Resistivity Logging-Based Calculation Method and Related Devices for Skin Factor in Oil and Gas Wells

Abstract

This invention discloses a method and related apparatus for calculating the skin factor of oil and gas wells based on resistivity logging. The method includes: acquiring resistivity logging data and well test skin factor data of the target formation in an oil and gas field; establishing a skin factor calculation model based on the resistivity logging data and well test skin factor data; and calculating the skin factor of the well to be logged in the oil and gas field based on the skin factor calculation model. This invention utilizes resistivity logging data and well test skin factor data of the target formation in an oil and gas field to establish a skin factor calculation model, and calculates the skin factor of the well to be logged in the oil and gas field based on this model. This provides more accurate production capacity prediction for the oil and gas field production and development field, and avoids the determination of various process parameters and the calculation process of complex skin factor models, effectively solving the problem of difficulty in determining the skin factor in actual production and development.

Description

Technical Field

[0001] This invention relates to the field of oil and gas field development, and specifically to a method and apparatus for calculating the skin factor of oil and gas wells based on resistivity logging, as well as a computing device and computer storage medium. Background Technology

[0002] In the field of oil and gas field development, the calculation of the skin factor is one of the most important parameters in the production capacity prediction process. However, its value is often difficult to predict, which makes the production capacity prediction of oil and gas wells difficult.

[0003] Currently, the prediction method for skin factors in oil and gas wells typically decomposes them into various skin factors under different influencing factors, such as drilling contamination skin, perforation skin, and well deviation skin. Then, calculation models are established for each skin factor, and finally, these models are summed to obtain the skin factor. However, currently, accurate acquisition of various skin factors is only possible through drill pipe formation testing combined with well test analysis. The challenge of obtaining these skin factors lies in the fact that the calculation model for skin factors requires determining many difficult-to-obtain process parameters, such as contamination zone radius, perforation penetration depth, and compaction zone thickness. This necessitates a large amount of detailed production process data as support. However, drill pipe formation testing is costly and is usually used for comparing and verifying model calculation results, not for predicting skin factors.

[0004] Therefore, developing a simple, fast, and relatively accurate method for predicting epidermal factors is particularly important for production capacity forecasting. Summary of the Invention

[0005] In view of the above problems, the present invention is proposed to provide a method, apparatus, computing device and computer storage medium for calculating the skin factor of oil and gas wells based on resistivity logging, which overcomes the problems of low accuracy and complex process parameters required for skin factor calculation.

[0006] According to one aspect of the present invention, a method for calculating the skin factor of oil and gas wells based on resistivity logging is provided, comprising:

[0007] Obtain resistivity logging data and well test skin factor data for the target formation in the oil and gas field;

[0008] Based on the resistivity logging data and well test skin factor data, a skin factor calculation model is established;

[0009] The skin factor of the well to be logged in the oil and gas field is calculated based on the skin factor calculation model.

[0010] In one alternative approach, the resistivity logging data includes at least one of the following: deep resistivity logging values, shallow resistivity logging values, and microspherical focused resistivity logging values.

[0011] In an alternative approach, establishing a skin factor calculation model based on the resistivity logging data and well test skin factor data further includes:

[0012] Based on the resistivity logging data, calculate the difference between deep resistivity logging values ​​and micro spherical focused resistivity logging values, as well as the difference between deep resistivity logging values ​​and shallow resistivity logging values.

[0013] Based on the deep resistivity logging values, shallow resistivity logging values, micro-spherical focused resistivity logging values, the difference between deep resistivity logging values ​​and micro-spherical focused resistivity logging values, the difference between deep resistivity logging values ​​and shallow resistivity logging values, and the well test skin factor data, a skin factor calculation model is established.

[0014] In an alternative approach, establishing a skin factor calculation model based on the resistivity logging data and well test skin factor data further includes:

[0015] Linear regression fitting was performed on the deep resistivity logging values, shallow resistivity logging values, micro spherical focused resistivity logging values, and well test skin factor data to obtain the first regression relationship.

[0016] In an alternative approach, establishing a skin factor calculation model based on the resistivity logging data and well test skin factor data further includes:

[0017] The difference between the deep resistivity logging value and the micro-spherical focused resistivity logging value, the difference between the deep resistivity logging value and the shallow resistivity logging value, and the well test skin factor data were subjected to linear regression fitting to obtain the second regression relationship.

[0018] In an alternative approach, the method further includes:

[0019] Obtain resistivity logging data and well test skin factor data from verification wells in oil and gas fields;

[0020] Based on the resistivity logging data and well test skin factor data of the oil and gas field verification wells, the average error of the skin factor calculation model is calculated.

[0021] The epidermal factor calculation model is optimized based on the average error.

[0022] In an alternative approach, establishing a skin factor calculation model based on the resistivity logging data and well test skin factor data further includes:

[0023] The regression coefficients of the epidermal factor calculation model were obtained using the least squares method.

[0024] According to another aspect of the present invention, an oil and gas well skin factor calculation device based on resistivity logging is provided, comprising:

[0025] The data acquisition module is used to acquire resistivity logging data and well test skin factor data of the target formation in the oil and gas field.

[0026] The model building module is used to build a skin factor calculation model based on the resistivity logging data and well test skin factor data.

[0027] The calculation module is used to calculate the skin factor of the well to be logged in the oil and gas field according to the skin factor calculation model.

[0028] According to another aspect of the present invention, a computing device is provided, comprising: a processor, a memory, a communication interface, and a communication bus, wherein the processor, the memory, and the communication interface communicate with each other via the communication bus;

[0029] The memory is used to store at least one executable instruction, which causes the processor to perform the operation corresponding to the above-described method for calculating the skin factor of oil and gas wells based on resistivity logging.

[0030] According to another aspect of the present invention, a computer storage medium is provided, the storage medium storing at least one executable instruction, the executable instruction causing a processor to perform an operation corresponding to the above-described method for calculating the skin factor of oil and gas wells based on resistivity logging.

[0031] According to the scheme provided by this invention, resistivity logging data and well test skin factor data of the target formation in an oil and gas field are obtained; a skin factor calculation model is established based on the resistivity logging data and well test skin factor data; and the skin factor of the well to be logged in the oil and gas field is calculated based on the skin factor calculation model. This invention utilizes resistivity logging data and well test skin factor data of the target formation in an oil and gas field to establish a skin factor calculation model, and calculates the skin factor of the well to be logged in the oil and gas field based on this model. This provides more accurate production capacity prediction for the oil and gas field production and development field, and avoids the determination of various process parameters and the calculation process of complex skin factor models, effectively solving the problem of difficulty in determining the skin factor in actual production and development.

[0032] The above description is merely an overview of the technical solution of the present invention. In order to better understand the technical means of the present invention and to implement it in accordance with the contents of the specification, and in order to make the above and other objects, features and advantages of the present invention more apparent and understandable, specific embodiments of the present invention are described below. Attached Figure Description

[0033] Various other advantages and benefits will become apparent to those skilled in the art upon reading the following detailed description of preferred embodiments. The accompanying drawings are for illustrative purposes only and are not intended to limit the invention. Furthermore, the same reference numerals denote the same parts throughout the drawings. In the drawings:

[0034] Figure 1 A flowchart illustrating the method for calculating the skin factor of oil and gas wells based on resistivity logging according to an embodiment of the present invention is shown.

[0035] Figure 2 A flowchart illustrating another embodiment of the present invention is shown.

[0036] Figure 3 This illustrates the difference (R) between the surface resistivity (S) and the surface resistivity (R) of an oil and gas field test well in embodiment A of the present invention. D -R MSL A schematic diagram of the relationship curve;

[0037] Figure 4 This illustrates the difference (R) between the surface resistivity (S) and the surface resistivity (R) of an oil and gas field test well in embodiment A of the present invention. D -R S A schematic diagram of the relationship curve;

[0038] Figure 5 This diagram illustrates a comparison of the effects of the surface layer S of the well test in oil and gas field A and the regression model according to an embodiment of the present invention.

[0039] Figure 6 This diagram illustrates a comparison of the effects of the surface layer S of the well test in oil and gas field A and the regression model S2 according to an embodiment of the present invention.

[0040] Figure 7 A schematic diagram of the structure of the oil and gas well skin factor calculation device based on resistivity logging according to an embodiment of the present invention is shown.

[0041] Figure 8 A schematic diagram of the structure of a computing device according to an embodiment of the present invention is shown. Detailed Implementation

[0042] Exemplary embodiments of the invention will now be described in more detail with reference to the accompanying drawings. While exemplary embodiments of the invention are shown in the drawings, it should be understood that the invention may be implemented in various forms and should not be limited to the embodiments set forth herein. Rather, these embodiments are provided so that this invention will be thorough and complete, and will fully convey the scope of the invention to those skilled in the art.

[0043] Figure 1This diagram illustrates a flowchart of the method for calculating the skin factor of oil and gas wells based on resistivity logging, according to an embodiment of the present invention. This method is based on resistivity logging data that reflects actual formation contamination, and combines this data with well test skin factor data to establish a universally applicable calculation model for the skin factor of oil and gas wells. Specifically, as... Figure 1 As shown, it includes the following steps:

[0044] Step S101: Obtain resistivity logging data and well test skin factor data for the target formation in the oil and gas field.

[0045] Through research and analysis, resistivity logging technology can qualitatively reflect the state of formation mud contamination. To establish a skin factor calculation model—that is, a model for calculating the skin factor of well tests using resistivity logging data—it is first necessary to obtain resistivity logging data and well test skin factor data for the target formation in the oil and gas field. For example, acquiring and statistically analyzing the resistivity logging values ​​and well test skin factor data for the target formation in an offshore oil and gas field (A).

[0046] Step S102: Based on resistivity logging data and well test skin factor data, establish a skin factor calculation model.

[0047] Specifically, the skin factor data from well testing is used as the dependent variable, and various resistivity logging values ​​from resistivity logging data are used as independent variables. A skin factor calculation model is established by fitting the data based on the causal relationship between multiple independent variables and the dependent variable.

[0048] The epidermal factor calculation model can be a multiple linear regression model, for example:

[0049] h θ (x)=θ0+θ1x1+θ2x2+θ3x3 (1-1)

[0050] Among them, h θ (x) represents the dependent variable, x1, x2, x3 represent the independent variables, θ0 represents the deviation, and θ1, θ2, θ3 represent the weight coefficients or regression coefficients of the independent variables.

[0051] Step S103: Calculate the skin factor of the well to be logged in the oil and gas field according to the skin factor calculation model.

[0052] Input various resistivity logging values ​​from the resistivity logging data of the well to be logged into the skin factor calculation model to calculate the skin factor of the well to be logged in the oil and gas field.

[0053] The solution provided by the above embodiments of the present invention obtains resistivity logging data and well test skin factor data of the target formation in an oil and gas field; establishes a skin factor calculation model based on the resistivity logging data and well test skin factor data; and calculates the skin factor of the well to be logged in the oil and gas field based on the skin factor calculation model. The present invention utilizes resistivity logging data and well test skin factor data of the target formation in an oil and gas field to establish a skin factor calculation model, and calculates the skin factor of the well to be logged in the oil and gas field based on this model. This provides more accurate production capacity prediction for the oil and gas field production and development field, and avoids the determination of various process parameters and the calculation process of complex skin factor models, effectively solving the problem of difficulty in determining the skin factor in actual production and development.

[0054] Figure 2 A flowchart illustrating another embodiment of the present invention for calculating the skin factor of oil and gas wells based on resistivity logging is shown. Specifically, as... Figure 2 As shown, it includes the following steps:

[0055] Step S201: Obtain resistivity logging data and well test skin factor data for the target formation in the oil and gas field.

[0056] Extensive experimental analysis revealed that deep resistivity logging R... D Shallow resistivity logging R S Miniature spherical focused resistivity logging R MSL It can qualitatively reflect the state of formation mud contamination. Therefore, in this embodiment of the invention, the obtained resistivity logging data includes at least one of the following: deep resistivity logging values, shallow resistivity logging values, and micro-spherical focused resistivity logging values.

[0057] Step S202: Based on resistivity logging data, calculate the difference between deep resistivity logging values ​​and micro-spherical focused resistivity logging values, as well as the difference between deep resistivity logging values ​​and shallow resistivity logging values; based on deep resistivity logging values, shallow resistivity logging values, micro-spherical focused resistivity logging values, the difference between deep resistivity logging values ​​and micro-spherical focused resistivity logging values, the difference between deep resistivity logging values ​​and shallow resistivity logging values, and well test skin factor data, establish a skin factor calculation model.

[0058] Specifically, based on resistivity logging data, the differences between deep resistivity logging values ​​and micro-spherical focused resistivity logging values, as well as the differences between deep resistivity logging values ​​and shallow resistivity logging values, are calculated.

[0059] For example, based on the average resistivity curve of the target layer in a certain offshore oil and gas field A and the data of the skin factor from well testing (some wells did not have micro-spherical focused resistivity logging values), the deep resistivity logging R is calculated. D With micro spherical focused resistivity logging value R MSL The difference, and the R of deep resistivity loggingD With shallow resistivity logging R S difference.

[0060] To better understand the resistivity logging data in this embodiment, sample original logging data from a certain oil and gas field A are shown in Table 1:

[0061] Table 1: Sample Original Well Logging Data from Oil and Gas Field A

[0062]

[0063]

[0064] Furthermore, based on the differences between deep resistivity logging values ​​and micro-spherical focused resistivity logging values, the differences between deep resistivity logging values ​​and shallow resistivity logging values, and well test skin factor data, a skin factor calculation model is established.

[0065] like Figure 3 , Figure 4 As shown, scatter plots were created to represent the differences between deep resistivity logging values ​​and micro-spherical focused resistivity logging values, the differences between deep resistivity logging values ​​and shallow resistivity logging values, and the corresponding well test skin factor data. The plots show that the well test skin factor S is related to the difference between deep resistivity logging values ​​and micro-spherical focused resistivity logging values ​​(R...). D -R MSL There is a linear relationship between the well test skin factor S and the difference between deep resistivity logging values ​​and shallow resistivity logging values ​​(R). D -R S There is also a linear relationship.

[0066] Using the well test skin factor as the dependent variable, the difference (R) between the deep resistivity logging value and the micro spherical focused resistivity logging value was calculated. D -R MSL The difference between deep resistivity logging values ​​and shallow resistivity logging values ​​(R) D -R S Using as the independent variable, a multiple linear regression was performed, based on the pre-defined correlation R0. 2 The obtained relationship fitting table is shown in Table 2.

[0067] Table 2: Fitting table of the relationship between the skin surface S and the difference ΔR of various resistivity logging values ​​in Oilfield A.

[0068]

[0069] Depend on Figure 3 , Figure 4 The fitted curves and their corresponding correlation R values ​​in Table 2 are shown in the table. 2 It can be seen that the difference between the surface S of the well test and the resistivity logging value ΔR(R) D -RMSL R D -R S It exhibits good linear and polynomial relationships, with the correlation R of the polynomial relationship being [value missing]. 2 All are above 0.86.

[0070] Furthermore, based on deep resistivity logging values, shallow resistivity logging values, micro-spherical focused resistivity logging values, the difference between deep resistivity logging values ​​and micro-spherical focused resistivity logging values, the difference between deep resistivity logging values ​​and shallow resistivity logging values, and well test skin factor data, a skin factor calculation model is established.

[0071] In one alternative approach, a linear regression fit is performed on the deep resistivity logging values, shallow resistivity logging values, micro-spherical focused resistivity logging values, and well test skin factor data to obtain the first regression relationship.

[0072] Since there is a good linear relationship between the well test surface and the resistivity logging values, a multiple linear regression equation between the well test surface and the resistivity logging values ​​can be established, as shown below:

[0073] S1=a1R MSL +a2R S +a3R D +a4 (2-1)

[0074] Among them, a1 to a4 are regression coefficients, and regression equation (2-1) shows the curve relationship between the skin factor and each resistivity logging value.

[0075] In one alternative approach, a second regression equation is obtained by performing linear regression fitting on the differences between deep resistivity logging values ​​and micro-spherical focused resistivity logging values, the differences between deep resistivity logging values ​​and shallow resistivity logging values, and the well test skin factor data.

[0076] Since there is a good linear relationship between the well test surface and the differences in resistivity logging values, a multiple linear regression equation for the difference between the well test surface and resistivity logging values ​​can be established, as shown below:

[0077] S2=b1(R D -R MSL )+b2(R D -R S )+b3 (2-2)

[0078] Among them, b1~b3 are regression coefficients, and regression equation (2-2) shows the curve relationship between the skin factor and the difference of each resistivity logging value.

[0079] In one alternative approach, the regression coefficients of the epidermal factor calculation model are obtained using the least squares method.

[0080] Using actual data from an oil and gas field A, the two multiple regression equations mentioned above were obtained through the least squares method, as shown in Table 3:

[0081] Table 3: Multiple linear regression equations between the skin surface S and resistivity logging values ​​R in Oil and Gas Field A

[0082]

[0083]

[0084] In one alternative approach, resistivity logging data and well test skin factor data are obtained from oil and gas field verification wells;

[0085] Based on resistivity logging data and well test skin factor data from oil and gas field verification wells, the average error of the skin factor calculation model is calculated.

[0086] The epidermal factor calculation model was optimized based on the average error.

[0087] Error is a metric used to measure the difference between model predictions and actual results. Error analysis allows us to adjust the size of the dataset and the number of features, and try different models.

[0088] To verify the application effect of the well test skin factor model, in this embodiment, two oil and gas wells from an offshore oilfield A in the same region that have been tested and analyzed were used for error analysis. The basic data of the verification wells are shown in Table 4:

[0089] Table 4: Basic data of resistivity logging values ​​of various verification wells in Oil and Gas Field A

[0090] Well number <![CDATA[Microsphere resistance R MSL > <![CDATA[Shallow resistance R S > <![CDATA[Deep resistance R D > <![CDATA[R D -R MSL ]]> <![CDATA[R D -R S ]]> 1 5.8728 10.8087 15.2161 9.3433 4.4074 2 3.4252 15.5758 18.5495 15.1243 2.9737

[0091] Table 5 shows a comparison between the calculation results of the skin factor calculation model for the verification well in oil and gas field A and the actual results:

[0092] Table 5: Comparison of Calculation Results and Actual Results of Skin Factor Calculation Model for Verification Wells in Oil and Gas Field A

[0093]

[0094] As shown in Table 5, both the multinomial calculation model and the multiple linear regression model for the epidermal factor can obtain effective epidermal factor results. The multinomial model S2 and the multiple linear regression model S3 showed low average errors, approximately 31.1% ((48.99%+13.12%) / 2=31.1%) and 17.7% ((13.92%+21.48%) / 2=17.7%), respectively, which have good predictive effects.

[0095] The comparison between the epidermal factor calculated based on the epidermal factor calculation model and the actual epidermal factor data is as follows: Figure 5 , Figure 6 As shown, the epidermal factor calculation model has a good fit.

[0096] Step S203: Calculate the skin factor of the well to be logged in the oil and gas field according to the skin factor calculation model.

[0097] The solution provided in the above embodiments of the present invention calculates the difference between deep resistivity logging values ​​and micro-spherical focused resistivity logging values, as well as the difference between deep and shallow resistivity logging values, based on resistivity logging data. A skin factor calculation model is established based on the deep resistivity logging values, shallow resistivity logging values, micro-spherical focused resistivity logging values, the difference between deep and shallow resistivity logging values, and well test skin factor data. This further provides more accurate production capacity prediction for the oil and gas field production and development field. The universally applicable calculation model between resistivity logging values ​​and skin factors has been widely applied in some offshore oil fields, providing good technical support for production capacity prediction in oil and gas field production and development.

[0098] Figure 7 A schematic diagram of the structure of an oil and gas well skin factor calculation device based on resistivity logging according to an embodiment of the present invention is shown. The oil and gas well skin factor calculation device 700 based on resistivity logging includes: a data acquisition module 710, a model building module 720, and a calculation module 730.

[0099] The data acquisition module 710 is used to acquire resistivity logging data and well test skin factor data of the target formation in the oil and gas field.

[0100] The model building module 720 is used to build a skin factor calculation model based on the resistivity logging data and well test skin factor data.

[0101] The calculation module 730 is used to calculate the skin factor of the well to be logged in the oil and gas field according to the skin factor calculation model.

[0102] In one alternative approach, the resistivity logging data includes at least one of the following: deep resistivity logging values, shallow resistivity logging values, and microspherical focused resistivity logging values.

[0103] In an alternative embodiment, the model building module 720 is further configured to:

[0104] Based on the resistivity logging data, calculate the difference between deep resistivity logging values ​​and micro spherical focused resistivity logging values, as well as the difference between deep resistivity logging values ​​and shallow resistivity logging values.

[0105] Based on the deep resistivity logging values, shallow resistivity logging values, micro-spherical focused resistivity logging values, the difference between deep resistivity logging values ​​and micro-spherical focused resistivity logging values, the difference between deep resistivity logging values ​​and shallow resistivity logging values, and the well test skin factor data, a skin factor calculation model is established.

[0106] In an alternative embodiment, the model building module 720 is further configured to:

[0107] Linear regression fitting was performed on the deep resistivity logging values, shallow resistivity logging values, micro spherical focused resistivity logging values, and well test skin factor data to obtain the first regression relationship.

[0108] In an alternative embodiment, the model building module 720 is further configured to:

[0109] The difference between the deep resistivity logging value and the micro-spherical focused resistivity logging value, the difference between the deep resistivity logging value and the shallow resistivity logging value, and the well test skin factor data were subjected to linear regression fitting to obtain the second regression relationship.

[0110] In an alternative embodiment, after the model building module 720 builds the epidermal factor calculation model, the method further includes:

[0111] Obtain resistivity logging data and well test skin factor data from verification wells in oil and gas fields;

[0112] Based on the resistivity logging data and well test skin factor data of the oil and gas field verification wells, the average error of the skin factor calculation model is calculated.

[0113] The epidermal factor calculation model is optimized based on the average error.

[0114] In an alternative embodiment, the model building module 720 is further configured to:

[0115] The regression coefficients of the epidermal factor calculation model were obtained using the least squares method.

[0116] The solution provided by the above embodiments of the present invention obtains resistivity logging data and well test skin factor data of the target formation in an oil and gas field; establishes a skin factor calculation model based on the resistivity logging data and well test skin factor data; and calculates the skin factor of the well to be logged in the oil and gas field based on the skin factor calculation model. The present invention utilizes resistivity logging data and well test skin factor data of the target formation in an oil and gas field to establish a skin factor calculation model, and calculates the skin factor of the well to be logged in the oil and gas field based on this model. This provides more accurate production capacity prediction for the oil and gas field production and development field, and avoids the determination of various process parameters and the calculation process of complex skin factor models, effectively solving the problem of difficulty in determining the skin factor in actual production and development.

[0117] Figure 8 The diagram shows a structural schematic of an embodiment of the computing device of the present invention. The specific embodiments of the present invention do not limit the specific implementation of the computing device.

[0118] like Figure 8 As shown, the computing device may include: a processor 802, a communications interface 804, a memory 806, and a communications bus 808.

[0119] The processor 802, communication interface 804, and memory 806 communicate with each other via communication bus 808. Communication interface 804 is used to communicate with other network elements such as clients or other servers. Processor 802 executes program 810, specifically performing the relevant steps in the above-described embodiment of the oil and gas well skin factor calculation method based on resistivity logging.

[0120] Specifically, program 810 may include program code that includes computer operation instructions.

[0121] Processor 802 may be a central processing unit (CPU), an application-specific integrated circuit (ASIC), or one or more integrated circuits configured to implement embodiments of the present invention. The computing device includes one or more processors, which may be processors of the same type, such as one or more CPUs; or processors of different types, such as one or more CPUs and one or more ASICs.

[0122] Memory 806 is used to store program 810. Memory 806 may include high-speed RAM memory, and may also include non-volatile memory, such as at least one disk storage device.

[0123] Specifically, program 810 can be used to cause processor 802 to perform the following operations:

[0124] Obtain resistivity logging data and well test skin factor data for the target formation in the oil and gas field;

[0125] Based on the resistivity logging data and well test skin factor data, a skin factor calculation model is established;

[0126] The skin factor of the well to be logged in the oil and gas field is calculated based on the skin factor calculation model.

[0127] In one alternative approach, the resistivity logging data includes at least one of the following: deep resistivity logging values, shallow resistivity logging values, and microspherical focused resistivity logging values.

[0128] In an alternative manner, the program 810 causes the processor to perform the following operations:

[0129] Based on the resistivity logging data, calculate the difference between deep resistivity logging values ​​and micro spherical focused resistivity logging values, as well as the difference between deep resistivity logging values ​​and shallow resistivity logging values.

[0130] Based on the deep resistivity logging values, shallow resistivity logging values, micro-spherical focused resistivity logging values, the difference between deep resistivity logging values ​​and micro-spherical focused resistivity logging values, the difference between deep resistivity logging values ​​and shallow resistivity logging values, and the well test skin factor data, a skin factor calculation model is established.

[0131] In an alternative manner, the program 810 causes the processor to perform the following operations:

[0132] Linear regression fitting was performed on the deep resistivity logging values, shallow resistivity logging values, micro spherical focused resistivity logging values, and well test skin factor data to obtain the first regression relationship.

[0133] In an alternative manner, the program 810 causes the processor to perform the following operations:

[0134] The difference between the deep resistivity logging value and the micro-spherical focused resistivity logging value, the difference between the deep resistivity logging value and the shallow resistivity logging value, and the well test skin factor data were subjected to linear regression fitting to obtain the second regression relationship.

[0135] In an alternative approach, after establishing the epidermal factor calculation model, the program 810 causes the processor to perform the following operations:

[0136] Obtain resistivity logging data and well test skin factor data from verification wells in oil and gas fields;

[0137] Based on the resistivity logging data and well test skin factor data of the oil and gas field verification wells, the average error of the skin factor calculation model is calculated.

[0138] The epidermal factor calculation model is optimized based on the average error.

[0139] In an alternative manner, the program 810 causes the processor to perform the following operations:

[0140] The regression coefficients of the epidermal factor calculation model were obtained using the least squares method.

[0141] The solution provided by the above embodiments of the present invention obtains resistivity logging data and well test skin factor data of the target formation in an oil and gas field; establishes a skin factor calculation model based on the resistivity logging data and well test skin factor data; and calculates the skin factor of the well to be logged in the oil and gas field based on the skin factor calculation model. The present invention utilizes resistivity logging data and well test skin factor data of the target formation in an oil and gas field to establish a skin factor calculation model, and calculates the skin factor of the well to be logged in the oil and gas field based on this model. This provides more accurate production capacity prediction for the oil and gas field production and development field, and avoids the determination of various process parameters and the calculation process of complex skin factor models, effectively solving the problem of difficulty in determining the skin factor in actual production and development.

[0142] This invention provides a non-volatile computer storage medium storing at least one executable instruction that can execute the oil and gas well skin factor calculation method based on resistivity logging in any of the above method embodiments.

[0143] The algorithms or displays provided herein are not inherently related to any particular computer, virtual system, or other device. Various general-purpose systems can also be used in conjunction with the teachings herein. The required structure for constructing such systems is apparent from the above description. Furthermore, the embodiments of the present invention are not directed to any particular programming language. It should be understood that the content of the invention described herein can be implemented using various programming languages, and the above description of specific languages ​​is for the purpose of disclosing the best mode of implementation of the invention.

[0144] Numerous specific details are set forth in the specification provided herein. However, it will be understood that embodiments of the invention may be practiced without these specific details. In some instances, well-known methods, structures, and techniques have not been shown in detail so as not to obscure the understanding of this specification.

[0145] Similarly, it should be understood that, in order to simplify the invention and aid in understanding one or more of the various inventive aspects, features of the embodiments of the invention are sometimes grouped together in a single embodiment, figure, or description thereof in the above description of exemplary embodiments of the invention. However, this disclosure should not be construed as reflecting an intention that the claimed invention requires more features than are expressly recited in each claim. Rather, as reflected in the following claims, inventive aspects lie in fewer than all features of a single foregoing disclosed embodiment. Therefore, the claims following the detailed description are hereby expressly incorporated into this detailed description, wherein each claim itself is a separate embodiment of the invention.

[0146] Those skilled in the art will understand that modules in the device of the embodiments can be adaptively changed and placed in one or more devices different from that embodiment. Modules, units, or components in the embodiments can be combined into a single module, unit, or component, and further, they can be divided into multiple sub-modules, sub-units, or sub-components. Except where at least some of such features and / or processes or units are mutually exclusive, any combination can be used to combine all features disclosed in this specification (including the accompanying claims, abstract, and drawings) and all processes or units of any method or device so disclosed. Unless expressly stated otherwise, each feature disclosed in this specification (including the accompanying claims, abstract, and drawings) may be replaced by an alternative feature that serves the same, equivalent, or similar purpose.

[0147] Furthermore, those skilled in the art will understand that although some embodiments herein include certain features included in other embodiments but not others, combinations of features from different embodiments are intended to be within the scope of the invention and form different embodiments. For example, in the following claims, any of the claimed embodiments can be used in any combination.

[0148] The various component embodiments of the present invention can be implemented in hardware, or as software modules running on one or more processors, or a combination thereof. Those skilled in the art will understand that microprocessors or digital signal processors (DSPs) can be used in practice to implement some or all of the functions of some or all of the components according to the embodiments of the present invention. The present invention can also be implemented as a device or apparatus program (e.g., a computer program and computer program product) for performing part or all of the methods described herein. Such programs implementing the present invention can be stored on a computer-readable medium, or can be in the form of one or more signals. Such signals can be downloaded from an Internet website, provided on a carrier signal, or provided in any other form.

[0149] It should be noted that the above embodiments are illustrative of the invention and not restrictive, and that those skilled in the art can devise alternative embodiments without departing from the scope of the appended claims. In the claims, any reference signs placed between parentheses should not be construed as limiting the claims. The word "comprising" does not exclude the presence of elements or steps not listed in the claims. The word "a" or "an" preceding an element does not exclude the presence of a plurality of such elements. The invention can be implemented by means of hardware comprising several different elements and by means of a suitably programmed computer. In the unit claims enumerating several means, several of these means may be embodied by the same item of hardware. The use of the words first, second, and third, etc., does not indicate any order. These words can be interpreted as names. The steps in the above embodiments, unless otherwise specified, should not be construed as limiting the order of execution.

Claims

1. A method for calculating the skin factor of oil and gas wells based on resistivity logging, characterized in that, include: Obtain resistivity logging data and well test skin factor data for the target formation in the oil and gas field; the resistivity logging data includes deep resistivity logging values, shallow resistivity logging values, and micro spherical focused resistivity logging values; Based on the resistivity logging data and well test skin factor data, a skin factor calculation model is established. Specifically, based on the resistivity logging data, the differences between deep resistivity logging values ​​and micro-spherical focused resistivity logging values, as well as the differences between deep resistivity logging values ​​and shallow resistivity logging values, are calculated. Linear regression fitting is performed on the deep resistivity logging values, shallow resistivity logging values, micro-spherical focused resistivity logging values, and well test skin factor data to obtain a first regression equation. Linear regression fitting is then performed on the differences between deep resistivity logging values ​​and micro-spherical focused resistivity logging values, and the differences between deep resistivity logging values ​​and shallow resistivity logging values, as well as the well test skin factor data, to obtain a second regression equation, thereby establishing the skin factor calculation model. The skin factor of the well to be logged in the oil and gas field is calculated based on the skin factor calculation model.

2. The method for calculating the skin factor of oil and gas wells based on resistivity logging according to claim 1, characterized in that, The method further includes: Obtain resistivity logging data and well test skin factor data from verification wells in oil and gas fields; Based on the resistivity logging data and well test skin factor data of the oil and gas field verification wells, the average error of the skin factor calculation model is calculated. The epidermal factor calculation model is optimized based on the average error.

3. The method for calculating the skin factor of oil and gas wells based on resistivity logging according to claim 1, characterized in that, The step of establishing a skin factor calculation model based on the resistivity logging data and well test skin factor data further includes: The regression coefficients of the epidermal factor calculation model were obtained using the least squares method.

4. A device for calculating the skin factor of oil and gas wells based on resistivity logging, characterized in that, include: The data acquisition module is used to acquire resistivity logging data and well test skin factor data of the target formation in the oil and gas field; the resistivity logging data includes deep resistivity logging values, shallow resistivity logging values ​​and micro spherical focused resistivity logging values; The model building module is used to establish a skin factor calculation model based on the resistivity logging data and well test skin factor data. Specifically, based on the resistivity logging data, the differences between deep resistivity logging values ​​and micro-spherical focused resistivity logging values, as well as the differences between deep resistivity logging values ​​and shallow resistivity logging values, are calculated. Linear regression fitting is performed on the deep resistivity logging values, shallow resistivity logging values, micro-spherical focused resistivity logging values, and well test skin factor data to obtain a first regression relationship. Linear regression fitting is then performed on the differences between deep resistivity logging values ​​and micro-spherical focused resistivity logging values, and the differences between deep resistivity logging values ​​and shallow resistivity logging values, as well as the well test skin factor data, to obtain a second regression relationship, thereby establishing the skin factor calculation model. The calculation module is used to calculate the skin factor of the well to be logged in the oil and gas field according to the skin factor calculation model.

5. A computing device, comprising: The processor, memory, communication interface, and communication bus are provided, wherein the processor, memory, and communication interface communicate with each other via the communication bus. The memory is used to store at least one executable instruction, which causes the processor to perform the operation corresponding to the oil and gas well skin factor calculation method based on resistivity logging as described in any one of claims 1-3.

6. A computer storage medium storing at least one executable instruction that causes a processor to perform an operation corresponding to the oil and gas well skin factor calculation method based on resistivity logging as described in any one of claims 1-3.

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