Formation permeability determination method and device, electronic equipment and storage medium

Abstract

The invention discloses a formation permeability determination method and device, electronic equipment and a storage medium. The method comprises the following steps: establishing a mud dynamic invasion process simulation model, and simulating the mud dynamic invasion process under different model parameter value conditions in a preset value range through the simulation model to obtain simulation results corresponding to the different model parameter value conditions; logging data of a target shaft and a rock electricity experiment result of a core sample of a drilled shaft in an area where the target shaft is located are obtained, and initial model parameter values of a target simulation model for the target shaft are determined according to the logging data and the rock electricity experiment result; and determining a resistivity profile in a simulation result corresponding to the initial model parameter value of the target model parameter, and if the resistivity profile is matched with a resistivity logging curve in the logging data, determining a numerical value corresponding to the formation permeability in the initial model parameter value of the target model parameter. The timeliness of stratum permeability calculation is improved, and time and capital cost are saved.

Description

Technical Field

[0001] The present invention relates to the technical field of petroleum exploration, and in particular to a formation permeability determination method, device, electronic equipment and storage medium. Background Art

[0002] Porosity, permeability and saturation are three key parameters for formation evaluation. Porosity and saturation belong to volume model parameters. At present, there are relatively mature evaluation technical methods. Permeability has no effective logging means to achieve direct measurement. Usually, formation permeability parameters are obtained by core experiments or formation tests. Although core experiments can achieve the goal of permeability evaluation, there are many factors such as long parameter acquisition cycle, high core sampling cost, and difficulty in obtaining core samples. Usually, only key reservoirs that have been explored before drilling are cored and tested. Potential layers encountered during drilling and reservoirs that are significantly different from exploration wells are often missed due to unpredictable formation changes in the early planning. Formation testing is relatively lagging. At the same time, formation testing is to calibrate permeability through pressure and mobility. It is measured in an environment after the mud filtrate of the wellbore is seriously invaded. There are errors in the calculation and evaluation of effective permeability. On the other hand, formation testing is a scattered point measurement and lacks the ability to continuously evaluate the formation. Summary of the invention

[0003] The present invention provides a formation permeability determination method, device, electronic equipment and storage medium, which improve the timeliness of formation permeability calculation without increasing well logging projects, and save time and financial costs.

[0004] According to one aspect of the present invention, a method for determining formation permeability is provided, the method comprising:

[0005] Establishing a simulation model for the dynamic invasion process of mud, simulating the dynamic invasion process of mud under different model parameter value conditions within a preset value range through the simulation model, and obtaining simulation results corresponding to different model parameter value conditions; wherein the preset value range is determined according to the geological information of the area where the target wellbore is located, and the model parameters include formation permeability and formation resistivity;

[0006] Acquire the logging data of the target wellbore and the rock electrical test results of the core samples of the drilled wellbore in the area where the target wellbore is located, and determine the initial model parameter values ​​of the target simulation model for the target wellbore according to the logging data and the rock electrical test results;

[0007] Determine the resistivity profile in the simulation result corresponding to the initial model parameter value of the target model parameter. If the resistivity profile matches the resistivity logging curve in the logging data, determine the value corresponding to the formation permeability in the initial model parameter value of the target model parameter.

[0008] According to another aspect of the present invention, there is provided a formation permeability determination device, the device comprising:

[0009] A simulation result determination module is used to establish a simulation model of the mud dynamic invasion process, and to simulate the mud dynamic invasion process under different model parameter value conditions within a preset value range through the simulation model to obtain simulation results corresponding to different model parameter value conditions; wherein the preset value range is determined according to the geological information of the area where the target wellbore is located, and the model parameters include formation permeability and formation resistivity;

[0010] An initial model parameter value determination module is used to obtain the well logging data of the target wellbore and the rock electrical experiment results of the core samples of the drilled wellbore in the area where the target wellbore is located, and determine the initial model parameter values ​​of the target simulation model for the target wellbore according to the well logging data and the rock electrical experiment results;

[0011] The formation permeability determination module is used to determine the resistivity profile in the simulation result corresponding to the initial model parameter value of the target model parameter. If the resistivity profile matches the resistivity logging curve in the logging data, the numerical value corresponding to the formation permeability in the initial model parameter value of the target model parameter is determined.

[0012] According to another aspect of the present invention, there is provided an electronic device, the electronic device comprising:

[0013] at least one processor; and

[0014] a memory communicatively connected to the at least one processor; wherein,

[0015] The memory stores a computer program that can be executed by the at least one processor, and the computer program is executed by the at least one processor so that the at least one processor can execute the formation permeability determination method described in any embodiment of the present invention.

[0016] According to another aspect of the present invention, a computer-readable storage medium is provided, wherein the computer-readable storage medium stores computer instructions, and the computer instructions are used to enable a processor to implement the formation permeability determination method described in any embodiment of the present invention when executed.

[0017] The technical solution of the embodiment of the present invention is to establish a simulation model of the mud dynamic invasion process, and simulate the mud dynamic invasion process under different model parameter value conditions within a preset value range through the simulation model to obtain simulation results corresponding to different model parameter value conditions; wherein the preset value range is determined according to the geological information of the area where the target wellbore is located, and the model parameters include formation permeability and formation resistivity; obtain the logging data of the target wellbore and the rock electrical experiment results of the core samples of the drilled wellbore in the area where the target wellbore is located, and determine the initial model parameter values ​​of the target simulation model for the target wellbore according to the logging data and the rock electrical experiment results; determine the resistivity profile in the simulation result corresponding to the initial model parameter value of the target model parameter, and if the resistivity profile matches the resistivity logging curve in the logging data, determine the numerical value corresponding to the formation permeability in the initial model parameter value of the target model parameter. The technical solution of the embodiment of the present application can perform inversion calculation on the formation permeability of the target wellbore after obtaining the logging data of the target wellbore through simulation results and rock electrical experiment results of core samples of drilled wellbores in the area where the target wellbore is located. This improves the timeliness of the formation permeability calculation without increasing the logging projects, saving time and financial costs.

[0018] It should be understood that the contents described in this section are not intended to identify the key or important features of the embodiments of the present invention, nor are they intended to limit the scope of the present invention. Other features of the present invention will become easily understood through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings in the following description are only some embodiments of the present invention. For ordinary technicians in this field, other accompanying drawings can be obtained based on these accompanying drawings without paying creative work.

[0020] Figure 1 is a flow chart of a method for determining formation permeability provided according to Embodiment 1 of the present invention;

[0021] Figure 2 is a structural schematic diagram of a mud dynamic invasion process simulation model provided according to the first embodiment of the present invention;

[0022] Figure 3 A graph showing the change of resistivity profile over time according to the first embodiment of the present invention is shown in FIG.

[0023] Figure 4 is a flow chart of a method for determining formation permeability provided according to Embodiment 2 of the present invention;

[0024] Figure 5 is a schematic diagram of a process for determining formation permeability according to Embodiment 2 of the present invention;

[0025] Figure 6 is a schematic diagram of inversion results of formation resistivity and formation permeability provided according to the second embodiment of the present invention;

[0026] Figure 7 is a specific implementation flow chart of a formation permeability determination method provided according to Embodiment 3 of the present invention;

[0027] Figure 8 is a schematic structural diagram of a formation permeability determination device provided according to a fourth embodiment of the present invention;

[0028] Fig. 9 It is a schematic diagram of the structure of an electronic device for implementing the formation permeability determination method according to an embodiment of the present invention. DETAILED DESCRIPTION

[0029] In order to enable those skilled in the art to better understand the scheme of the present invention, the technical scheme in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work should fall within the scope of protection of the present invention.

[0030] It should be noted that the terms "first", "second", etc. in the specification and claims of the present invention and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchanged where appropriate, so that the embodiments of the present invention described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device that includes a series of steps or units is not necessarily limited to those steps or units that are clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0031] Embodiment 1

[0032] Figure 1A flow chart of a formation permeability determination method is provided for the first embodiment of the present invention. This embodiment is applicable to determining the formation permeability during drilling. The method can be executed by a formation permeability determination device. The formation permeability determination device can be implemented in the form of hardware and / or software. The formation permeability determination device can be configured in an electronic device. Figure 1 As shown, the method includes:

[0033] S110. Establish a simulation model for the dynamic invasion process of mud. Use the simulation model to simulate the dynamic invasion process of mud under different model parameter value conditions within a preset value range to obtain simulation results corresponding to different model parameter value conditions. The preset value range is determined based on the geological information of the area where the target wellbore is located, and the model parameters include formation permeability and formation resistivity.

[0034] The simulation model of the dynamic invasion process of mud includes the wellbore, the resistivity logging tool placed in the center of the wellbore, the mud filled in the wellbore, and the formations around the wellbore. The simulation model of the dynamic invasion process of mud is infinitely thick in the vertical direction, and does not consider the vertical formation stratification. For example, Figure 2 The schematic diagram of the structure of a mud dynamic invasion process simulation model is shown. The model parameters of the mud dynamic invasion process simulation model include formation permeability and formation resistivity, as well as other geological parameters and drilling parameters.

[0035] In an embodiment of the present invention, the value ranges of various model parameters of the established mud dynamic invasion process simulation model can be predetermined according to the geological information of the area where the target wellbore is located, and by selecting different model parameter values ​​within the predetermined value range, different target wellbores and their surrounding formation environmental conditions and different drilling conditions can be simulated to obtain simulation results of the mud dynamic invasion process under formation environmental conditions and different drilling conditions, thereby providing a basis for the formation permeability inversion of the target wellbore.

[0036] Optionally, the preset value range is determined according to the geological information of the area where the target wellbore is located, including: determining the upper and lower limits of the static parameters in the model parameters according to the geological information of the area where the target wellbore is located, and determining the value set of the static parameters according to a preset change step; wherein the static parameters include wellbore diameter, mud resistivity, mud viscosity, mud filtrate mineralization, formation water mineralization, formation porosity, formation permeability and formation resistivity; in combination with the value set of the static parameters, setting the value range of the dynamic parameters in the model parameters; wherein the dynamic parameters include wellbore mud cake thickness, mud cake permeability, drilling fluid static pressure, formation pressure, fluid viscosity and formation water saturation.

[0037] The model parameters can be divided into static parameters and dynamic parameters, the static parameters include wellbore diameter, mud resistivity, mud viscosity, mud filtrate salinity, formation water salinity, formation porosity, formation permeability and formation resistivity, and the dynamic parameters include wellbore mud cake thickness, mud cake permeability, drilling fluid static pressure, formation pressure, fluid viscosity and formation water saturation. In the embodiment of the present invention, the methods for determining the preset value ranges of static parameters and dynamic parameters are different.

[0038] In an embodiment of the present invention, for the static parameters in the model parameters of the mud dynamic invasion process simulation model, the upper and lower limits of the static parameters can be first determined according to the geological information of the area where the target wellbore is located, and then the value set of the static parameters can be determined according to the upper and lower limits and the preset change step. Among them, the preset change step can be set according to the actual situation, and different static parameters correspond to different preset change steps. For the dynamic parameters in the model parameters of the mud dynamic invasion process simulation model, it is necessary to directly determine the upper and lower limits of the dynamic parameters in combination with the determined value set of the static parameters, and use them as the value range of the dynamic parameters.

[0039] Optionally, the simulation model is used to simulate the dynamic invasion process of mud under different model parameter value conditions within a preset value range to obtain simulation results corresponding to different model parameter value conditions, including: for each set of model parameter value conditions, a numerical simulation algorithm is established through seepage theory to simulate the dynamic invasion process of mud; the resistivity profile during the simulation process is recorded, and a correlation relationship between the model parameters and the resistivity profile under each set of model parameter value conditions is established.

[0040] Among them, seepage theory is mainly used to describe the flow behavior of fluids in porous media. Seepage theory defines the relationship between fluid permeability and flow rate based on Darcy's law. Resistivity profile is a longitudinal or transverse resistivity distribution map of a geological body obtained through resistivity measurement, and is an important tool for underground structure and stratigraphic analysis.

[0041] In the embodiment of the present invention, each set of model parameter value conditions can be regarded as a model instance. For each model instance, the dynamic intrusion process of mud is simulated by a numerical simulation algorithm based on seepage theory, and the change of resistivity profile over time during the simulation process is recorded and associated with the model parameter value conditions corresponding to the model instance as the simulation results corresponding to different model parameter value conditions. For example, Figure 3 A graph of resistivity profile versus time is shown.

[0042] S120, obtaining logging data of the target wellbore and rock electrical test results of core samples of drilled wellbores in the area where the target wellbore is located, and determining initial model parameter values ​​of a target simulation model for the target wellbore based on the logging data and the rock electrical test results.

[0043] Among them, rock electrical experiment is an experimental method to study the electrical characteristics of rocks and the resistivity of formations, which is widely used in geological exploration, mineral resource development, engineering construction and other fields. It can be understood that the formation conditions in the same area are roughly the same. Therefore, by obtaining the rock electrical experiment results of the core samples of the drilled wellbore in the area where the target wellbore is located, the complicated core experiment and formation testing process can be avoided, saving time and capital costs.

[0044] In an embodiment of the present invention, the initial model parameter values ​​of some model parameters of the target simulation model of the target wellbore can be directly determined according to the well logging data of the target wellbore. However, the initial model parameter values ​​of some model parameters cannot be directly determined by the well logging data of the target wellbore, such as the formation permeability. In this case, the rock electrical test results of the core samples of the drilled wellbore in the area where the target wellbore is located can be analyzed to determine the correlation between the model parameters so as to estimate the initial model parameter values ​​of the model parameters that cannot be directly determined. The dynamic mud invasion process of the target wellbore is constrainedly inverted by the rock electrical test results of the core samples of the drilled wellbore in the area where the target wellbore is located. Without increasing the well logging items, the problem of multiple solutions caused by insufficient well logging data in a single well can be solved.

[0045] S130, determining the resistivity profile in the simulation result corresponding to the initial model parameter value of the target model parameter, and if the resistivity profile matches the resistivity logging curve in the logging data, determining the value corresponding to the formation permeability in the initial model parameter value of the target model parameter.

[0046] In the embodiment of the present invention, after the initial model parameter value of the target model parameter is determined, the resistivity profile in the simulation result corresponding to the initial model parameter value can be determined from the simulation results corresponding to the different model parameter value conditions determined in step S110, and when the resistivity profile matches the resistivity logging curve in the logging data, the value corresponding to the formation permeability in the initial model parameter value of the target model parameter is determined, and the real-time calculation of the formation permeability of the target wellbore is completed, thereby improving the timeliness of the formation permeability calculation. Optionally, when the resistivity profile matches the resistivity logging curve in the logging data, the value corresponding to the formation resistivity in the initial model parameter value of the target model parameter can also be determined, thereby eliminating the influence of the environment, especially the mud invasion, on the formation resistivity measurement value, which is of great significance to the fine evaluation of formation evaluation and the accurate calculation of reserves.

[0047] The technical solution of the embodiment of the present invention is to establish a simulation model of the mud dynamic invasion process, and simulate the mud dynamic invasion process under different model parameter value conditions within a preset value range through the simulation model to obtain simulation results corresponding to different model parameter value conditions; wherein the preset value range is determined according to the geological information of the area where the target wellbore is located, and the model parameters include formation permeability and formation resistivity; obtain the logging data of the target wellbore and the rock electrical experiment results of the core samples of the drilled wellbore in the area where the target wellbore is located, and determine the initial model parameter values ​​of the target simulation model for the target wellbore according to the logging data and the rock electrical experiment results; determine the resistivity profile in the simulation result corresponding to the initial model parameter value of the target model parameter, and if the resistivity profile matches the resistivity logging curve in the logging data, determine the value corresponding to the formation permeability in the initial model parameter value of the target model parameter. The technical solution of the embodiment of the present invention can perform inversion calculation on the formation permeability of the target wellbore after obtaining the logging data of the target wellbore through simulation results and rock electrical experiment results of core samples of drilled wellbores in the area where the target wellbore is located. Without increasing the logging projects, the timeliness of the formation permeability calculation is improved, saving time and financial costs.

[0048] Embodiment 2

[0049] Figure 4 This is a flow chart of a formation permeability determination method provided in the second embodiment of the present invention. The embodiment of the present invention is optimized based on the above embodiment. For solutions not described in detail in the embodiment of the present invention, please refer to the above embodiment. Figure 4 As shown, the method includes:

[0050] S210, establishing a simulation model for the dynamic intrusion process of mud, simulating the dynamic intrusion process of mud under different model parameter value conditions within a preset value range through the simulation model, and obtaining simulation results corresponding to different model parameter value conditions.

[0051] S220, obtaining the logging data of the target wellbore and the rock electrical test results of the core samples of the drilled wellbore in the area where the target wellbore is located.

[0052] In an embodiment of the present invention, the logging data of the target wellbore can be obtained through various logging instruments installed during the drilling process of the target wellbore, and the rock electrical test results of the core samples of the drilled wellbore in the area where the target wellbore is located can be obtained by selecting the core samples of the drilled wellbore around the target wellbore, taking samples from different directions to make core plungers, and performing rock electrical experiments on the core plungers to obtain the data.

[0053] S230. Determine the regression relationship between the formation resistivity and other model parameters based on the rock electrical experiment results as the statistical law between the formation resistivity and other model parameters.

[0054] In an embodiment of the present invention, a regression equation of formation resistivity with respect to other model parameters is determined based on rock electrical experimental results as a statistical law between formation resistivity and other model parameters, so as to estimate the initial model values ​​of the model parameters of the target simulation model that cannot be directly determined based on the statistical law.

[0055] Optionally, a regression equation of formation resistivity with respect to other model parameters is determined based on the rock electrical experiment results, including: determining the measured values ​​of formation resistivity in the rock electrical experiment results and the corresponding model parameter values; determining the simulated values ​​of formation resistivity in the simulation results under the same model parameter value conditions, and calculating a scaling factor based on the measured values ​​and the simulated values; scaling the simulated values ​​of formation resistivity and the corresponding model parameter values ​​by the scaling factor, and supplementing the rock electrical experiment results based on the scaled data; determining changes in formation resistivity with changes in the values ​​of other model parameters based on the supplemented rock electrical experiment results, and forming a regression equation through regression fitting.

[0056] Among them, the formation resistivity simulation value can be determined by the resistivity simulation curve corresponding to the resistivity profile, and the resistivity simulation curve can be obtained by using the resistivity profile in the simulation result obtained in step S210, combined with the resistivity logging tool set in the mud dynamic invasion process simulation model to simulate the resistivity. Specifically, according to the working principle of the resistivity logging tool and Maxwell's equations, the working constitutive equation of the resistivity logging tool is constructed, and numerical simulation is performed according to the resistivity profile and the sampling finite element method to obtain the signal response of the resistivity logging tool receiving antenna, and the received signal (voltage / current) is scaled into resistivity through nonlinear transformation to obtain the resistivity simulation curve.

[0057] In an embodiment of the present invention, after determining the formation resistivity measurement value and the corresponding model parameter value in the rock electrical experiment result, the resistivity simulation curve and the formation resistivity measurement value can be compared and calibrated, the formation resistivity measurement value and the formation resistivity simulation value under the same model parameter value conditions can be determined, and the scaling factor can be calculated according to the measurement value and the simulation value, and the formation resistivity simulation value can be scaled to the formation resistivity measurement value by the scaling factor, and the model parameter value corresponding to the formation resistivity simulation value can be scaled in the same way, and the rock electrical experiment result can be supplemented according to the scaled data to obtain richer sample data, and the intrinsic connection between the change of formation resistivity with the change of other model parameter values ​​can be determined, and a regression relationship can be formed by regression fitting as the statistical law between formation resistivity and other model parameters. Optionally, after the regression relationship is formed by regression fitting, the regression relationship can be expanded by Taylor polynomial, and the polynomial coefficients can be saved in order.

[0058] S240, determining initial model parameter values ​​of a target simulation model for a target wellbore according to the well logging data and statistical laws.

[0059] In the embodiment of the present invention, the initial model parameter values ​​of the target simulation model for the target wellbore may be determined according to the well logging data of the target wellbore and the statistical law between the formation resistivity and other model parameters.

[0060] Optionally, initial model parameter values ​​of the target simulation model for the target wellbore are determined based on the logging data and the statistical law, including: extracting initial model parameter values ​​of target model parameters other than formation permeability from the logging data; estimating the initial model parameter values ​​of formation permeability based on the statistical law, the resistivity logging curve in the logging data, and the initial model parameter values ​​of the target model parameters other than formation permeability.

[0061] In an embodiment of the present invention, initial model parameter values ​​of target model parameters other than formation permeability can be extracted from the logging data of the target wellbore, such as deep lateral resistivity can be used as formation resistivity, and formation porosity can be determined according to neutron porosity logging curves, etc. After the initial model parameter values ​​of target model parameters other than formation permeability are determined, the initial model parameter values ​​of formation permeability can be estimated according to the statistical law between formation resistivity and other model parameters, the resistivity logging curve in the logging data, and the initial model parameter values ​​of target model parameters other than formation permeability. Specifically, the range of formation permeability can be determined by the range of formation resistivity and the regression relationship, and the mean value of formation permeability can be used as the initial model parameter value of formation permeability of the current formation.

[0062] S250, determining the resistivity profile in the simulation result corresponding to the initial model parameter value of the target model parameter, if the resistivity profile matches the resistivity logging curve in the logging data, then determining the value corresponding to the formation permeability in the initial model parameter value of the target model parameter.

[0063] S260. If the resistivity profile does not match the resistivity logging curve in the logging data, the initial model parameter values ​​in the target model parameters are updated until the resistivity profile matches the resistivity logging curve in the logging data, and the value corresponding to the formation permeability in the updated initial model parameter values ​​is determined.

[0064] In an embodiment of the present invention, if the resistivity profile does not match the resistivity logging curve in the logging data, it means that the initial model parameter values ​​of the model parameters do not match the model parameter values ​​of the target wellbore in the current formation, and the initial model parameter values ​​in the target model parameters need to be updated until the resistivity profile matches the resistivity logging curve in the logging data, and the quality control curve is obtained. The numerical value corresponding to the formation permeability in the updated initial model parameter values ​​is determined to complete the real-time calculation of the formation permeability of the target wellbore, thereby improving the timeliness of the formation permeability calculation. Specifically, the initial model parameter values ​​in the target model parameters are updated, and the initial model parameter values ​​of the constant parameters in the model parameters can be fixed, and the initial model parameter values ​​of the formation resistivity and formation permeability are updated. The resistivity profile in the simulation result corresponding to the initial model parameter values ​​of the target model parameters is re-obtained to determine whether the resistivity profile matches the resistivity logging curve in the logging data. Exemplarily, Figure 5 A schematic diagram of a process for determining formation permeability is shown. Figure 6 A schematic diagram of the inversion results of formation resistivity and formation permeability is shown.

[0065] The technical solution of the embodiment of the present invention is to establish a simulation model of the mud dynamic invasion process, and simulate the mud dynamic invasion process under different model parameter value conditions within a preset value range through the simulation model to obtain simulation results corresponding to different model parameter value conditions; obtain the logging data of the target wellbore and the rock electrical experiment results of the core samples of the drilled wellbore in the area where the target wellbore is located; determine the regression relationship between the formation resistivity and other model parameters according to the rock electrical experiment results, as the statistical law between the formation resistivity and other model parameters; determine the target wellbore according to the logging data and the statistical law. The initial model parameter values ​​of the target simulation model are determined; the resistivity profile in the simulation result corresponding to the initial model parameter values ​​of the target model parameters is determined; if the resistivity profile matches the resistivity logging curve in the logging data, the value corresponding to the formation permeability in the initial model parameter values ​​of the target model parameters is determined; if the resistivity profile does not match the resistivity logging curve in the logging data, the initial model parameter values ​​in the target model parameters are updated until the resistivity profile matches the resistivity logging curve in the logging data, and the value corresponding to the formation permeability in the updated initial model parameter values ​​is determined. The technical solution of the embodiment of the present invention, through the simulation results and the rock electrical experiment results of the core samples of the drilled wellbore in the area where the target wellbore is located, after obtaining the logging data of the target wellbore, the formation permeability of the target wellbore can be inverted and calculated, which improves the timeliness of the formation permeability calculation without increasing the logging project, saving time and financial costs.

[0066] Embodiment 3

[0067] Figure 7 The specific implementation flow chart of a formation permeability determination method provided in the third embodiment of the present invention provides a preferred embodiment based on the above embodiment, which is as follows:

[0068] S310, establishing a simulation model of the mud dynamic invasion process, and setting static parameters in the model parameters.

[0069] In the embodiment of the present invention, a resistivity logging tool HLAT is selected, which can provide 5 resistivity measurement curves with a resistivity measurement range of 0.1 to 10000 ohm.m; the tool is placed inside the wellbore at the center of the mud dynamic invasion process simulation model, the wellbore is filled with mud, and the formation is infinitely thick. The values ​​of the static parameters are determined according to the geological information of the target wellbore area as shown in Table 1 below.

[0070] Table 1 Static parameter value table

[0071]

[0072] S320. Determine the value range of the dynamic parameters in the model parameters in combination with the static parameters, simulate the dynamic invasion process of the mud, and obtain simulation results corresponding to different model parameter value conditions.

[0073] In the embodiment of the present invention, first, the value range of the dynamic parameters is determined in combination with the static parameter values ​​in step S310, for example, mud cake thickness 0-2 mm, mud cake permeability 1-1000 mD, drilling fluid static pressure 3000-8000 psi, formation pressure 3000-8000 psi, fluid viscosity 0.8-1.2 cp, formation water saturation 0-1 v / v, to form a model instance.

[0074] Secondly, the physical model of mud filtrate intrusion into the formation is established by using the phase seepage theory and diffusion theory in porous media, and then the formation electrical parameters are calculated by the rock electrical theory. Considering the influence of capillary force and ignoring the influence of gravity, the following one-dimensional radial flow pressure and saturation equations and auxiliary equations can be obtained according to the seepage theory:

[0075]

[0076] P c (S w )=P o -P w ;

[0077] S o +S w =1;

[0078] Where K represents the formation permeability, μ is the fluid viscosity, S is the formation water saturation, represents the initial undisturbed formation water saturation, C represents the mineralization, P c represents the capillary pressure, r represents the distance from the well axis, and r b represents the wellbore radius, P b Indicates the drilling fluid column pressure, P mf represents the initial undisturbed formation pressure, and the subscripts ro and rw refer to the wetting phase (water) and non-wetting phase (oil), respectively.

[0079] Due to the difference in mineralization between mud filtrate and formation water, mud filtrate will mix with formation water during the invasion process. The convection-diffusion equation is used to simulate the mixing process of mud filtrate and formation water, which shows the diffusion process of salt ions from high concentration to low concentration.

[0080]

[0081] Among them, C w0 Indicates the mineralization of the original stratum, C mf Indicates the mineralization of mud filtrate.

[0082] The water saturation and formation mineralization profiles at each moment can be obtained by solving the two-phase seepage equation and convection-diffusion equation using the finite difference method. When the formation water mineralization profile is known, the formation water resistivity profile can be obtained using the following formula:

[0083]

[0084] Then, according to the formation water saturation distribution profile and formation water resistivity distribution profile at different times, the radial resistivity R is calculated according to the Archie formula. t The distribution profile of:

[0085]

[0086] The calculated resistivity profiles are matched one by one with the model parameters and saved in the order of the model parameters to obtain the simulation results corresponding to different model parameter value conditions.

[0087] S330, obtaining the logging data of the target wellbore and the rock electrical test results of the core samples of the drilled wellbore in the area where the target wellbore is located, and determining the regression relationship between the formation resistivity and other model parameters based on the rock electrical test results as the statistical law between the formation resistivity and other model parameters.

[0088] Core samples from drilled wellbores in the target wellbore area are selected, and core plungers are cut from different directions to conduct mud invasion experiments. By winding electrodes on the core plungers, the change in resistance between two adjacent electrodes during the invasion process is detected in real time, thereby realizing dynamic monitoring of mud invasion.

[0089] The experiment first measured the permeability, porosity and resistivity of the dry core, and recorded the resistivity changes during the experiment. The specific method is as follows:

[0090] 1. The invasion cycle pressure difference was set to 2.8MPa and 5.6MPa according to the mud column pressure (5 cores were used for 10 invasion tests); the formation pressure was set to 7.5MP confining pressure;

[0091] 2. Measure the core porosity, permeability and resistivity before the experiment;

[0092] 3. The invasion time depends on the specific invasion situation of the experiment. For cores with a diameter less than 10mD, the invasion time is not less than 12h. For high porosity and permeability formations that are easy to invade, the invasion experiment can be stopped after the dynamic monitoring of the core resistivity remains unchanged for a long time (more than 10 minutes). For core experiments with fast invasion speed, the frequency of core resistivity monitoring and recording needs to be increased.

[0093] 4. Dynamically monitor the resistivity changes at different positions of the core during the experiment and record them accurately to facilitate the characteristic analysis of the intrusion experiment in the next step;

[0094] 5. When changing the pressure difference to conduct a secondary experiment, the salt washing and drying operations must be carried out first.

[0095] 6. According to the core invasion experiment, the relationship between mud invasion depth, resistivity change and permeability, porosity and saturation is classified and statistically analyzed to form a regular chart, as shown in Table 2 below.

[0096] Table 2 Statistics of change rules

[0097]

[0098] Combining the resistivity logging tool and its working principle, the finite element method is used to perform forward simulation of the instrument response. The electric field problem of array lateral logging can be described by differential equations. Using u(x,y,z) to represent the potential and σ to represent the conductivity, in the rectangular coordinate system (x,y,z), the potential u satisfies the differential equation:

[0099]

[0100] For objects with rotational symmetry we can use:

[0101]

[0102] The boundary conditions include the following two types:

[0103] The first type of boundary condition: On the constant voltage electrode, u is a known constant, and on the infinite boundary, this known constant is zero. On the constant voltage electrode, u is an unknown constant.

[0104] The second type of boundary conditions: on the surface of the constant current electrode: Among them, I A represents the constant-current electrode current, σ m Represents the conductivity of the mud. On the insulating boundary surface, it satisfies:

[0105] The universal function constructed according to the above-mentioned problem is:

[0106]

[0107] in:

[0108]

[0109] For objects with rotational symmetry:

[0110]

[0111] Where Ω is the entire space enclosed by the instrument surface and the infinite boundary.

[0112] u is approximated by the following formula:

[0113]

[0114] Among them, u i is the voltage on each vertex, l i For each point, the shape function is used to discretize the expression:

[0115]

[0116] You can get:

[0117] {u} T {G}{u}={u} T {I};

[0118] Where {G} is the overall stiffness matrix or conductivity matrix. In order to make the functional reach the extreme value, it is necessary to make We get: {G}{u}={I}. By solving this matrix, we can get the voltage at each point.

[0119] Through nonlinear transformation, the received signal (voltage / current) is scaled into resistivity, and the resistivity obtained from the core experiment under the same model parameters is compared with the numerical simulation resistivity. Through the operation of the amplification factor, the simulated resistivity is scaled to the core experiment resistivity value. The amplification factor is fixed, and by changing and encrypting the numerical model sampling, a richer model logging response is calculated, and the relationship between the resistivity change and the model invasion depth, porosity, saturation, and permeability is statistically analyzed, and a regression relationship is formed through regression fitting. The relationship formed by regression fitting is expanded by Taylor polynomials, and the polynomial coefficients are saved in order.

[0120] S340, determining initial model parameter values ​​of a target simulation model for a target wellbore according to the well logging data and statistical laws.

[0121] Collect measured data from the well site, including resistivity logging data, neutron density logging data, neutron porosity logging data, and natural gamma logging data. Collect logging time information in the drilling log, and convert the logging time corresponding to each measurement point according to the logging depth. Collect formation water information, wellbore parameters, mud parameters, formation pressure numbers, mud static pressure, formation water salinity, and mud cake information in the target area, and extract initial model parameter values ​​corresponding to some model parameters that can be directly determined, such as deep lateral resistivity can be used as formation resistivity, and formation porosity can be determined according to neutron porosity logging curves. After determining the initial model parameter values ​​of the target model parameters other than formation permeability, the initial model parameter values ​​of formation permeability can be estimated according to the statistical laws between formation resistivity and other model parameters, the resistivity logging curves in the logging data, and the initial model parameter values ​​of the target model parameters other than formation permeability. Specifically, the value range of the formation permeability can be determined through the value range of the formation resistivity and the regression relationship, and the mean value of the formation permeability is used as the initial model parameter value of the formation permeability of the current formation.

[0122] S350, determine the resistivity profile in the simulation result corresponding to the initial model parameter value of the target model parameter, judge whether the resistivity profile matches the resistivity logging curve in the logging data, modify the model according to the matching situation until the accuracy requirement is met, obtain the quality control curve, and output the inversion result.

[0123] 1. Fix the wellbore diameter, mud resistivity, drilling pressure, formation pressure, salinity, porosity, formation density and other constant parameters, and query the corresponding resistivity profile in the simulation result obtained in step S320;

[0124] 2. Extract the intrusion zone resistivity, intrusion depth, formation resistivity and permeability parameters according to the least squares method. The query formula is as follows:

[0125]

[0126] Where n is the number of logging curves, x i represents the measured value, y i represents the model response value, α i represents the weighting coefficient, and

[0127] 3. Based on the extracted invasion depth, invasion zone resistivity, formation resistivity, and permeability parameters, combined with the unchanged parameters, an updated model is formed. The forward response of the updated model is compared with the resistivity curve of the measured well to evaluate the model accuracy. The accuracy of the model is evaluated based on the model.

[0128] 4. If the accuracy does not meet the requirements, repeat steps 2-3. If the accuracy meets the requirements, proceed to the next step.

[0129] 5. Output the inversion results and quality control curves. The inversion results include the resistivity of the invasion zone RXO, the resistivity of the original formation RT, and the permeability calculation result KEW.

[0130] The embodiment of the present invention has the same beneficial effects as the above embodiment.

[0131] Embodiment 4

[0132] Figure 8 This is a schematic diagram of the structure of a formation permeability determination device provided in Embodiment 4 of the present invention. Figure 8 As shown, the device comprises:

[0133] The simulation result determination module 410 is used to establish a simulation model of the mud dynamic invasion process, and simulate the mud dynamic invasion process under different model parameter value conditions within a preset value range through the simulation model to obtain simulation results corresponding to different model parameter value conditions; wherein the preset value range is determined according to the geological information of the area where the target wellbore is located, and the model parameters include formation permeability and formation resistivity;

[0134] An initial model parameter value determination module 420 is used to obtain the well logging data of the target wellbore and the rock electrical test results of the core samples of the drilled wellbore in the area where the target wellbore is located, and determine the initial model parameter values ​​of the target simulation model for the target wellbore according to the well logging data and the rock electrical test results;

[0135] The formation permeability determination module 430 is used to determine the resistivity profile in the simulation result corresponding to the initial model parameter value of the target model parameter. If the resistivity profile matches the resistivity logging curve in the logging data, the numerical value corresponding to the formation permeability in the initial model parameter value of the target model parameter is determined.

[0136] Optionally, the preset value range is determined according to geological information of the area where the target wellbore is located, including:

[0137] Determine the upper and lower limits of the static parameters in the model parameters according to the geological information of the area where the target wellbore is located, and determine the value set of the static parameters according to the preset change step; wherein the static parameters include well diameter, mud resistivity, mud viscosity, mud filtrate mineralization, formation water mineralization, formation porosity, formation permeability and formation resistivity;

[0138] In combination with the value set of the static parameters, the value range of the dynamic parameters in the model parameters is set; wherein the dynamic parameters include wellbore mud cake thickness, mud cake permeability, drilling fluid static pressure, formation pressure, fluid viscosity and formation water saturation.

[0139] Optionally, the simulation result determination module 410 includes:

[0140] The mud dynamic invasion process simulation unit is used to simulate the mud dynamic invasion process by establishing a numerical simulation algorithm through seepage theory for each set of model parameter value conditions;

[0141] The simulation result determination unit is used to record the resistivity profile during the simulation process and establish the correlation relationship between the model parameters under each set of model parameter value conditions and the resistivity profile.

[0142] Optionally, the initial model parameter value determination module 420 includes:

[0143] A statistical law determination unit, used to determine a regression relationship between formation resistivity and other model parameters according to the rock electrical test results, as a statistical law between formation resistivity and other model parameters;

[0144] The initial model parameter value determination unit is used to determine the initial model parameter values ​​of the target simulation model for the target wellbore according to the well logging data and the statistical law.

[0145] Optionally, the statistical law determination unit is specifically used to: determine the formation resistivity measurement value and the corresponding model parameter value in the rock electrical experiment results; determine the formation resistivity simulation value in the simulation result under the same model parameter value conditions, and calculate the scaling factor according to the measurement value and the simulation value; scale the formation resistivity simulation value and the corresponding model parameter value by the scaling factor, and supplement the rock electrical experiment results according to the scaled data; determine the change of formation resistivity with the change of other model parameter values ​​according to the supplemented rock electrical experiment results, and form a regression relationship through regression fitting.

[0146] Optionally, the initial model parameter value determination unit is specifically used to: extract initial model parameter values ​​of target model parameters other than formation permeability from the logging data; and estimate the initial model parameter values ​​of formation permeability based on the statistical laws, the resistivity logging curves in the logging data, and the initial model parameter values ​​of the target model parameters other than formation permeability.

[0147] Optionally, the device further comprises:

[0148] The initial model parameter value updating module is used to update the initial model parameter values ​​in the target model parameters if the resistivity profile does not match the resistivity logging curve in the logging data until the resistivity profile matches the resistivity logging curve in the logging data, and determine the numerical value corresponding to the formation permeability in the updated initial model parameter values.

[0149] The formation permeability determination device provided in the embodiment of the present invention can execute the formation permeability determination method provided in any embodiment of the present invention, and has the corresponding functional modules and beneficial effects of the execution method.

[0150] Embodiment 5

[0151] Fig. 9 A schematic diagram of the structure of an electronic device 10 that can be used to implement an embodiment of the present invention is shown. The electronic device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device can also represent various forms of mobile devices, such as personal digital processing, cellular phones, smart phones, wearable devices (such as helmets, glasses, watches, etc.) and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely examples and are not intended to limit the implementation of the present invention described and / or required herein.

[0152] like Fig. 9As shown, the electronic device 10 includes at least one processor 11, and a memory connected to the at least one processor 11, such as a read-only memory (ROM) 12, a random access memory (RAM) 13, etc., wherein the memory stores a computer program that can be executed by at least one processor, and the processor 11 can perform various appropriate actions and processes according to the computer program stored in the read-only memory (ROM) 12 or the computer program loaded from the storage unit 18 to the random access memory (RAM) 13. In the RAM 13, various programs and data required for the operation of the electronic device 10 can also be stored. The processor 11, the ROM 12, and the RAM 13 are connected to each other through a bus 14. An input / output (I / O) interface 15 is also connected to the bus 14.

[0153] A number of components in the electronic device 10 are connected to the I / O interface 15, including: an input unit 16, such as a keyboard, a mouse, etc.; an output unit 17, such as various types of displays, speakers, etc.; a storage unit 18, such as a disk, an optical disk, etc.; and a communication unit 19, such as a network card, a modem, a wireless communication transceiver, etc. The communication unit 19 allows the electronic device 10 to exchange information / data with other devices through a computer network such as the Internet and / or various telecommunication networks.

[0154] The processor 11 may be a variety of general and / or special processing components with processing and computing capabilities. Some examples of the processor 11 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various dedicated artificial intelligence (AI) computing chips, various processors running machine learning model algorithms, a digital signal processor (DSP), and any appropriate processor, controller, microcontroller, etc. The processor 11 executes the various methods and processes described above, such as a formation permeability determination method.

[0155] In some embodiments, the formation permeability determination method may be implemented as a computer program, which is tangibly contained in a computer-readable storage medium, such as a storage unit 18. In some embodiments, part or all of the computer program may be loaded and / or installed on the electronic device 10 via the ROM 12 and / or the communication unit 19. When the computer program is loaded into the RAM 13 and executed by the processor 11, one or more steps of the formation permeability determination method described above may be performed. Alternatively, in other embodiments, the processor 11 may be configured to perform the formation permeability determination method in any other appropriate manner (e.g., by means of firmware).

[0156] Various implementations of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), application specific standard products (ASSPs), systems on chips (SOCs), load programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various implementations can include: being implemented in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which can be a special purpose or general purpose programmable processor that can receive data and instructions from a storage system, at least one input device, and at least one output device, and transmit data and instructions to the storage system, the at least one input device, and the at least one output device.

[0157] Computer programs for implementing the methods of the present invention may be written in any combination of one or more programming languages. These computer programs may be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing device, so that when the computer program is executed by the processor, the functions / operations specified in the flow chart and / or block diagram are implemented. The computer program may be executed entirely on the machine, partially on the machine, partially on the machine and partially on a remote machine as a stand-alone software package, or entirely on a remote machine or server.

[0158] In the context of the present invention, a computer-readable storage medium may be a tangible medium that may contain or store a computer program for use by or in conjunction with an instruction execution system, device, or equipment. A computer-readable storage medium may include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, devices, or equipment, or any suitable combination of the foregoing. Alternatively, a computer-readable storage medium may be a machine-readable signal medium. A more specific example of a machine-readable storage medium may include an electrical connection based on one or more lines, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

[0159] To provide interaction with a user, the systems and techniques described herein may be implemented on an electronic device having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and a pointing device (e.g., a mouse or trackball) through which the user can provide input to the electronic device. Other types of devices may also be used to provide interaction with the user; for example, the feedback provided to the user may be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user may be received in any form (including acoustic input, voice input, or tactile input).

[0160] The systems and techniques described herein may be implemented in a computing system that includes backend components (e.g., as a data server), or a computing system that includes middleware components (e.g., an application server), or a computing system that includes frontend components (e.g., a user computer with a graphical user interface or a web browser through which a user can interact with implementations of the systems and techniques described herein), or a computing system that includes any combination of such backend components, middleware components, or frontend components. The components of the system may be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: a local area network (LAN), a wide area network (WAN), a blockchain network, and the Internet.

[0161] A computing system may include a client and a server. The client and the server are generally remote from each other and usually interact through a communication network. The client and server relationship is generated by computer programs running on the corresponding computers and having a client-server relationship with each other. The server may be a cloud server, also known as a cloud computing server or cloud host, which is a host product in the cloud computing service system to solve the defects of difficult management and weak business scalability in traditional physical hosts and VPS services.

[0162] It should be understood that the various forms of processes shown above can be used to reorder, add or delete steps. For example, the steps described in the present invention can be executed in parallel, sequentially or in different orders, as long as the desired results of the technical solution of the present invention can be achieved, and this document does not limit this.

[0163] The above specific implementations do not constitute a limitation on the protection scope of the present invention. It should be understood by those skilled in the art that various modifications, combinations, sub-combinations and substitutions can be made according to design requirements and other factors. Any modification, equivalent substitution and improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.

Claims

1. A method for determining formation permeability, characterized in that: The method comprises: Establishing a simulation model for the dynamic invasion process of mud, simulating the dynamic invasion process of mud under different model parameter value conditions within a preset value range through the simulation model, and obtaining simulation results corresponding to different model parameter value conditions; wherein the preset value range is determined according to the geological information of the area where the target wellbore is located, and the model parameters include formation permeability and formation resistivity; Acquire the logging data of the target wellbore and the rock electrical test results of the core samples of the drilled wellbore in the area where the target wellbore is located, and determine the initial model parameter values ​​of the target simulation model for the target wellbore according to the logging data and the rock electrical test results; Determine the resistivity profile in the simulation result corresponding to the initial model parameter value of the target model parameter. If the resistivity profile matches the resistivity logging curve in the logging data, determine the value corresponding to the formation permeability in the initial model parameter value of the target model parameter.

2. The method according to claim 1, characterized in that The preset value range is determined according to the geological information of the area where the target wellbore is located, including: Determine the upper and lower limits of the static parameters in the model parameters according to the geological information of the area where the target wellbore is located, and determine the value set of the static parameters according to the preset change step; wherein the static parameters include well diameter, mud resistivity, mud viscosity, mud filtrate mineralization, formation water mineralization, formation porosity, formation permeability and formation resistivity; In combination with the value set of the static parameters, the value range of the dynamic parameters in the model parameters is set; wherein the dynamic parameters include wellbore mud cake thickness, mud cake permeability, drilling fluid static pressure, formation pressure, fluid viscosity and formation water saturation.

3. The method according to claim 1, characterized in that The simulation model is used to simulate the dynamic intrusion process of mud under different model parameter value conditions within a preset value range, and simulation results corresponding to different model parameter value conditions are obtained, including: According to each set of model parameter value conditions, a numerical simulation algorithm is established through seepage theory to simulate the dynamic invasion process of mud; The resistivity profile during the simulation process is recorded, and the correlation relationship between the model parameters and the resistivity profile under each set of model parameter value conditions is established.

4. The method according to claim 1, characterized in that: Determining initial model parameter values ​​of a target simulation model for the target wellbore according to the well logging data and the rock electrical experiment results includes: Determine a regression relationship between formation resistivity and other model parameters according to the rock electrical test results as a statistical law between formation resistivity and other model parameters; Initial model parameter values ​​of a target simulation model for the target wellbore are determined according to the well logging data and the statistical law.

5. The method according to claim 4, characterized in that The regression relationship between formation resistivity and other model parameters is determined based on the rock electrical test results, including: Determine the formation resistivity measurement value and the corresponding model parameter value in the rock electrical experiment result; Determine a simulated value of formation resistivity in a simulation result under the same model parameter value conditions, and calculate a scaling factor according to the measured value and the simulated value; Scaling the formation resistivity simulation value and the corresponding model parameter value by a scaling factor, and supplementing the rock electrical test result according to the scaled data; According to the supplemented rock electrical experimental results, the changes of formation resistivity with the change of other model parameters are determined, and the regression relationship is formed through regression fitting.

6. The method according to claim 4, characterized in that Determining initial model parameter values ​​of a target simulation model for the target wellbore according to the well logging data and the statistical law includes: Extracting initial model parameter values ​​of target model parameters except formation permeability from the well logging data; The initial model parameter values ​​of the formation permeability are estimated according to the statistical law, the resistivity logging curve in the logging data, and the initial model parameter values ​​of the target model parameters except the formation permeability.

7. The method according to claim 1, characterized in that The method further comprises: If the resistivity profile does not match the resistivity logging curve in the logging data, the initial model parameter values ​​in the target model parameters are updated until the resistivity profile matches the resistivity logging curve in the logging data, and the numerical value corresponding to the formation permeability in the updated initial model parameter values ​​is determined.

8. A formation permeability determination device, characterized in that: The device comprises: A simulation result determination module is used to establish a simulation model of the mud dynamic invasion process, and to simulate the mud dynamic invasion process under different model parameter value conditions within a preset value range through the simulation model to obtain simulation results corresponding to different model parameter value conditions; wherein the preset value range is determined according to the geological information of the area where the target wellbore is located, and the model parameters include formation permeability and formation resistivity; An initial model parameter value determination module is used to obtain the well logging data of the target wellbore and the rock electrical experiment results of the core samples of the drilled wellbore in the area where the target wellbore is located, and determine the initial model parameter values ​​of the target simulation model for the target wellbore according to the well logging data and the rock electrical experiment results; The formation permeability determination module is used to determine the resistivity profile in the simulation result corresponding to the initial model parameter value of the target model parameter. If the resistivity profile matches the resistivity logging curve in the logging data, the numerical value corresponding to the formation permeability in the initial model parameter value of the target model parameter is determined.

9. An electronic device, characterized in that: The electronic device comprises: at least one processor; and a memory communicatively connected to the at least one processor; wherein, The memory stores a computer program executable by the at least one processor, and the computer program is executed by the at least one processor so that the at least one processor can perform the formation permeability determination method according to any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores computer instructions, and the computer instructions are used to enable a processor to implement the formation permeability determination method according to any one of claims 1 to 7 when executed.

Images