A method, device and equipment for identifying shale oil dessert in a salted lake basin

Abstract

The application discloses a method, device and equipment for identifying sweet spots of shale oil in a salinized lake basin. The method comprises the following steps: firstly, determining a rock structure factor of a stratum based on a natural gamma curve, volume density logging data and skeleton density logging data of the stratum, so as to determine the lithology of the stratum and identify reservoir rock layers in the stratum based on the rock structure factor; then, determining a lamination index of the rock in the stratum based on electrical imaging logging data, so as to identify source rock layers in the stratum based on the lamination index; and finally, identifying a sweet spot development section of the shale oil in the salinized lake basin based on the reservoir rock layers in the stratum and the source rock layers in the stratum. The method can quickly identify the sweet spot development section of the shale oil in the target area, and provides a basis for later oil and gas development.

Description

Technical Field

[0001] This invention relates to the field of petroleum geological exploration and well logging interpretation technology, and in particular to a method, apparatus and equipment for identifying sweet spots in shale oil in saline lacustrine basins. Background Technology

[0002] Shale oil deposits have been discovered in multiple regions worldwide, with a total geological resource value of approximately 4090 × 10⁻⁶. 8 The data shows promising exploration prospects. In recent years, many regions have strengthened their exploration and research in the shale oil field, especially with the gradual improvement of key technologies such as vertical well and horizontal well volumetric fracturing. They have actively carried out shale oil exploration and development test technology research, and some exploration wells have obtained industrial oil flows. Industrial development of shale oil has been achieved in different reservoirs in multiple regions.

[0003] Due to the complex geological backgrounds in some regions, especially in the saline lacustrine basin shale oil sector, there are significant differences in the characteristics of source rocks, reservoir types, and source-reservoir combinations. Particularly in identifying sweet spots in saline lacustrine basin shale oil, characteristics include low organic matter abundance, thin single-layer thickness, and high laminar density. Accurately identifying high-quality sweet spots has become a key issue restricting large-scale exploration and efficient development of saline lacustrine basin shale oil.

[0004] Current research on shale oil sweet spot identification methods is extensive, but primarily focuses on freshwater lake basin shale oil, with less research on saline lake basins. The existing technologies reviewed by the inventors are also mostly applied to freshwater lake basin shale oil evaluation. The difference between freshwater and saline lake basins lies in the varying salinity of the water during sedimentation, leading to different sedimentary rock lithologies. Freshwater lake basins are dominated by sandstone, while saline lake basins are dominated by carbonate rocks. Summary of the Invention

[0005] The inventors discovered that high-density, bedding-laden dolomitic shale is a characteristic lithology of saline lacustrine basins, and this lithology is crucial for sweet spot evaluation, but it is difficult to identify during well logging. In view of the above problems, this invention is proposed to provide a method, apparatus, and device for identifying sweet spots in shale oil from saline lacustrine basins, overcoming or at least partially solving the aforementioned problems.

[0006] In a first aspect, embodiments of the present invention provide a method for identifying sweet spots in shale oil from saline lacustrine basins, the method comprising:

[0007] Based on the natural gamma curves, volumetric density logging data, and framework density logging data of the target area strata, the rock structure factors of the strata are determined, and the lithology of the strata is determined and the reservoir rock layers in the strata are identified based on the rock structure factors.

[0008] Based on electrical imaging logging data, the lamination index of rocks in the formation is determined, so as to identify the source rock layers in the formation based on the lamination index;

[0009] Based on the reservoir rock layers and source rock layers in the strata, the sweet spot development zone of the saline lacustrine basin shale oil is identified.

[0010] Optionally, the step of determining the lamination index of rocks in the formation based on electrical imaging logging data, in order to identify the source rock strata in the formation based on the lamination index, may include:

[0011] Based on the aforementioned electrical imaging logging data, the single-electrode resistivity curves were identified.

[0012] Based on the single-electrode resistivity curve, the number of resistivity values ​​within a preset window length that are higher or lower than the data points at adjacent depths is counted to determine the lamination index of the rock strata.

[0013] The source rock layers in the strata are identified based on the laminarity index.

[0014] Optionally, identifying the source rock strata in the formation based on the laminarity index may include:

[0015] Based on the positive correlation between the lamination index and the total organic carbon content of the strata core in the target area, the source rock strata in the strata are identified.

[0016] Optionally, identifying source rock strata in the strata based on the positive correlation between the lamination index and the total organic carbon content of the stratigraphic cores in the target area may include:

[0017] Based on the preset threshold of total organic carbon content in source rocks and the product of lamination index, the source rock strata in the target area are identified by extracting data from the relationship between the lamination index and the total organic carbon content in the core samples.

[0018] Optionally, the rock structure factor for determining the strata is obtained by the following formula:

[0019]

[0020] Where RFF is the rock structure factor; RHOG is the framework density; RHOB is the bulk density; GR is the natural gamma curve; GR max This represents the maximum value of the natural gamma curve.

[0021] Optionally, determining the lithology of the formation and identifying the reservoir layers within the formation based on the rock structure factors may include:

[0022] Based on the rock structure factor and the natural gamma curve of the strata in the target area, a cross-plot of rock structure factor and natural gamma curve is established.

[0023] Based on well logging interpretation, reservoir rock formations in the formation were identified in the cross plot.

[0024] Optionally, the method may further include:

[0025] Acquire natural gamma curves, volumetric density logging data, skeletal density logging data, and electrical imaging logging data of the formation in the target area.

[0026] Optionally, the skeleton density logging data is obtained through the following methods:

[0027] Acquire inelastic scattering gamma spectrum and captured gamma spectrum;

[0028] Based on the collected inelastic scattering gamma spectrum and the captured gamma spectrum, the relative yields of inelastic scattering elements and the relative yields of captured elements are determined.

[0029] Based on the principle of oxide closure, the relative yield of elements is converted into the dry weight percentage content of elements.

[0030] Based on the elemental dry weight percentage, the mineral content and framework density are determined using the corresponding interpretation model, in order to determine the framework density logging data.

[0031] In a second aspect, embodiments of the present invention provide a sweet spot identification device for shale oil in saline lake basins, the device comprising:

[0032] The reservoir rock formation identification module is used to determine the rock structure factors of the formation based on the natural gamma curve, volume density logging data and skeleton density logging data of the formation in the target area, so as to determine the lithology of the formation and identify the reservoir rock formations in the formation based on the rock structure factors.

[0033] The source rock strata identification module is used to determine the bedding index of rocks in the formation based on electrical imaging logging data, so as to identify the source rock strata in the formation based on the bedding index.

[0034] The sweet spot development segment identification module is used to identify the sweet spot development segment of the saline lacustrine basin shale oil based on the reservoir rock layer and the source rock layer in the formation.

[0035] Thirdly, 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 method for identifying sweet spots in saline lacustrine basin shale oil as described in the first aspect.

[0036] Fourthly, 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 method for identifying sweet spots in saline lacustrine shale oil as described in the first aspect.

[0037] The beneficial effects of the above-described technical solutions provided in the embodiments of the present invention include at least the following:

[0038] This invention provides a method, apparatus, and equipment for identifying sweet spots in shale oil in saline lacustrine basins. The method first determines the rock structure factors of the formations based on natural gamma curves, volumetric density logging data, and framework density logging data of the target area. This rock structure factor is then used to determine the lithology of the formations and identify reservoir layers. Next, the method determines the lamination index of the rocks in the formations based on electrical imaging logging data. This lamination index is then used to identify source rock layers. Finally, based on the reservoir layers and source rock layers, the sweet spot development zones of shale oil in saline lacustrine basins are identified. This method can quickly identify sweet spot development zones of shale oil in target areas, providing a basis for subsequent oil and gas development.

[0039] 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.

[0040] 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

[0041] 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:

[0042] Figure 1 This is a flowchart of the method for identifying sweet spots in shale oil from saline lake basins provided in this embodiment of the invention;

[0043] Figure 2 This is a cross-plot of rock structure factor-natural gamma curves provided in an embodiment of the present invention;

[0044] Figure 3 Here is the flowchart for step S11;

[0045] Figure 4 This is a flowchart illustrating the determination of skeleton density provided in an embodiment of the present invention;

[0046] Figure 5 Here is the execution flowchart for step S12;

[0047] Figure 6 This is a schematic diagram illustrating the positive correlation between laminar density and TOC in the target region cored segment provided in this embodiment of the invention;

[0048] Figure 7This is a TOC distribution map of different lithofacies provided in the embodiments of the present invention;

[0049] Figure 8 This is a diagram showing the lithofacies and sweet spot identification results of the core section of the example well;

[0050] Figure 9 This is a schematic diagram of the structure of the sweet spot identification device for saline lake basin shale oil provided in an embodiment of the present invention. Detailed Implementation

[0051] 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.

[0052] This invention provides a method for identifying sweet spots in shale oil from saline lacustrine basins, referring to... Figure 1 As shown, the method may include the following steps:

[0053] Step S11: Based on the natural gamma curve, volumetric density logging data and skeleton density logging data of the target area strata, determine the rock structure factor of the strata, and determine the lithology of the strata and identify the reservoir rock layers in the strata based on the rock structure factor.

[0054] In this embodiment of the invention, logging data from a single well in the target area are mainly used to identify and evaluate logging sweet spots. In specific implementation, the sweet spot situation of each well in the target area can also be evaluated.

[0055] Step S12: Determine the lamination index of rocks in the formation based on electrical imaging logging data, so as to identify source rock layers in the formation based on the lamination index.

[0056] Step S13: Identify sweet spot development zones for shale oil in saline lacustrine basins based on reservoir strata and source rock strata in the formation.

[0057] In a specific example, the upper section of the Lower Ganchaigou Formation of the Paleogene in the Ganchaigou area is a set of saline lacustrine carbonate rock strata. The lithofacies are mainly micritic-silty dolomite, dolomitic shale, felsic shale, siltstone, inequigranulated sandstone and massive mudstone. The above geological conditions are suitable for the application of the present invention.

[0058] In this example, the lithofacies assemblage of micritic-silty dolomite and dolomitic shale represents the optimal sweet spot. The micritic-silty dolomite has a high carbonate mineral content, numerous intercrystalline and solution pores, and a porosity of 5-12%, exhibiting the best physical properties and making it suitable as a reservoir. The dolomitic shale develops clay and calcite laminae, with a porosity of 3-8% and an average TOC (total organic carbon) of 0.8% and a maximum of 2.3%, making it the highest quality source rock. Therefore, the lithofacies assemblage of the best-particle-physical micritic-silty dolomite and the highest-TOC dolomitic shale represents the optimal sweet spot.

[0059] The sweet spot identification method for saline lacustrine basin shale oil provided in this embodiment of the invention first determines the rock structure factors of the formation based on the natural gamma curve, volumetric density logging data, and framework density logging data of the target area formation. Then, based on these rock structure factors, the lithology of the formation is determined, and the reservoir layers within the formation are identified. Combined with... Figure 2 As shown, micritic-silty dolomite and calcareous mudstone were identified, with the micritic-silty dolomite serving as the reservoir strata. However, the boundary between the dolomitic shale and the felsic shale was unclear, making it impossible to distinguish the source rock strata. The inventors innovatively proposed using electrical imaging logging data to determine the source rock strata in the formation.

[0060] The inventors discovered that sedimentary water bodies are relatively calm and have weak source wave influence when laminae are well-developed, which is conducive to the flourishing of microorganisms and the formation of organic matter dominated by sapropel. Furthermore, in strata with well-developed laminae, organic matter is enriched along the bedding planes. Acidic fluids generated during hydrocarbon generation dissolve and modify adjacent carbonate laminae, easily forming densely packed dissolution pores along the bedding planes, which is beneficial for oil and gas accumulation and migration. In addition, the bonding force between the bedding organic matter and the rock strata is relatively weak, easily forming numerous bedding fractures during hydrocarbon generation and pressurization. These fractures, along with organic and inorganic pores, form a fracture-pore system, which is beneficial for the storage, seepage, and preservation of oil and gas.

[0061] Electro-imaging logging offers high resolution and is the most sensitive logging method for bedding density. Its vertical resolution can reach up to 5 mm, making it the logging method with the highest vertical resolution. By utilizing the variation characteristics of the single-electrode resistivity curve in electro-imaging logging, the degree of formation rock bedding development can be characterized. In this embodiment of the invention, the bedding index of rocks in the formation is determined based on electro-imaging logging data to identify source rock layers within the formation.

[0062] Then, micritic-silty dolomite was identified as a high-quality reservoir, and dolomitic shale as a high-quality source rock. The vertically superimposed sections of these two lithofacies were designated as sweet spot development zones. This method can quickly identify sweet spot development zones for shale oil in target areas, providing a basis for subsequent oil and gas development.

[0063] In an optional embodiment, refer to Figure 1 As shown, the method may further include the following steps:

[0064] S10. Obtain natural gamma curves, volumetric density logging data, skeleton density logging data, and electrical imaging logging data of the formation in the target area.

[0065] In this embodiment of the invention, the CLS-5700 and MAXIS-500 logging series instruments can be used to measure the formation natural gamma curve (GR), bulk density (RHOB), framework density (RHOG), electrical imaging, and elemental capture energy spectrum logging data.

[0066] More specifically, the electrical imaging logging data in the embodiments of the present invention can be processed and acquired using existing software, such as Ciflog and Geolog.

[0067] In another optional embodiment, the rock structure factor of the strata determined in step S11 above is obtained by the following formula:

[0068]

[0069] Where RFF is the rock structure factor; RHOG is the framework density; RHOB is the bulk density; GR is the natural gamma curve; GR max This represents the maximum value of the natural gamma curve.

[0070] For example, for a natural gamma GR = 90, the maximum natural gamma GR is... max =150, bulk density RHOB=2.70, skeletal density RHOG=2.85, and RFF=3.1579 calculated using the rock structure factor formula.

[0071] In another specific embodiment, refer to Figure 3 As shown, determining the lithology of strata and identifying reservoir strata based on rock structure factors may include the following steps:

[0072] Step S111: Based on the rock structure factor and the natural gamma curve of the strata in the target area, establish a cross-plot of the rock structure factor and the natural gamma curve. Combined with... Figure 2 The figure shown is a cross-plot of the rock structure factor and natural gamma curves within the target area.

[0073] Step S112: Identify reservoir rock layers in the formation based on well logging interpretation in the cross plot.

[0074] In the above examples of this invention, the terrigenous clastic content is the sum of the contents of quartz, feldspar, and clay minerals in the rock, and the carbonate mineral content is the sum of the contents of calcite and dolomite in the rock. This content can be obtained from X-ray diffraction experimental data or from elemental capture energy dispersive spectroscopy (EDS) logging data. By collecting core samples and analyzing X-ray diffraction data, the contents of various minerals in the rock can be provided. Combined with... Figure 2As shown, the two charts represent the regions on the map where different lithologies are located, derived from core analysis data. However, core data is limited and cannot identify the entire well section, necessitating the use of well logging data. Using charts defined by core data, well logging data is projected onto these charts, and the lithology can be determined based on the region where the data points are located. It should be noted that in this embodiment of the invention, existing well logging interpretation software can be used to determine the lithology of the aforementioned strata; this embodiment does not impose specific limitations on this method.

[0075] In another alternative embodiment, refer to Figure 4 As shown, the above-mentioned skeleton density logging data were obtained through the following methods:

[0076] Step S41: Acquire inelastic scattering gamma spectrum and captured gamma spectrum.

[0077] Step S42: Based on the collected inelastic scattering gamma spectrum and captured gamma spectrum, determine the relative yield of inelastic scattering elements and the relative yield of captured elements.

[0078] Step S43: Based on the principle of oxide closure, convert the relative yield of elements into the dry weight percentage of elements.

[0079] Step S44: Based on the elemental dry weight percentage content, use the corresponding interpretation model to determine the mineral content and framework density, so as to determine the framework density logging data.

[0080] In an optional embodiment, refer to Figure 5 As shown, step S12 above may specifically include:

[0081] Step S121: Based on the electrical imaging logging data, identify the single-electrode resistivity curve.

[0082] Step S122: Based on the single-electrode resistivity curve, count the number of resistivity values ​​within a preset window length that are higher or lower than the data points at adjacent depths above and below, in order to determine the laminarity index of the rock strata.

[0083] In this step, the number of resistivity values ​​within a fixed window length (10cm) that are higher or lower than the data points at adjacent depths above and below is counted, thus obtaining the laminar index curve with a vertical sampling interval of 0.1m.

[0084] Step S123: Identify the source rock strata in the formation based on the laminarity index.

[0085] In practice, this step identifies source rock strata in the formation based on the positive correlation between the laminarity index (LI) and the total organic carbon content of the core samples in the target area. It should be noted that the total organic carbon (TOC) data from the core samples in this embodiment can be obtained using existing technology equipment, such as CS230, multi N / C 3100, and SieversInnovOx Onli.

[0086] Specifically, based on the preset threshold of total organic carbon content in source rocks and the product of laminarity index, the source rock strata in the strata are identified by extracting data from the relationship between laminarity index and total organic carbon content in the core samples of the target area.

[0087] For example, refer to Figure 6 and Figure 7 As shown, the parameter LI*TOC was established, and a cutoff value for LI*TOC was obtained in the core section. Layers with a value greater than this cutoff value were identified as shale sections. Analysis of the core section revealed a LI*TOC cutoff value of 35 for the target area. The two types of shale differ in carbonate mineral content; cutoff values ​​were obtained based on core data to distinguish between them. This figure shows the TOC range for different lithofacies in the target area, indicating that compared to other lithofacies, dolomitic shale has the highest TOC, followed by felsic shale. Analysis of the core section showed that shale in the study area with a carbonate mineral content greater than 33% is dolomitic shale, while those with a content less than or equal to 33% are felsic shale.

[0088] Combination Figure 8 As shown in the example, the identification of lithofacies and high-quality sweet spots in a core section of a well achieved a consistency rate of 85%; the identification of shale oil sweet spots in the same core section also yielded good results. Oil testing of the high-quality sweet spots in the core section of the well yielded a daily oil production of 32.3 tons, demonstrating the practicality of the method.

[0089] Based on the same inventive concept, this invention also provides a device for identifying sweet spots in shale oil from saline lake basins, referring to... Figure 9 As shown, the device may include: a reservoir stratum identification module 11, a source rock stratum identification module 12, and a sweet spot development section identification module 13, and its working principle is as follows:

[0090] The reservoir rock formation identification module 11 is used to determine the rock structure factor of the formation based on the natural gamma curve, volume density logging data and skeleton density logging data of the formation in the target area, so as to determine the lithology of the formation and identify the reservoir rock formation in the formation based on the rock structure factor.

[0091] The source rock strata identification module 12 is used to determine the bedding index of rocks in the formation based on electrical imaging logging data, so as to identify the source rock strata in the formation based on the bedding index.

[0092] The sweet spot development zone identification module 13 is used to identify sweet spot development zones of saline lacustrine basin shale oil based on reservoir rock layers and source rock layers in the formation.

[0093] In an optional embodiment, the source rock strata identification module 12 is specifically used for:

[0094] Based on the aforementioned electrical imaging logging data, the single-electrode resistivity curves were identified.

[0095] Based on the single-electrode resistivity curve, the number of resistivity values ​​within a preset window length that are higher or lower than the data points at adjacent depths is counted to determine the lamination index of the rock strata.

[0096] The source rock layers in the strata are identified based on the laminarity index.

[0097] In another optional embodiment, the source rock strata identification module 12 identifies the source rock strata in the strata based on the positive correlation between the lamination index and the total organic carbon content of the strata core in the target area.

[0098] In one specific embodiment, the source rock strata identification module 12 identifies the source rock strata in the strata by extracting the product of the preset threshold of the total organic carbon content of the source rock and the lamination index from the relationship map between the lamination index and the total organic carbon content of the strata core of the target area.

[0099] In another optional embodiment, the reservoir formation identification module 11 determines the formation's rock structure factor using the following formula:

[0100] RFF = RHORGH - ORGHOB × (GR max -GR)

[0101] Where RFF is the rock structure factor; RHOG is the framework density; RHOB is the bulk density; GR is the natural gamma curve; GR max This represents the maximum value of the natural gamma curve.

[0102] In another optional embodiment, the reservoir formation identification module 11 is specifically used for:

[0103] Based on the rock structure factor and the natural gamma curve of the strata in the target area, a cross-plot of rock structure factor and natural gamma curve is established.

[0104] Based on well logging interpretation, reservoir rock formations in the formation were identified in the cross plot.

[0105] In another alternative embodiment, refer to Figure 5As shown, it may also include an acquisition module 10, which is used to acquire the natural gamma curve, volumetric density logging data, skeleton density logging data and electrical imaging logging data of the formation in the target area.

[0106] 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 identifying sweet spots in saline lacustrine basin shale oil.

[0107] 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 identifying sweet spots in saline lacustrine basin shale oil.

[0108] 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.

[0109] 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.

[0110] 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.

[0111] 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.

[0112] 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.

[0113] 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 identifying sweet spots in shale oil from saline lacustrine basins, characterized in that, include: Based on the natural gamma curves, volumetric density logging data, and framework density logging data of the target area strata, the rock structure factors of the strata are determined, and the lithology of the strata is determined and the reservoir rock layers in the strata are identified based on the rock structure factors. Based on electrical imaging logging data, the single-electrode resistivity curves were identified. Based on the single-electrode resistivity curve, the number of resistivity values ​​within a preset window length that are higher or lower than the data points at adjacent depths is counted to determine the lamination index of the rock strata. Based on the positive correlation between the lamination index and the total organic carbon content of the stratigraphic cores in the target area, the source rock strata in the strata are identified; Based on the reservoir rock layers and source rock layers in the strata, the sweet spot development zone of the saline lacustrine basin shale oil is identified.

2. The method according to claim 1, characterized in that, Based on the positive correlation between the lamination index and the total organic carbon content of the stratigraphic cores in the target area, the source rock strata in the strata are identified, including: Based on the preset threshold of total organic carbon content in source rocks and the product of lamination index, the source rock strata in the target area are identified by extracting data from the relationship between the lamination index and the total organic carbon content in the core samples.

3. The method according to any one of claims 1 to 2, characterized in that, The rock structure factor for determining the strata is obtained by the following formula: ； Where RFF is the rock structure factor; RHOG is the framework density; RHOB is the bulk density; GR is the natural gamma curve; GR max This represents the maximum value of the natural gamma curve.

4. The method according to claim 3, characterized in that, The process of determining the lithology of strata and identifying reservoir strata based on the rock structure factors includes: Based on the rock structure factor and the natural gamma curve of the strata in the target area, a cross-plot of rock structure factor and natural gamma curve is established. Based on well logging interpretation, reservoir rock formations in the formation were identified in the cross plot.

5. The method according to claim 3, characterized in that, Also includes: Acquire natural gamma curves, volumetric density logging data, skeletal density logging data, and electrical imaging logging data of the formation in the target area.

6. The method according to claim 5, characterized in that, The skeleton density logging data was obtained through the following methods: Acquire inelastic scattering gamma spectrum and captured gamma spectrum; Based on the collected inelastic scattering gamma spectrum and the captured gamma spectrum, the relative yields of inelastic scattering elements and the relative yields of captured elements are determined. Based on the principle of oxide closure, the relative yield of elements is converted into the dry weight percentage content of elements. Based on the elemental dry weight percentage, the mineral content and framework density are determined using the corresponding interpretation model, in order to determine the framework density logging data.

7. A device for identifying sweet spots in shale oil from saline lacustrine basins, characterized in that, include: The reservoir rock formation identification module is used to determine the rock structure factors of the formation based on the natural gamma curve, volume density logging data and skeleton density logging data of the formation in the target area, so as to determine the lithology of the formation and identify the reservoir rock formations in the formation based on the rock structure factors. The source rock strata identification module is used to identify the single-electrode resistivity curve based on electrical imaging logging data; based on the single-electrode resistivity curve, it counts the number of resistivity values ​​within a preset window that are higher or lower than the adjacent depth data points to determine the lamination index of the strata; based on the positive correlation between the lamination index and the total organic carbon content of the formation core of the target area, it identifies the source rock strata in the formation. The sweet spot development segment identification module is used to identify the sweet spot development segment of the saline lacustrine basin shale oil based on the reservoir rock layer and the source rock layer in the formation.

8. A computer-readable storage medium having a computer program stored thereon, characterized in that, When executed by the processor, the program implements the method for identifying sweet spots in saline lacustrine shale oil as described in any one of claims 1 to 6.

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 identifying sweet spots in saline lacustrine shale oil as described in any one of claims 1 to 6.

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