S202, extracting fracture mesh data from the simulation results for different well spacings (e.g., 300 meters, 400 meters) and fracturing parameters (e.g., fluid volume from 500 to 1133 cubic meters). The software renders multiple sets of fracture network morphologies using visualization algorithms and displays them side-by-side to compare parameter sensitivity. For example, under high-pressure fracturing fluid volume, the fracture network exhibits complex branching, while the fractures are simpler at low fluid volumes. Figure 6 Its function includes visually demonstrating the differences between 80 sets of simulated cases, proving that the calculation of the first and second parameters covers a variety of working conditions.

[0158] Figure 7 This is a schematic diagram of the SRV numerical model for capacity simulation provided in the embodiments of this application, as shown below. Figure 7 As shown, this figure is a schematic diagram of a reservoir dynamics velocimetric (SRV) model generated by reservoir simulation software (such as CMG or ECLIPSE). The generation process is based on the production simulation in step S205. After inputting the fracture network and reservoir properties, the software uses a dual-pore, dual-permeability model to simulate fluid flow and calculate the fracturing fluid invasion range and gas production. The software outputs the pressure and gas-water distribution in the SRV region and displays the connection between the SRV and the wellbore in three dimensions. For example, when an old well is producing at a constant gas rate, the software simulates the water-to-gas ratio in the first week and renders the fluid migration path within the SRV. Figure 7 Its function includes providing average water-to-gas ratio data for the first week, which is used to quantify the impact of pressure channeling on the productivity of old wells (step S205).

[0159] Figure 8 This is a schematic diagram of a model generated by the history matching module of numerical simulation software to complete a history fitting study, as provided in an embodiment of this application. Figure 8 As shown, this figure represents a model generated using the history matching module of numerical simulation software, completing a history fitting study. The generation process is based on historical production data (such as cumulative gas production) collected from old wells in S201. The software adjusts geological parameters (such as permeability) using optimization algorithms (such as the least squares method) to ensure the simulated production curve overlaps with the actual data. The model displays the relationship between time and production in graphical form, for example, comparing the simulated gas production curve with actual data points, with the error controlled within 10%. Figure 8 Its functions include:

[0160] 1. Through historical data fitting, it is demonstrated that the numerical model can accurately predict production capacity, providing a reliable basis for parameter calculation in steps S205 and S206;

[0161] 2. Verify the rationality of geological and engineering parameters.

[0162] Figure 9 This is a schematic diagram of the initial calculation provided for an embodiment of this application. Figure 9 The scatter plot data is directly derived from the calculation results of S203 to S205. For 80 sets of orthogonal simulation experiments (covering different combinations of parameters such as well spacing and fracturing fluid volume), the dimensionless well connectivity parameter (first parameter, S203 result), the dimensionless fracturing fluid volume parameter (second parameter, S204 result), and the average water-gas ratio in the first week (third parameter, S205 result) for each set were summarized into a dataset. For example, for a well spacing of 300 meters and a fluid volume of 1133 cubic meters, the first parameter = 0.243, the second parameter = 0.378, and the third parameter = 2.619. Specifically, Figure 9 The generation method involves using data processing software (such as Python's Matplotlib library or Excel) to plot a two-dimensional scatter plot, using the first parameter value as the x-axis and the second parameter value as the y-axis. The color or size of each scatter point is mapped to the third parameter value (e.g., the higher the third parameter, the darker the point), forming a preliminary plot. The axis labels clearly indicate the parameter name and unit, and the legend displays the color gradient of the third parameter value.

[0163] Figure 10 This is a schematic diagram illustrating the selection of critical parameters provided in an embodiment of this application. Figure 10 The generation method is based on field experience (such as the production capacity data of wells in the Daning-Jixian area), and the critical value of the third parameter is preset to 1.2 cubic meters per thousand cubic meters (meaning that if the water-gas ratio in the first week exceeds this value, the production capacity is considered to be significantly damaged). Figure 9The dataset is filtered to identify all scatter points with a third parameter value > 1.2 as high-risk cases. Next, statistical characteristics (such as mean, standard deviation, or quantiles) are calculated for the first and second parameter values ​​of high-risk cases. For example, the mean of the first parameter for high-risk points is approximately 0.15, and the mean of the second parameter is approximately 0.2, which serves as a critical benchmark. Then, the boundaries of the high-risk region are defined centered on the statistical characteristics of the first and second parameters. For example, the x-axis range is the first parameter mean ± standard deviation (0.12-0.18), and the y-axis range is the second parameter mean ± standard deviation (0.18-0.22), forming a... Figure 10 The red shaded area in the upper right quadrant. The boundary line can be a straight line or a curve (such as the boundary line generated by a clustering algorithm).

[0164] Furthermore, risk area labels can be added, highlighting high-risk scatter plots. Legends can also be added to explain the meaning of the areas (e.g., red represents "high risk," green represents "safe"). Figure 10 This is a practical tool ultimately delivered to the field engineer. After the user inputs the first and second parameter values ​​for the new well design, if the point falls within the red high-risk zone, the fracturing scale needs to be adjusted (such as reducing the fluid volume or increasing the well spacing) to avoid interference with the old wells. For example, if the calculated first parameter for the new well is 0.2 and the second parameter is 0.25, and the point is located within the red zone, the system will trigger an early warning.

[0165] It should be noted that, Figure 10 The threshold range can be dynamically adjusted according to different blocks. For example, in areas with significant differences in geological conditions, new critical values ​​for the third parameter and boundaries of high-risk areas can be generated by rerunning steps S201-S205, making the map more universal.

[0166] Figure 11 This is a schematic diagram of the adjacent well crosstalk early warning threshold determination device provided in this application, as shown below. Figure 11 As shown, the device 110 includes:

[0167] The acquisition module 1101 is used to acquire the geological parameters and geostress parameters of the target reservoir and the surrounding rock formations, as well as the historical fracturing engineering parameters and historical production data of the target fracturing well in the target reservoir.

[0168] The simulation module 1102 is used to generate multiple sets of simulation parameters for the wells to be fractured, including the well distance between the wells to be fractured and the target fractured well, and the fracturing engineering parameters of the wells to be fractured.

[0169] Based on geological parameters, geostress parameters, historical fracturing engineering parameters, historical production data, and multiple sets of simulation parameters, a first parameter, a second parameter, and a third parameter are generated for each set of simulation parameters. The first parameter indicates the degree of overlap between the inter-well fracture and reservoir stimulation volumes of the target fracturing well and the well to be fractured. The second parameter indicates the degree of influence of the fracturing fluid volume of the target fracturing well and the well to be fractured on the crosstalk intensity. The third parameter indicates the degree of reduction in the production capacity of the target fracturing well due to crosstalk from the well to be fractured. The value of the third parameter is positively correlated with the degree of reduction.

[0170] The threshold determination module 1103 is used to determine a target third parameter that is greater than a preset threshold among the third parameters;

[0171] Based on the first and second parameters associated with the target third parameter, determine the first threshold range of the first parameter and the second threshold range of the second parameter.

[0172] The apparatus provided in this embodiment can execute the method provided in the above method embodiment. Its implementation principle and technical effect are similar, and will not be described in detail here.

[0173] Figure 12 A schematic diagram of the structure of the electronic device provided in this application. Figure 12 As shown, the electronic device 120 provided in this embodiment includes at least one processor 1201 and a memory 1202. Optionally, the device 120 further includes a communication component 1203. The processor 1201, the memory 1202, and the communication component 1203 are connected via a bus 1204.

[0174] In a specific implementation, at least one processor 1201 executes computer execution instructions stored in memory 1202, causing at least one processor 1201 to perform the above-described method.

[0175] The specific implementation process of processor 1201 can be found in the above method embodiments, and its implementation principle and technical effect are similar. It will not be repeated here.

[0176] In the above embodiments, it should be understood that the processor can be a Central Processing Unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), etc. The general-purpose processor can be a microprocessor or any conventional processor. The steps of the method disclosed in this invention can be directly implemented by a hardware processor, or implemented by a combination of hardware and software modules within the processor.

[0177] The memory may include random access memory (RAM) and may also include non-volatile memory (NVM), such as at least one disk storage device.

[0178] The bus can be an Industry Standard Architecture (ISA) bus, a Peripheral Component Interconnect (PCI) bus, or an Extended Industry Standard Architecture (EISA) bus, etc. Buses can be categorized as address buses, data buses, control buses, etc. For ease of illustration, the buses shown in the accompanying drawings are not limited to a single bus or a single type of bus.

[0179] This application also provides a computer program product, including a computer program that, when executed by a processor, implements the above-described method.

[0180] This application also provides a computer-readable storage medium storing computer-executable instructions, which, when executed by a processor, implement the above-described method.

[0181] The aforementioned readable storage medium can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic storage, flash memory, magnetic disk, or optical disk. The readable storage medium can be any available medium accessible to a general-purpose or special-purpose computer.

[0182] An exemplary readable storage medium is coupled to a processor, enabling the processor to read information from and write information to the readable storage medium. Of course, the readable storage medium can also be a component of the processor. The processor and the readable storage medium can reside in an Application Specific Integrated Circuit (ASIC). Alternatively, the processor and the readable storage medium can exist as discrete components in the device.

[0183] The division of units is merely a logical functional division; in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be indirect coupling or communication connection through some interfaces, devices, or units, and may be electrical, mechanical, or other forms.

[0184] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0185] In addition, the functional units in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit.

[0186] If a function is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this invention, or the part that contributes to the prior art, or a part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods of the various embodiments of this invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0187] Those skilled in the art will understand that all or part of the steps of the above-described method embodiments can be implemented by hardware related to program instructions. The aforementioned program can be stored in a computer-readable storage medium. When executed, the program performs the steps of the above-described method embodiments; and the aforementioned storage medium includes various media capable of storing program code, such as ROM, RAM, magnetic disks, or optical disks.

[0188] Finally, it should be noted that other embodiments of the invention will readily occur to those skilled in the art upon consideration of the specification and practice of the invention disclosed herein. This invention is intended to cover any variations, uses, or adaptations of the invention that follow the general principles of the invention and include common knowledge or customary techniques in the art not disclosed herein, and is not limited to the precise structures described above and shown in the accompanying drawings, and various modifications and changes can be made without departing from its scope. The scope of the invention is limited only by the appended claims.

Claims

1. A method for determining the early warning threshold for crosstalk between adjacent wells, characterized in that, The method includes: Obtain geological parameters and geostress parameters of the target reservoir and the surrounding rock formations, as well as historical fracturing engineering parameters and historical production data of the target fracturing well in the target reservoir; Multiple sets of simulation parameters for wells to be fractured are generated, including the well distance between the well to be fractured and the target fractured well, and the fracture engineering parameters of the well to be fractured. Based on the geological parameters, the geostress parameters, the historical fracturing engineering parameters, the historical production capacity data, and multiple sets of simulation parameters, a first parameter, a second parameter, and a third parameter are generated for each set of simulation parameters. The first parameter indicates the degree of overlap between the inter-well fracture and reservoir stimulation volumes of the target fracturing well and the well to be fractured. The second parameter indicates the influence of the fracturing fluid volume of the target fracturing well and the well to be fractured on the crosstalk intensity. The third parameter indicates the degree to which the crosstalk from the well to the target fracturing well reduces the production capacity of the target fracturing well, and the value of the third parameter is positively correlated with the degree of reduction. Among the third parameters, a target third parameter greater than a preset threshold is determined; Based on the first parameter and the second parameter associated with the target third parameter, a first threshold range for the first parameter and a second threshold range for the second parameter are determined.

2. The method according to claim 1, characterized in that, The process of generating a first parameter, a second parameter, and a third parameter corresponding to each set of simulation parameters based on the geological parameters, the geostress parameters, the historical fracturing engineering parameters, the historical production capacity data, and multiple sets of simulation parameters includes: Based on the geological parameters, the geostress parameters, the historical fracturing engineering parameters, and the simulation parameters, the average fracture half-length of the target fracturing well and the well to be fractured on the crosstalk side, the projected area of ​​the reservoir stimulation volume of the target fracturing well and the well to be fractured on the plane, and the inter-well control area of ​​the target fracturing well and the well to be fractured are determined. The first parameter is determined based on the average fracture half-length, the well spacing, the projected area, and the inter-well control area.

3. The method according to claim 1, characterized in that, The process of generating a first parameter, a second parameter, and a third parameter corresponding to each set of simulation parameters based on the geological parameters, the geostress parameters, the historical fracturing engineering parameters, the historical production capacity data, and multiple sets of simulation parameters includes: Based on the geological parameters, the geostress parameters, the historical fracturing engineering parameters, the historical production data, and the simulation parameters, the first average fracture half-length on the crosstalk side of the well to be fractured, the second average fracture half-length on the non-crosstalk side of the well to be fractured, the first total fracturing fluid volume of the target fracturing well, the second total fracturing fluid volume of the well to be fractured, the cumulative fluid production of the target fracturing well before the fracturing of the well to be fractured, the average porosity of the coal seam in the target reservoir, and the inter-well coal seam volume between the target fracturing well and the well to be fractured are determined. The second parameter is determined based on the first average fracture half-length, the second average fracture half-length, the first total fracturing fluid volume, the second total fracturing fluid volume, the cumulative fluid production volume, the average porosity of the coal seam, and the inter-well coal seam volume.

4. The method according to claim 1, characterized in that, The process of generating a first parameter, a second parameter, and a third parameter corresponding to each set of simulation parameters based on the geological parameters, the geostress parameters, the historical fracturing engineering parameters, the historical production capacity data, and multiple sets of simulation parameters includes: Based on the geological parameters, the geostress parameters, the historical fracturing engineering parameters, the historical production capacity data, and the simulation parameters, the average daily water production and average daily gas production of the target fracturing well are determined after the fracturing of the well to be fractured is completed. The third parameter is determined based on the average daily water production and the average daily gas production.

5. The method according to claim 4, characterized in that, The process of determining the third parameter based on the average daily water production and average daily gas production includes: The third parameter is determined based on the ratio of the average daily water production to the average daily gas production.

6. The method according to claim 1, characterized in that, After determining the first threshold range of the first parameter and the second threshold range of the second parameter based on the first parameter and the second parameter associated with the target third parameter, the method further includes: Based on the first threshold range, the horizontal coordinate range of the high-risk area in the target two-dimensional coordinate system is determined, and based on the second threshold range, the vertical coordinate range of the high-risk area is determined. The target two-dimensional coordinate system is displayed on the target interactive interface, and the high-risk area is displayed on the target interactive interface according to the range of the horizontal coordinate and the range of the vertical coordinate.

7. The method according to any one of claims 1-6, characterized in that, The step of determining a first threshold range for the first parameter and a second threshold range for the second parameter based on the first parameter and the second parameter associated with the target third parameter includes: Obtain the first statistical feature value of the first parameter associated with the target third parameter, and the second statistical feature value of the second parameter associated with the target third parameter; The first threshold range is determined based on the first statistical feature value, and the second threshold range is determined based on the second statistical feature value.

8. A device for determining the early warning threshold of inter-well crosstalk, characterized in that, The device includes: The acquisition module is used to acquire geological parameters and geostress parameters of the target reservoir and the surrounding rock strata, as well as historical fracturing engineering parameters and historical production data of the target fracturing well in the target reservoir. The simulation module is used to generate multiple sets of simulation parameters for wells to be fractured, including the well distance between the well to be fractured and the target fractured well, and the fracture engineering parameters of the well to be fractured. Based on the geological parameters, the geostress parameters, the historical fracturing engineering parameters, the historical production capacity data, and multiple sets of simulation parameters, a first parameter, a second parameter, and a third parameter are generated for each set of simulation parameters. The first parameter indicates the degree of overlap between the inter-well fracture and reservoir stimulation volumes of the target fracturing well and the well to be fractured. The second parameter indicates the influence of the fracturing fluid volume of the target fracturing well and the well to be fractured on the crosstalk intensity. The third parameter indicates the degree to which the crosstalk from the well to the target fracturing well reduces the production capacity of the target fracturing well, and the value of the third parameter is positively correlated with the degree of reduction. A threshold determination module is used to determine a target third parameter that is greater than a preset threshold among the third parameters; Based on the first parameter and the second parameter associated with the target third parameter, a first threshold range for the first parameter and a second threshold range for the second parameter are determined.

9. An electronic device, characterized in that, include: A processor, and a memory communicatively connected to the processor; The memory stores computer-executed instructions; The processor executes computer execution instructions stored in the memory to implement the method as described in any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer-executable instructions, which, when executed by a processor, are used to implement the method as described in any one of claims 1 to 7.

Images