Shale oil reservoir lithology identification method and device based on high-resolution resistivity imaging

Abstract

The present application relates to the technical field of oil and gas exploration and development, and particularly relates to a shale oil reservoir lithology identification method and device based on high-resolution resistivity imaging. The method comprises: obtaining well logging curves and electrical imaging data of a target layer in a work area; preprocessing the electrical imaging data to obtain an electrical imaging image and a high-resolution resistivity curve; optimizing a well logging curve with the highest lithology correlation among all the well logging curves; combining the well logging curve with the highest lithology correlation, the high-resolution resistivity curve and the electrical imaging image to identify the reservoir lithology of the target layer. The present application solves the problem that the existing lithology identification method uses conventional well logging and formation element data for lithology identification, but the resolution of conventional well logging and formation element data is low, which leads to high difficulty in lithology identification and low identification accuracy.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of oil and gas exploration and development, and particularly relates to a shale oil reservoir lithology identification method and device based on imaging high-resolution resistivity. BACKGROUND

[0002] The existing shale oil reservoir lithology identification method is mostly based on rock physics experiments such as X-ray diffraction and whole rock mineral analysis, and uses conventional logging data combined with formation element logging to qualitatively identify lithology and quantitatively calculate mineral content. However, the special sedimentary environment leads to serious thin interbedding, various types and rapid changes in shale oil reservoirs. The shale reservoirs have the characteristics of developed foliation, diverse mineral components, high clay content and small logging response difference. The resolution of conventional logging and formation element logging data is low, the thin layer identification precision is not high, and it is difficult to accurately calculate the mineral components and identify the lithology, which brings great challenges to the accurate evaluation of shale oil "sweet spots" by logging. SUMMARY

[0003] The present application provides a shale oil reservoir lithology identification method and device based on imaging high-resolution resistivity, to solve the problem that the existing lithology identification method uses conventional logging and formation element data to identify lithology, but the resolution of conventional logging and formation element data is low, which makes it difficult to identify lithology and cannot further improve the identification accuracy.

[0004] According to one aspect of the present application, a shale oil reservoir lithology identification method based on imaging high-resolution resistivity is provided, comprising:

[0005] Obtaining logging curves and electrical imaging data of a target layer in a work area;

[0006] Preprocessing the electrical imaging data to obtain electrical imaging images and high-resolution resistivity curves;

[0007] Preferably, all the logging curves with the highest lithology correlation are selected;

[0008] Combining the logging curves with the highest lithology correlation, the high-resolution resistivity curves and the electrical imaging images to identify the reservoir lithology of the target layer.

[0009] Preferably, the preprocessing includes but is not limited to velocity correction, image equalization, gain voltage correction and bad electrode correction.

[0010] Preferably, the method of selecting all the logging curves with the highest lithology correlation comprises:

[0011] Obtaining lithology fine description data corresponding to the target layer;

[0012] Respectively, all logging curves are compared with the lithology fine description data, and a logging curve with the highest lithology correlation is selected.

[0013] Preferably, the method of combining the logging curve with the highest lithology correlation, the high-resolution resistivity curve and the electrical imaging image comprises:

[0014] A lithology identification chart is established between the logging curve with the highest lithology correlation and the high-resolution resistivity curve, and reservoir lithology is identified according to the lithology identification chart and the electrical imaging image.

[0015] If the logging curve with the highest lithology correlation is a natural gamma curve, the natural gamma curve needs to be normalized.

[0016] Preferably, the method of identifying the reservoir lithology of the target interval comprises:

[0017] Typical structures and structural features on the electrical imaging image are identified, and lithology is identified according to the typical structures and structural features and corresponding high-resolution resistivity values on the lithology identification chart, specifically comprising:

[0018] It is judged whether the electrical imaging image of the target interval has high-low resistivity thin interbedded features, whether the local definition of the high-low resistivity change interface meets a predetermined definition, and whether the corresponding high-resolution resistivity on the lithology identification chart is in a first predetermined range, and if so, the identified lithology is layered shale.

[0019] It is judged whether the electrical imaging image of the target interval has laminated structure, whether the local definition of the high-low resistivity change interface meets a predetermined definition, and whether the corresponding high-resolution resistivity on the lithology identification chart is in a second predetermined range, and if so, the identified lithology is laminated shale.

[0020] It is judged whether the electrical imaging image of the target interval has a first predetermined color feature, whether the image has layered or massive structure, and whether the corresponding high-resolution resistivity on the lithology identification chart is in a third predetermined range, and if so, the identified lithology is siltstone.

[0021] It is judged whether the electrical imaging image of the target interval has a second predetermined color feature, whether the image has layered or massive structure, and whether the corresponding high-resolution resistivity on the lithology identification chart is greater than a predetermined value, and if so, the identified lithology is shell limestone.

[0022] judging whether the target interval has a second predetermined color feature on the electrical image, has a lens body feature on the image, and whether the corresponding high-resolution resistivity on the lithology identification chart is in a fourth predetermined range, if yes, the identified lithology is dolomite.

[0023] Preferably, the method for identifying typical structures and structural features on the electrical image comprises:

[0024] determining the number of stratification per meter of the formation on the electrical image;

[0025] if the number of stratification per meter is greater than a first predetermined number of layers, it is classified as laminated structure, if the number of stratification per meter is less than or equal to the first number of layers and greater than or equal to a second number of layers, it is classified as layered structure, and if the number of stratification per meter is less than the second number of layers, it is classified as massive structure.

[0026] Preferably, the first predetermined range is 10-50Ω·m;

[0027] the second predetermined range is 3-20Ω·m;

[0028] the third predetermined range is 50-1000Ω·m;

[0029] the predetermined value is 3000Ω·m;

[0030] the fourth predetermined range is 200-500Ω·m.

[0031] According to an aspect of the present application, there is provided a shale oil reservoir lithology identification device based on imaging high-resolution resistivity, comprising:

[0032] an acquisition unit configured to acquire well logging curves and electrical imaging data of a target interval in a work area;

[0033] a preprocessing unit configured to preprocess the electrical imaging data to obtain an electrical imaging image and a high-resolution resistivity curve;

[0034] a well logging curve optimization unit configured to optimize a well logging curve with the highest lithology correlation among all the well logging curves;

[0035] a reservoir lithology identification unit configured to combine the well logging curve with the highest lithology correlation, the high-resolution resistivity curve, and the electrical imaging image to identify the reservoir lithology of the target interval.

[0036] The present application has at least the following beneficial effects:

[0037] The shale oil reservoir lithology identification device based on imaging high-resolution resistivity is provided, high-resolution characteristics of electric imaging logging data are utilized, and the lithology is accurately identified in combination with logging data, thin layers are finely divided, the shale oil reservoir lithology identification coincidence rate is improved, and a reliable foundation is established for subsequent reservoir parameter calculation and evaluation. BRIEF DESCRIPTION OF DRAWINGS

[0038] The drawings incorporated in and forming a part of the specification, illustrate embodiments consistent with the present application and, together with the description, serve to explain the principles of the application.

[0039] Figure 1 A flow chart of a shale oil reservoir lithology identification method based on imaging high-resolution resistivity according to an embodiment of the present application is shown;

[0040] Figure 2 A typical electric imaging characteristic diagram according to an embodiment of the present application is shown;

[0041] Figure 3 A shale oil logging characteristic diagram according to an embodiment of the present application is shown;

[0042] Figure 4 A shale oil reservoir five lithology characteristic diagram of XX1 well according to an embodiment of the present application is shown;

[0043] Figure 5 A lithology profile and natural gamma curve characteristic diagram according to an embodiment of the present application is shown;

[0044] Figure 6 A natural gamma and high-resolution resistivity lithology identification chart according to an embodiment of the present application is shown. EMBODIMENTS

[0045] Various exemplary embodiments, features, and aspects of the present application will be described in detail below with reference to the accompanying drawings. The same reference numbers in different drawings represent the same or similar elements. Although various aspects of the embodiments are shown in the drawings, the drawings are not necessarily drawn to scale unless specifically indicated.

[0046] The word "exemplary" is used herein to mean "serving as an example, instance, or illustration." Any implementation described herein as "exemplary" is not necessarily to be construed as preferred or advantageous over other implementations.

[0047] The term "and / or", used herein merely describes association relationships of associated objects, and indicates that three relationships can exist, for example, A and / or B can represent three cases of A existing alone, A and B existing simultaneously, and B existing alone. In addition, the term "at least one" used herein indicates any one of multiple or any combination of at least two of multiple, for example, at least one of A, B and C can indicate any one or more elements selected from a set consisting of A, B and C.

[0048] In addition, in order to better illustrate the present application, numerous specific details are given in the following specific embodiments. It should be understood by those skilled in the art that the present application can also be implemented without certain specific details. In some examples, methods, means, elements and circuits well known to those skilled in the art are not described in detail in order to highlight the main idea of the present application.

[0049] Figure 1 A flow chart of a shale oil reservoir lithology identification method based on imaging high-resolution resistivity according to an embodiment of the present application is shown; Figure 2 A typical electrical imaging feature map according to an embodiment of the present application is shown; Figure 3 A shale oil logging feature map according to an embodiment of the present application is shown; Figure 4 A shale oil reservoir five-lithology feature map of XX1 well according to an embodiment of the present application is shown; Figure 5 A lithology profile and natural gamma curve feature map according to an embodiment of the present application is shown; Figure 6 A natural gamma and high-resolution resistivity lithology identification chart according to an embodiment of the present application is shown. As Figures 1-6 The shale oil reservoir lithology identification method based on imaging high-resolution resistivity includes: step S01: acquiring well logging curves and electrical imaging data of a target layer section in a work area; step S02: pre-processing the electrical imaging data to obtain an electrical imaging image and a high-resolution resistivity curve; step S03: selecting a well logging curve with the highest lithology correlation from all the well logging curves; and step S04: combining the well logging curve with the highest lithology correlation, the high-resolution resistivity curve and the electrical imaging image to identify the reservoir lithology of the target layer section.

[0050] The shale oil reservoir lithology identification method based on imaging high-resolution resistivity provided by the embodiment of the present application specifically includes the following steps:

[0051] Step S01: acquiring well logging curves and electrical imaging data of a target layer section in a work area.

[0052] In the embodiment of the present application, the well logging curves include natural gamma, dual laterolog and formation element well logging curves, etc. The resolution of conventional well logging curves is low, and therefore it is necessary to combine electrical imaging data for lithology identification.

[0053] Step S02: Preprocess the electrical imaging data to obtain electrical imaging images and high-resolution resistivity curves.

[0054] In this invention, the preprocessing includes, but is not limited to: speed correction and / or image equalization and / or gain voltage correction and / or bad electrode correction.

[0055] In this embodiment of the invention, the electrical imaging data undergoes preprocessing such as velocity correction, bad electrode correction, and electrode depth alignment in the electrical imaging processing and interpretation software. Taking an FMI (Resistivity Imaging) logging instrument as an example, the instrument has 8 electrodes, each with 24 electrical contacts, and each contact can acquire conductivity data.

[0056] Electrical imaging data consists of electrical conductivity values. Preprocessing of these conductivity values ​​includes:

[0057] Velocity correction: During downhole operations, the movement of the instrument is affected by differential viscosity and wellbore roughness. This causes the instrument to wobble slightly up and down during logging. This irregular movement leads to incorrect data and reduces the quality of the logging. Velocity correction can correct for non-uniform motion of the electrical imaging logging instrument during logging, relative motion between electrodes, and image stretching, compression, or electrode misalignment at the same depth caused by jamming or unjamming.

[0058] Image equalization: The conductivity data of each electrical connector is uniformly modified to eliminate the influence of mud erosion or noise data in the well logging;

[0059] Gain voltage correction: Adjust the gain voltage to maintain the proportional relationship between electrical imaging data and formation conductivity;

[0060] Bad electrode correction: Repairs image data loss or discontinuity caused by partial electrode damage or abnormality;

[0061] The conductivity values ​​are calibrated using shallow lateral logging curves to generate high-resolution resistivity curves and electrical imaging images.

[0062] Electron imaging can be used to identify various structures, textures, and typical geological features, such as massive structures, horizontal bedding, convoluted bedding, fractures, faults, and gravel, etc. Figure 2 As shown, in Figure 2 In the diagram, a shows convoluted bedding and cross-bedding; b shows faults; c shows gravel; d shows horizontal bedding; e shows high-resistivity fractures; f shows high-conductivity fractures; g shows massive structures and dissolution cavities; and h shows deformed structures. The main structures and textures in shale oil reservoirs are layered and lamellar structures, with lenses developing locally.

[0063] Step S03: Select the logging curve with the highest correlation to lithology among all the logging curves.

[0064] In this invention, the method for selecting the logging curve with the highest correlation to lithology among all the logging curves includes: obtaining lithological detailing data corresponding to the target interval; comparing all logging curves with the lithological detailing data respectively, and selecting the logging curve with the highest correlation to lithology.

[0065] In this embodiment of the invention, the lithological detailing data is obtained by testing and observing core samples from geological logging data of the work area. Using the acquired shale oil reservoir lithological description data (lithological detailing data) as a standard, logging curves such as natural gamma ray, dual lateral, formation element logging, and high-resolution resistivity curves are compared with the lithological data. Figure 3 As shown, in Figure 3 In the image, from left to right, the first channel is the lithology curve (natural gamma), the second is the depth channel, the third is the logging curve used to calculate porosity, the fourth is the porosity curve, the fifth is the dynamic and static electro-imaging images (electro-imaging images), the sixth is the high-resolution resistivity curve of electro-imaging, the seventh is the detailed lithological description data, and the eighth is the stratigraphic element and mineral profile. It can be seen that conventional logging and stratigraphic element logging data have low resolution, and local thin-layer lithology cannot be identified. The high-resolution resistivity curve responds better in thin layers or areas with developed lenses, showing obvious changes in resistivity values. It also shows good correspondence with the lithological variation interfaces in the detailed lithology description data, indicating that lithology identification using electro-imaging data is more accurate.

[0066] In addition to electrical imaging images and high-resolution resistivity curves, among other logging curves, the logging curve with the best correlation to lithology was selected and combined with electrical imaging images and high-resolution resistivity curves for lithology identification. Among all logging curves, the natural gamma ray curve was preferred as it had the highest correlation with lithology.

[0067] Step S04: Combine the logging curve with the highest correlation to lithology, the high-resolution resistivity curve, and the electrical imaging image to identify the reservoir lithology of the target section.

[0068] In this invention, the method for combining the logging curve with the highest lithological correlation, the high-resolution resistivity curve, and the electrical imaging image includes: establishing a lithological identification chart by combining the logging curve with the high-resolution resistivity curve, and performing reservoir lithology identification based on the lithological identification chart and the electrical imaging image. If the logging curve with the highest lithological correlation is a natural gamma curve, then the natural gamma curve needs to be normalized.

[0069] In this embodiment of the invention, a lithology identification chart is established by combining a preferred natural gamma curve with a high-resolution resistivity curve, as shown below. Figure 6The natural gamma ray logging can measure the total content of radioactive elements in the formation, the radioactive element content of mudstone and shale is high, the radioactive element content of sandstone is lower, the change of the natural gamma ray curve can reflect the change of the lithology, has the function of distinguishing the lithology, and the effect is better than that of other logging curves; as shown in the lithology profile and the natural gamma ray curve characteristic comparison graph of Fig. Figure 5 As shown in the a graph of Fig. Figure 5 The mudstone natural gamma ray value is large, and is between 90 and 120 API, the sandstone natural gamma ray value is small, and is between 50 and 70 API; as shown in the b graph of Fig. Figure 5 In the shale oil reservoir, because the sandstone, shell limestone and dolomite exist in the form of thin interlayer or lens, the natural gamma ray curve value is less different from that of the shale. Therefore, the natural gamma ray curve (GRv) is normalized, a scatter plot is drawn with the high-resolution resistivity curve, the structure and structure identified by the electrical imaging are combined, a lithology identification chart (Fig. Figure 6 ) is established, and the shale oil reservoir lithology is divided into the following five kinds: layered shale, laminated shale, siltstone, shell limestone and dolomite.

[0070] In the present application, the method for identifying the reservoir lithology of the target interval includes: identifying typical structure and structural features on the electrical imaging image, identifying the lithology according to the typical structure, structural features and corresponding high-resolution resistivity value on the lithology identification chart, and specifically including:

[0071] Judging whether the electrical imaging image of the target interval has high-low resistance thin interbedded features, whether the local definition of the high-low resistance change interface meets the predetermined definition, and whether the corresponding high-resolution resistivity on the lithology identification chart is in a first predetermined range, if yes, the identified lithology is layered shale;

[0072] Judging whether the electrical imaging image of the target interval has laminated structure, whether the local definition of the high-low resistance change interface meets the predetermined definition, and whether the corresponding high-resolution resistivity on the lithology identification chart is in a second predetermined range, if yes, the identified lithology is laminated shale;

[0073] Judging whether the electrical imaging image of the target interval has a first predetermined color feature, whether the image has layered or massive structure, and whether the corresponding high-resolution resistivity on the lithology identification chart is in a third predetermined range, if yes, the identified lithology is siltstone;

[0074] Judging whether the electrical imaging image of the target interval has a second predetermined color feature, whether the image has layered or massive structure, and whether the corresponding high-resolution resistivity on the lithology identification chart is greater than a predetermined value, if yes, the identified lithology is shell limestone;

[0075] determining whether the target interval has a second predetermined color feature on the electrical image, a lens feature on the image, and a corresponding high-resolution resistivity in a fourth predetermined range on the lithology identification chart, and if yes, the identified lithology is dolomite.

[0076] In the present application, the method for identifying typical structures and construction features on the electrical image comprises: determining the number of stratification per meter of the stratum on the electrical image; if the number of stratification per meter is greater than a first predetermined number of layers, the stratum is classified as a laminated structure; if the number of stratification per meter is less than or equal to the first number of layers and greater than or equal to a second number of layers, the stratum is classified as a layered structure; and if the number of stratification per meter is less than the second number of layers, the stratum is classified as a massive structure.

[0077] In the present application, the first predetermined range is 10-50Ω·m; the second predetermined range is 3-20Ω·m; the third predetermined range is 50-1000Ω·m; the predetermined value is 3000Ω·m; and the fourth predetermined range is 200-500Ω·m.

[0078] In the present application, the well XX1 in the work area is taken as an example, and the electrical imaging data is preprocessed by using an electrical imaging processing software (GeoFrame, CIFLog-Geospace or Techlog software) to obtain electrical imaging dynamic and static images and high-resolution resistivity curves, i.e. image enhancement, shallow lateral scaling and other processing. Taking the GeoFrame software as an example, the electrical imaging image is displayed by using 16-bit or 64-bit color scale normalization, the highest and lowest resistivity values correspond to white and black respectively, the intermediate values are gradually transitioned by yellow, each resistivity value of the electrical imaging data is assigned to a corresponding color, and finally the electrical imaging image is generated; the lighter and brighter the color on the image is, the higher the resistivity of the stratum is, and the darker and darker the color on the image is, the lower the resistivity of the stratum is.

[0079] In the present application, the first predetermined number is 30, and the second predetermined number is 10. The typical structures and construction features include laminated structure, layered structure, massive structure, lens and high-low resistivity thin interbedded features. The number of stratification per meter is determined according to the electrical imaging image to divide the stratum construction, the number of stratification per meter is greater than 30 layers to divide the laminated structure, the number of stratification per meter is 10-30 layers to divide the layered structure, and the number of stratification per meter is less than 10 layers to divide the massive structure.

[0080] The method for identifying the lens body is that: the part with high resistivity value on the electroimaging image, i.e. the part with a color corresponding to an elliptical or intestinal feature with a resistivity value greater than a predetermined value, is the lens body, i.e. the part with high resistivity value on the electroimaging image presents a white elliptical or intestinal feature as the lens body; the method for identifying the thin interbedding of high and low resistivity is that: the electroimaging image has a high and low resistivity value cross variation, which is the thin interbedding of high and low resistivity, i.e. the electroimaging image has a cross appearance of dark color and light color, the boundary between the light color with high resistivity value and the dark color with low resistivity value is clear, i.e. reaches a predetermined clear degree.

[0081] The typical structure and structural feature of the XX1 well are identified by using the electroimaging image, as shown in the following table. Figure 4 As shown in the table, the block structure (c), the layered structure (b, d), the lamination structure (a) and the lens body (e) features can be seen by identifying the electroimaging image of the XX1 well, and the thin interbedding of high and low resistivity features is also present. Figure 4 Figure 4 Figure 4 Figure 4

[0082] The natural gamma and high-resolution resistivity curves of the shale oil reservoir are dropped into the lithology identification chart (as shown in the following table), and five kinds of lithology can be identified by combining the electroimaging image. Figure 6

[0083] Layered shale: the electroimaging image shows the thin interbedding of high and low resistivity features, the high and low resistivity variation interface is locally unclear, i.e. does not meet the predetermined clear degree, and the high-resolution resistivity in the corresponding lithology identification chart is between 10-50Ω·m.

[0084] Lamination shale: the electroimaging image shows that the horizontal lamination develops, i.e. the lamination structure, the high and low resistivity variation interface is clear and complete, i.e. meets the predetermined clear degree, and the lamination thickness is small, and the high-resolution resistivity in the corresponding lithology identification chart is between 3-20Ω·m.

[0085] Siltstone: the electroimaging image shows high-resistivity bright yellow, i.e. the first predetermined color feature, and presents a layered or block structure, and the high-resolution resistivity in the corresponding lithology identification chart is between 50-1000Ω·m.

[0086] Shell limestone: the electroimaging image shows high-resistivity white or bright yellow, i.e. the second predetermined color feature, and presents a layered or block structure, and the average high-resolution resistivity in the corresponding lithology identification chart is >3000Ω·m.

[0087] Dolomite: the electroimaging image shows high-resistivity white or bright yellow, and presents a lens body feature, and the high-resolution resistivity in the corresponding lithology identification chart is between 200-500Ω·m.

[0088] ​​​​​It can be understood that the above-mentioned various method embodiments of the present application can be combined with each other to form combined embodiments without deviating from the principle logic. However, the present application will not be described again due to the limited space.

[0089] The execution subject of the shale oil reservoir lithology identification method based on high-resolution resistivity imaging can be a shale oil reservoir lithology identification device based on high-resolution resistivity imaging. For example, the shale oil reservoir lithology identification method based on high-resolution resistivity imaging can be executed by a terminal device or a server or other processing device. The terminal device can be a user equipment (UE), a mobile device, a user terminal, a terminal, a cellular phone, a cordless phone, a personal digital assistant (PDA), a handheld device, a computing device, a vehicle-mounted device, a wearable device, etc. In some possible implementation manners, the shale oil reservoir lithology identification method based on high-resolution resistivity imaging can be realized by a processor calling computer readable instructions stored in a memory.

[0090] It can be understood by those skilled in the art that the writing order of each step in the above method of the specific embodiment does not mean a strict execution order and does not constitute any limitation on the implementation process. The specific execution order of each step should be determined by its function and possible internal logic.

[0091] The present application also provides a shale oil reservoir lithology identification device based on high-resolution resistivity imaging, comprising: an acquisition unit configured to acquire well logging curves and electrical imaging data of a target layer section in a work area; a preprocessing unit configured to preprocess the electrical imaging data to obtain an electrical imaging image and a high-resolution resistivity curve; a well logging curve optimization unit configured to optimize a well logging curve with the highest lithology correlation among all the well logging curves; and a reservoir lithology identification unit configured to combine the well logging curve with the highest lithology correlation, the high-resolution resistivity curve, and the electrical imaging image to identify the reservoir lithology of the target layer section.

[0092] In some embodiments, the device provided by the embodiments of the present application has functions or includes modules and units, which can be used to execute the methods described in the above method embodiments. The specific implementation can refer to the description of the above method embodiments, and will not be described here again for brevity.

[0093] The application is a method for lithology identification by combining conventional logging, formation element logging and high-resolution resistivity curve obtained by using electrical imaging data. In current oilfield exploration and development, the conventional oil exploration degree of XX basin is high, and it has entered the fine exploration stage. Shale resources have great potential and gradually become an important field of oil and gas replacement, which is an oilfield extension project and has important strategic significance. In 2020-2024, it is expected to drill 184 new wells and establish 6 experimental areas. In the next three years, it is expected to submit 1 billion tons of proven reserves and produce more than 1 million tons. The shale reservoir in this area has the characteristics of foliation development, mineral composition diversity, high clay content and small logging response difference, and it is difficult to accurately calculate the mineral composition and identify the lithology.

[0094] Taking the array lateral instrument of Schlumberger Company as an example, the instrument has high longitudinal resolution, and the longitudinal resolution is 12in (about 30.5cm) in 8in borehole. The electrical imaging data (taking FMI as an example) has a longitudinal geometric resolution of 0.2in (about 0.5cm), and 192 electrical conductivity curves can be measured at each depth point of FMI. Therefore, high-resolution resistivity curves can be obtained by processing, and the method of the application is used to identify the structure and structure of the electrical imaging image, combined with conventional logging data, rock physical experiment and geological data, to establish a lithology identification chart, thin layer precision, identify shale oil reservoir lithology, improve the coincidence rate of shale oil reservoir lithology identification, and the coincidence rate of the established lithology identification chart reaches more than 90%, which establishes the foundation for subsequent reservoir parameter calculation and evaluation.

[0095] The above has described various embodiments of the application, and the above description is exemplary, not exhaustive, and is not limited to the disclosed embodiments. Many modifications and changes are obvious to those skilled in the art without departing from the scope and spirit of the described embodiments. The selection of terms used herein is intended to best explain the principles, practical application or technical improvement in the market of the embodiments, or to enable other ordinary skilled persons in the art to understand the embodiments disclosed herein.

Claims

1. A method for lithological identification of shale oil reservoirs based on high-resolution imaging resistivity, characterized in that, include: Acquire well logging curves and electrical imaging data for the target formation in the work area; The electrical imaging data is preprocessed to obtain electrical imaging images and high-resolution resistivity curves; The best logging curve is the one with the highest correlation to lithology among all the logging curves mentioned. By combining the logging curves with the highest correlation to lithology, the high-resolution resistivity curves, and the electrical imaging images, the reservoir lithology of the target interval can be identified. The method for combining the logging curve with the highest lithological correlation, the high-resolution resistivity curve, and the electrical imaging image includes: establishing a lithological identification chart by combining the logging curve with the high-resolution resistivity curve, and performing reservoir lithological identification based on the lithological identification chart and the electrical imaging image; if the logging curve with the highest lithological correlation is a natural gamma curve, then the natural gamma curve needs to be normalized. The method for identifying the reservoir lithology of the target stratum includes: identifying typical structural and tectonic features on the electro-imaging image; identifying the lithology based on the typical structural and tectonic features and the corresponding high-resolution resistivity value on the lithology identification chart; specifically, determining whether the electro-imaging image of the target stratum has thin interbedded high and low resistivity layers, whether the local clarity of the interface between high and low resistivity does not meet a predetermined clarity, and whether the corresponding high-resolution resistivity on the lithology identification chart is within a first predetermined range; if so, the identified lithology is layered shale; determining whether the electro-imaging image of the target stratum has lamellar structures, whether the local clarity of the interface between high and low resistivity meets a predetermined clarity, and whether the corresponding high-resolution resistivity on the lithology identification chart is within a second predetermined range; if so, the method further determines whether the lithology is layered shale. The identified lithology is layered shale. The process involves determining whether the target stratum has a first predetermined color feature in the electro-optical imaging image, whether the image shows layered or massive structures, and whether the corresponding high-resolution resistivity on the lithology identification chart is within a third predetermined range. If yes, the identified lithology is siltstone. The process also involves determining whether the target stratum has a second predetermined color feature in the electro-optical imaging image, whether the image shows layered or massive structures, and whether the corresponding high-resolution resistivity on the lithology identification chart is greater than a predetermined value. If yes, the identified lithology is shell limestone. Finally, the process involves determining whether the target stratum has a second predetermined color feature in the electro-optical imaging image, whether the image shows lenticular features, and whether the corresponding high-resolution resistivity on the lithology identification chart is within a fourth predetermined range. If yes, the identified lithology is dolomite.

2. The lithological identification method for shale oil reservoirs based on high-resolution imaging resistivity according to claim 1, characterized in that: The preprocessing includes: speed correction and / or image equalization and / or gain voltage correction and / or bad electrode correction.

3. The lithological identification method for shale oil reservoirs based on high-resolution imaging resistivity according to claim 1, characterized in that, The method for selecting the logging curve with the highest correlation to lithology among all the logging curves includes: Obtain detailed lithological data corresponding to the target stratigraphic interval; All logging curves were compared with the lithological detailing data, and the logging curve with the highest correlation to lithology was selected.

4. The lithological identification method for shale oil reservoirs based on high-resolution imaging resistivity according to claim 1, characterized in that, The method for identifying typical structures and structural features on the electro-imaging image includes: Determine the stratigraphic number per meter of the formation in the electrical imaging image; If the number of stratifications per meter is greater than a first predetermined number of bars, it is classified as a lamellar structure; if the number of stratifications per meter is less than or equal to the first number of bars but greater than or equal to the second number of bars, it is classified as a layered structure; if the number of stratifications per meter is less than the second number of bars, it is classified as a blocky structure.

5. The lithological identification method for shale oil reservoirs based on high-resolution imaging resistivity according to claim 4, characterized in that: The first predetermined range is: 10-50 Ω·m; The second predetermined range is: 3-20 Ω·m; The third predetermined range is: 50-1000 Ω·m; The predetermined value is: 3000 Ω·m; The fourth predetermined range is 200-500 Ω·m.

6. A shale oil reservoir lithology identification device based on high-resolution imaging resistivity, characterized in that, include: The acquisition unit is used to acquire well logging curves and electrical imaging data of the target layer in the work area. The preprocessing unit is used to preprocess the electrical imaging data to obtain electrical imaging images and high-resolution resistivity curves. A logging curve optimization unit is used to optimize the logging curve with the highest correlation to lithology among all the logging curves. The reservoir lithology identification unit is used to combine the logging curves with the highest lithology correlation, the high-resolution resistivity curves, and the electrical imaging images to identify the reservoir lithology of the target interval. The method for combining the logging curve with the highest lithological correlation, the high-resolution resistivity curve, and the electrical imaging image includes: establishing a lithological identification chart by combining the logging curve with the high-resolution resistivity curve, and performing reservoir lithological identification based on the lithological identification chart and the electrical imaging image; if the logging curve with the highest lithological correlation is a natural gamma curve, then the natural gamma curve needs to be normalized. The method for identifying the reservoir lithology of the target stratum includes: identifying typical structural and tectonic features on the electro-imaging image; identifying the lithology based on the typical structural and tectonic features and the corresponding high-resolution resistivity value on the lithology identification chart; specifically, determining whether the electro-imaging image of the target stratum has thin interbedded high and low resistivity layers, whether the local clarity of the interface between high and low resistivity does not meet a predetermined clarity, and whether the corresponding high-resolution resistivity on the lithology identification chart is within a first predetermined range; if so, the identified lithology is layered shale; determining whether the electro-imaging image of the target stratum has lamellar structures, whether the local clarity of the interface between high and low resistivity meets a predetermined clarity, and whether the corresponding high-resolution resistivity on the lithology identification chart is within a second predetermined range; if so, the method further determines whether the lithology is layered shale. The identified lithology is layered shale. The process involves determining whether the target stratum has a first predetermined color feature in the electro-optical imaging image, whether the image shows layered or massive structures, and whether the corresponding high-resolution resistivity on the lithology identification chart is within a third predetermined range. If yes, the identified lithology is siltstone. The process also involves determining whether the target stratum has a second predetermined color feature in the electro-optical imaging image, whether the image shows layered or massive structures, and whether the corresponding high-resolution resistivity on the lithology identification chart is greater than a predetermined value. If yes, the identified lithology is shell limestone. Finally, the process involves determining whether the target stratum has a second predetermined color feature in the electro-optical imaging image, whether the image shows lenticular features, and whether the corresponding high-resolution resistivity on the lithology identification chart is within a fourth predetermined range. If yes, the identified lithology is dolomite.

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