Terrestrial clastic rock reservoir stratum division method, device, equipment and medium

Abstract

The invention provides a terrestrial clastic rock reservoir stratum division method, device, equipment and medium, and belongs to the field of geological classification, and the method comprises the steps: collecting and obtaining electric imaging data of each preset point location of a target well wall; classifying preset point locations of all depths and orientations according to the electric imaging data, and determining storage types to which the preset point locations belong; determining the depth boundary of each reservoir layer section according to the quantitative proportion of the preset point locations of different reservoir types; and according to the depth boundary, carrying out fine division on the reservoir interval. In this way, the coverage rate and resolution of stratum information can be increased, the sensitivity of geological response is enhanced, heterogeneous and nonlinear changes of geological details are highlighted, and more accurate division of reservoir intervals is achieved. According to the method, the distribution and characteristics of the reservoir stratum can be more accurately determined through fine division of the reservoir stratum sections, fine evaluation of the stratum is facilitated, and powerful support is provided for oil-gas exploration and development.

Description

Technical Field

[0001] The present invention belongs to the field of geological classification, and particularly relates to a method, device, equipment and medium for dividing terrigenous clastic rock reservoirs. Background Art

[0002] Terrigenous clastic rocks (different from volcanic clastic rocks, usually simply referred to as clastic rocks) refer to sedimentary rocks mainly composed of terrigenous clastic materials, including conglomerate, sandstone, siltstone and claystone, accounting for more than 3 / 4 of the total sedimentary rocks. As an important type of oil and gas reservoir, terrigenous clastic rocks can form both conventional oil and gas reservoirs and unconventional oil and gas reservoirs (such as shale oil and gas, tight oil and gas, etc.). Therefore, improving the logging interpretation and evaluation technology of terrigenous clastic rock reservoirs helps to improve the efficiency of exploration and development and promote the increase of reserves and production in oil and gas fields.

[0003] Integrating various logging data, dividing the reservoir (distinguishing from non-reservoirs) from the measured stratigraphic section, and determining the top and bottom interfaces of each layer are usually simply referred to as "layer division" in the processing and interpretation of logging data. It is an important basis and prerequisite for further carrying out reservoir parameter calculation (such as shale content, porosity, permeability, water saturation, etc.), interpretation conclusion determination (such as oil layer, gas layer, water layer, dry layer, etc.), reservoir evaluation and oil and gas production, etc.

[0004] In production practice, logging interpretation engineers usually first compile and print the comprehensive curve charts of various logging data, and then, based on the charts and combined with their own experience and understanding, use the manual interpretation method to carry out the work of dividing terrigenous clastic rock reservoirs on the logging curves. In order to improve the work efficiency of layer division interpretation, scientific research workers in the logging industry have carried out extensive research on the method of automatic layer division, and formed a variety of digital processing means of logging data with a computer as the basic tool and discrete data as the processing object. The reservoir automatic division technology based on digital logging data includes statistical analysis method, frequency domain transformation method and artificial intelligence method.

[0005] The statistical analysis method mainly uses the method of mathematical statistics to determine the reservoir boundary according to the time-domain characteristic parameters of the logging curves obtained by statistics. There are mainly 3 ideas: (1) Search and determine the optimal statistical segmentation point between the reservoir and non-reservoir through variance analysis (small variance within the layer and large variance between layers); (2) According to the geometric shape change of the curve, find the inflection point or half amplitude point by differentiating the logging curve, finding the extreme value point of the slope or the "activity" cut-off value, as the depth boundary of the reservoir; (3) Determine the rock property or membership degree according to the statistical characteristics of the logging values, and merge the same lithology, so as to realize the division of terrigenous clastic rock reservoirs (different from the clustering analysis in machine learning, the focus of this method is traditional multivariate statistics, represented by ordered clustering analysis and extreme value variance clustering method).

[0006] The frequency-domain transformation method processes logging data in the frequency domain and simulates the manual interpretation process of "from coarse to fine, hierarchical by level" through multi-scale analysis, including methods such as Fourier transform, wavelet transform, and Walsh transform.

[0007] When artificial intelligence is used for logging stratification, it includes fuzzy recognition methods and machine learning methods. The fuzzy recognition method is an automatic stratification method that comprehensively applies multiple curves. The main method is to construct a closeness formula in the number domain and use the closeness criterion to give a unified stratification point. Machine learning methods include supervised learning methods (presetting artificial labels in advance, including support vector machines, decision trees, random forests, artificial neural networks, etc.) and unsupervised learning methods (not presetting data labels in advance and automatically classifying by the computer, including k-Means clustering, DBSCAN clustering, principal component analysis, and self-organizing mapping neural networks, etc.).

[0008] In the above-mentioned prior art, manual stratification of logging curves is time-consuming, laborious, and has low efficiency, and is easily affected by the experience and understanding of analysts, and there may be subjective biases. The statistical analysis method for automatic stratification based on the digital processing of logging data is conducive to improving the efficiency of reservoir division. However, the statistical analysis method depends on the numerical or morphological characteristics of logging data and requires complex computer programs to be compiled according to strict mathematical methods for processing. It may be greatly affected by noise interference and the resolution of logging data, resulting in a large error in the final stratification result; it is difficult to determine the specific stratification scale by the frequency-domain transformation method, and it still depends on the empirical judgment of the interpreter, resulting in subjective errors; it is difficult to obtain results that conform to geological understanding when artificial intelligence stratification lacks interpreter intervention (fuzzy pattern recognition, unsupervised machine learning), and it can only be used for data preprocessing. The supervised stratification method requires adding labels to the data in advance to form a large training sample library, and still requires a large amount of manual processing work. Moreover, the trained model has poor generalization and can only be applied to objects similar to the learning sample characteristics, and there are great errors in the application of non-similar objects. Especially in the case of variable geological morphology, the method of stratification by artificial intelligence can only be used in very few cases.

[0009] In addition, in the above prior art, only conventional one-dimensional logging curves are used, so the non-homogeneous and non-linear changes of the formation are not considered, and the resolution of the formation is relatively low, which is not conducive to the division of thin layers or even ultra-thin layers.

[0010] Therefore, there are large errors in the division of terrigenous clastic rock reservoirs in the prior art, and the non-homogeneous and non-linear changes of the formation in terms of composition, structure, and physical properties are not considered, resulting in inaccurate division of reservoirs. Summary of the Invention

[0011] To solve the problem of poor accuracy in the division of terrigenous clastic rock reservoirs in the prior art, the present invention provides a method, device, equipment and medium for dividing terrigenous clastic rock reservoirs.

[0012] To achieve the above object, the present invention provides the following technical solutions: Firstly, a method for dividing terrigenous clastic rock reservoirs is provided, and the method includes: Collecting the electrical imaging data of each preset point in the target wellbore of the terrigenous clastic rock; Classifying the preset points according to the electrical imaging data to determine the reservoir type to which the preset points belong; the reservoir types include reservoirs and non-reservoirs; Determining the depth boundaries of each reservoir section according to the quantity ratio of the preset points of different reservoir types; the depth boundaries are the interfaces between the reservoir sections and the non-reservoir sections of the reservoirs in the longitudinal section; Dividing the reservoir sections according to the depth boundaries.

[0013] Optionally, classifying the preset points according to the electrical imaging data to determine the reservoir type to which the preset points belong, including: Determining the electrical buckle value of each preset point according to the electrical imaging data; Generating a histogram of electrical buckle values for the entire well section with the electrical buckle value and its corresponding occurrence frequency as the horizontal and vertical coordinates respectively; Determining one or more candidate electrical buckle values corresponding to a preset frequency according to the histogram of electrical buckle values; In the case where there is one candidate electrical buckle value, determining this candidate electrical buckle value as the target electrical buckle value; in the case where there are multiple candidate electrical buckle values, determining the maximum value among the multiple candidate electrical buckle values as the target electrical buckle value; Determining that the preset points with electrical buckle values greater than the target electrical buckle value belong to non-reservoirs, and the preset points with electrical buckle values less than or equal to the target electrical buckle value belong to reservoirs.

[0014] Optionally, the determining the depth boundaries of each reservoir section according to the quantity ratio of the preset points in the reservoir includes: Determining the proportion of the preset points of the reservoir in the wellbore circumference direction , and the specific formula is: ; Wherein, N r is the number of preset points of the reservoir, N b is the total number of preset points; On the vertical section of the wellbore, the proportion of preset points is statistically analyzed depth by depth to obtain a reservoir proportion curve; Screen the data of the reservoir proportion curve, and determine that the layer segments in the reservoir proportion curve data that are greater than or equal to the preset proportion threshold are reservoir layer segments, and the layer segments less than the preset proportion threshold are non-reservoir layer segments, so as to determine the depth boundary of the reservoir layer segments.

[0015] Optionally, the method further includes: when statistically calculating the proportion of preset points depth by depth, the statistical thickness is limited to 0.1 meter, and the non-reservoir layer interlayers with a thickness less than 0.1 meter are merged into the adjacent reservoir layer segments; discard the reservoir layer segments with a total thickness less than 0.1 meter.

[0016] Optionally, before classifying the preset points according to the electrical imaging data, the electrical imaging data is also preprocessed, including: Perform depth alignment, equalization correction, and bad electrical button removal on the electrical button values in the electrical imaging data; Perform color matching on the processed electrical imaging data to generate a static image of the electrical imaging logging.

[0017] Secondly, a terrigenous clastic rock reservoir division device is provided, and the device includes: An acquisition module, configured to collect electrical imaging data of each preset point in the target wellbore of the terrigenous clastic rock; A classification module, configured to classify the preset points according to the electrical imaging data to determine the reservoir type to which the preset points belong; the reservoir type includes a reservoir layer and a non-reservoir layer; A determination module, configured to determine the depth boundary of each reservoir layer segment according to the quantity ratio of preset points of different reservoir types; the depth boundary is the interface between the reservoir layer segment and the non-reservoir layer segment of the reservoir layer in the longitudinal section; A division module, configured to divide the reservoir layer segments according to the depth boundary.

[0018] In addition, a computer-readable storage medium is also provided, and the storage medium stores a computer program, and when the computer program is executed by a processor, the above-mentioned method for dividing a terrigenous clastic rock reservoir is implemented.

[0019] Finally, a computer device is provided, including a memory, a processor, and a computer program stored on the memory and executable on the processor, and when the processor executes the program, the above-mentioned method for dividing a terrigenous clastic rock reservoir is implemented.

[0020] The method for dividing a terrigenous clastic rock reservoir provided by the present invention has the following beneficial effects: First, collect the electrical imaging array data of the target wellbore. On the one hand, it improves the coverage rate and resolution of formation information, ensuring comprehensive, uniform, and high-precision measurement of the target wellbore and avoiding missing key information. On the other hand, it enhances the sensitivity of geological responses and highlights the heterogeneous and non-linear changes in geological details. Based on the classification results of the electrical imaging data, rapid formation identification can be achieved, accurately identifying the reservoir type to which the preset points belong, which is beneficial for more refined division of reservoir intervals later and reducing the subjectivity of human judgment. Determining the depth boundary of the reservoir interval by statistically analyzing the proportion of the number of preset points of different reservoir types helps to more accurately evaluate the reserves and quality of the reservoir. Through the refined division of the reservoir, the distribution and characteristics of the reservoir can be more accurately determined, not only improving the accuracy and precision of formation division but also providing strong support for oil and gas exploration and development. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] To more clearly illustrate the embodiments of the present invention and their design schemes, the accompanying drawings required for this embodiment will be briefly introduced below. The drawings in the following description are only partial embodiments of the present invention. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts.

[0022] Figure 1 FIG. is a schematic flow chart of a method for dividing terrigenous clastic rock reservoirs provided according to an exemplary embodiment of the present invention.

[0023] Figure 2 FIG. is a schematic structural diagram of an electrical imaging logging tool provided according to an exemplary embodiment of the present invention.

[0024] Figure 3 FIG. is a schematic diagram of histogram segmentation of electrical imaging logging data provided according to an exemplary embodiment of the present invention.

[0025] Figure 4 FIG. is a schematic diagram of refined division of a reservoir based on electrical imaging logging provided according to an exemplary embodiment of the present invention.

[0026] Figure 5 FIG. is a block diagram of a device for dividing terrigenous clastic rock reservoirs provided according to an exemplary embodiment of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0027] To enable those skilled in the art to better understand the technical solutions of the present invention and implement them, the present invention will be described in detail below with reference to the accompanying drawings and specific embodiments. The following embodiments are only used to more clearly illustrate the technical solutions of the present invention and should not be used to limit the protection scope of the present invention.

[0028] The technical solutions provided in each embodiment of the present invention will be described in detail below with reference to the accompanying drawings.

[0029] First, the present invention provides a method for dividing terrigenous clastic rock reservoirs, specifically as follows: Figure 1 The method includes the following steps: S101. Collect the electrical imaging data of each preset point in the target wellbore of the terrigenous clastic rock.

[0030] The terrigenous clastic rock formation is mainly composed of sand, gravel clastic and muddy parts. The reservoir is mainly composed of the former, with relatively high resistivity, while the latter constitutes the non-reservoir with good conductivity.

[0031] During logging, the electrical imaging logging instrument contacts the formation near the wellbore through its arrayed electrical buttons. The measured value of each button represents the response of the formation within a certain detection depth range centered on the button and with the distance between adjacent buttons as the radius. Compared with the conventional one-dimensional logging curve, the electrical imaging logging instrument uses a uniformly spaced button electrode array and performs uniform measurement in the wellbore, which can ensure high-resolution, high-coverage and high-sensitivity acquisition of the wellbore formation information.

[0032] Among them, the preset point can be an arrayed measurement point preset according to the measurement requirements.

[0033] For example, taking the electrical imaging logging instrument FMI as an example, as shown in the electrical imaging logging instrument, there are 8 measurement plates in total. Each measurement plate has 24 button electrode arrays, with a total of 192 button electrodes. The diameter of each button is 0.2 inches (about 5 mm). Each two buttons are closely adjacent, but there is a certain spacing between adjacent plates. In an 8-inch wellbore, the coverage rate of the button measurement points around the well is about 80%, thus achieving high-precision acquisition of the heterogeneous information of the formation beside the well. Figure 2 For the collected electrical imaging logging data, each data point represents the electrical response of the formation near the "preset point" at a certain definite depth and azimuth. Obtain the measured values of the buttons at each preset point for subsequent processing and analysis.

[0034] S102. Classify the preset points according to the electrical imaging data to determine the reservoir type to which the preset point belongs.

[0035] Among them, the reservoir type includes reservoir and non-reservoir.

[0036] Since the button values of different reservoir types are different, for the terrigenous clastic rock formation, the micro-resistivity value of the formation measured by each button can be judged to belong to the reservoir or non-reservoir in turn according to the electrical imaging logging data.

[0037]

[0038] ​Specifically, determine the electrical buckle values at each preset point based on the electrical imaging data; generate a histogram of the electrical buckle values for the entire well section with the electrical buckle values and their corresponding occurrence frequencies as the horizontal and vertical coordinates respectively; determine one or more candidate electrical buckle values corresponding to a preset frequency according to the histogram of the electrical buckle values; in the case where there is only one candidate electrical buckle value, determine the candidate electrical buckle value as the target electrical buckle value; in the case where there are multiple candidate electrical buckle values, determine the maximum value among the multiple candidate electrical buckle values as the target electrical buckle value; determine that the preset points with electrical buckle values greater than the target electrical buckle value belong to non-reservoir layers, and the preset points with electrical buckle values less than or equal to the target electrical buckle value belong to reservoir layers.

[0039] For example, for the electrical imaging logging data of terrigenous clastic rock formations, the "one percent" empirical criterion can be used for segmentation to distinguish reservoir layers and non-reservoir layers. The electrical buckle values in electrical imaging logging ( C Pre ) have the meaning of formation conductivity, and a histogram statistics for the entire well section is performed on it (the horizontal coordinate is C Pre , increasing gradually from left to right). According to experience, the corresponding value at the statistical frequency of 0.01 (i.e., 1%) of the electrical buckle is the best segmentation threshold for the entire well, as shown in Figure 3 .

[0040] To the left of this segmentation point are relatively high-resistance reservoir layer data points, while to its right are relatively high-conductivity non-reservoir layer data points. Read the horizontal coordinate value ( C X ) of this point and regard it as the segmentation point of the electrical buckle values for the entire well. That is, if the preprocessed electrical buckle value ( C Pre ) is greater than this value ( C X ), then this electrical buckle value is regarded as a measurement point of the non-reservoir layer, otherwise it is a measurement point of the reservoir layer; according to this criterion, the preprocessed electrical imaging logging data can be converted into binary data ( C Bin ):

[0041] C Bin =( C Pre > C X )? Non-reservoir layer measurement point (0): Reservoir layer measurement value (1).

[0042] Based on the above formula, determine the reservoir type to which each preset point belongs according to the electrical buckle value of each preset point.

[0043] In addition, before classifying the preset points according to the electrical imaging data, the electrical imaging data can also be preprocessed.

[0044] Use professional software (such as the Geology processing package of GeoFrame, Techlog, CIFLog, etc.) to perform depth alignment, equalization correction, and bad electrical button removal on the electrical button values in the electrical imaging data collected in the field; Apply the common Yellow color palette to color the processed electrical imaging data to generate static images of the electrical imaging logging for intuitive analysis.

[0045] S103. Determine the depth boundaries of each reservoir interval according to the proportional number of preset points for different reservoir types.

[0046] Specifically, determine the depth boundaries of each reservoir interval according to the proportional number of preset points of the reservoir. This depth boundary is the interface between the reservoir interval and the non-reservoir interval in the longitudinal section of the reservoir.

[0047] In this step, first determine the proportion of the preset points of the reservoir in the wellbore circumference direction R r It is: Wherein, N r is the number of preset points of the reservoir, N b is the number of electrical buttons of the electrical imaging logging instrument. Taking the FMI electrical imaging logging instrument as an example, N b = 192, that is, at each depth sampling point, a total of 192 electrical button measurement values are formed around the wellbore.

[0048] Based on the proportion of the preset points of the reservoir in the wellbore circumference direction, determine the reservoir. On the vertical section of the wellbore wall, perform statistics on the proportion of the preset points depth by depth, and a continuous and high-resolution reservoir proportion curve can be obtained.

[0049] Secondly, screen the data of the reservoir proportion curve, and determine that the layer segments in the reservoir proportion curve greater than or equal to the preset proportion threshold are reservoir intervals, and the layer segments less than the preset proportion threshold are non-reservoir intervals, so as to determine the depth boundaries of each reservoir interval.

[0050] For example, vertically, further statistics are performed on the reservoir proportion curve. Taking 0.5 of the reservoir proportion as the preset proportion threshold, the layer segments with the reservoir preset point proportion of 0.5 (i.e., 50%) and above are divided into reservoir intervals, and the depth segments with the reservoir preset point proportion lower than 0.5 are regarded as non-reservoir intervals.

[0051] In addition, considering the current production process level, when statistically calculating the proportion of preset points layer by layer, the statistical thickness is limited to 0.1 m. The non-reservoir interlayers with a thickness less than 0.1 m are merged into the adjacent reservoir sections; meanwhile, the reservoir sections with a total thickness less than 0.1 m are discarded.

[0052] S104. Divide the reservoir sections according to the depth boundary.

[0053] After the reservoir is finely divided based on the depth boundary, an electrical imaging static map, a binary segmentation map (displayed in blue and green, with green indicating the reservoir measurement points and blue indicating the non-reservoir measurement points), a reservoir proportion curve (scale from 0 to 1.2, and the part greater than 0.5 on the right is filled with orange to indicate the reservoir section), and a stratification result map (each reservoir section is represented by a red rectangle, and the number on the right is the layer number) can be drawn side by side, which are used to display the electrical imaging logging image, the segmentation and statistical process, and the final stratification result, as specifically Figure 4 shown.

[0054] Based on the above fine division results, as shown in Table 1, the present invention can more accurately determine the top and bottom interfaces and thickness of the reservoir section (only two decimal places are retained in the present invention, but according to the resolution of the electrical imaging logging, it can actually be accurate to 5 decimal places). Thin layers with a thickness less than 0.6 m can be identified (theoretically, thin layers with a thickness less than 0.1 m can be identified, but due to the limitation of the current production process level, ultra-thin layers with a thickness less than 0.1 m are not statistically considered in the present invention).

[0055] Table 1 Electrical imaging logging stratification results By adopting the above method, first, the electrical imaging array data of the target wellbore are collected. On the one hand, it improves the coverage rate and resolution of the formation information, can ensure comprehensive, uniform and high-precision measurement of the target wellbore, and avoid missing key information. On the other hand, it enhances the sensitivity of the geological response and highlights the heterogeneous and non-linear changes of geological details. Based on the classification results of the electrical imaging data, rapid formation identification can be realized, and the reservoir type to which the preset points belong can be accurately identified, which is beneficial to subsequent finer division of the reservoir section and reduces the subjectivity of human judgment. Determining the depth boundary of the reservoir section by statistically calculating the proportion of the number of preset points in the reservoir helps to more accurately evaluate the reserves and quality of the reservoir. Through the fine division of the reservoir, the distribution and characteristics of the reservoir can be more accurately determined, which not only improves the accuracy and precision of formation division, but also provides strong support for oil and gas exploration and development.

[0056] Secondly, the present invention also provides a terrigenous clastic rock reservoir division device, as Figure 5 shown, including: An acquisition module 501 is configured to collect the electrical imaging data of each preset point in the target wellbore of the terrigenous clastic rock.

[0057] A classification module 502 is configured to classify the preset points according to the electrical imaging data and determine the reservoir type to which the preset points belong; the reservoir types include reservoir formations and non-reservoir formations.

[0058] A determination module 503 is configured to determine the depth boundaries of each reservoir interval according to the quantity ratio of the preset points of different reservoir types; the depth boundaries are the interfaces between the reservoir intervals and the non-reservoir intervals of the reservoir formations in the longitudinal section.

[0059] A division module 504 is configured to divide the reservoir intervals according to the depth boundaries.

[0060] By using the above device, first, the electrical imaging array data of the target wellbore is collected. On the one hand, it improves the coverage rate and resolution of the formation information, can ensure a comprehensive, uniform and high-precision measurement of the target wellbore, and avoid missing key information. On the other hand, it enhances the sensitivity of the geological response and highlights the heterogeneous and non-linear changes of the geological details. Based on the classification results of the electrical imaging data, rapid formation identification can be achieved, and the reservoir type to which the preset points belong can be accurately identified, which is beneficial to the subsequent more refined division of the reservoir intervals and reduces the subjectivity of human judgment. By statistically calculating the quantity ratio of the preset points in the reservoir formation to determine the depth boundaries of the reservoir intervals, it helps to more accurately evaluate the reserves and quality of the reservoir formation. By finely dividing the reservoir formation, the distribution and characteristics of the reservoir can be more accurately determined, which not only improves the accuracy and precision of the formation division, but also provides strong support for oil and gas exploration and development.

[0061] The present invention also provides a computer-readable storage medium storing a computer program, which can be used to execute the steps of the method for dividing the terrigenous clastic rock reservoir formation provided above. Figure 1 The steps of the method for dividing the terrigenous clastic rock reservoir formation provided above.

[0062] The present invention also provides a computer device. At the hardware level, the computer device includes a processor, an internal bus, a network interface, a memory, and a non-volatile memory. Of course, it may also include other hardware required for other services. The processor reads the corresponding computer program from the non-volatile memory into the memory and then runs it to implement the steps of the method for dividing the terrigenous clastic rock reservoir formation provided above. Figure 1 The steps of the method for dividing the terrigenous clastic rock reservoir formation provided above.

[0063] Those skilled in the art should understand that the embodiments of the present invention can be provided as a method, a system, or a computer program product. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Moreover, the present invention can take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) that contain computer-usable program code.

[0064] The present invention is described with reference to the flowcharts and / or block diagrams of methods, apparatuses (systems), and computer program products according to the embodiments of the present invention. It should be understood that each flow and / or block in the flowchart and / or block diagram can be implemented by computer program instructions, and the combination of flows and / or blocks in the flowchart and / or block diagram can also be implemented. These computer program instructions can be provided to the processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing devices to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing devices generate a device for implementing the functions specified in Figure 1 one flow or multiple flows and / or blocks Figure 1 one block or multiple blocks.

[0065] These computer program instructions can also be stored in a computer-readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer-readable memory generate a manufactured article including an instruction device, and the instruction device implements the functions specified in Figure 1 one flow or multiple flows and / or blocks Figure 1 one block or multiple blocks.

[0066] These computer program instructions can also be loaded onto a computer or other programmable data processing device, so that a series of operation steps are executed on the computer or other programmable device to generate a computer-implemented process, and thus the instructions executed on the computer or other programmable device provide steps for implementing the functions specified in Figure 1 one flow or multiple flows and / or blocks Figure 1 one block or multiple blocks.

[0067] It should be noted that the above specific embodiments can enable those skilled in the art to understand the present invention more comprehensively, but do not limit the present invention in any way. Therefore, although this specification has described the present invention in detail, those skilled in the art should understand that the present invention can still be modified or equivalently replaced; and all technical solutions and improvements that do not depart from the spirit and scope of the present invention are covered by the protection scope of the patent of the present invention. Any reference signs in the claims should not be construed as limiting the claims involved.

Claims

1. A method for dividing terrigenous clastic rock reservoirs, characterized in that, The method includes: Collecting the electrical imaging data of each preset point in the target wellbore of terrigenous clastic rock; Classifying the preset points according to the electrical imaging data to determine the reservoir type to which the preset points belong; the reservoir type includes reservoir layers and non-reservoir layers; Determining the depth boundaries of each reservoir interval according to the quantity ratio of the preset points of different reservoir types; the depth boundary is the interface between the reservoir interval and the non-reservoir interval of the reservoir layer in the longitudinal section; Dividing the reservoir intervals according to the depth boundaries.

2. The method for dividing a terrigenous clastic rock reservoir according to claim 1, wherein Classifying the preset points according to the electrical imaging data to determine the reservoir type to which the preset points belong, including: Determining the electrical buckling value of each preset point according to the electrical imaging data; Generating a histogram of electrical buckling values for the entire well section with the electrical buckling value and its corresponding occurrence frequency as the horizontal and vertical coordinates respectively; Determining one or more candidate electrical buckling values corresponding to a preset frequency according to the histogram of electrical buckling values; In the case where there is one candidate electrical buckling value, determining this candidate electrical buckling value as the target electrical buckling value; in the case where there are multiple candidate electrical buckling values, determining the maximum value among the multiple candidate electrical buckling values as the target electrical buckling value; Determining that the preset points with electrical buckling values greater than the target electrical buckling value belong to non-reservoir layers, and the preset points with electrical buckling values less than or equal to the target electrical buckling value belong to reservoir layers.

3. A method for dividing terrigenous clastic rock reservoirs according to claim 1, characterized in that, The determining the depth boundaries of each reservoir interval according to the quantity ratio of the preset points in the reservoir layer includes: Determine the proportion of preset points in the reservoir in the wellbore circumference direction , and the specific formula is as follows: ； Among them, N r is the number of preset points of the reservoir formation, N b is the total number of preset points; On the vertical section of the wellbore, statistically calculating the proportion of preset points depth by depth to obtain a reservoir proportion curve; Screening the data of the reservoir proportion curve to determine that the intervals in the data of the reservoir proportion curve that are greater than or equal to a preset proportion threshold are reservoir intervals, and the intervals less than the preset proportion threshold are non-reservoir intervals, thereby determining the depth boundaries of the reservoir intervals.

4. A method for dividing terrigenous clastic rock reservoirs according to claim 3, characterized in that The method further includes: when statistically calculating the proportion of preset points depth by depth, the statistical thickness is limited to 0.1 m, merging the non-reservoir interlayers with a thickness less than 0.1 m into the adjacent reservoir intervals; discarding the reservoir intervals with a total thickness less than 0.1 m.

5. A method for dividing terrigenous clastic rock reservoirs according to claim 1, characterized in that, Before classifying the preset points according to the electrical imaging data, preprocessing the electrical imaging data is also performed, including: Performing depth alignment, equalization correction, and bad electrical buckling value elimination on the electrical buckling values in the electrical imaging data; Performing color matching on the processed electrical imaging data to generate a static image of the electrical imaging logging.

6. A terrigenous clastic rock reservoir division device, characterized in that The device includes: An acquisition module for collecting the electrical imaging data of each preset point in the target wellbore of terrigenous clastic rock; A classification module for classifying the preset points according to the electrical imaging data to determine the reservoir type to which the preset points belong; the reservoir type includes reservoir layers and non-reservoir layers; A determination module for determining the depth boundaries of each reservoir interval according to the quantity ratio of the preset points of different reservoir types; the depth boundary is the interface between the reservoir interval and the non-reservoir interval of the reservoir layer in the longitudinal section; A division module for dividing the reservoir intervals according to the depth boundaries.

7. A computer-readable storage medium, characterized in that, The storage medium stores a computer program, and when the computer program is executed by a processor, the method described in any one of the above claims 1 to 5 is implemented.

8. A computer device, characterized in that, It includes a memory, a processor, and a computer program stored on the memory and executable on the processor. When the processor executes the program, the method described in any one of the above claims 1 to 5 is implemented.

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