A sandwich-type compact reservoir dessert classification method, device and equipment

Abstract

The application discloses a sandwich type compact reservoir dessert classification method, device and equipment, and the method can comprise the following steps: constructing a lithology density correlation coefficient-porosity correlation of a target reservoir section based on the lithology density correlation coefficient and the porosity of the reservoir at different depths; and comparing the lithology density correlation coefficient-porosity correlation of the target reservoir section with a pre-constructed sandwich type compact reservoir dessert classification evaluation chart of a research block where the target reservoir section is located, so as to determine the dessert type of the target reservoir section. The method creatively establishes a dessert classification identification chart by deeply studying the correlation between conventional logging and nuclear magnetic resonance logging data, and avoids the high dependence on rock physical experiment data in the conventional compact reservoir dessert evaluation method. The method is highly operable, practical and efficient, and can solve the actual production problems in oilfield sites.

Description

Technical Field

[0001] This invention relates to the field of petroleum geological exploration and development technology, and in particular to a method, apparatus and equipment for classifying sweet spots in interlayered tight reservoirs. Background Technology

[0002] In recent years, tight reservoir oil and gas resources, as important oil and gas resources, have seen steady progress in production capacity construction and rapid increases in output, expanding their share of total production. Existing technologies, such as "short horizontal wells, high-angle well water injection development and long horizontal well advanced energy replenishment development technology," "multi-layered systems, small well spacing, long horizontal sections, and densely cut development technology," and "vertical-horizontal well combination + fracture network-volume fracturing + platform-based mode," have all achieved breakthroughs in tight oil reservoir production, while also improving the internal rate of return and reducing investment costs.

[0003] Although tight reservoir oil and gas has become the mainstay of new area production capacity construction, the development process has gradually revealed contradictions such as strong heterogeneity of continental tight reservoirs, difficulty in drilling high-quality sweet spot oil layers, and large differences in production. Summary of the Invention

[0004] During the production process, the inventors discovered that tight reservoirs, especially interlayered tight reservoirs, exhibit significant differences in the physical properties and geomechanical characteristics of oil layers across different blocks and strata. They are characterized by "contiguous reservoir development with scattered sweet spots," often consisting of complex, multi-layered structures, making it difficult to encounter high-quality sweet spots. In view of these problems, this invention aims to provide a method, apparatus, and equipment for classifying sweet spots in interlayered tight reservoirs to overcome or at least partially solve these problems.

[0005] In a first aspect, embodiments of the present invention provide a method for classifying sweet spots in sandwich-type dense reservoirs, which may include:

[0006] Based on the lithological density correlation coefficient and porosity of the target reservoir section at different depths, a lithological density correlation coefficient-porosity correlation relationship is constructed for the target reservoir section.

[0007] Based on the lithological density correlation coefficient-porosity correlation relationship of the target reservoir segment, it is compared with the pre-constructed sweet spot classification and evaluation chart of interlayered tight reservoirs in the study block where the target reservoir segment is located to determine the sweet spot type of the target reservoir segment.

[0008] Optionally, the step of constructing the lithology density correlation coefficient-porosity correlation relationship of the target reservoir segment based on the lithology density correlation coefficient and porosity of the target reservoir segment at different depths may include:

[0009] Obtain natural gamma logging curves, density logging curves, and nuclear magnetic resonance logging curves for the target reservoir section;

[0010] The logging data of the natural gamma ray logging curve and the density logging curve at different depths are normalized respectively. The difference between the normalized natural gamma ray logging data and the density logging data is then calculated to obtain the lithological density correlation coefficient of the reservoir at different depths.

[0011] The nuclear magnetic resonance logging curves are interpreted to determine the porosity of reservoirs at different depths;

[0012] Based on the lithological density correlation coefficient and porosity of the target reservoir segment at different depths, a lithological density correlation coefficient-porosity correlation relationship for the target reservoir segment is constructed.

[0013] Optionally, the sandwich-type dense reservoir sweet spot classification evaluation chart is pre-constructed according to the following steps:

[0014] Obtain vertical well test production data for different reservoir sections in the study block, and classify different reservoir sections into sweet spot types based on a preset daily oil production threshold and the daily oil production included in the vertical well test production data.

[0015] Natural gamma logging, density logging, and nuclear magnetic resonance logging curves were obtained for reservoir sections corresponding to different dessert types.

[0016] The logging data of the natural gamma logging curve and the density logging curve of the reservoir segment corresponding to different sweetness types are normalized at different depths. The difference between the normalized natural gamma logging data and the density logging data is used to obtain the lithological density correlation coefficient of the reservoir at different depths.

[0017] The nuclear magnetic resonance logging curves of reservoir sections corresponding to different sweet spot types were interpreted to determine the porosity of reservoirs at different depths.

[0018] Based on the correlation coefficients of lithological density and porosity of reservoir segments corresponding to different sweet spot types at different depths, the correlation relationships between lithological density and porosity of reservoir segments of different sweet spot types were constructed.

[0019] Based on the correlation coefficient between lithology density and porosity of reservoir segments with different sweet spot types, a classification and evaluation chart for sweet spots in interlayered tight reservoirs is established.

[0020] Optionally, the method may further include: adjusting the preset daily oil production threshold based on the correlation coefficient between lithology density and porosity of different sweet spot reservoir sections.

[0021] Optionally, the step of classifying different reservoir sections into sweet spot types based on a preset daily oil production threshold and the daily oil production included in the vertical well test production data may include:

[0022] Based on the preset daily oil production threshold and the daily oil production included in the vertical well test production data, different reservoir sections are classified into sweet spot types to be divided into at least three sweet spot types.

[0023] The daily oil production thresholds are as follows: 5-6 tons for Class I sweet spot reservoirs, 3-5 tons for Class II sweet spot reservoirs, and 1-3 tons for Class III sweet spot reservoirs.

[0024] Optionally, the comparison between the lithology-density correlation coefficient and porosity correlation of the target reservoir segment and a pre-constructed sweet spot classification and evaluation map of interlayered tight reservoirs in the study block where the target reservoir segment is located, to determine the sweet spot type of the target reservoir segment, may include:

[0025] Based on the correlation coefficient between lithology density and porosity of the target reservoir section, the correlation curve between lithology density and porosity of the target reservoir section is fitted.

[0026] Based on the distribution range and curvature of the lithology-density correlation coefficient-porosity correlation curve of the target reservoir section, it is compared with the sweet spot classification and evaluation chart of the interlayered tight reservoir to determine the sweet spot type of the target reservoir section.

[0027] Optionally, normalizing the well logging data may include:

[0028] Based on the maximum and minimum values ​​in the well logging data, the well logging data is normalized so that the normalization result of the well logging data is mapped within the interval [0,1].

[0029] In a second aspect, embodiments of the present invention provide a sandwich-type dense reservoir dessert sorting device, which may include:

[0030] A construction module is used to construct the lithology density correlation coefficient-porosity correlation relationship of the target reservoir segment based on the lithology density correlation coefficient and porosity of the target reservoir segment at different depths;

[0031] The determination module is used to compare the lithology-density correlation coefficient-porosity correlation of the target reservoir segment with a pre-constructed sweet spot classification and evaluation chart of interlayered tight reservoirs in the study block where the target reservoir segment is located, in order to determine the sweet spot type of the target reservoir segment.

[0032] Thirdly, embodiments of the present invention provide a method for oil and gas extraction from interlayered tight reservoirs, comprising: designing a corresponding extraction scheme based on the sweet spot type of the target reservoir section of the interlayered tight reservoir, so as to extract oil and gas from the target reservoir section;

[0033] The sweet spot type of the target reservoir segment is determined according to the sweet spot classification method for sandwich-type tight reservoirs described in the first aspect.

[0034] Fourthly, embodiments of the present invention provide a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the sandwich-type dense reservoir sweet spot classification method as described in the first aspect.

[0035] Fifthly, embodiments of the present invention provide a computer device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the sandwich-type dense reservoir sweet spot classification method as described in the first aspect.

[0036] The beneficial effects of the above-described technical solutions provided in the embodiments of the present invention include at least the following:

[0037] This invention provides a method, apparatus, and equipment for classifying sweet spots in interlayered tight reservoirs. This method creatively establishes a sweet spot classification and identification chart by deeply studying the correlation between conventional logging and nuclear magnetic resonance logging data. This avoids the high dependence on rock physical experimental data in conventional tight reservoir sweet spot evaluation methods. This method is highly operable, and the technical means are practical and efficient. It can solve actual production problems in oilfields and improve the accuracy of sweet spot prediction.

[0038] Other features and advantages of the invention will be set forth in the following description, and will be apparent in part from the description, or may be learned by practicing the invention. The objects and other advantages of the invention may be realized and obtained by means of the structures particularly pointed out in the written description and the accompanying drawings.

[0039] The technical solution of the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. Attached Figure Description

[0040] The accompanying drawings are provided to further illustrate the invention and form part of the specification. They are used in conjunction with embodiments of the invention to explain the invention and do not constitute a limitation thereof. In the drawings:

[0041] Figure 1 This is a flowchart of the method for classifying sweet spots in sandwich-type dense reservoirs provided in an embodiment of the present invention;

[0042] Figure 2 This is a flowchart illustrating the construction process of a sweet spot classification and evaluation chart for sandwich-type dense reservoirs provided in this embodiment of the invention.

[0043] Figure 3 This is a graph showing the correlation between lithological density and porosity of a type of sweet spot reservoir provided in an embodiment of the present invention.

[0044] Figure 4 This is a graph showing the correlation between lithological density and porosity of two types of sweet spot type reservoirs provided in this embodiment of the invention.

[0045] Figure 5 This is a graph showing the correlation between lithological density and porosity for three types of sweet spot reservoirs provided in this embodiment of the invention.

[0046] Figure 6 This is one of the schematic diagrams for classifying and evaluating the sweet spot of the sandwich-type dense reservoir provided in the embodiments of the present invention;

[0047] Figure 7 This is the second schematic diagram of the sweet spot classification and evaluation chart provided in the embodiments of the present invention;

[0048] Figure 8 This is a correlation diagram constructed by the sum of normalized natural gamma logging data and density logging data and porosity in the embodiments of the present invention;

[0049] Figure 9 This is a schematic diagram of the structure of the sandwich-type dense reservoir dessert sorting device provided in an embodiment of the present invention. Detailed Implementation

[0050] Exemplary embodiments of the present disclosure will now be described in more detail with reference to the accompanying drawings. While exemplary embodiments of the present disclosure are shown in the drawings, it should be understood that the present disclosure may be implemented in various forms and should not be limited to the embodiments set forth herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the scope of the disclosure to those skilled in the art.

[0051] The inventors have developed an invention to classify and evaluate the sweet spot types in interlayered tight reservoirs, particularly tight reservoirs with interlayers. This invention addresses a series of technical challenges, including the significant differences in oil layer properties and geomechanical characteristics across different blocks and strata, the presence of "contiguous reservoir development with scattered sweet spot oil layers," and the prevalence of complex, multi-layered, and difficult-to-drill high-quality sweet spot oil layers.

[0052] This invention provides a method for classifying sweet spots in sandwich-type dense reservoirs, referring to... Figure 1 As shown, the method may include the following steps:

[0053] Step S11: Based on the lithology-density correlation coefficient and porosity of the target reservoir segment at different depths, construct the lithology-density correlation coefficient-porosity correlation relationship of the target reservoir segment. In this embodiment of the invention, considering the characteristics of interlayered tight reservoirs with strong heterogeneity and rapid changes in reservoir characteristics, the inventors conducted long-term analysis of logging curves and logging parameters, and found that the logging parameters most sensitive to sweet spot types are lithology, density, and porosity. Based on the lithology-density correlation coefficient and porosity data, their correlation relationship was constructed.

[0054] In practice, the lithology-density correlation coefficient of the above-mentioned reservoir is obtained based on the natural gamma logging curve and density logging curve, which are most sensitive to the response characteristics of sweet spot type, and the porosity of the reservoir is obtained based on the seismic interpretation of the nuclear magnetic resonance logging curve.

[0055] Step S12: Based on the lithological density correlation coefficient-porosity correlation relationship of the target reservoir section, compare it with the pre-constructed sweet spot classification and evaluation chart of interlayered tight reservoirs in the study block where the target reservoir section is located, in order to determine the sweet spot type of the target reservoir section.

[0056] The inventors analyzed the correlation coefficients between lithology density and porosity in tight reservoirs of different sweet spot types. They found a positive correlation between lithology density and porosity in reservoirs with high daily oil production, a negative correlation between lithology density and porosity in reservoirs with low daily oil production, and no significant correlation between lithology density and porosity in reservoirs with intermediate daily oil production. This step involves comparing the lithology density correlation coefficient-porosity relationship of the target reservoir segment with a pre-constructed sweet spot classification and evaluation map of interlayered tight reservoirs in the study block. Based on the comparison results, the sweet spot type of the target reservoir segment is determined.

[0057] This invention, through in-depth research on the correlation between conventional logging and nuclear magnetic resonance logging data, creatively establishes a sweet spot classification and identification chart, avoiding the high dependence on rock physics experimental data in conventional tight reservoir sweet spot evaluation methods. This method is highly operable, with practical and efficient technical means, and can solve actual production problems in oilfields, improving the accuracy of sweet spot prediction.

[0058] In an optional embodiment, refer to Figure 2 As shown, the above-mentioned sandwich-type dense reservoir sweet spot classification and evaluation chart was pre-constructed according to the following steps:

[0059] Step S21: Obtain vertical well test production data of different reservoir sections in the study block, and classify different reservoir sections into sweet spot types based on the preset daily oil production threshold and the daily oil production included in the vertical well test production data.

[0060] In practice, this step classifies different reservoir sections into sweet spot types based on preset daily oil production thresholds and the daily oil production included in the vertical well test production data, so as to classify them into at least three sweet spot types. The daily oil production thresholds are as follows: 5-6 tons for Class I sweet spot reservoirs, 3-5 tons for Class II sweet spot reservoirs, and 1-3 tons for Class III sweet spot reservoirs.

[0061] Step S22: Obtain the natural gamma logging curve, density logging curve, and nuclear magnetic resonance logging curve for the reservoir sections corresponding to different dessert types.

[0062] This step involves the inventors conducting long-term analysis of reservoir sweet spots and discovering that the most sensitive logging response characteristics to sweet spot types are the measured natural gamma logging curve, density logging curve, and nuclear magnetic resonance logging curve. Therefore, the above data is obtained for further analysis.

[0063] Step S23: Normalize the natural gamma logging curves and density logging curves of reservoir sections corresponding to different sweet spot types at different depths, and then subtract the normalized natural gamma logging data and density logging data to obtain the lithological density coefficients of reservoirs at different depths.

[0064] In its implementation, the inventors determined the lithological characteristics interpreted from natural gamma ray logging data and the density of that lithology interpreted from density logging curves, thus obtaining a lithology-density correlation coefficient. The normalized gamma and density logging curves can be compared within the same data scale range of 0 to 1. Analysis revealed that within this range, the normalized gamma logging data values ​​are generally greater than the density logging data values. The difference between the normalized gamma and density logging data represents the area of ​​the polygon formed by the boundary lines of the two curves. The trend of this polygon area changing with depth shows a clear regularity with the porosity interpreted from nuclear magnetic resonance logging. The inventors use this regular trend as an important basis for classifying sweet spots in interlayered tight reservoirs.

[0065] The specific steps for normalizing the above well logging data are as follows:

[0066] The natural gamma logging data is normalized, and the resulting values ​​are mapped to the range [0,1]. The transformation function is as follows:

[0067]

[0068] In the formula, GR' is the normalized natural gamma value, GR is the original natural gamma value, min(GR) is the minimum natural gamma value in the dataset, and max(GR) is the maximum natural gamma value in the dataset.

[0069] Normalizing density logging data maps the resulting values ​​to the range [0,1]. The transformation function is as follows:

[0070]

[0071] In the formula, RHOB' is the normalized density value, RHOB is the original density value, min(RHOB) is the minimum density value in the dataset, and max(RHOB) is the maximum density value in the dataset.

[0072] In this step, the difference between the normalized natural gamma logging data and the density logging data is calculated to obtain the density of reservoirs at different depths, i.e., the difference between the normalized natural gamma and the density, which is calculated according to the following formula:

[0073] D = GR'-RHOB'

[0074] In the formula, D is the difference between the normalized natural gamma and the density.

[0075] Step S24: Interpret the nuclear magnetic resonance logging curves of reservoir sections corresponding to different sweet spot types to determine the porosity of reservoirs at different depths.

[0076] This step involves interpreting the nuclear magnetic resonance logging curves to determine the porosity of reservoirs at different depths. It should be noted that in this embodiment of the invention, steps S23 and S24 are not performed in any particular order; step S23 can be performed before step S24, or vice versa. Of course, steps S23 and S24 can also be performed simultaneously. This embodiment of the invention does not impose any specific limitations on this.

[0077] In a specific example, refer to Tables 1 to 3 below for the lithological density correlation coefficients and porosity data of different sweet spot type reservoirs.

[0078] Table 1. Correlation coefficients of lithology density and porosity data for a type of sweet-sweet reservoir.

[0079]

[0080] Table 2. Lithology-density correlation coefficients and porosity data for Class II sweet spot type reservoirs.

[0081]

[0082] Table 3. Correlation coefficients of lithology density and porosity data for three types of sweet spot reservoirs.

[0083]

[0084]

[0085] Step S25: Based on the lithological density correlation coefficient and porosity of reservoir segments corresponding to different sweet spot types at different depths, construct the lithological density correlation coefficient-porosity correlation relationship of reservoir segments of different sweet spot types.

[0086] Reference Figures 3-5 As shown, a correlation between lithology-density and porosity was established for different sweet spot reservoir types, with the difference between natural gamma ray and density (D) as the abscissa of a rectangular coordinate system and the porosity data interpreted by nuclear magnetic resonance (NMR) logging as the ordinate. The figure shows that for type I sweet spot reservoirs, the difference between natural gamma ray and density logging data is positively correlated with the porosity data interpreted by NMR logging; for type III sweet spot reservoirs, the difference is negatively correlated; and for type II sweet spot reservoirs, the correlation between the difference is not significant.

[0087] Step S26: Based on the correlation coefficient between lithology density and porosity of reservoir segments with different sweet spot types, establish a classification and evaluation chart for sweet spots in interlayered tight reservoirs.

[0088] This step is based on the above. Figures 3-5 A chart is established based on the distribution range and correlation of sweet spots in the data. In specific implementation, a classification and evaluation chart of sweet spots in interlayered tight reservoirs is obtained based on the difference between the natural gamma-density logging data of the first, second and third types of sweet spots and the distribution range and correlation characteristics of the porosity data interpreted by nuclear magnetic resonance logging in the rectangular coordinate system.

[0089] The method for constructing the sweet spot classification and evaluation chart of interlayered tight reservoirs provided in this embodiment of the invention classifies sweet spot types based on daily oil production. By deeply studying the correlation between conventional logging and nuclear magnetic resonance logging data, a quantitative sweet spot classification and identification chart of interlayered tight reservoirs is creatively established. This avoids the high dependence on rock physics experimental data in conventional tight reservoir sweet spot evaluation methods. The method is highly operable, and the technical means are practical and efficient, which can solve actual production problems in oilfields.

[0090] In another alternative embodiment, reference is also made to Figure 2 As shown, the process of constructing the above-mentioned classification and evaluation chart for sandwich-type tight reservoirs may also include:

[0091] Step S27: Adjust the preset daily oil production threshold according to the correlation coefficient between lithology density and porosity of different sweet spot reservoir sections.

[0092] Reference Figure 7As shown, when the inventors initially classified sweet spot reservoirs based on daily oil production, they divided them into two categories based on a preset daily oil production threshold: Category I and Category II sweet spot reservoirs. In Category I sweet spot reservoirs, the difference in natural gamma-density logging data was positively correlated with the porosity data interpreted by nuclear magnetic resonance (NMR) logging, while the difference in natural gamma-density logging data for Category II sweet spot reservoirs showed no significant correlation with the porosity data interpreted by NMR logging. The inventors then readjusted the preset daily oil production threshold using this chart, further subdividing the reservoirs into three categories. Analysis of these sweet spot reservoirs showed... Figure 6 As shown, the difference between natural gamma-density logging data and nuclear magnetic resonance logging interpretation porosity data are negatively correlated.

[0093] It should be noted that the preset daily oil production threshold in the embodiments of the present invention can be adjusted according to the actual situation, and the dessert types are not simply divided into three categories. A more detailed division can be made according to actual needs. The embodiments of the present invention do not impose specific limitations on this.

[0094] Reference Figure 8 As shown, the inventors constructed a correlation between the sum of normalized natural gamma-ray logging data and density logging data and porosity. They found that the sum of natural gamma-ray and density data for different types of sweet spots did not show a significant correlation with porosity, making it impossible to finely identify and classify sweet spot types. Similarly, the inventors also performed correlations based on other conventional logging curves, but found no strong linear correlation that could effectively distinguish sweet spot types. However, the difference between normalized natural gamma-ray and density data and porosity showed a strong trend, which can effectively identify the type of sweet spot in interlayered tight reservoirs.

[0095] In another optional embodiment, step S11 above constructs a lithology density correlation coefficient-porosity correlation relationship for the target reservoir segment based on the lithology density correlation coefficient and porosity of the target reservoir segment at different depths. Specifically, this may include:

[0096] First, natural gamma ray logging (NGR), density logging, and nuclear magnetic resonance (NMR) logging curves for the target reservoir section are acquired. Then, the NGR and density logging data at different depths are normalized. The difference between the normalized NGR and density logging data is used to obtain the lithology-density correlation coefficient for reservoirs at different depths. Next, the NMR logging curves are interpreted to determine the porosity of reservoirs at different depths. Finally, based on the lithology-density correlation coefficients and porosity of the target reservoir section at different depths, a lithology-density correlation relationship is constructed for the target reservoir section.

[0097] It should be noted that the normalization process described above in this step can refer to the normalization process when creating the drawing plate, and will not be repeated here in this embodiment of the invention.

[0098] In another optional embodiment, step S12 above, based on the lithological density correlation coefficient-porosity correlation relationship of the target reservoir segment, compares it with a pre-constructed sweet spot classification and evaluation map of interlayered tight reservoirs in the study block where the target reservoir segment is located, to determine the sweet spot type of the target reservoir segment. Specifically, this may include:

[0099] First, based on the correlation between lithology density and porosity of the target reservoir section, a correlation curve between lithology density and porosity of the target reservoir section is fitted. Then, based on the distribution range and curvature of the correlation curve between lithology density and porosity of the target reservoir section, it is compared with the sweet spot classification and evaluation chart of interlayer tight reservoirs to determine the sweet spot type of the target reservoir section.

[0100] It should be noted that the embodiments of the present invention use three types of sweet spot reservoirs as examples. If the reservoirs are divided into more types, it will be difficult to determine the sweet spot type of the target reservoir segment when comparing the lithology density correlation coefficient-porosity correlation relationship of the target reservoir segment with the lithology density correlation coefficient-porosity correlation relationship of different sweet spot reservoir segments in the figure. Therefore, the inventors accurately identify the sweet spot type of the target reservoir segment by comparing the correlation curve and the curvature and distribution range of the correlation curve.

[0101] In another optional embodiment, the normalization process for well logging data may specifically include: normalizing the well logging data based on the maximum and minimum values ​​in the data, so that the normalized result maps to the interval [0,1]. Normalization facilitates more standardized data and better reflects the correlation between lithological density correlation coefficient and porosity.

[0102] In this invention, the inventors, addressing the strong heterogeneity and rapid changes in reservoir characteristics of interlayered tight reservoirs, established a method for classifying and evaluating sweet spots in interlayered tight reservoirs by combining conventional logging with nuclear magnetic resonance (NMR) logging. In creating the map, the difference between normalized natural gamma ray and density logging data is first calculated and plotted against corresponding depth NMR logging data on a pre-defined rectangular coordinate system. Correlation analysis of the natural gamma ray-density logging difference and NMR logging data yields a classification and evaluation map for interlayered tight reservoir sweet spots. Based on this evaluation map, sweet spots in tight reservoirs within the target stratigraphic interval can be classified and evaluated, determining the type of sweet spot within the target interval and laying a geological foundation for characterizing the reservoir heterogeneity of sweet spots in tight reservoirs.

[0103] Based on the same inventive concept, this invention also provides a sandwich-type dense storage dessert sorting device, referring to... Figure 9 As shown, the device may include a construction module 81 and a determination module 82, and its working principle is as follows:

[0104] Module 81 is used to construct the lithology density correlation coefficient-porosity correlation relationship of the target reservoir segment based on the lithology density correlation coefficient and porosity of the target reservoir segment at different depths;

[0105] The determination module 82 is used to compare the lithological density correlation coefficient-porosity correlation of the target reservoir section with the pre-constructed sweet spot classification evaluation chart of interlayered tight reservoirs in the study block where the target reservoir section is located, in order to determine the sweet spot type of the target reservoir section.

[0106] In an optional embodiment, the construction module 81 is specifically used for:

[0107] Obtain natural gamma logging curves, density logging curves, and nuclear magnetic resonance logging curves for the target reservoir section;

[0108] The logging data of the natural gamma ray logging curve and the density logging curve at different depths are normalized respectively. The difference between the normalized natural gamma ray logging data and the density logging data is then calculated to obtain the lithological density correlation coefficient of the reservoir at different depths.

[0109] The nuclear magnetic resonance logging curves are interpreted to determine the porosity of reservoirs at different depths;

[0110] Based on the lithological density correlation coefficient and porosity of the target reservoir segment at different depths, a lithological density correlation coefficient-porosity correlation relationship for the target reservoir segment is constructed.

[0111] In another alternative embodiment, refer to Figure 8 As shown, the device may further include: a drawing board creation module 83, which is specifically used for:

[0112] Obtain vertical well test production data for different reservoir sections in the study block, and classify different reservoir sections into sweet spot types based on a preset daily oil production threshold and the daily oil production included in the vertical well test production data.

[0113] Natural gamma logging, density logging, and nuclear magnetic resonance logging curves were obtained for reservoir sections corresponding to different dessert types.

[0114] The logging data of the natural gamma logging curve and the density logging curve of the reservoir segment corresponding to different sweetness types are normalized at different depths. The difference between the normalized natural gamma logging data and the density logging data is used to obtain the lithological density correlation coefficient of the reservoir at different depths.

[0115] The nuclear magnetic resonance logging curves of reservoir sections corresponding to different sweet spot types were interpreted to determine the porosity of reservoirs at different depths.

[0116] Based on the correlation coefficients of lithological density and porosity of reservoir segments corresponding to different sweet spot types at different depths, the correlation relationships between lithological density and porosity of reservoir segments of different sweet spot types were constructed.

[0117] Based on the correlation coefficient between lithology density and porosity of reservoir segments with different sweet spot types, a classification and evaluation chart for sweet spots in interlayered tight reservoirs is established.

[0118] In another optional embodiment, the above-mentioned plotting module 83 can also be used to: adjust the preset daily oil production threshold according to the lithological density correlation coefficient-porosity correlation relationship of different sweet spot type reservoir sections.

[0119] In another optional embodiment, the above-mentioned drawing board creation module 83 can also be used for:

[0120] Based on the preset daily oil production threshold and the daily oil production included in the vertical well test production data, different reservoir sections are classified into sweet spot types to be divided into at least three sweet spot types.

[0121] The daily oil production thresholds are as follows: 5-6 tons for Class I sweet spot reservoirs, 3-5 tons for Class II sweet spot reservoirs, and 1-3 tons for Class III sweet spot reservoirs.

[0122] In another alternative embodiment, the determining module 82 is specifically used for:

[0123] Based on the correlation coefficient between lithology density and porosity of the target reservoir section, the correlation curve between lithology density and porosity of the target reservoir section is fitted.

[0124] Based on the distribution range and curvature of the lithology-density correlation coefficient-porosity correlation curve of the target reservoir section, it is compared with the sweet spot classification and evaluation chart of the interlayered tight reservoir to determine the sweet spot type of the target reservoir section.

[0125] Based on the same inventive concept, this embodiment of the invention also provides a method for oil and gas extraction from interlayered tight reservoirs. The method may include: designing a corresponding extraction scheme according to the sweet spot type of the target reservoir section of the interlayered tight reservoir, so as to extract oil and gas from the target reservoir section; wherein, the sweet spot type of the target reservoir section is determined according to the above-mentioned sweet spot classification method for interlayered tight reservoirs.

[0126] Based on the same inventive concept, this embodiment of the invention also provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the above-described method for classifying sweet spots in sandwich-type dense reservoirs.

[0127] Based on the same inventive concept, this embodiment of the invention also provides a computer device, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the program, it implements the above-mentioned method for classifying sweet spots in sandwich-type dense reservoirs.

[0128] The principles by which the above-mentioned devices, media, and related equipment in the embodiments of the present invention solve the problem are similar to those of the aforementioned methods. Therefore, their implementation can refer to the implementation of the aforementioned methods, and repeated details will not be repeated.

[0129] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, systems, or computer program products. 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. Furthermore, the present invention can take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage and optical storage) containing computer-usable program code.

[0130] This invention is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart illustrations and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0131] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0132] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0133] Obviously, those skilled in the art can make various modifications and variations to this invention without departing from its spirit and scope. Therefore, if these modifications and variations fall within the scope of the claims of this invention and their equivalents, this invention also intends to include these modifications and variations.

Claims

1. A method for classifying desserts in sandwich-type dense reservoirs, characterized in that, include: Obtain natural gamma logging curves, density logging curves, and nuclear magnetic resonance logging curves for the target reservoir section; The logging data of the natural gamma ray logging curve and the density logging curve at different depths are normalized respectively. The difference between the normalized natural gamma ray logging data and the density logging data is then calculated to obtain the lithological density correlation coefficient of the reservoir at different depths. The nuclear magnetic resonance logging curves are interpreted to determine the porosity of reservoirs at different depths; Based on the lithological density correlation coefficient and porosity of the target reservoir segment at different depths, a lithological density correlation coefficient-porosity correlation relationship for the target reservoir segment is constructed. Based on the lithological density correlation coefficient-porosity correlation relationship of the target reservoir section, it is compared with the pre-constructed sweet spot classification and evaluation chart of interlayered tight reservoirs in the study block where the target reservoir section is located, so as to determine the sweet spot type of the target reservoir section. The sandwich-type dense reservoir sweet spot classification and evaluation chart is pre-constructed according to the following steps: Obtain vertical well test production data for different reservoir sections in the study block, and classify different reservoir sections into sweet spot types based on a preset daily oil production threshold and the daily oil production included in the vertical well test production data. Natural gamma logging, density logging, and nuclear magnetic resonance logging curves were obtained for reservoir sections corresponding to different dessert types. The logging data of the natural gamma logging curve and the density logging curve of the reservoir segment corresponding to different sweetness types are normalized at different depths. The difference between the normalized natural gamma logging data and the density logging data is used to obtain the lithological density correlation coefficient of the reservoir at different depths. The nuclear magnetic resonance logging curves of reservoir sections corresponding to different sweet spot types were interpreted to determine the porosity of reservoirs at different depths. Based on the correlation coefficients of lithological density and porosity of reservoir segments corresponding to different sweet spot types at different depths, the correlation relationships between lithological density and porosity of reservoir segments of different sweet spot types were constructed. Based on the correlation coefficient between lithology density and porosity of reservoir segments with different sweet spot types, a classification and evaluation chart for sweet spots in interlayered tight reservoirs is established.

2. The method according to claim 1, characterized in that, Also includes: Based on the correlation coefficient between lithology density and porosity of different sweet spot reservoir sections, the preset daily oil production threshold is adjusted.

3. The method according to claim 1, characterized in that, The method of classifying different reservoir sections into sweet spot types based on a preset daily oil production threshold and the daily oil production included in the vertical well test production data includes: Based on the preset daily oil production threshold and the daily oil production included in the vertical well test production data, different reservoir sections are classified into sweet spot types to be divided into at least three sweet spot types. The daily oil production thresholds are as follows: 5-6 tons for Class I sweet spot reservoirs, 3-5 tons for Class II sweet spot reservoirs, and 1-3 tons for Class III sweet spot reservoirs.

4. The method according to any one of claims 1 to 3, characterized in that, The correlation coefficient between lithology density and porosity of the target reservoir segment is compared with a pre-constructed sweet spot classification and evaluation map of interlayered tight reservoirs in the study block where the target reservoir segment is located to determine the sweet spot type of the target reservoir segment, including: Based on the correlation coefficient between lithology density and porosity of the target reservoir section, the correlation curve between lithology density and porosity of the target reservoir section is fitted. Based on the distribution range and curvature of the lithology-density correlation coefficient-porosity correlation curve of the target reservoir section, it is compared with the sweet spot classification and evaluation chart of the interlayered tight reservoir to determine the sweet spot type of the target reservoir section.

5. The method according to any one of claims 1 to 3, characterized in that, Normalizing the well logging data includes: Based on the maximum and minimum values ​​in the well logging data, the well logging data is normalized so that the normalization result of the well logging data is mapped within the interval [0,1].

6. A sandwich-type dense storage dessert sorting device, characterized in that, include: A construction module is used to acquire natural gamma ray logging curves, density logging curves, and nuclear magnetic resonance logging curves of the target reservoir section; normalize the logging data of the natural gamma ray logging curves and density logging curves at different depths, and obtain the lithology-density correlation coefficient of the reservoir at different depths by subtracting the normalized natural gamma ray logging data and density logging data; interpret the nuclear magnetic resonance logging curves to determine the porosity of the reservoir at different depths; and construct the lithology-density correlation coefficient-porosity correlation relationship of the target reservoir section based on the lithology-density correlation coefficient and porosity of the target reservoir section at different depths. The determination module is used to compare the lithology-density correlation coefficient-porosity correlation relationship of the target reservoir segment with a pre-constructed sweet spot classification and evaluation chart of interlayered tight reservoirs in the study block where the target reservoir segment is located, so as to determine the sweet spot type of the target reservoir segment. The sandwich-type dense reservoir sweet spot classification and evaluation chart is pre-constructed according to the following steps: Obtain vertical well test production data for different reservoir sections in the study block, and classify different reservoir sections into sweet spot types based on a preset daily oil production threshold and the daily oil production included in the vertical well test production data. Natural gamma logging, density logging, and nuclear magnetic resonance logging curves were obtained for reservoir sections corresponding to different dessert types. The logging data of the natural gamma logging curve and the density logging curve of the reservoir segment corresponding to different sweetness types are normalized at different depths. The difference between the normalized natural gamma logging data and the density logging data is used to obtain the lithological density correlation coefficient of the reservoir at different depths. The nuclear magnetic resonance logging curves of reservoir sections corresponding to different sweet spot types were interpreted to determine the porosity of reservoirs at different depths. Based on the correlation coefficients of lithological density and porosity of reservoir segments corresponding to different sweet spot types at different depths, the correlation relationships between lithological density and porosity of reservoir segments of different sweet spot types were constructed. Based on the correlation coefficient between lithology density and porosity of reservoir segments with different sweet spot types, a classification and evaluation chart for sweet spots in interlayered tight reservoirs is established.

7. A method for oil and gas extraction from interlayered tight reservoirs, characterized in that, include: Based on the sweet spot type of the target reservoir section of the interlayered tight reservoir, a corresponding exploitation scheme is designed to exploit oil and gas in the target reservoir section. The sweet spot type of the target reservoir segment is determined by the sweet spot classification method for sandwich-type tight reservoirs according to any one of claims 1 to 5.

8. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the program is executed by the processor, it implements the method for classifying sweet spots in sandwich-type dense reservoirs as described in any one of claims 1 to 5.

9. A computer device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the program, it implements the method for classifying sandwich-type dense reservoir sweet spots as described in any one of claims 1 to 5.

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