Shale oil heterogeneity evaluation method based on imaging logging and related equipment

Abstract

The invention discloses a shale oil heterogeneity evaluation method based on imaging logging and related equipment, and belongs to the field of oil exploration logging. Based on the characteristics of high resolution and high coverage rate of micro-resistivity imaging logging, the shale oil heterogeneity evaluation method can be used for evaluating shale oil heterogeneity through resistivity data in a static image; four heterogeneity quantitative characterization parameters including a vertical heterogeneity index, a horizontal heterogeneity index, a comprehensive heterogeneity index and a heterogeneity coefficient are constructed, and a heterogeneity parameter classification evaluation standard is constructed in combination with historical oil testing data of a research area; and finally, evaluating and predicting the heterogeneity of the new well shale bed series by utilizing a heterogeneity parameter classification evaluation standard. The evaluation result obtained by adopting the method is not influenced by other intermediate parameters and factors and is only related to the logging original acquisition quality, and the method is consistent in calculation standard and high in precision.

Description

Technical Field

[0001] The invention belongs to the field of petroleum exploration and logging, and in particular relates to a shale oil heterogeneity evaluation method based on imaging logging and related equipment. Background Art

[0002] The lithology of continental shale oil is complex, with strong vertical and horizontal heterogeneity, which results in differences in the reservoir properties and production capacity of shale formations in the plane and vertical direction, making it difficult to select the optimal sweet spot. Therefore, conducting quantitative characterization and evaluation research on the heterogeneity of shale formations is of great significance for the optimization of shale oil exploration sweet spots and the realization of industrial production.

[0003] In recent years, there have been many methods for evaluating shale heterogeneity, which can be roughly divided into two categories: one is the microscopic heterogeneity evaluation method using experimental methods. Guo Yinghai and Zhao Difei (2015) proposed the "shale reservoir microscopic heterogeneity control gas" theory based on scanning electron microscopy and other methods, but failed to quantitatively evaluate the strength of microscopic heterogeneity; Li Wenhao et al. (2022) proposed a method for quantitatively characterizing the microscopic heterogeneity of tight reservoirs based on scanning electron microscopy large field of view stitching technology. "Quantitative Characterization Method of Shale Heterogeneity" CN110161208B (2022) invented a method based on shale sample testing to determine multiple quantitative heterogeneity parameters such as organic carbon content and porosity that control reservoirs and oil and gas content, but experimental measurements were not accurate. The test has the limitations of limited sample quantity, low timeliness and high cost; the second type is the macro-structural heterogeneity evaluation method characterized by well logging curves. Zhong Hongli et al. (2023) proposed a method for quantitatively characterizing macro-heterogeneity based on conventional well logging curves, such as interlayer identification statistics, permeability statistics and Lorentz curve construction. "A method for quantitative evaluation of macro-heterogeneity of tight oil reservoirs and its application" CN112228050A (2021) invented a comprehensive quantitative evaluation method for reservoir heterogeneity based on conventional well logging curves to obtain reservoir sand-to-formation ratio, sand body structure index, porosity and other parameters. However, since the vertical resolution of conventional well logging curves is up to 10 cm, its ability to reflect thin shale layers is relatively weak. Summary of the invention

[0004] In order to overcome the shortcomings of the above-mentioned technology, the present invention provides a shale oil heterogeneity evaluation method and related equipment based on imaging logging, which can solve the technical problem that the existing evaluation methods are insufficient in accuracy and cannot meet the technical problem of the detailed evaluation requirements of continental shale oil heterogeneity.

[0005] In order to achieve the above object, the present invention adopts the following technical solution:

[0006] A shale oil heterogeneity evaluation method based on imaging logging, comprising:

[0007] S1: Obtain resistivity data based on static images of microresistivity imaging logging;

[0008] S2: vertical heterogeneity index, horizontal heterogeneity index, comprehensive heterogeneity index and heterogeneity coefficient are calculated based on resistivity data;

[0009] S3: Based on the vertical heterogeneity index, horizontal heterogeneity index, comprehensive heterogeneity index and heterogeneity coefficient, combined with historical oil testing data, a classification and evaluation standard for heterogeneity parameters is established;

[0010] S4: The heterogeneity of shale formations in new wells is evaluated and predicted using the heterogeneity parameter classification evaluation standard.

[0011] Furthermore, in S1, a static image of the microresistivity imaging logging data is obtained by preprocessing the microresistivity imaging logging data; wherein the preprocessing includes image enhancement processing of the microresistivity imaging logging data.

[0012] Furthermore, in S2, the specific steps of obtaining the vertical heterogeneity index include:

[0013] The resistivity data average value of each depth point is calculated based on the resistivity data, and multiple resistivity data average values ​​are selected to calculate the data distribution concentration to obtain the vertical heterogeneity index.

[0014] Furthermore, in S2, the specific steps of obtaining the horizontal heterogeneity index include:

[0015] The average values ​​of multiple depth point data of each column of resistivity data are calculated based on the resistivity data. The average values ​​of multiple depth point data are selected to calculate the data distribution concentration and obtain the horizontal heterogeneity index.

[0016] Furthermore, in S2, the specific steps of obtaining the comprehensive heterogeneity index include:

[0017] The comprehensive average value of resistivity data at multiple depth points is calculated based on the resistivity data, and the data distribution concentration of each resistivity data is calculated based on the comprehensive average value to obtain the comprehensive heterogeneity index.

[0018] Furthermore, the calculation formula of the heterogeneity coefficient is:

[0019] Heterogeneity coefficient = vertical heterogeneity index / horizontal heterogeneity index.

[0020] Furthermore, the specific steps of S3 include:

[0021] The VINDEX-HINDEX crossplot was obtained based on the vertical heterogeneity index and the horizontal heterogeneity index combined with historical oil testing data. The SINDEX-HEGVHR crossplot was obtained based on the comprehensive heterogeneity index and the heterogeneity coefficient combined with historical oil testing data. The classification and evaluation criteria of heterogeneity parameters were obtained by analyzing the VINDEX-HINDEX crossplot and the SINDEX-HEGVHR crossplot.

[0022] A shale oil heterogeneity evaluation system based on imaging logging, used to implement the steps of the shale oil heterogeneity evaluation method based on imaging logging, comprising:

[0023] A data acquisition module, used for acquiring resistivity data according to a static image of microresistivity imaging logging;

[0024] A parameter calculation module, used to calculate the vertical heterogeneity index, horizontal heterogeneity index, comprehensive heterogeneity index and heterogeneity coefficient according to the resistivity data;

[0025] The standard establishment module is used to establish the heterogeneity parameter classification evaluation standard based on the vertical heterogeneity index, horizontal heterogeneity index, comprehensive heterogeneity index and heterogeneity coefficient, combined with historical oil testing data;

[0026] The evaluation and prediction module uses the heterogeneity parameter classification evaluation standard to evaluate and predict the heterogeneity of the shale formation in the new well.

[0027] A device comprising:

[0028] Memory for storing computer programs;

[0029] A processor is used to implement the steps of the above-mentioned shale oil heterogeneity evaluation method based on imaging logging when executing the computer program.

[0030] A computer-readable storage medium stores a computer program, which, when executed by a processor, is used to implement the steps of the above-mentioned shale oil heterogeneity evaluation method based on imaging logging.

[0031] Compared with the prior art, the present invention has the following beneficial effects:

[0032] The invention provides a shale oil heterogeneity evaluation method based on imaging logging. The method is based on the high resolution and high coverage characteristics of microresistivity imaging logging. Through the resistivity data in the static image, four heterogeneity quantitative characterization parameters, namely, vertical heterogeneity index, horizontal heterogeneity index, comprehensive heterogeneity index and heterogeneity coefficient, are constructed. In combination with the historical oil test data of the study area, a heterogeneity parameter classification evaluation standard is constructed. Finally, the standard shale formation heterogeneity strength and sweet spot distribution relationship are used for evaluation and prediction. The method is based on microresistivity imaging logging data, with a vertical resolution of up to 5 mm. By analyzing the static image concentration, heterogeneity research in different directions is carried out to quickly obtain continuous and high-resolution heterogeneity quantitative characterization parameters, which meets the fine evaluation requirements of continental shale oil with developed lamination and strong heterogeneity, and plays an important guiding role in the optimization of vertical sweet spots of shale oil. Moreover, the calculation result is not affected by other intermediate parameters and factors, but is only related to the original logging acquisition quality, and the calculation standard is consistent and the accuracy is high. BRIEF DESCRIPTION OF THE DRAWINGS

[0033] Figure 1 A VINDEX-HINDEX cross-plot of the oil-testing layer section of the oil-testing wells in the study area provided by the embodiment of the present invention;

[0034] Figure 2 A HEGVHR-SINDEX cross-plot of the oil-testing layer section of the oil-testing wells in the study area provided by the embodiment of the present invention;

[0035] Figure 3 A diagram showing the processing results of shale oil heterogeneity parameters in the 2221-2230m section of Well A provided in an embodiment of the present invention;

[0036] Figure 4 The embodiment of the present invention provides a result diagram of shale oil heterogeneity parameter processing in the 1990-1998m well section of Well B;

[0037] Figure 5 A flowchart of a shale oil heterogeneity evaluation method based on imaging logging provided by the present invention;

[0038] Figure 6 A schematic structural diagram of a shale oil heterogeneity evaluation system based on imaging logging provided by the present invention. DETAILED DESCRIPTION

[0039] The present invention provides a shale oil heterogeneity evaluation method based on imaging logging, such as Figure 5 As shown, the following steps are included:

[0040] A shale oil heterogeneity evaluation method based on imaging logging, comprising:

[0041] S1: Obtain resistivity data based on static images of microresistivity imaging logging.

[0042] Among them, the static image of microresistivity imaging logging is obtained by preprocessing the microresistivity imaging logging data; wherein, the preprocessing includes image enhancement processing of the microresistivity imaging logging data, the purpose of which is that the instrument parameters of different series of microresistivity imaging logging instruments are different, so in the processing process, attention should be paid to selecting the processing parameters of the corresponding instrument series; since the static image of microresistivity imaging logging is obtained by using uniform processing parameters for the entire well section, when the static image of the target layer section cannot distinguish different lithological formations, it is necessary to re-statically enhance the target layer depth section (microresistivity imaging logging data).

[0043] The preprocessing here also includes routine data cleaning, denoising and other processing.

[0044] S2: Based on the resistivity data, the vertical heterogeneity index, horizontal heterogeneity index, comprehensive heterogeneity index and heterogeneity coefficient are calculated respectively.

[0045] The specific steps of obtaining the vertical heterogeneity index include:

[0046] The resistivity data average value of each depth point is calculated based on the resistivity data, and multiple resistivity data average values ​​are selected to calculate the data distribution concentration to obtain the vertical heterogeneity index.

[0047] The specific steps to obtain the horizontal heterogeneity index include:

[0048] The average values ​​of multiple depth point data of each column of resistivity data are calculated based on the resistivity data. The average values ​​of multiple depth point data are selected to calculate the data distribution concentration and obtain the horizontal heterogeneity index.

[0049] The specific steps to obtain the comprehensive heterogeneity index include:

[0050] The comprehensive average value of resistivity data at multiple depth points is calculated based on the resistivity data, and the data distribution concentration of each resistivity data is calculated based on the comprehensive average value to obtain the comprehensive heterogeneity index.

[0051] The calculation formula of the heterogeneity coefficient is:

[0052] Heterogeneity coefficient = vertical heterogeneity index / horizontal heterogeneity index.

[0053] S3: Based on the vertical heterogeneity index, horizontal heterogeneity index, comprehensive heterogeneity index and heterogeneity coefficient, combined with historical oil testing data, a classification and evaluation standard for heterogeneity parameters is established.

[0054] The specific steps of S3 include:

[0055] The VINDEX-HINDEX crossplot was obtained based on the vertical heterogeneity index and the horizontal heterogeneity index combined with historical oil testing data. The SINDEX-HEGVHR crossplot was obtained based on the comprehensive heterogeneity index and the heterogeneity coefficient combined with historical oil testing data. The classification and evaluation criteria of heterogeneity parameters were obtained by analyzing the VINDEX-HINDEX crossplot and the SINDEX-HEGVHR crossplot.

[0056] S4: The heterogeneity of shale formations in new wells is evaluated and predicted using the heterogeneity parameter classification evaluation standard.

[0057] like Figure 6 As shown, the present invention also provides a shale oil heterogeneity evaluation system based on imaging logging, including: a data acquisition module, used to acquire resistivity data according to the static image of microresistivity imaging logging; a parameter calculation module, used to calculate the vertical heterogeneity index, horizontal heterogeneity index, comprehensive heterogeneity index and heterogeneity coefficient according to the resistivity data; a standard establishment module, used to establish a heterogeneity parameter classification evaluation standard based on the vertical heterogeneity index, horizontal heterogeneity index, comprehensive heterogeneity index and heterogeneity coefficient in combination with historical oil test data; an evaluation and prediction module, which uses the heterogeneity parameter classification evaluation standard to evaluate and predict the heterogeneity of the shale formation system in a new well.

[0058] The present invention also provides a device, comprising: a memory for storing a computer program; and a processor for implementing the steps of the shale oil heterogeneity evaluation method based on imaging logging when executing the computer program.

[0059] When the processor executes the computer program, the above-mentioned steps of evaluating the heterogeneity of shale oil based on imaging logging are implemented, for example: obtaining resistivity data according to the static image of microresistivity imaging logging; calculating the vertical heterogeneity index, horizontal heterogeneity index, comprehensive heterogeneity index and heterogeneity coefficient according to the resistivity data; establishing a heterogeneity parameter classification evaluation standard based on the vertical heterogeneity index, horizontal heterogeneity index, comprehensive heterogeneity index and heterogeneity coefficient in combination with historical oil testing data; and evaluating and predicting the heterogeneity of shale formations in new wells using the heterogeneity parameter classification evaluation standard.

[0060] Alternatively, when the processor executes the computer program, the functions of each module in the above system are realized, for example: a data acquisition module, which is used to acquire resistivity data based on the static image of microresistivity imaging logging; a parameter calculation module, which is used to calculate the vertical heterogeneity index, horizontal heterogeneity index, comprehensive heterogeneity index and heterogeneity coefficient respectively according to the resistivity data; a standard establishment module, which is used to establish a heterogeneity parameter classification evaluation standard based on the vertical heterogeneity index, horizontal heterogeneity index, comprehensive heterogeneity index and heterogeneity coefficient in combination with historical oil testing data; an evaluation and prediction module, which uses the heterogeneity parameter classification evaluation standard to evaluate and predict the heterogeneity of the shale formation system in a new well.

[0061] Exemplarily, the computer program can be divided into one or more modules / units, and the one or more modules / units are stored in the memory and executed by the processor to complete the present invention. The one or more modules / units can be a series of computer program instruction segments that can complete the preset functions, and the instruction segments are used to describe the execution process of the computer program in the shale oil heterogeneity evaluation device based on imaging logging. For example, the computer program can be divided into a data acquisition module, a parameter calculation module, a standard establishment module, and an evaluation prediction module; the specific functions of each module are as follows: a data acquisition module, which is used to acquire resistivity data according to the static image of microresistivity imaging logging; a parameter calculation module, which is used to calculate the vertical heterogeneity index, horizontal heterogeneity index, comprehensive heterogeneity index and heterogeneity coefficient according to the resistivity data; a standard establishment module, which is used to establish a heterogeneity parameter classification evaluation standard based on the vertical heterogeneity index, horizontal heterogeneity index, comprehensive heterogeneity index and heterogeneity coefficient, combined with historical oil test data; an evaluation prediction module, which uses the heterogeneity parameter classification evaluation standard to evaluate and predict the heterogeneity of the new well shale formation.

[0062] The shale oil heterogeneity evaluation device based on imaging logging can be a computing device such as a desktop computer, a notebook, a PDA, and a cloud server. The shale oil heterogeneity evaluation device based on imaging logging may include, but is not limited to, a processor and a memory. Those skilled in the art can understand that the above is an example of a shale oil heterogeneity evaluation device based on imaging logging, and does not constitute a limitation on the shale oil heterogeneity evaluation device based on imaging logging. It may include more components than the above, or a combination of certain components, or different components. For example, the shale oil heterogeneity evaluation device based on imaging logging may also include input and output devices, network access devices, buses, etc.

[0063] The processor may be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor may be a microprocessor or the processor may be any conventional processor, etc. The processor is the control center of the imaging logging-based shale oil heterogeneity evaluation, and uses various interfaces and lines to connect various parts of the entire imaging logging-based shale oil heterogeneity evaluation device.

[0064] The memory can be used to store the computer program and / or module, and the processor implements various functions of the shale oil heterogeneity evaluation device based on imaging logging by running or executing the computer program and / or module stored in the memory and calling the data stored in the memory.

[0065] The memory may mainly include a program storage area and a data storage area, wherein the program storage area may store an operating system, an application required for at least one function (such as a sound playback function, an image playback function, etc.), etc.; the data storage area may store data created according to the use of the mobile phone (such as audio data, a phone book, etc.), etc. In addition, the memory may include a high-speed random access memory, and may also include a non-volatile memory, such as a hard disk, a memory, a plug-in hard disk, a smart memory card (SmartMediaCard, SMC), a secure digital (SecureDigital, SD) card, a flash card (FlashCard), at least one disk storage device, a flash memory device, or other volatile solid-state storage devices.

[0066] The present invention also provides a computer-readable storage medium, which stores a computer program. When the computer program is executed by a processor, the steps of the shale oil heterogeneity evaluation method based on imaging logging are implemented.

[0067] If the integrated modules / units of the shale oil heterogeneity evaluation system based on imaging logging are implemented in the form of software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium.

[0068] Based on such understanding, the present invention implements all or part of the processes in the above-mentioned shale oil heterogeneity evaluation method based on imaging logging, and can also be completed by instructing related hardware through a computer program, and the computer program can be stored in a computer-readable storage medium, and when the computer program is executed by a processor, the steps of the above-mentioned shale oil heterogeneity evaluation method based on imaging logging can be implemented. Among them, the computer program includes computer program code, and the computer program code can be in source code form, object code form, executable file or preset intermediate form, etc.

[0069] The computer-readable storage medium may include: any entity or device capable of carrying the computer program code, recording medium, USB flash drive, mobile hard disk, magnetic disk, optical disk, computer memory, read-only memory (ROM), random access memory (RAM), electrical carrier signal, telecommunication signal and software distribution medium, etc.

[0070] It should be noted that the content contained in the computer-readable storage medium can be appropriately increased or decreased according to the requirements of legislation and patent practice in the jurisdiction. For example, in some jurisdictions, according to legislation and patent practice, computer-readable storage media do not include electrical carrier signals and telecommunication signals.

[0071] Example

[0072] The present invention will be further described below in conjunction with the embodiments and drawings:

[0073] In order to solve the technical problem that the existing evaluation methods mentioned in the background technology are not accurate enough and cannot meet the needs of fine evaluation of continental shale oil heterogeneity, this embodiment provides a shale oil heterogeneity evaluation method based on imaging logging. In order to better understand this method, first of all, Figure 1 — Figure 4 To explain:

[0074] Figure 1 This is the intersection diagram of the vertical heterogeneity index (VINDEX) and the horizontal heterogeneity index (HINDEX) of the tested oil wells in the study area. The horizontal axis is the horizontal heterogeneity index and the vertical axis is the vertical heterogeneity index. Based on this diagram, the vertical heterogeneity index and horizontal heterogeneity index boundaries of different sweet spots of shale oil can be determined.

[0075] Figure 2 This is a cross plot of the heterogeneity coefficient (HEGVHR) and the comprehensive heterogeneity index (SINDEX) of the tested wells in the study area. The horizontal axis is the comprehensive heterogeneity index and the vertical axis is the heterogeneity coefficient. Based on this figure, the boundaries of the comprehensive heterogeneity index and heterogeneity coefficient of different sweet spots of shale oil can be determined.

[0076] Figure 3 , Figure 4 The following are examples of processing the heterogeneity parameters of shale formations for two wells, A and B. Track 3 in the figure is a static image of microresistivity imaging logging, and Tracks 4 to 7 are the processed shale oil heterogeneity parameters such as vertical heterogeneity index, horizontal heterogeneity index, comprehensive heterogeneity index and heterogeneity coefficient.

[0077] This embodiment provides a shale oil heterogeneity evaluation method based on imaging logging, comprising the following steps:

[0078] Step 1: Preprocess the microresistivity imaging logging data, including image enhancement processing, to obtain a static image of the microresistivity imaging logging, and obtain resistivity data based on the static image of the microresistivity imaging logging.

[0079] Since different series of microresistivity imaging logging instruments have different instrument parameters, it is necessary to select the processing parameters of the corresponding instrument series during the processing; since the static image of microresistivity imaging logging is obtained using unified processing parameters for the entire well section, when the static image of the target layer section cannot distinguish between different lithological formations, it is necessary to re-perform static enhancement processing on the depth section of the target layer.

[0080] Step 2: For the resistivity data, within the window length of M depth points, first calculate the average value of N columns of microresistivity data at each depth point, then calculate the data distribution concentration of the M average values ​​to obtain the vertical heterogeneity index of the target layer;

[0081] The vertical heterogeneity index is the vertical distribution concentration of image data within a specified depth window length. Let the average value of N resistivity data at each depth point be x i , the vertical heterogeneity index of M depth points can be calculated by the following formula:

[0082]

[0083] In the formula, k v is the order, generally k v ≥3; is M depth points x i The average value of C V (x) is a normalized, dimensionless quantity. The smaller the data value, the lower the concentration of the data distribution and the more uniform the distribution. The vertical heterogeneity index reflects the changes in the grain size, lithology, physical properties and electrical properties of the reservoir in the vertical direction within a certain window length.

[0084] Step 3: For the resistivity data, within the window length of M depth points, first calculate the average data of the M depth points of each column of resistivity data, and then calculate the data distribution concentration of the N average values ​​to obtain the horizontal heterogeneity index of the target layer.

[0085] The horizontal heterogeneity index is the distribution concentration of the image data in the horizontal direction within the specified depth window length. Let the average value of the M depth point data corresponding to each column of resistivity data be y i , the horizontal heterogeneity index of N columns of resistivity data can be calculated by the following formula:

[0086]

[0087] In the formula, k h is the order, generally k h ≥3; N y i The average value of C H (y) is a normalized, dimensionless quantity. The smaller the data value, the lower the concentration of the data distribution and the more uniform the distribution. The horizontal heterogeneity index reflects the changes in the grain size, lithology, physical properties and electrical properties of the reservoir in the horizontal direction within a certain window length.

[0088] Step 4: For the resistivity data, within the window length of M depth points, calculate the distribution concentration of M depth points and N columns of resistivity data to obtain the comprehensive heterogeneity index of the target layer.

[0089] The comprehensive heterogeneity index is the distribution concentration of image data in the vertical and horizontal directions within the specified window length. Let the value of N columns of resistivity data at M depth points be z i , L=M﹡N, the comprehensive heterogeneity index can be calculated by the following formula:

[0090]

[0091] In the formula, k s is the order, generally k S ≥3; is M * N z i The comprehensive average value of C S (z) is a normalized, dimensionless quantity. The smaller the data value, the lower the concentration of the data distribution and the more uniform the distribution. The comprehensive heterogeneity index reflects the comprehensive changes in the grain size, lithology, physical properties and electrical properties of the reservoir in the vertical and horizontal directions within a certain window length.

[0092] Step 5: Calculate the ratio of the vertical heterogeneity index to the horizontal heterogeneity index to obtain the heterogeneity coefficient of the target layer.

[0093] The calculated heterogeneity coefficient of the target layer is the ratio of the vertical heterogeneity index to the horizontal heterogeneity index, which reflects the difference in the vertical and lateral heterogeneity of the shale formation.

[0094] Step 6: In the wells that have been tested, by analyzing the relationship between the test results and the heterogeneity parameters of the target layer, the classification and evaluation criteria of the shale oil heterogeneity parameters can be determined, so as to conduct a comprehensive evaluation of the heterogeneity of the shale formation in the new well and predict the sweet spot.

[0095] Among them, establishing the evaluation standard of shale oil heterogeneity requires as much known oil test data as possible, and at the same time, it is necessary to consider the impact of different oil test measures on the oil test conclusions.

[0096] The following is an example of actual data on the analysis and evaluation of shale formation heterogeneity using microresistivity imaging logging data in a certain study area of ​​the Ordos Basin, combined with the accompanying drawings to further explain the specific implementation methods of the present invention in detail.

[0097] 1. Collect conventional logging data, microresistivity imaging logging data of relevant wells in the study area and the oil testing data of all tested wells, pre-process and image enhance the microresistivity imaging logging data to obtain static image data of microresistivity imaging logging.

[0098] 2. The static image plate data of microresistivity imaging logging is used as input to calculate four parameters for evaluating the heterogeneity of the target layer, namely the vertical heterogeneity index, horizontal heterogeneity index, comprehensive heterogeneity index and heterogeneity coefficient.

[0099] 3. In the wells that have been tested, the relationship between the test results and the heterogeneity parameters of the target layer is analyzed, and it is found that as the heterogeneity of the target layer increases, its test oil production decreases; as the heterogeneity decreases, its test oil production increases. Figure 1 , Figure 2As shown in the figure, according to the classification standard of the test oil production in the study area, the vertical heterogeneity index-horizontal heterogeneity index (VINDEX-HINDEX) cross plot and the comprehensive heterogeneity index-heterogeneity coefficient (SINDEX-HEGVHR) cross plot are used to determine the classification standard of shale oil heterogeneity parameters. The vertical heterogeneity index and horizontal heterogeneity index of Class I shale oil sweet spots are both less than 0.1, the comprehensive heterogeneity index is less than 0.2, and the heterogeneity coefficient is less than 0.3. This type of sweet spot has weak heterogeneity, good reservoir performance, and daily oil production is generally above 10 tons; the vertical heterogeneity index of Class II shale oil sweet spots is between 0.1 and 0.3, the horizontal heterogeneity index is between 0.1 and 0.2, the comprehensive heterogeneity index is between 0.2 and 0.5, and the heterogeneity coefficient is between 0.3 and 1.0. This type of sweet spot has medium heterogeneity, poor reservoir performance, and daily oil production is generally less than 10 tons; the vertical heterogeneity index of Class III shale oil sweet spots is greater than 0.3, the horizontal heterogeneity index is greater than 0.2, the comprehensive heterogeneity index is greater than 0.5, and the heterogeneity coefficient is greater than 1.0. This type of sweet spot has strong heterogeneity, poor reservoir performance, and almost no oil production capacity.

[0100] 4. According to the above shale oil heterogeneity evaluation standard, the shale oil heterogeneity parameter processing results are used to evaluate the heterogeneity of the new well shale formation. Figure 3 It can be seen that the heterogeneity parameter values ​​of the 2222-2226m well section of Well A are relatively low overall, with the average values ​​of VINDEX, HINDEX, SINDEX and HEGVHR parameters being 0.01, 0.04, 0.09 and 0.29 respectively, and all falling into the Class I zone on the VINDEX-HINDEX and SINDEX-HEGVHR crossplots, indicating that the heterogeneity of this section is relatively weak and is a favorable shale oil sweet spot. Figure 4 It can be seen that the heterogeneity parameter values ​​of the 1992-1996m well section of Well B are relatively high, and the average values ​​of VINDEX, HINDEX, SINDEX and HEGVHR parameters are 0.23, 0.31, 0.55 and 1.03 respectively. They all fall into the Class III area on the VINDEX-HINDEX and SINDEX-HEGVHR cross plots, indicating that this section has strong heterogeneity and is not a favorable shale oil sweet spot. Subsequently, the 2222-2226m well section of Well A produced 10.8t of oil per day in the oil test, and the 1992-1996m well section of Well B produced only oil flowers and a small amount of water. The heterogeneity evaluation results are consistent with the oil test conclusions.

[0101] The present embodiment provides a shale oil heterogeneity evaluation method based on imaging logging, which realizes the quantitative characterization and evaluation of continental shale oil heterogeneity, and has the following advantages:

[0102] This method utilizes the high resolution and high coverage of microresistivity imaging logging. Through processing and interpretation of static images, four quantitative characterization parameters and evaluation methods of heterogeneity, including vertical heterogeneity index, horizontal heterogeneity index, comprehensive heterogeneity index and heterogeneity coefficient, are constructed. Combined with the test results of oil trials in the study area, the relationship between the strength of shale stratum heterogeneity and the distribution of sweet spots is determined, thus forming a set of shale oil heterogeneity quantitative characterization and evaluation methods based on microresistivity imaging logging data. This method is based on microresistivity imaging logging data with a vertical resolution of up to 5 mm. Through the analysis of static image concentration, heterogeneity research in different directions is carried out to quickly obtain continuous and high-resolution quantitative characterization parameters of heterogeneity, which meets the needs of fine evaluation of continental shale oil with developed lamination and strong heterogeneity, and plays an important guiding role in the optimization of vertical sweet spots for shale oil. Moreover, the calculation results are not affected by other intermediate parameters and factors, but are only related to the original acquisition quality of logging. The calculation standards are consistent and the accuracy is high.

[0103] The above embodiment is only one of the implementation methods that can realize the technical solution of the present invention. The scope of protection claimed by the present invention is not limited only to this embodiment, but also includes changes, replacements and other implementation methods that can be easily thought of by any technician familiar with the technical field within the technical scope disclosed by the present invention.

Claims

1. A shale oil heterogeneity evaluation method based on imaging logging, characterized in that: include: S1: Obtain resistivity data based on static images of microresistivity imaging logging; S2: vertical heterogeneity index, horizontal heterogeneity index, comprehensive heterogeneity index and heterogeneity coefficient are calculated based on resistivity data; S3: Based on the vertical heterogeneity index, horizontal heterogeneity index, comprehensive heterogeneity index and heterogeneity coefficient, combined with historical oil testing data, a classification and evaluation standard for heterogeneity parameters is established; S4: Use the heterogeneity parameter classification evaluation standard to evaluate and predict the heterogeneity of shale formations in new wells.

2. The shale oil heterogeneity evaluation method based on imaging logging according to claim 1 is characterized in that: In S1, a static image of the microresistivity imaging logging data is obtained by preprocessing the microresistivity imaging logging data; wherein the preprocessing includes image enhancement processing of the microresistivity imaging logging data.

3. The shale oil heterogeneity evaluation method based on imaging logging according to claim 1 is characterized in that: In S2, the specific steps of obtaining the vertical heterogeneity index include: The resistivity data average value of each depth point is calculated based on the resistivity data, and multiple resistivity data average values ​​are selected to calculate the data distribution concentration to obtain the vertical heterogeneity index.

4. The shale oil heterogeneity evaluation method based on imaging logging according to claim 1, characterized in that: In S2, the specific steps of obtaining the horizontal heterogeneity index include: The average values ​​of multiple depth point data of each column of resistivity data are calculated based on the resistivity data. The average values ​​of multiple depth point data are selected to calculate the data distribution concentration and obtain the horizontal heterogeneity index.

5. The shale oil heterogeneity evaluation method based on imaging logging according to claim 1, characterized in that: In S2, the specific steps of obtaining the comprehensive heterogeneity index include: The comprehensive average value of resistivity data at multiple depth points is calculated based on the resistivity data, and the data distribution concentration of each resistivity data is calculated based on the comprehensive average value to obtain the comprehensive heterogeneity index.

6. The shale oil heterogeneity evaluation method based on imaging logging according to claim 1, characterized in that: The calculation formula of the heterogeneity coefficient is: Heterogeneity coefficient = vertical heterogeneity index / horizontal heterogeneity index.

7. The method for evaluating shale oil heterogeneity based on imaging logging according to claim 1, characterized in that: The specific steps of S3 include: The VINDEX-HINDEX crossplot was obtained based on the vertical heterogeneity index and the horizontal heterogeneity index combined with historical oil testing data. The SINDEX-HEGVHR crossplot was obtained based on the comprehensive heterogeneity index and the heterogeneity coefficient combined with historical oil testing data. The classification and evaluation criteria of heterogeneity parameters were obtained by analyzing the VINDEX-HINDEX crossplot and the SINDEX-HEGVHR crossplot.

8. A shale oil heterogeneity evaluation system based on imaging logging, used to implement the steps of the shale oil heterogeneity evaluation method based on imaging logging according to any one of claims 1 to 7, characterized in that: include: A data acquisition module, used for acquiring resistivity data according to a static image of microresistivity imaging logging; A parameter calculation module, used to calculate the vertical heterogeneity index, horizontal heterogeneity index, comprehensive heterogeneity index and heterogeneity coefficient according to the resistivity data; The standard establishment module is used to establish the heterogeneity parameter classification evaluation standard based on the vertical heterogeneity index, horizontal heterogeneity index, comprehensive heterogeneity index and heterogeneity coefficient, combined with historical oil testing data; The evaluation and prediction module uses the heterogeneity parameter classification evaluation standard to evaluate and predict the heterogeneity of the shale formation in the new well.

9. A device, characterized in that: include: Memory for storing computer programs; A processor is used to implement the steps of the shale oil heterogeneity evaluation method based on imaging logging as described in any one of claims 1 to 7 when executing the computer program.

10. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, it is used to implement the steps of the shale oil heterogeneity evaluation method based on imaging logging as described in any one of claims 1 to 7.

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