Interlayer fluid channeling coupling interpretation method and device for stratified injection well test data and electronic equipment

Abstract

The invention provides an interlayer fluid channeling coupling interpretation method and device for layered injection well test data and electronic equipment. Comprising the following steps: identifying whether fluid channeling exists between layers based on pressure time sequence data collected by each layer in a discharge and mining test process, and quantifying a channeling channel; the quantized fluid channeling result is used for correcting the actually-measured flow data of all the layers so as to eliminate fluid channeling interference, and the real stratum parameters of all the layers are inversed based on the corrected data; and establishing a coupling relation model of the layered real flow and the interlayer cross flow, performing comparison verification on the total flow calculated by the model and the actually measured total flow, and performing iterative optimization on model parameters according to a verification result. Interlayer fluid channeling can be quantified through pressure curve synchronous fluctuation characteristics and fluid channeling coefficients, the fluid channeling which is regarded as interference in a traditional model is converted into interpretable parameters, and the interpretation precision is remarkably improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of computers, in particular to a method and device for interlayer channeling coupling interpretation of layered injection well test data and an electronic device. BACKGROUND

[0002] In the process of carbon dioxide geological storage or oil and gas field development, layered injection wells are often used for multilayer drainage test or injection operation. In the traditional interpretation model, each layer is usually regarded as an independent system, and parameter inversion is only based on single-layer flow and pressure data, ignoring the interference of interlayer channeling on test results. This simplified processing leads to large deviations in the obtained formation parameters (such as permeability, skin factor, and formation pressure), which further affects the subsequent injection scheme design, storage safety evaluation, and adjustment effect of measures.

[0003] Especially in multilayer system storage wells, the formation structure is complex, and there may be high-permeability channels, fractures, or pore-type channeling between layers. The traditional model cannot identify and quantify the influence of channeling, resulting in interpretation results that do not match the actual formation behavior, making it difficult to guide precise profile control or channeling plugging operations.

[0004] Therefore, there is an urgent need for a layered test data interpretation method that can effectively identify, quantify, and eliminate the interference of interlayer channeling to improve the accuracy and reliability of formation parameter inversion. SUMMARY

[0005] The purpose of the embodiments of the present application is to provide a method and device for interlayer channeling coupling interpretation of layered injection well test data to alleviate the above technical problems.

[0006] In a first aspect, the present application provides a method for interlayer channeling coupling interpretation of layered injection well test data, comprising the following steps: Based on the pressure time series data collected during the drainage test of each layer, it is identified whether there is fluid channeling between layers, and the channeling channels are quantified; Using the quantified channeling results, the measured flow data of each layer is corrected to eliminate channeling interference, and the real formation parameters of each layer are inverted based on the corrected data; A coupling relationship model of layered real flow and interlayer channeling flow is established, the total flow calculated by the model is compared and verified with the measured total flow, and the model parameters are iteratively optimized according to the verification results.

[0007] In an optional embodiment, based on the pressure time series data collected during the drainage test of each layer, it is identified whether there is fluid channeling between layers, and the channeling channels are quantified, specifically including: Draw the curve of pressure change with time for each layer; analyzing fluctuation characteristics of the pressure curve, when the first layer pressure changes, if the second layer pressure changes in the same trend and synchronously without injection or production adjustment, it is determined that there is a channeling channel between the first layer and the second layer; Based on the pressure change amplitude and the initial physical parameters of each layer, a channeling coefficient representing the channeling capacity is calculated. According to the numerical value of the channeling coefficient, the type of the channeling channel is qualitatively divided.

[0008] In an optional embodiment, the qualitative division of the type of the channeling channel according to the numerical value of the channeling coefficient comprises: When the channeling coefficient is greater than a first set threshold, the channeling channel is divided into a high-permeability channeling channel; When the channeling coefficient is between the first set threshold and a second set threshold, the channeling channel is divided into a fracture-type channeling channel; When the channeling coefficient is less than or equal to the second set threshold, the channeling channel is divided into a pore-type channeling channel.

[0009] In an optional embodiment, the measured flow data of each layer is corrected by using the quantified channeling result to eliminate channeling interference, and the real formation parameters of each layer are inverted based on the corrected data, comprising: According to the channeling direction, the measured flow of the channeling-out layer is corrected by subtracting the channeling flow, and the measured flow of the channeling-in layer is corrected by adding the channeling flow, so as to obtain the real flow of each layer; The real flow and the corresponding pressure data are substituted into the Darcy flow equation to calculate the permeability and skin factor of each layer; In the calculation process, an adaptive coefficient related to the lithology of the formation is introduced to correct the calculation model.

[0010] In an optional embodiment, when the target layer is a sandstone formation, a first preset value is used as the adaptive coefficient; when the target layer is a carbonate rock formation, a second preset value is used as the adaptive coefficient.

[0011] In an optional embodiment, a coupling relationship model of the real flow of each layer and the interlayer channeling flow is established, the total flow calculated by the model is compared and verified with the measured total flow, and the model parameters are iteratively optimized according to the verification result, comprising: A material balance equation is constructed, in which the sum of the real flow of each layer and the interlayer channeling flow is used as the total flow; The deviation between the total flow obtained by the material balance equation and the field measured total flow is calculated; If the deviation is greater than an allowable error threshold, a crossflow coefficient is returned and adjusted, and the measured flow data of each layer is corrected using the quantized crossflow result to eliminate the crossflow interference, and the real formation parameters of each layer are inverted based on the corrected data until the deviation is less than or equal to the allowable error threshold.

[0012] In an optional embodiment, the method further comprises: After the interpretation is completed and subsequent engineering measures are implemented, production dynamic data after the measures are collected; The actual effect after the measures is compared with the model predicted effect; If the comparison deviation exceeds a set range, the adaptability coefficient in the lithology adaptation sub-step is corrected to update the model.

[0013] In a second aspect, the present application provides a device for interlayer crossflow coupling interpretation of layered injection well test data, comprising: An identification module is configured to identify whether fluid crossflow exists between layers based on pressure time series data collected during the drainage test of each layer, and to quantify the crossflow channels; A layering module is configured to correct the measured flow data of each layer using the quantized crossflow result to eliminate the crossflow interference, and to invert the real formation parameters of each layer based on the corrected data; A comparison module is configured to establish a coupling relationship model of the layered real flow and the interlayer crossflow, to compare and verify the total flow calculated by the model with the measured total flow, and to iteratively optimize the model parameters according to the verification result.

[0014] In an optional embodiment, a correction module is further included and configured to: After the interpretation is completed and subsequent engineering measures are implemented, production dynamic data after the measures are collected; The actual effect after the measures is compared with the model predicted effect; If the comparison deviation exceeds a set range, the adaptability coefficient in the lithology adaptation sub-step is corrected to update the model.

[0015] In a third aspect, the present application provides an electronic device comprising a processor and a memory, wherein the memory stores machine executable instructions capable of being executed by the processor, and the processor can execute the machine executable instructions to implement the method of any one of the preceding embodiments.

[0016] According to the present application, the crossflow between layers is quantized by the synchronous fluctuation characteristics of the pressure curve, the crossflow in the traditional model is converted into an interpretable parameter, and the interpretation accuracy is significantly improved.

[0017] On the basis of removing channeling interference, the real permeability, skin factor and formation pressure of each layer are inverted, and the error is reduced compared with the traditional model.

[0018] The introduction of the adaptive coefficient of each layer can adjust the calculation model according to different lithology such as sandstone and carbonate rock, and enhance the universality and practicality of the method.

[0019] Through the verification of coupled equations and the feedback of production dynamics, a "interpretation-verification-optimization" closed loop is formed to ensure that the model is continuously optimized with actual production data.

[0020] The output results include channeling channel type and treatment recommendations, which can provide direct basis for subsequent profile control, channeling plugging or injection scheme optimization, and improve sealing safety and development efficiency.

[0021] It is suitable for interpretation requirements of multi-layer sealing wells. BRIEF DESCRIPTION OF DRAWINGS

[0022] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the following will briefly introduce the drawings needed to be used in the embodiments of the present application. It should be understood that the following drawings only show some embodiments of the present application, and therefore should not be regarded as a limitation on the scope. For those skilled in the art, other related drawings can also be obtained without creative labor on the basis of these drawings.

[0023] Figure 1 A layering interlayer channeling coupling interpretation method for layered injection well test data provided by the embodiments of the present application is shown in the flowchart. Figure 2 A layering interlayer channeling coupling interpretation device structure provided by the embodiments of the present application is shown in the structural diagram. Figure 3 An electronic device structure provided by the embodiments of the present application is shown in the structural diagram. DETAILED DESCRIPTION

[0024] The technical solutions in the embodiments of the present application will be described below in combination with the drawings in the embodiments of the present application.

[0025] Figure 1 A layering interlayer channeling coupling interpretation method for layered injection well test data provided by the embodiments of the present application is shown in the flowchart. As shown in Figure 1 the following steps are included: S110, based on the pressure time series data collected in the process of each layer in the drainage test, whether there is fluid channeling between layers is identified, and the channeling channel is quantified.

[0026] In some embodiments, a curve of pressure of each layer over time can be drawn; fluctuation characteristics of the pressure curve are analyzed, when the pressure of the first layer changes, if the pressure of the second layer changes synchronously and in the same trend without injection or production adjustment, it is determined that a channeling channel exists between the first layer and the second layer; a channeling coefficient representing channeling capacity is calculated based on the pressure change amplitude and initial physical property parameters of each layer; and the type of the channeling channel is qualitatively divided according to the numerical value of the channeling coefficient.

[0027] When the channeling coefficient is greater than a first set threshold value, the channeling channel is divided into a high-permeability channeling channel; when the channeling coefficient is between the first set threshold value and a second set threshold value, the channeling channel is divided into a fracture-type channeling channel; and when the channeling coefficient is less than or equal to the second set threshold value, the channeling channel is divided into a pore-type channeling channel.

[0028] The interlayer fluid channeling identification and channeling channel quantification can be achieved through the following steps: Pressure curve drawing and analysis: pressure-time sequence data of each layer during production testing is collected, and a pressure-time curve (P-t curve) is drawn; the curve fluctuation characteristics are analyzed, if the pressure of the first layer changes (such as ), the pressure of the second layer changes synchronously and in the same trend (such as , and rises / falls) without injection / production adjustment (without external interference), it is determined that a channeling channel exists between the two layers.

[0029] Channeling coefficient calculation: the channeling coefficient (a quantitative parameter representing channeling capacity) is calculated based on the pressure change amplitude ( , ) and initial physical property parameters (such as layer permeability , , and porosity , ). The formula can be:

[0030] In the formula: , are the pressure change amplitudes of the two layers; , are the initial permeabilities of the layers; , are the initial porosities of the layers.

[0031] The type can be divided according to the comparison of C and the threshold value: : high-permeability channeling channel (strongest channeling capacity); : fracture-type channeling channel (channeling through a fracture). : interporosity channeling (channeling through matrix porosity, ).

[0032] S120, using the quantified channeling result, correcting the measured flow data of each layer to eliminate channeling interference, and based on the corrected data, inverting the real formation parameters of each layer.

[0033] In some embodiments, the measured flow of the channeling-out layer can be corrected by subtracting the channeling flow according to the channeling direction, and the measured flow of the channeling-in layer can be corrected by adding the channeling flow, so as to obtain the real flow of each layer; the real flow and the corresponding pressure data are substituted into the Darcy flow equation to calculate the permeability and skin factor of each layer; in the calculation process, the adaptive coefficient related to the lithology of the formation is introduced to correct the calculation model.

[0034] When the target layer is a sandstone formation, the first preset value is used as the adaptive coefficient; when the target layer is a carbonate rock formation, the second preset value is used as the adaptive coefficient.

[0035] The measured flow correction and real formation parameter inversion can be realized by the following steps: The measured flow can be corrected according to the channeling direction to distinguish the channeling-out layer (the layer where the fluid flows out) and the channeling-in layer (the layer where the fluid flows in).

[0036] The real flow of the channeling-out layer is: (q) is the channeling flow of the channeling-out layer; The real flow of the channeling-in layer is: (q) is the channeling flow of the channeling-in layer.

[0037] Real formation parameter inversion: substitute the real flow into the Darcy flow equation (equation describing linear flow of fluid in porous medium) to invert the permeability (the ability of the formation to allow fluid to pass through) and the skin factor (a dimensionless parameter reflecting the pollution / improvement of the formation near the wellbore). The Darcy equation considering skin effect can be used:

[0038] Wherein: is the real flow; k is the permeability; h is the formation thickness; is the pressure difference; is the fluid viscosity; B is the fluid volume coefficient; is the supply radius; is the wellbore radius; S is the skin factor (positive value indicating pollution, negative value indicating improvement).

[0039] Solve the permeability:

[0040] In some embodiments, an adaptive coefficient (adjusting the correction parameters of the model according to the lithology) can be introduced to correct the permeability calculation:

[0041] Wherein: the sandstone formation takes (the first preset value), the carbonate rock formation takes (the second preset value, determined by experiments).

[0042] S130, a coupling relationship model of the layered real flow and the interlayer channeling flow is established, the total flow calculated by the model is compared and verified with the measured total flow, and the model parameters are iteratively optimized according to the verification result.

[0043] In some embodiments, a material balance equation can be constructed with the sum of the real flow of each layer and the interlayer channeling flow as the total flow; the deviation between the total flow obtained by the material balance equation and the field measured total flow is calculated; if the deviation is greater than the allowable error threshold, the channeling coefficient is adjusted, and the measured flow data of each layer is corrected again using the quantified channeling result to eliminate the channeling interference, and the real formation parameters of each layer are inverted based on the corrected data until the deviation is less than or equal to the allowable error threshold.

[0044] In some embodiments, the method can further include: after completing the interpretation and implementing the subsequent engineering measures, collecting the production dynamic data after the measures; comparing the actual effect after the measures with the model prediction effect; if the comparison deviation exceeds the set range, the adaptive coefficient in the lithology adaptation sub-step is corrected to update the model.

[0045] The coupling relationship model establishment and iterative optimization can be realized by the following steps: establishing a coupling relationship model (considering the interaction between the layered real flow and the channeling flow), based on the material balance equation (mass conservation): .

[0046] Wherein: is the total flow calculated by the model; is the real flow of the i-th layer; is the channeling flow between the i-th layer and the j-th layer (considering the direction, channeling out is negative, channeling in is positive).

[0047] The deviation between the total flow calculated by the model and the measured total flow is calculated , if , the channeling coefficient C is adjusted, and the flow is corrected again and the parameters are inverted in S120 until (the iterative optimization process).

[0048] After implementing engineering measures, compare the actual production dynamic with the model prediction effect. If the deviation exceeds the set range, correct the adaptive coefficient ) and update the model.

[0049] In the above embodiment, fluid channeling is the uncontrolled cross-layer flow of fluid between different reservoir layers.

[0050] Channeling channel is a physical path that causes channeling (such as a fracture, a high-permeability interlayer).

[0051] Channeling coefficient (C) is a dimensionless parameter representing the channeling capacity. The larger the value, the stronger the channeling.

[0052] Initial physical property parameters are the physical properties of the formation before production (permeability, porosity, formation thickness, etc.).

[0053] True flow is the actual output / injection flow of the layer itself after removing the interference of channeling.

[0054] Permeability (k) is the ability of the formation to allow fluid to pass through, with units of mD (millidarcy) or m 2 .

[0055] Skin factor (S) is a dimensionless parameter that reflects the pollution / improvement of the formation near the wellbore. A positive value indicates pollution, and a negative value indicates improvement.

[0056] Adaptive coefficient (α) is a correction parameter for adjusting the flow model according to the lithology (sandstone / carbonate rock).

[0057] Material balance equation is a mass conservation equation that describes the total flow as the sum of the true flow and the channeling flow of each layer.

[0058] Allowed error threshold (ε) is the maximum acceptable deviation of the model calculated total flow from the measured total flow.

[0059] Iterative optimization: a process of repeatedly adjusting parameters (such as channeling coefficient) to make the model deviation less than ε.

[0060] Figure 2 A device for interlayer channeling coupling interpretation of layered injection well test data is provided for the embodiments of the present application. As shown in Figure 2 , the device comprises: An identification module 201 for identifying whether there is fluid channeling between layers based on the pressure time series data collected during the production test of each layer, and quantifying the channeling channel; A layered module 202 for correcting the measured flow data of each layer using the quantified channeling results to remove channeling interference, and inverting the true formation parameters of each layer based on the corrected data; The comparison module 203 is configured to establish a coupling relationship model of the layered real traffic and the interlayer traffic, compare the total traffic calculated by the model with the measured total traffic, and iteratively optimize the model parameters according to the verification result.

[0061] In some embodiments, the method further comprises a correction module configured to: After the explanation and implementation of the subsequent engineering measures are completed, collecting production dynamic data after the measures are taken; Comparing the actual effect after the measures with the predicted effect of the model; If the comparison deviation exceeds the set range, the adaptability coefficient in the lithology adaptation sub-step is corrected to update the model.

[0062] The device embodiment corresponds to the foregoing method embodiment, and can be understood with reference to each other.

[0063] Referring to Figure 3 The electronic device 300 provided by the embodiments of the present application at least includes a processor 301, a memory 302, and a computer program stored in the memory 302 and executable on the processor 301, and the processor 301 implements the method provided by the embodiments of the present application when executing the computer program.

[0064] The electronic device 300 provided by the embodiments of the present application can further include a bus 303 connected to different components (including the processor 301 and the memory 302). The bus 303 represents one or more of several bus structures, including a memory bus, a peripheral bus, a local bus, and the like.

[0065] The memory 302 can include a readable storage medium in the form of a volatile memory, such as a random access memory (RAM) 3021 and / or a cache memory 3022, and can further include a read-only memory (ROM) 3023. The memory 302 can also include a program tool 3025 having a set of (at least one) program modules 3024, including but not limited to an operating subsystem, one or more application programs, other program modules, and program data, each of which or some combination thereof can include the implementation of a network environment.

[0066] The processor 301 can be one processing element or a collective term for a plurality of processing elements. For example, the processor 301 can be a central processing unit (CPU), or one or more integrated circuits configured to implement methods provided by embodiments of the present application. Specifically, the processor 301 can be a general-purpose processor, including but not limited to a CPU, an application specific integrated circuit (ASIC), a field programmable gate array (FPGA) or other programmable logic device, a discrete gate or transistor logic, a discrete hardware component, and the like.

[0067] The electronic device 300 can communicate with one or more external devices 304 (such as a keyboard, a remote control, and the like) by means of the I / O interface 305. Also, the electronic device 300 can communicate with one or more devices that enable a user to interact with the electronic device 300 (such as a personal computer, a printer, and the like) and / or one or more devices that enable the electronic device 300 to communicate with one or more other electronic devices 300 (such as a router, a modem, and the like). Such communication can occur via the I / O interface 305. Also, the electronic device 300 can communicate with one or more networks (such as a local area network (LAN), a wide area network (WAN), and / or the public network, such as the Internet) by means of the network adapter 306. As Figure 3 illustrated, the network adapter 306 communicates with the other components of the electronic device 300 via the bus 303. It should be understood that although not shown, other hardware and / or software components that are commonly used in computing can also be included in the electronic device 300, such as a microcode, a device driver, a redundant processor, an external disk drive array, a RAID subsystem, a tape drive, and a data backup storage subsystem, and the like. Figure 3 It should be understood that, although not shown, other hardware and / or software components that are commonly used in computing can also be included in the electronic device 300, such as a microcode, a device driver, a redundant processor, an external disk drive array, a RAID subsystem, a tape drive, and a data backup storage subsystem, and the like.

[0068] It should be noted that the electronic device 300 as shown is merely an example, and should not impose any limitation on the functions and use range of embodiments of the present application. Figure 3 It should be noted that the electronic device 300 as shown is merely an example, and should not impose any limitation on the functions and use range of embodiments of the present application.

[0069] The computer-readable storage medium provided in the embodiments of this application is described below. The computer-readable storage medium provided in the embodiments of this application stores computer instructions, which, when executed by a processor, implement the methods provided in the embodiments of this application. Specifically, the computer instructions may be built into or installed in a processor, so that the processor can implement the methods provided in the embodiments of this application by executing the built-in or installed computer instructions.

[0070] Furthermore, the method provided in this application embodiment can also be implemented as a computer program product, which includes program code that implements the method provided in this application embodiment when run on a processor.

[0071] The computer program product provided in this application embodiment may employ one or more computer-readable storage media, which may be, but is not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any suitable combination thereof. Specifically, more specific examples (a non-exhaustive list) of computer-readable storage media include: electrical connections having one or more wires, portable disks, hard disks, RAM, ROM, erasable programmable read-only memory (EPROM), optical fibers, portable compact disc read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof.

[0072] The computer program product provided in this application embodiment can be a CD-ROM and include program code, and can also run on electronic devices such as computers. However, the computer program product provided in this application embodiment is not limited thereto. In this application embodiment, the computer-readable storage medium can be any tangible medium that contains or stores program code, which can be used by or in conjunction with an instruction execution system, device, or apparatus.

[0073] It should be noted that although several units or sub-units of the device have been mentioned in the detailed description above, this division is merely exemplary and not mandatory. In fact, according to embodiments of this application, the features and functions of two or more units described above can be embodied in one unit. Conversely, the features and functions of one unit described above can be further divided and embodied by multiple units.

[0074] Furthermore, although the operations of the method(s) herein can be described in a particular, sequential order, this order is not meant to be a limitation and one or more of the operations described can be performed in parallel, or in a different order, including before or after other operations described. The various steps described can be implemented in hardware, software, or a combination thereof. The subject specification can be implemented by computer software implemented by one or more processors of a computing device.

[0075] While the preferred embodiments of the application have been described above, it should be understood that they have been presented by way of example only, and not limitation. Numerous changes to the embodiments can be made in addition to those described and nevertheless accomplish the same objectives of the application. Thus, while the application is susceptible to various modifications and alternative forms, specific embodiments have been shown by way of example in order to elucidate the application. It should be understood, therefore, that the application is not to be limited to the particular embodiments described but it is intended to cover any and all modifications and equivalents within the scope of the appended claims.

[0076] Obviously, numerous modifications and variations of the present application are possible in light of the above teachings. It is therefore to be understood that within the scope of the appended claims, the application can be practiced otherwise than as specifically described herein.

Claims

1. A method for interpreting inter-layer crossflow coupling in stratified injection well test data, characterized in that, Includes the following steps: Based on the pressure time series data collected during the drainage test of each layer, the presence of fluid cross-flow between layers is identified, and the cross-flow channels are quantified. Using the quantified crossflow results, the measured flow data of each layer are corrected to eliminate crossflow interference, and the true formation parameters of each layer are retrieved based on the corrected data. A coupling model between the actual flow rate at different levels and the flow rate between different levels is established. The total flow rate calculated by the model is compared and verified with the actual total flow rate. Based on the verification results, the model parameters are iteratively optimized.

2. The method according to claim 1, characterized in that, Based on the pressure time series data collected during the drainage test of each layer, the existence of fluid cross-flow between layers is identified, and the cross-flow channels are quantified, specifically including: Plot the pressure changes over time for each layer; Analyzing the fluctuation characteristics of the pressure curve, if the pressure of the second layer changes synchronously with the same trend when the pressure of the first layer changes, without any injection or output adjustment, it is determined that there is a crossflow channel between the first layer and the second layer. Based on the pressure change amplitude and the initial physical property parameters of each layer, the crossflow coefficient characterizing the crossflow capability is calculated; Based on the magnitude of the crossflow coefficient, the types of crossflow channels are qualitatively classified.

3. The method according to claim 2, characterized in that, The qualitative classification of crossflow channels based on the magnitude of the crossflow coefficient includes: When the crossflow coefficient is greater than a first set threshold, the crossflow channel is classified as a high-permeability crossflow channel. When the crossflow coefficient is between a first set threshold and a second set threshold, the crossflow channel is classified as a crack-type crossflow channel. When the crossflow coefficient is less than or equal to the second set threshold, the crossflow channel is classified as a pore-type crossflow channel.

4. The method according to claim 1, characterized in that, Using the quantified crossflow results, the measured flow data of each layer are corrected to eliminate crossflow interference. Based on the corrected data, the true formation parameters of each layer are retrieved, including: Based on the direction of the flow, the measured flow rate of the outflow layer is corrected by subtracting the flow rate, and the measured flow rate of the inflow layer is corrected by adding the flow rate, so as to obtain the true flow rate of each layer. Substituting the actual flow rate and corresponding pressure data into the Darcy flow equation, the permeability and skin coefficient of each layer are calculated. In the calculation process, an adaptation coefficient related to the lithology of the strata is introduced to correct the calculation model.

5. The method according to claim 4, characterized in that, in, When the target stratum is sandstone, a first preset value is used as the adaptability coefficient; when the target stratum is carbonate rock, a second preset value is used as the adaptability coefficient.

6. The method according to claim 1, characterized in that, A coupling model between the actual flow rate at different levels and the crossflow flow between levels is established. The total flow rate calculated by the model is compared and verified with the actual total flow rate. Based on the verification results, the model parameters are iteratively optimized, including: Construct a material balance equation with the sum of the actual flow rate of each layer and the interlayer crossflow as the total flow rate; The deviation between the total flow rate calculated from the material balance equation and the total flow rate measured on site; If the deviation is greater than the allowable error threshold, the process returns to adjust the crossflow coefficient and re-executes the process using the quantized crossflow results to correct the measured flow data of each layer in order to eliminate crossflow interference. Based on the corrected data, the true formation parameters of each layer are inverted until the deviation is less than or equal to the allowable error threshold.

7. The method according to claim 6, characterized in that, The method further includes: After completing the explanation and implementing subsequent engineering measures, collect dynamic production data following the implementation of these measures; Compare the actual effects of the measures with the model predictions; If the comparison deviation exceeds the set range, the fitness coefficient in the lithology adaptation sub-step is corrected to update the model.

8. A device for interpreting inter-layer crossflow coupling of test data from stratified injection wells, characterized in that, include: The identification module is used to identify whether there is fluid cross-flow between layers based on the pressure time series data collected during the drainage test of each layer, and to quantify the cross-flow channels. The layered module is used to correct the measured flow data of each layer using the quantized crossflow results in order to eliminate crossflow interference, and to retrieve the real formation parameters of each layer based on the corrected data. The comparison module is used to establish a coupling relationship model between the actual flow rate in each layer and the cross-flow rate between layers. It compares and verifies the total flow rate calculated by the model with the actual total flow rate, and iteratively optimizes the model parameters based on the verification results.

9. The apparatus according to claim 8, characterized in that, It also includes a correction module for: After completing the explanation and implementing subsequent engineering measures, collect dynamic production data following the implementation of these measures; Compare the actual effects of the measures with the model predictions; If the comparison deviation exceeds the set range, the fitness coefficient in the lithology adaptation sub-step is corrected to update the model.

10. An electronic device, characterized in that, The method includes a processor and a memory, the memory storing machine-executable instructions that can be executed by the processor to implement the method according to any one of claims 1 to 7.

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