Method for predicting distribution of oil and gas layers in oil well reservoirs

Abstract

This application discloses a method for predicting the distribution of oil and gas layers in an oil well reservoir. The method includes: determining the reference clay content and reference resistivity of the oil and gas layers under each porosity range; determining the reference clay content and reference resistivity at each logging depth; determining the difference in clay content between the measured clay content and the reference clay content at each logging depth, and the difference in resistivity between the measured resistivity and the reference resistivity; determining the resistivity correction value for each depth to be predicted in the oil well reservoir using a resistivity correction model; correcting the measured resistivity at each depth to be predicted in the oil well reservoir using the resistivity correction value to obtain the standard resistivity at each depth to be predicted, and determining the distribution of oil and gas layers at each depth to be predicted in the oil well reservoir. The technical solution provided by this application can improve the accuracy of predicting the distribution of oil and gas layers based on resistivity.

Description

Technical Field

[0001] This application belongs to the field of oil and gas layer distribution detection technology in oil well reservoirs, and particularly relates to a method for predicting the distribution of oil and gas layers in oil well reservoirs. Background Technology

[0002] In oil well reservoirs, the organic matter content of oil and gas layers is higher than that of non-oil and gas layers. Conversely, the water content of non-oil and gas layers is higher than that of oil and gas layers. Therefore, the resistivity of oil and gas layers is higher than that of non-oil and gas layers, and the difference in resistivity between the two is significant. Based on this, resistivity is widely used to detect oil and gas reservoirs, that is, to determine whether a formation is an oil and gas layer by measuring changes in underground resistivity. However, the porosity and clay content of the formation have a significant impact on the measured resistivity, which reduces the accuracy of predicting the distribution of oil and gas layers. Therefore, how to improve the accuracy of predicting the distribution of oil and gas layers based on resistivity is an urgent technical problem to be solved. Summary of the Invention

[0003] The embodiments of this application provide a method for predicting the distribution of oil and gas reservoirs in oil wells, thereby improving the accuracy of predicting the distribution patterns of oil and gas reservoirs based on resistivity.

[0004] Other features and advantages of this application will become apparent from the following detailed description, or may be learned in part from practice of this application.

[0005] According to a first aspect of the embodiments of this application, a method for predicting the distribution of oil and gas layers in an oil well reservoir is provided, characterized in that the method includes: acquiring production test data and logging data of the oil well reservoir, and based on the production test data, determining the reference clay content and reference resistivity corresponding to the oil and gas layers under each porosity interval, wherein the logging data includes measured clay content, measured resistivity, and porosity at each logging depth in the oil well reservoir; determining the reference clay content and reference resistivity at each logging depth according to the porosity interval to which the porosity at each logging depth belongs; and determining the clay content between the measured clay content and the reference clay content at each logging depth. The resistivity difference between the measured resistivity and the reference resistivity is calculated. Based on the differences in clay content, resistivity, and porosity at each logging depth, a resistivity correction model is constructed. Based on the measured clay content and porosity at each depth to be predicted in the oil well reservoir, the resistivity correction value at each depth to be predicted in the oil well reservoir is determined using the resistivity correction model. The measured resistivity at each depth to be predicted in the oil well reservoir is corrected using the resistivity correction value to obtain the standard resistivity at each depth to be predicted. Based on the standard resistivity at each depth to be predicted, the oil and gas layer distribution at each depth to be predicted in the oil well reservoir is determined.

[0006] In some embodiments of this application, based on the foregoing scheme, the porosity interval includes a first porosity interval, a second porosity interval, a third porosity interval, and a fourth porosity interval, wherein the porosity of the first porosity interval is less than or equal to 5%, the porosity of the second porosity interval is greater than 5% and less than or equal to 10%, the porosity of the third porosity interval is greater than 10% and less than or equal to 20%, and the porosity of the fourth porosity interval is greater than 20%.

[0007] In some embodiments of this application, based on the foregoing scheme, the difference in mud content between the measured mud content and the reference mud content is determined by the following formula:

[0008]

[0009] Wherein, ΔGR represents the difference in mud content, GR represents the measured mud content, and GR s1 GR represents the baseline clay content corresponding to the oil and gas reservoir in the first porosity range. s2 GR represents the baseline clay content corresponding to the oil and gas reservoir in the second porosity range. s3 GR represents the baseline clay content corresponding to the oil and gas reservoir in the third porosity range. s4 This indicates the baseline clay content corresponding to the oil and gas reservoir in the fourth porosity range. Indicates porosity.

[0010] In some embodiments of this application, based on the foregoing scheme, the resistivity difference between the measured resistivity and the reference resistivity is determined by the following formula:

[0011]

[0012] Wherein, ΔRT represents the resistivity difference, RT represents the measured resistivity, and RT s1 RT represents the reference resistivity of the oil and gas reservoir in the first porosity range. s2 RT represents the reference resistivity of the oil and gas reservoir in the second porosity range. s3 RT represents the reference resistivity of the oil and gas reservoir in the third porosity range. s4 This represents the reference resistivity corresponding to the oil and gas reservoir in the fourth porosity range. Indicates porosity.

[0013] In some embodiments of this application, based on the aforementioned scheme, the step of constructing a resistivity correction model according to the differences in clay content, resistivity, and porosity at each logging depth, and determining the resistivity correction value at each depth to be predicted in the oil well reservoir using the resistivity correction model based on the measured clay content and porosity at each depth to be predicted in the oil well reservoir, includes: performing linear fitting on the resistivity difference and clay content difference at each target logging depth to obtain the resistivity correction model, wherein the porosity at each target logging depth belongs to the same porosity interval; the resistivity correction model includes the following formula:

[0014]

[0015] Wherein, ΔRT-GR represents the resistivity correction value, and ΔGR represents the difference in clay content. d1, d2, d3, d4 represent porosity, and k1, k2, k3, k4, d1, d2, d3, d4 represent linear fitting coefficients.

[0016] In some embodiments of this application, based on the aforementioned scheme, the step of constructing a resistivity correction model according to the differences in clay content, resistivity, and porosity at each logging depth, and determining the resistivity correction value at each depth to be predicted in the oil well reservoir using the resistivity correction model based on the measured clay content and porosity at each depth to be predicted in the oil well reservoir, includes: performing plane fitting on the resistivity differences, clay content differences, and porosity at each logging depth to obtain the resistivity correction model; the resistivity correction model includes the following formula:

[0017]

[0018] in, This represents the resistivity correction value, and ΔGR represents the difference in clay content. denoted as porosity, and a, b, c represent plane fitting coefficients.

[0019] In some embodiments of this application, based on the foregoing scheme, determining the distribution of oil and gas layers at each predicted depth in the oil well reservoir according to the standard resistivity of each predicted depth includes: if the standard resistivity is greater than or equal to a preset resistivity threshold, it indicates that the oil well reservoir distribution at the predicted depth corresponding to the standard resistivity is an oil and gas layer; if the standard resistivity is less than the preset resistivity threshold, it indicates that the oil well reservoir distribution at the predicted depth corresponding to the standard resistivity is a non-oil and gas layer.

[0020] According to a second aspect of the embodiments of this application, a computer program product is provided, the computer program product including computer instructions stored in a computer-readable storage medium and adapted to be read and executed by a processor to cause a computer device having the processor to perform an operation as described in any of the embodiments of the first aspect above.

[0021] According to a third aspect of the embodiments of this application, a computer-readable storage medium is provided, the computer-readable storage medium storing at least one computer program instruction, the at least one computer program instruction being loaded and executed by a processor to perform the operation performed by the method described in any of the embodiments of the first aspect above.

[0022] According to a fourth aspect of the present application, an electronic device is provided, the electronic device including one or more processors and one or more memories, the one or more memories storing at least one computer program instruction, the at least one computer program instruction being loaded and executed by the one or more processors to perform the operation performed by the method as described in any of the embodiments of the first aspect above.

[0023] In this application, firstly, by determining the benchmark clay content and benchmark resistivity of the oil and gas layers corresponding to each porosity range through the production test data of the oil well reservoir, and by determining the benchmark clay content and benchmark resistivity of each logging depth according to the porosity range to which the porosity belongs, the rationality and accuracy of the benchmark clay content and benchmark resistivity can be improved. Furthermore, it is possible to determine the benchmark clay content and benchmark resistivity corresponding to each measurement well depth, which not only provides data support for subsequent steps but also improves the accuracy of predicting the distribution patterns of oil and gas layers. Secondly, by measuring the clay content and benchmark clay content at each logging depth... The first step is to determine the difference in clay content by comparing the measured resistivity at each logging depth with the baseline resistivity. This improves the accuracy of data processing and enhances the accuracy of predicting oil and gas reservoir distribution patterns based on resistivity. The second step is to construct a resistivity correction model based on the differences in clay content, resistivity, and porosity at each logging depth, and determine the corrected resistivity value. Correcting the measured resistivity with this value eliminates the influence of porosity and clay content on the measured resistivity, yielding a standard resistivity that further improves the accuracy of predicting oil and gas reservoir distribution patterns based on resistivity.

[0024] It should be understood that the above general description and the following detailed description are exemplary and explanatory only, and do not limit this application. Attached Figure Description

[0025] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this application and, together with the description, serve to explain the principles of this application. It is obvious that the drawings described below are merely some embodiments of this application, and those skilled in the art can obtain other drawings based on these drawings without any inventive effort. In the drawings:

[0026] Figure 1 A flowchart illustrating a method for predicting the distribution of oil and gas reservoirs in an oil well according to one embodiment of this application is shown.

[0027] Figure 2 A comparison graph of measured resistivity and standard resistivity in one embodiment of this application is shown;

[0028] Figure 3 A schematic diagram of the structure of an electronic device according to one embodiment of this application is shown. Detailed Implementation

[0029] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0030] Furthermore, the described features, structures, or characteristics can be combined in any suitable manner in one or more embodiments. Numerous specific details are provided in the following description to give a thorough understanding of embodiments of this application. However, those skilled in the art will recognize that the technical solutions of this application can be practiced without one or more of the specific details, or other methods, components, apparatuses, steps, etc., can be employed. In other instances, well-known methods, apparatuses, implementations, or operations are not shown or described in detail to avoid obscuring various aspects of this application.

[0031] The block diagrams shown in the accompanying drawings are merely functional entities and do not necessarily correspond to physically independent entities. That is, these functional entities can be implemented in software, in one or more hardware modules or integrated circuits, or in different network and / or processor devices and / or microcontroller devices.

[0032] The flowcharts shown in the accompanying drawings are merely illustrative and do not necessarily include all content and operations / steps, nor do they necessarily have to be performed in the described order. For example, some operations / steps can be broken down, while others can be combined or partially combined; therefore, the actual execution order may change depending on the specific circumstances.

[0033] It should also be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this application are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such uses of these terms can be interchanged where appropriate so that the embodiments of this application described herein can be implemented in orders other than those illustrated or described.

[0034] To enable those skilled in the art to better understand this application, a brief explanation will first be given of the proposed method for predicting the distribution of oil and gas reservoirs based on resistivity.

[0035] In oil well reservoirs, the organic matter content of oil and gas layers is higher than that of non-oil and gas layers. Conversely, the water content of non-oil and gas layers is higher than that of oil and gas layers. Therefore, the resistivity of oil and gas layers is higher than that of non-oil and gas layers, and the difference in resistivity between the two is significant. Based on this, resistivity is widely used in oil and gas reservoir detection; that is, by measuring changes in underground resistivity, it can be determined whether a formation is an oil and gas layer. However, the porosity and clay content of the formation have a significant impact on the measured resistivity. Therefore, the inventors of this application propose a method for predicting the distribution of oil and gas layers in oil well reservoirs. This method predicts the distribution of oil and gas layers in oil well reservoirs using resistivity and can eliminate the influence of porosity and clay content on the measured resistivity, thereby improving the accuracy of predicting the distribution patterns of oil and gas layers based on resistivity.

[0036] Next, we will combine Figure 1 The method for predicting the distribution of oil and gas layers in oil well reservoirs proposed in this application is described in detail.

[0037] See Figure 1 This document illustrates a flowchart of a method for predicting the distribution of oil and gas reservoirs in an oil well, according to one embodiment of this application. The method can be executed by a device with computational processing capabilities, such as... Figure 1 As shown, the method may include at least steps 110 to 150:

[0038] Step 110: Obtain production test data and logging data of the oil well reservoir, and based on the production test data, determine the reference clay content and reference resistivity of the oil and gas layer under each porosity range. The logging data includes the measured clay content, measured resistivity and porosity at each logging depth in the oil well reservoir.

[0039] Step 120: Determine the reference clay content and reference resistivity for each logging depth based on the porosity range to which the porosity belongs.

[0040] Step 130: Determine the difference in mud content between the measured mud content and the reference mud content at each logging depth, and the difference in resistivity between the measured resistivity and the reference resistivity.

[0041] Step 140: Based on the differences in clay content, resistivity, and porosity at each logging depth, construct a resistivity correction model, and determine the resistivity correction value at each depth to be predicted in the oil well reservoir based on the measured clay content and porosity at each depth to be predicted in the oil well reservoir using the resistivity correction model.

[0042] Step 150: Correct the measured resistivity of each depth to be predicted in the oil well reservoir using the resistivity correction value to obtain the standard resistivity of each depth to be predicted, and determine the oil and gas layer distribution of each depth to be predicted in the oil well reservoir based on the standard resistivity of each depth to be predicted.

[0043] In this application, the production test data may specifically include various physical properties of the oil and gas reservoir, such as permeability, saturation pressure, crude oil viscosity, and crude oil density.

[0044] In this application, it should be noted that the mud content can be specifically determined by measuring the natural gamma curve and by using the natural gamma curve, and the measured resistivity is the resistivity obtained by direct measurement.

[0045] In this application, the porosity range includes a first porosity range, a second porosity range, a third porosity range, and a fourth porosity range. The porosity of the first porosity range is less than or equal to 5%, the porosity of the second porosity range is greater than 5% and less than or equal to 10%, the porosity of the third porosity range is greater than 10% and less than or equal to 20%, and the porosity of the fourth porosity range is greater than 20%. A porosity of less than or equal to 5% in the first porosity range indicates poor oil and gas reservoir quality; a porosity of greater than 5% and less than or equal to 10% in the second porosity range indicates moderate oil and gas reservoir quality; a porosity of greater than 10% and less than or equal to 20% in the third porosity range indicates relatively good oil and gas reservoir quality; and a porosity of greater than 20% in the fourth porosity range indicates the potential for cavern formation in the oil and gas reservoir.

[0046] In this application, firstly, by determining the benchmark clay content and benchmark resistivity of the oil and gas layers corresponding to each porosity range through the production test data of the oil well reservoir, and by determining the benchmark clay content and benchmark resistivity of each logging depth according to the porosity range to which the porosity belongs, the rationality and accuracy of the benchmark clay content and benchmark resistivity can be improved. Furthermore, it is possible to determine the benchmark clay content and benchmark resistivity corresponding to each measurement well depth, which not only provides data support for subsequent steps but also improves the accuracy of predicting the distribution patterns of oil and gas layers. Secondly, by measuring the clay content and benchmark clay content at each logging depth... The first step is to determine the difference in clay content by comparing the measured resistivity at each logging depth with the baseline resistivity. This improves the accuracy of data processing and enhances the accuracy of predicting oil and gas reservoir distribution patterns based on resistivity. The second step is to construct a resistivity correction model based on the differences in clay content, resistivity, and porosity at each logging depth, and determine the corrected resistivity value. Correcting the measured resistivity with this value eliminates the influence of porosity and clay content on the measured resistivity, yielding a standard resistivity that further improves the accuracy of predicting oil and gas reservoir distribution patterns based on resistivity.

[0047] In step 130 above, the difference in mud content between the measured mud content and the reference mud content can be determined using the following formula (1):

[0048]

[0049] Wherein, ΔGR represents the difference in mud content, GR represents the measured mud content, and GR s1 GR represents the baseline clay content corresponding to the oil and gas reservoir in the first porosity range. s2 GR represents the baseline clay content corresponding to the oil and gas reservoir in the second porosity range. s3 GR represents the baseline clay content corresponding to the oil and gas reservoir in the third porosity range. s4 This indicates the baseline clay content corresponding to the oil and gas reservoir in the fourth porosity range. Indicates porosity.

[0050] In step 130 above, the resistivity difference between the measured resistivity and the reference resistivity can be determined using the following formula (2):

[0051]

[0052] Wherein, ΔRT represents the resistivity difference, RT represents the measured resistivity, and RT s1 RT represents the reference resistivity of the oil and gas reservoir in the first porosity range. s2RT represents the reference resistivity of the oil and gas reservoir in the second porosity range. s3 RT represents the reference resistivity of the oil and gas reservoir in the third porosity range. s4 This represents the reference resistivity corresponding to the oil and gas reservoir in the fourth porosity range. Indicates porosity.

[0053] In this application, the reference clay content is the baseline value of clay content in the corresponding porosity range, which can be determined by the production test data of the oil well reservoir. Similarly, the reference resistivity is the baseline value of clay content in the corresponding porosity range, which can also be determined by the production test data of the oil well reservoir.

[0054] In this application, by determining the difference in mud content and the difference in resistivity, a data basis can be provided for the subsequent steps to eliminate the influence of mud content on resistivity, thereby improving the accuracy of predicting the distribution pattern of oil and gas reservoirs based on resistivity. According to different porosity intervals, determining the difference in mud content and the difference in resistivity corresponding to each porosity interval can reduce the influence of porosity on the measured resistivity to a certain extent, thereby further improving the accuracy of predicting the distribution pattern of oil and gas reservoirs based on resistivity.

[0055] In step 140 above, a resistivity correction model is constructed based on the differences in clay content, resistivity, and porosity at each logging depth. The resistivity correction value for each depth in the oil well reservoir is then determined using this model based on the measured clay content and porosity at each depth to be predicted. Specifically, this can be performed according to steps 141 to 142 below:

[0056] Step 141: Perform linear fitting on the resistivity difference and clay content difference at each target logging depth to obtain a resistivity correction model, wherein the porosity at each target logging depth belongs to the same porosity range.

[0057] Step 142, the resistivity correction model includes the following formula:

[0058]

[0059] Wherein, ΔRT-GR represents the resistivity correction value, and ΔGR represents the difference in clay content. d1, d2, d3, d4 represent porosity, and k1, k2, k3, k4, d1, d2, d3, d4 represent linear fitting coefficients.

[0060] In this application, a resistivity correction model is obtained by linearly fitting the resistivity difference and clay content difference at each target logging depth. Based on the measured clay content and porosity at each depth to be predicted in the oil well reservoir, the resistivity correction value at each depth to be predicted in the oil well reservoir is determined through the resistivity correction model. This can be used to eliminate the influence of clay content on the measured resistivity, thereby improving the accuracy of predicting the distribution pattern of oil and gas reservoirs based on resistivity. In addition, by dividing the porosity at each logging depth into multiple porosity intervals and considering the resistivity correction values ​​corresponding to multiple porosity intervals separately, the influence of porosity on the measured resistivity can be eliminated to a certain extent, thereby further improving the accuracy of predicting the distribution pattern of oil and gas reservoirs based on resistivity.

[0061] In step 140 above, a resistivity correction model is constructed based on the differences in clay content, resistivity, and porosity at each logging depth. The resistivity correction value for each depth in the oil well reservoir is then determined using this model based on the measured clay content and porosity at each depth to be predicted. Alternatively, steps 143 to 144 can be followed:

[0062] Step 143: Perform plane fitting on the resistivity difference, clay content difference, and porosity at each logging depth to obtain the resistivity correction model.

[0063] Step 144, the resistivity correction model includes the following formula:

[0064]

[0065] in, This represents the resistivity correction value, and ΔGR represents the difference in clay content. denoted as porosity, and a, b, c represent plane fitting coefficients.

[0066] In this application, a resistivity correction model is obtained by performing plane fitting on the resistivity difference, clay content difference, and porosity at each logging depth. Based on the measured clay content and porosity at each depth to be predicted in the oil well reservoir, the resistivity correction value at each depth to be predicted in the oil well reservoir is determined by the resistivity correction model. Considering the influence of clay content and porosity on the measured resistivity, the resistivity correction value obtained by formula (4) can eliminate the influence of clay content and porosity on the measured resistivity, thereby improving the accuracy of predicting the distribution law of oil and gas layers based on resistivity.

[0067] In step 150 above, determining the distribution of oil and gas layers at each depth in the oil well reservoir based on the standard resistivity of each depth to be predicted can be performed according to steps 151 to 152 as follows:

[0068] Step 151: If the standard resistivity is greater than or equal to a preset resistivity threshold, it indicates that the oil well reservoir depth distribution corresponding to the standard resistivity is an oil and gas layer.

[0069] Step 152: If the standard resistivity is less than the preset resistivity threshold, it indicates that the oil well reservoir depth to be predicted corresponding to the standard resistivity is a non-oil and gas layer.

[0070] In this application, the preset resistivity threshold can be specifically determined based on the material composition of the oil and gas layer in the oil well reservoir. For example, it can be 500Ω. Depending on actual needs, it can also be other values. This application does not make specific limitations in this regard.

[0071] In this application, by comparing the standard resistivity with a preset resistivity threshold, the distribution of gas layers at the depth to be predicted in the oil well reservoir can be accurately determined, provided that the influence of clay content and porosity on resistivity is eliminated.

[0072] For details, please refer to Figure 2 This illustrates a comparison graph of measured resistivity and standard resistivity in one embodiment of this application, as shown below. Figure 2 As shown, the natural gamma curve can be used to characterize the clay content of oil well reservoirs. The larger the natural gamma value, the greater the clay content of the oil well reservoir. It can also be seen from the figure that when the clay content and porosity vary greatly, the deviation between the measured resistivity and the standard resistivity is large. That is, the measured resistivity is affected by the clay content and porosity. After correcting the measured resistivity with the resistivity correction value, the standard resistivity is obtained. The distribution of oil and gas layers at the depth to be predicted in the oil well reservoir can be accurately determined by the standard resistivity.

[0073] In this application, by setting a preset resistivity threshold and comparing the standard resistivity with the preset resistivity threshold, the depth of the oil well reservoir to be predicted can be accurately determined based on the standard resistivity, thereby further improving the accuracy of predicting the distribution pattern of oil and gas layers based on resistivity.

[0074] In this application, firstly, by determining the benchmark clay content and benchmark resistivity of the oil and gas layers corresponding to each porosity range through the production test data of the oil well reservoir, and by determining the benchmark clay content and benchmark resistivity of each logging depth according to the porosity range to which the porosity belongs, the rationality and accuracy of the benchmark clay content and benchmark resistivity can be improved. Furthermore, it is possible to determine the benchmark clay content and benchmark resistivity corresponding to each measurement well depth, which not only provides data support for subsequent steps but also improves the accuracy of predicting the distribution patterns of oil and gas layers. Secondly, by measuring the clay content and benchmark clay content at each logging depth... The first step is to determine the difference in clay content by comparing the measured resistivity at each logging depth with the baseline resistivity. This improves the accuracy of data processing and enhances the accuracy of predicting oil and gas reservoir distribution patterns based on resistivity. The second step is to construct a resistivity correction model based on the differences in clay content, resistivity, and porosity at each logging depth, and determine the corrected resistivity value. Correcting the measured resistivity with this value eliminates the influence of porosity and clay content on the measured resistivity, yielding a standard resistivity that further improves the accuracy of predicting oil and gas reservoir distribution patterns based on resistivity.

[0075] Based on the same inventive concept, embodiments of this application provide a computer program product, the computer program product including computer instructions stored in a computer-readable storage medium and adapted to be read and executed by a processor to cause a computer device having the processor to perform the operations performed as described above.

[0076] Based on the same inventive concept, embodiments of this application provide a computer-readable storage medium storing at least one computer program instruction, which is loaded and executed by a processor to perform the operations described above.

[0077] Figure 3 A schematic diagram of the structure of an electronic device according to one embodiment of this application is shown.

[0078] Based on the same inventive concept, embodiments of this application also provide an electronic device. (Reference) Figure 3 The diagram shows a schematic of the structure of an electronic device according to an embodiment of this application. The electronic device includes one or more memories 304, one or more processors 302, and at least one computer program (program code) stored in the memory 304 and executable on the processor 302. When the processor 302 executes the computer program, it implements the method described above.

[0079] Among them, Figure 3In this document, a bus architecture (represented by bus 300) is used. Bus 300 may include any number of interconnected buses and bridges, linking various circuits including one or more processors represented by processor 302 and memory represented by memory 304. Bus 300 may also link various other circuits such as peripheral devices, voltage regulators, and power management circuits, which are well known in the art and therefore will not be described further herein. Bus interface 305 provides an interface between bus 300 and receiver 301 and transmitter 303. Receiver 301 and transmitter 303 may be the same element, i.e., a transceiver, providing a unit for communicating with various other devices over a transmission medium. Processor 302 is responsible for managing bus 300 and general processing, while memory 304 can be used to store data used by processor 302 during operation.

[0080] The functions described herein may be implemented in hardware, software executed by a processor, firmware, or any combination thereof. If implemented in software executed by a processor, the functions may be stored as one or more instructions or codes on or transmitted via a computer-readable medium. Other examples and embodiments are within the scope and spirit of this application and the appended claims. For example, due to the nature of software, the functions described above may be implemented using software executed by a processor, hardware, firmware, hardwired, or any combination thereof. Furthermore, the functional units may be integrated into a single processing unit, or each unit may exist physically separately, or two or more units may be integrated into a single unit.

[0081] In the several embodiments provided in this application, it should be understood that the disclosed technical content can be implemented in other ways. The device embodiments described above are merely illustrative; for example, the division of units can be a logical functional division, and in actual implementation, there may be other division methods. For instance, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the displayed or discussed mutual coupling, direct coupling, or communication connection may be through some interfaces; the indirect coupling or communication connection between units or modules may be electrical or other forms.

[0082] The units described as separate components may or may not be physically separate. Similarly, the components of the control device may or may not be physical units; they may be located in one place or distributed across multiple units. Some or all of the units can be selected to achieve the purpose of this embodiment, depending on actual needs.

[0083] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as a USB flash drive, read-only memory (ROM), random access memory (RAM), portable hard drive, magnetic disk, or optical disk.

[0084] The above description is merely an embodiment of this application and is not intended to limit this application. Various modifications and variations can be made to this application by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the scope of the claims of this application.

Claims

1. A method for predicting the distribution of oil and gas reservoirs in oil wells, characterized in that, The method includes: The production test data and logging data of the oil well reservoir are obtained, and based on the production test data, the reference clay content and reference resistivity of the oil and gas layer under each porosity range are determined. The logging data includes the measured clay content, measured resistivity and porosity at each logging depth in the oil well reservoir. Based on the porosity range to which the porosity at each logging depth belongs, determine the benchmark clay content and benchmark resistivity at each logging depth. Determine the difference in clay content between the measured clay content and the reference clay content at each logging depth, and the difference in resistivity between the measured resistivity and the reference resistivity; Based on the differences in clay content, resistivity, and porosity at each logging depth, a resistivity correction model is constructed. Based on the measured clay content and porosity at each depth to be predicted in the oil well reservoir, the resistivity correction value at each depth to be predicted in the oil well reservoir is determined through the resistivity correction model. The measured resistivity at each depth to be predicted in the oil well reservoir is corrected using the resistivity correction value to obtain the standard resistivity at each depth to be predicted, and the distribution of oil and gas layers at each depth to be predicted in the oil well reservoir is determined based on the standard resistivity at each depth to be predicted.

2. The method according to claim 1, characterized in that, The porosity range includes a first porosity range, a second porosity range, a third porosity range, and a fourth porosity range, wherein the porosity of the first porosity range is less than or equal to 5%, the porosity of the second porosity range is greater than 5% and less than or equal to 10%, the porosity of the third porosity range is greater than 10% and less than or equal to 20%, and the porosity of the fourth porosity range is greater than 20%.

3. The method according to claim 1, characterized in that, The difference in mud content between the measured mud content and the reference mud content is determined by the following formula: Wherein, ΔGR represents the difference in mud content, GR represents the measured mud content, and GR s1 GR represents the baseline clay content corresponding to the oil and gas reservoir in the first porosity range. s2 GR represents the baseline clay content corresponding to the oil and gas reservoir in the second porosity range. s3 GR represents the baseline clay content corresponding to the oil and gas reservoir in the third porosity range. s4 This indicates the baseline clay content corresponding to the oil and gas reservoir in the fourth porosity range. Indicates porosity.

4. The method according to claim 1, characterized in that, The resistivity difference between the measured resistivity and the reference resistivity is determined by the following formula: Wherein, ΔRT represents the resistivity difference, RT represents the measured resistivity, and RT s1 RT represents the reference resistivity of the oil and gas reservoir in the first porosity range. s2 RT represents the reference resistivity of the oil and gas reservoir in the second porosity range. s3 RT represents the reference resistivity of the oil and gas reservoir in the third porosity range. s4 This represents the reference resistivity corresponding to the oil and gas reservoir in the fourth porosity range. Indicates porosity.

5. The method according to claim 1, characterized in that, The process involves constructing a resistivity correction model based on the differences in clay content, resistivity, and porosity at various logging depths, and determining the resistivity correction value at each depth in the oil well reservoir based on the measured clay content and porosity at each depth to be predicted using the resistivity correction model. This includes: Linear fitting was performed on the resistivity difference and clay content difference at each target logging depth to obtain a resistivity correction model, wherein the porosity at each target logging depth belongs to the same porosity range. The resistivity correction model includes the following formula: Wherein, ΔRT-GR represents the resistivity correction value, and ΔGR represents the difference in clay content. d1, d2, d3, d4 represent porosity, and k1, k2, k3, k4, d1, d2, d3, d4 represent linear fitting coefficients.

6. The method according to claim 1, characterized in that, The process involves constructing a resistivity correction model based on the differences in clay content, resistivity, and porosity at various logging depths, and determining the resistivity correction value at each depth in the oil well reservoir based on the measured clay content and porosity at each depth to be predicted using the resistivity correction model. This includes: A resistivity correction model is obtained by performing plane fitting on the resistivity difference, clay content difference, and porosity at each logging depth. The resistivity correction model includes the following formula: in, This represents the resistivity correction value, and ΔGR represents the difference in clay content. denoted as porosity, and a, b, c represent plane fitting coefficients.

7. The method according to claim 1, characterized in that, The step of determining the distribution of oil and gas layers at each predicted depth in the oil well reservoir based on the standard resistivity of each predicted depth includes: If the standard resistivity is greater than or equal to the preset resistivity threshold, it indicates that the oil well reservoir depth distribution corresponding to the standard resistivity is an oil and gas layer. If the standard resistivity is less than the preset resistivity threshold, it indicates that the oil well reservoir depth to be predicted corresponding to the standard resistivity is a non-oil and gas layer.

8. A computer program product, characterized in that, The computer program product includes computer instructions stored in a computer-readable storage medium and adapted to be read and executed by a processor to cause a computer device having the processor to perform the method as claimed in any one of claims 1 to 7.

9. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores at least one piece of program code, which is loaded and executed by a processor to perform the operations performed by the method as described in any one of claims 1 to 7.

10. An electronic device, characterized in that, The method includes one or more processors and one or more memories, wherein at least one piece of program code is stored in the one or more memories, and the at least one piece of program code is loaded and executed by the one or more processors to implement the method as claimed in any one of claims 1 to 7.

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