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

Abstract

The invention relates to the technical field of petroleum and natural gas exploration and development, in particular to a shale oil reservoir lithology identification method and device based on imaging high-resolution resistivity. The method comprises the following steps: acquiring a logging curve and electric imaging data of a target interval of a work area; preprocessing the electric imaging data to obtain an electric imaging image and a high-resolution resistivity curve; preferably selecting the logging curve with the highest lithological correlation in all the logging curves; and combining the logging curve with the highest lithology correlation, the high-resolution resistivity curve and the electric imaging image, and identifying the lithology of the reservoir of the target interval. The invention aims to solve the problems that the lithology identification difficulty is high and the identification precision cannot be further improved because conventional logging and stratum element data are used for lithology identification in an existing lithology identification method and the resolution ratio of the conventional logging and stratum element data is relatively low.

Description

Technical Field

[0001] The present invention relates to the technical field of oil and gas exploration and development, and particularly to a method and device for identifying the lithology of shale oil reservoirs based on imaging high-resolution resistivity. Background Art

[0002] Existing methods for identifying the lithology of shale oil reservoirs are mostly based on rock physics experiments such as X-ray diffraction and whole-rock mineral analysis, and use conventional logging data combined with formation element logging for qualitative lithology identification and quantitative calculation of mineral content. However, the special sedimentary environment has led to serious thin interbeds, diverse types, and rapid changes in shale oil reservoirs. Shale reservoirs generally have characteristics such as well-developed bedding, diverse mineral components, high clay content, and small differences in logging responses. Conventional logging and formation element logging data have low resolution, low accuracy in thin layer identification, and great difficulty in accurately calculating mineral components and identifying lithology, which poses a huge challenge to accurately evaluating the "sweet spots" of shale oil by logging. Summary of the Invention

[0003] The present invention provides a method and device for identifying the lithology of shale oil reservoirs based on imaging high-resolution resistivity, so as to solve the problem that existing lithology identification methods use conventional logging and formation element data for lithology identification, but the resolution of conventional logging and formation element data is low, resulting in great difficulty in lithology identification and inability to further improve the identification accuracy.

[0004] According to one aspect of the present invention, there is provided a method for identifying the lithology of shale oil reservoirs based on imaging high-resolution resistivity, including: Obtaining logging curves and electrical imaging data of the target interval in the work area; Preprocessing the electrical imaging data to obtain an electrical imaging image and a high-resolution resistivity curve; Selecting the logging curve with the highest correlation with lithology from all the logging curves; Combining the logging curve with the highest correlation with lithology, the high-resolution resistivity curve, and the electrical imaging image to identify the lithology of the reservoir in the target interval.

[0005] Preferably, the preprocessing includes but is not limited to: velocity correction and / or image equalization and / or gain voltage correction and / or bad electrode correction.

[0006] Preferably, the method for selecting the logging curve with the highest correlation with lithology from all the logging curves includes: Obtaining the detailed lithology description data corresponding to the target interval; Comparing all the logging curves with the detailed lithology description data respectively, and selecting the logging curve with the highest correlation with lithology.

[0007] Preferably, the method of combining the logging curve with the highest lithology correlation, the high-resolution resistivity curve, and the electrical imaging image includes: Establish a lithology identification chart by combining the logging curve with the highest lithology correlation and the high-resolution resistivity curve, and identify the reservoir lithology based on the lithology identification chart in combination with the electrical imaging image; If the logging curve with the highest lithology correlation is the natural gamma curve, the natural gamma curve needs to be normalized.

[0008] Preferably, the method of identifying the reservoir lithology of the target interval includes: Identify the typical structures and tectonic features on the electrical imaging image, and identify the lithology based on the typical structures, tectonic features, and the corresponding high-resolution resistivity values on the lithology identification chart. Specifically, it includes: Judge whether the electrical imaging image of the target interval has the characteristics of thin interbeds of high and low resistivity, whether the local clarity of the high and low resistivity change interface meets the predetermined clarity, and whether the corresponding high-resolution resistivity on the lithology identification chart is within the first predetermined range. If so, the identified lithology is laminated shale; Judge whether the electrical imaging image of the target interval has a laminated structure, whether the local clarity of the high and low resistivity change interface meets the predetermined clarity, and whether the corresponding high-resolution resistivity on the lithology identification chart is within the second predetermined range. If so, the identified lithology is laminated shale; Judge whether the target interval has the first predetermined color feature on the electrical imaging image, whether it has a laminated or massive structure on the image, and whether the corresponding high-resolution resistivity on the lithology identification chart is within the third predetermined range. If so, the identified lithology is siltstone; Judge whether the electrical imaging image of the target interval has the second predetermined color feature, whether it has a laminated or massive structure on the image, and whether the corresponding high-resolution resistivity on the lithology identification chart is greater than the predetermined value. If so, the identified lithology is shell limestone; Judge whether the electrical imaging of the target interval has the second predetermined color feature, whether it has a lenticular feature on the image, and whether the corresponding high-resolution resistivity on the lithology identification chart is within the fourth predetermined range. If so, the identified lithology is dolomite.

[0009] Preferably, the method of identifying the typical structures and tectonic features on the electrical imaging image includes: Determine the number of bedding planes per meter of the formation on the electrical imaging image; If the number of bedding planes per meter is greater than a first predetermined number, it is classified as a laminated structure. If the number of bedding planes per meter is less than or equal to the first number and greater than or equal to a second number, it is classified as a layered structure. If the number of bedding planes per meter is less than the second number, it is classified as a massive structure.

[0010] Preferably, 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.

[0011] According to one aspect of the present invention, there is provided a device for identifying the lithology of shale oil reservoirs based on imaging high-resolution resistivity, comprising: An acquisition unit for acquiring logging curves and electrical imaging data of the target interval in the work area; A preprocessing unit for preprocessing the electrical imaging data to obtain an electrical imaging image and a high-resolution resistivity curve; A logging curve optimization unit for optimizing the logging curve with the highest lithology correlation among all the logging curves; A reservoir lithology identification unit for combining the logging curve with the highest lithology correlation, the high-resolution resistivity curve, and the electrical imaging image to identify the lithology of the reservoir in the target interval.

[0012] The present invention has at least the following beneficial effects: The present invention proposes a device for identifying the lithology of shale oil reservoirs based on imaging high-resolution resistivity. By utilizing the high-resolution characteristics of electrical imaging logging data and combining logging data, it accurately identifies the lithology, finely divides thin layers, improves the coincidence rate of shale oil reservoir lithology identification, and establishes a reliable basis for the subsequent calculation and evaluation of reservoir parameters. BRIEF DESCRIPTION OF THE DRAWINGS

[0013] The accompanying drawings herein are incorporated into the specification and form a part of the specification. These drawings illustrate embodiments consistent with the present invention and, together with the specification, are used to explain the technical solutions of the present invention.

[0014] Figure 1 A flowchart showing a method for identifying the lithology of shale oil reservoirs based on imaging high-resolution resistivity according to an embodiment of the present invention; Figure 2 A typical electrical imaging feature diagram showing an embodiment of the present invention; Figure 3 A logging feature diagram of shale oil showing an embodiment of the present invention; Figure 4 Show five lithology characteristic diagrams of the shale oil reservoir in Well XX1 according to an embodiment of the present invention; Figure 5 Show the lithology profile and natural gamma curve characteristic diagram according to an embodiment of the present invention; Figure 6 Show the lithology identification chart of natural gamma and high-resolution resistivity according to an embodiment of the present invention. Embodiment

[0015] Various exemplary embodiments, features and aspects of the present invention will be described in detail below with reference to the accompanying drawings. The same reference numerals in the drawings denote elements having the same or similar functions. Although various aspects of the embodiments are shown in the drawings, the drawings do not have to be drawn to scale unless otherwise specified.

[0016] The special term "exemplary" herein means "serving as an example, embodiment or illustration". Any embodiment described as "exemplary" herein does not have to be construed as superior to or better than other embodiments.

[0017] The term "and / or" herein merely describes an associated relationship of associated objects and indicates that three relationships may exist. For example, A and / or B may represent: A exists alone, A and B exist simultaneously, and B exists alone. In addition, the term "at least one" herein means any one of a plurality or any combination of at least two of a plurality. For example, including at least one of A, B, and C may represent including any one or more elements selected from the set composed of A, B, and C.

[0018] In addition, in order to better illustrate the present invention, numerous specific details are given in the following specific embodiments. Those skilled in the art should understand that the present invention can also be implemented without some specific details. In some instances, methods, means, elements and circuits well known to those skilled in the art are not described in detail so as to highlight the gist of the present invention.

[0019] Figure 1 Show the flowchart of the lithology identification method for shale oil reservoir based on imaging high-resolution resistivity according to an embodiment of the present invention; Figure 2 Show the typical characteristic diagram of electrical imaging according to an embodiment of the present invention; Figure 3 Show the logging characteristic diagram of shale oil according to an embodiment of the present invention; Figure 4 Show five lithology characteristic diagrams of the shale oil reservoir in Well XX1 according to an embodiment of the present invention; Figure 5 Show the lithology profile and natural gamma curve characteristic diagram according to an embodiment of the present invention; Figure 6 Show the lithology identification chart of natural gamma and high-resolution resistivity according to an embodiment of the present invention. As Figures 1-6As shown in the figure, a method for identifying the lithology of shale oil reservoirs based on imaging high-resolution resistivity includes the following steps: Step S01: Obtain the logging curves and electrical imaging data of the target interval in the work area; Step S02: Preprocess the electrical imaging data to obtain an electrical imaging image and a high-resolution resistivity curve; Step S03: Optimally select the logging curve with the highest correlation with lithology among all the logging curves; Step S04: Combine the logging curve with the highest correlation with lithology, the high-resolution resistivity curve, and the electrical imaging image to identify the lithology of the reservoir in the target interval.

[0020] The method for identifying the lithology of shale oil reservoirs based on imaging high-resolution resistivity provided by the embodiments of the present invention specifically includes the following steps: Step S01: Obtain the logging curves and electrical imaging data of the target interval in the work area.

[0021] In the embodiments of the present invention, the logging curves include natural gamma, dual laterolog, and formation element logging curves, etc. The resolution of conventional logging curves is relatively low, so it is necessary to combine electrical imaging data for lithology identification.

[0022] Step S02: Preprocess the electrical imaging data to obtain an electrical imaging image and a high-resolution resistivity curve.

[0023] In the present invention, the preprocessing includes but is not limited to: velocity correction and / or image equalization and / or gain voltage correction and / or bad electrode correction.

[0024] In the embodiments of the present invention, in the electrical imaging processing and interpretation software, preprocess the electrical imaging data through velocity correction, bad electrode correction, and alignment of the plate depth. Taking the FMI (resistivity imaging) logging tool as an example, this tool has 8 plates, each plate has 24 electrical contact electrodes, and each electrical contact can collect conductivity data.

[0025] The electrical imaging data is conductivity values. The preprocessing of the conductivity values specifically includes: Velocity correction: During downhole operations, the movement of the tool is affected by differential viscosity and borehole roughness. This causes the tool to slightly move up and down during logging. This irregular movement results in incorrect data and reduces the quality of logging. Velocity correction can correct the stretching, compression, or misalignment of the image at the same depth caused by non-uniform movement, relative movement between plates, and sticking and releasing during logging of the electrical imaging logging tool; Image equalization: Uniformly modify the conductivity data of each electrical contact to eliminate the influence of mud scouring or noise data during logging; Gain voltage correction: Adjust the gain voltage to maintain the proportional relationship between the electrical imaging data and the formation conductivity; Bad electrode correction: Repair the missing or interrupted image data caused by damage or abnormality of some electrodes; Use the shallow lateral logging curve to scale the conductivity value, and generate a high-resolution resistivity curve and an electrical imaging image.

[0026] Multiple structures, tectonics and typical geological features can be identified using the electrical imaging image, such as massive structure, horizontal bedding, convolute bedding, fractures, faults and gravel, etc. As Figure 2 shown, in Figure 2 a, Figure a shows convolute bedding and inclined bedding, Figure b shows a fault, Figure c shows gravel, Figure d shows horizontal bedding, Figure e shows high-resistance fractures, Figure f shows high-conductivity fractures, Figure g shows massive structure and dissolution pores, Figure h shows deformed structure; The main structures and tectonics in the shale oil reservoir are layered and laminated structures, and lenticles are locally developed.

[0027] Step S03: Optimize the logging curve with the highest lithology correlation among all the logging curves.

[0028] In the present invention, the method for optimizing the logging curve with the highest lithology correlation among all the logging curves includes: obtaining the detailed lithology description data corresponding to the target interval; respectively comparing all the logging curves with the detailed lithology description data, and optimizing the logging curve with the highest lithology correlation among them.

[0029] In the embodiment of the present invention, the detailed lithology description data is the actual lithology analysis result obtained by testing and observing the core in the geological logging data of the work area. Using the obtained shale oil reservoir lithology description data (detailed lithology description data) as a standard, logging curves such as natural gamma, dual laterolog, formation element logging, high-resolution resistivity curve, etc. are compared with the lithology. As Figure 3 shown, in Figure 3 from left to right, the first track is the lithology curve (natural gamma), the second track is the depth track, the third track is the logging curve used to calculate porosity, the fourth track is the porosity curve, the fifth track is the dynamic and static images of the electrical imaging (electrical imaging image), the sixth track is the high-resolution resistivity curve of the electrical imaging, the seventh track is the detailed lithology description data, and the eighth track is the formation element mineral profile. It can be seen that the resolution of conventional logging and formation element logging data is relatively low, and the lithology of local thin layers cannot be identified. The high-resolution resistivity curve has a good response at the development of thin layers or lenticles, and obvious resistivity value changes can be seen, which has a good correspondence with the lithology change interface of the detailed lithology description data, indicating that lithology identification through electrical imaging data will be more accurate.

[0030] In addition to the electrical imaging image and the high-resolution resistivity curve, among other logging curves, find the logging curve with the relatively best lithology correlation and combine the electrical imaging image and the high-resolution resistivity curve for lithology identification. Among them, the logging curve with the highest lithology correlation among all the optimized logging curves is the natural gamma curve.

[0031] Step S04: Combine the 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.

[0032] In the present invention, the method of combining the logging curve with the highest lithology correlation, the high-resolution resistivity curve, and the electrical imaging image includes: establishing a lithology identification chart by combining the logging curve with the highest lithology correlation and the high-resolution resistivity curve, and identifying the reservoir lithology based on the lithology identification chart in combination with the electrical imaging image. If the logging curve with the highest lithology correlation is the natural gamma curve, then normalization processing needs to be performed on the natural gamma curve.

[0033] In an embodiment of the present invention, the preferred natural gamma curve and the high-resolution resistivity curve are combined, and the established lithology identification chart is as Figure 6 shown. Natural gamma logging can measure the total content of radioactive elements in the formation. Mudstone and shale have high contents of radioactive elements, while sandstone has a lower content of radioactive elements. The change of the natural gamma curve can reflect the change of lithology and has the function of distinguishing lithology, with better effect compared to other logging curves; as shown in the Figure 5 lithology profile and the characteristic comparison chart of the natural gamma curve attached, as shown in Figure 5 Figure a, the natural gamma value of mudstone is large, between 90 and 120 API, and the natural gamma value of sandstone is small, between 50 and 70 API; as shown in Figure 5 Figure b, in the shale oil reservoir, due to sandstone, shell limestone, and dolomite existing in the form of thin interlayers or lenses, the natural gamma curve value has a small difference from that of shale. Therefore, normalization processing is performed on the natural gamma curve (GRv), a scatter plot is drawn with the high-resolution resistivity curve, and combined with the structure and structure identified by the electrical imaging, a lithology identification chart ( Figure 6 ) is established, and the lithology of the shale oil reservoir is divided into the following five types: laminated shale, laminated shale, siltstone, shell limestone, and dolomite.

[0034] In the present invention, the method of identifying the reservoir lithology of the target interval includes: identifying the typical structure and structure characteristics on the electrical imaging image, and identifying the lithology based on the typical structure and structure characteristics and the corresponding high-resolution resistivity value on the lithology identification chart, specifically including: Judging whether the electrical imaging image of the target interval has the characteristics of thin interbeds of high and low resistivity, whether the local clarity of the high and low resistivity change interface does not meet the predetermined clarity, and whether the corresponding high-resolution resistivity on the lithology identification chart is within the first predetermined range. If so, the identified lithology is laminated shale; Judge whether there is a laminated structure on the electrical imaging image of the target interval, whether the local clarity of the high and low resistivity change interface meets the predetermined clarity, and whether the high-resolution resistivity corresponding to the lithology identification chart is within the second predetermined range. If so, the identified lithology is laminated shale; Judge whether the target interval has a first predetermined color feature on the electrical imaging image, whether there is a layered or massive structure on the image, and whether the high-resolution resistivity corresponding to the lithology identification chart is within the third predetermined range. If so, the identified lithology is siltstone; Judge whether the electrical imaging image of the target interval has a second predetermined color feature, whether there is a layered or massive structure on the image, and whether the high-resolution resistivity corresponding to the lithology identification chart is greater than the predetermined value. If so, the identified lithology is shell limestone; Judge whether the electrical imaging of the target interval has a second predetermined color feature, whether there is a lenticular feature on the image, and whether the high-resolution resistivity corresponding to the lithology identification chart is within the fourth predetermined range. If so, the identified lithology is dolomite.

[0035] In the present invention, the method for identifying typical structural and tectonic features on the electrical imaging image includes: determining the number of bedding planes per meter of the formation on the electrical imaging image; if the number of bedding planes per meter is greater than the first predetermined number, it is classified as a laminated structure; if the number of bedding planes per meter is less than or equal to the first number and greater than or equal to the second number, it is classified as a layered structure; if the number of bedding planes per meter is less than the second number, it is classified as a massive structure.

[0036] In the present invention, 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.

[0037] In an embodiment of the present invention, taking Well XX1 in the work area as an example, using electrical imaging processing software (GeoFrame, CIFLog-Geospace or Techlog software can all be used), preprocess the electrical imaging data, that is, image enhancement, shallow lateral calibration, etc., to obtain dynamic and static electrical imaging images and high-resolution resistivity curves. Among them, taking GeoFrame software as an example, the electrical imaging image is displayed with 16-bit or 64-bit color scale normalization. The highest and lowest resistivity values correspond to white and black respectively, and the intermediate values are gradually transitioned through yellow. Each resistivity value of the electrical imaging data is assigned a corresponding color, and finally an electrical imaging image is generated; the lighter and brighter the color on the image, the higher the formation resistance, and the darker and darker the image color, the lower the formation resistance.

[0038] In the embodiments of the present invention, the first predetermined quantity is: 30, and the second predetermined quantity is 10. Typical structures and structural features include: laminar structure, layered structure, massive structure, lens body, and high-low resistivity thin interbed features. The formation structure is divided according to the number of bedding planes identified from the electrical imaging image. If the number of bedding planes per meter is greater than 30, it is divided into a laminar structure; if it is 10 - 30 per meter, it is a layered structure; and if it is less than 10 per meter, it is a massive structure.

[0039] Among them, the method for identifying the lens body is as follows: For the part with a high resistivity value on the electrical imaging image, that is, the part corresponding to the color where the resistivity value is greater than the predetermined value forms an elliptical or intestinal shape feature, then it is a lens body. That is, if the part with a high resistivity value on the electrical imaging image presents a white elliptical or intestinal shape, it is a lens body. The method for identifying the high-low resistivity thin interbed feature is as follows: If there are alternating high and low resistivity values on the electrical imaging image, it is a high-low resistivity thin interbed. That is, if dark and light colors alternate on the electrical imaging image, and the boundary between the light color with a high resistivity value and the dark color with a low resistivity value is clear, that is, it reaches the predetermined clarity level.

[0040] Using the electrical imaging image, typical structures and structural features of Well XX1 are identified, such as Figure 4 shown. By identifying the electrical imaging image of Well XX1, it can be seen that there are massive structures ( Figure 4 c), layered structures ( Figure 4 b, d), laminar structures ( Figure 4 a), and lens bodies ( Figure 4 e) features, and there are also high-low resistivity thin interbed features.

[0041] The natural gamma and high-resolution resistivity curves of the shale oil reservoir are placed in the lithology identification chart ( Figure 6 ). Combining with the electrical imaging image, 5 types of lithologies can be identified: Layered shale: The electrical imaging image shows high-low resistivity thin interbed features, and the interface of the high-low resistivity change is locally unclear, that is, it does not meet the predetermined clarity level. In the corresponding lithology identification chart, the high-resolution resistivity is between 10 - 50 Ω·m; Laminar shale: The electrical imaging image shows well-developed horizontal laminations, that is, a laminar structure. The interface of the high-low resistivity change is clear and complete, that is, it meets the predetermined clarity level. The lamination thickness is small. In the corresponding lithology identification chart, the high-resolution resistivity is between 3 - 20 Ω·m; Siltstone: The electrical imaging image shows high-resistivity bright yellow, that is, the first predetermined color feature, and it has a layered or massive structure. In the corresponding lithology identification chart, the high-resolution resistivity is between 50 - 1000 Ω·m; Shell limestone: The electrical imaging image shows high-resistivity white or bright yellow, that is, the second predetermined color feature, and it has a layered or massive structure. In the corresponding lithology identification chart, the average value of the high-resolution resistivity > 3000 Ω·m; Dolomite: The electrical imaging image shows high resistivity, white or bright yellow, with a lenticular feature. In the corresponding lithology identification chart, the high-resolution resistivity ranges from 200 to 500 Ω·m.

[0042] It can be understood that, without violating the principle logic, the above-mentioned method embodiments mentioned in the present invention can be combined with each other to form combined embodiments. Due to space limitations, the present invention will not elaborate further.

[0043] The execution subject of the lithology identification method for shale oil reservoirs based on imaging high-resolution resistivity can be a lithology identification device for shale oil reservoirs based on imaging high-resolution resistivity. For example, the lithology identification method for shale oil reservoirs based on imaging high-resolution resistivity can be executed by a terminal device, a server, or other processing devices. Among them, 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 lithology identification method for shale oil reservoirs based on imaging high-resolution resistivity can be implemented by a processor calling computer-readable instructions stored in a memory.

[0044] Those skilled in the art can understand that in the above method of the specific implementation manner, the writing order of each step 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 according to its function and possible internal logic.

[0045] The present invention also provides a lithology identification device for shale oil reservoirs based on imaging high-resolution resistivity, including: an acquisition unit for acquiring well logging curves and electrical imaging data of a target interval in a work area; a preprocessing unit for preprocessing the electrical imaging data to obtain an electrical imaging image and a high-resolution resistivity curve; a well logging curve optimization unit for optimizing the well logging curve with the highest lithology correlation among all the well logging curves; and a reservoir lithology identification unit for 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 interval.

[0046] In some embodiments, the functions or modules and units included in the device provided in the embodiments of the present invention 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. For the sake of brevity, it will not be elaborated here.

[0047] The present invention is a method for lithology identification that combines conventional logging and formation element logging and uses the high-resolution resistivity curve obtained from resistivity imaging data. In the current oilfield exploration and development, the conventional oil exploration in the XX Basin has reached a relatively high level and has entered the stage of fine exploration. The shale resource potential is large, and it has gradually become an important area for oil and gas succession. It is a project to extend the life of the oilfield and has important strategic significance. From 2020 to 2024, it is estimated that a total of 184 new wells will be drilled and 6 experimental areas will be established. In the past three years, it is estimated that 1 billion tons of proven reserves will be submitted and the production will increase by more than 1 million tons. The shale reservoir in this area generally has the characteristics of well-developed bedding, diverse mineral components, high clay content, and small differences in logging responses. It is difficult to accurately calculate the mineral components and identify the lithology.

[0048] Taking the array laterolog instrument of Schlumberger as an example, this instrument has a relatively high vertical resolution. In an 8-inch wellbore, the vertical resolution is 12 inches (about 30.5 cm), while the resistivity imaging data (taking FMI as an example) has a vertical geometric resolution of 0.2 inches (about 0.5 cm), and 192 conductivity curves can be measured at each depth point of FMI. Therefore, through processing, a high-resolution resistivity curve can be obtained. Using the method of the present invention to identify the structure and structure of resistivity imaging images, combined with conventional logging data, rock physics experiments, and geological data, a lithology identification chart is established, thin layers are accurately divided, the lithology of shale oil reservoirs is identified, the coincidence rate of lithology identification of shale oil reservoirs is improved, and the coincidence rate of the established lithology identification chart reaches more than 90%, laying a foundation for the subsequent calculation and evaluation of reservoir parameters.

[0049] The embodiments of the present invention have been described above. The above description is exemplary and not exhaustive, and is not limited to the disclosed embodiments. Many modifications and variations are obvious to those of ordinary skill in the art in the technical field without departing from the scope and spirit of the described embodiments. The selection of the terms used herein is intended to best explain the principles of the embodiments, practical applications, or improvements to the technology in the market, or to enable other ordinary skill in the art in the technical field to understand the embodiments disclosed herein.

Claims

1. A shale oil reservoir lithology identification method based on imaging high-resolution resistivity, characterized in that: include: Obtain well logging curves and electrical imaging data of the target layer in the work area; Preprocessing the electrical imaging data to obtain an electrical imaging image and a high-resolution resistivity curve; Preferentially selecting the well logging curve with the highest lithology correlation among all the well logging curves; The well logging curve with the highest correlation with lithology, the high-resolution resistivity curve and the electrical imaging image are combined to identify the reservoir lithology of the target layer.

2. The shale oil reservoir lithology identification method based on imaging high-resolution resistivity according to claim 1 is characterized in that: The preprocessing includes but is not limited to: speed correction and / or image equalization and / or gain voltage correction and / or bad electrode correction.

3. The shale oil reservoir lithology identification method based on imaging high-resolution resistivity according to claim 1 is characterized in that: The method of selecting the well logging curve with the highest lithology correlation among all the well logging curves comprises: Obtaining lithology precise description data corresponding to the target layer segment; All the well logging curves are compared with the lithology precise description data respectively, and the well logging curve with the highest correlation with the lithology is preferred.

4. The shale oil reservoir lithology identification method based on imaging high-resolution resistivity according to any one of claims 1 to 3, characterized in that: The method of combining the well logging curve with the highest correlation with lithology, the high-resolution resistivity curve and the electrical imaging image comprises: The well logging curve with the highest correlation with lithology and the high-resolution resistivity curve are used to establish a lithology identification chart, and reservoir lithology identification is performed based on the lithology identification chart combined with the electrical imaging image; If the well logging curve with the highest correlation with lithology is a natural gamma curve, the natural gamma curve needs to be normalized.

5. The shale oil reservoir lithology identification method based on imaging high-resolution resistivity according to claim 4 is characterized in that: The method for identifying the reservoir lithology of the target layer section comprises: Identify the typical structures and structural features on the electrical imaging image, and identify the lithology according to the typical structures and structural features and the corresponding high-resolution resistivity values ​​on the lithology identification plate, specifically including: Determine whether the electrical imaging image of the target layer segment has the characteristics of high-low resistivity thin interlayers, whether the local clarity of the high-low resistivity change interface does not meet the predetermined clarity, and whether the corresponding high-resolution resistivity on the lithology identification plate is within the first predetermined range. If yes, the identified lithology is layered shale; Determine whether the electrical imaging image of the target layer has a laminar structure, whether the local clarity of the high-low resistance change interface meets the predetermined clarity, and whether the corresponding high-resolution resistivity on the lithology identification plate is within a second predetermined range. If yes, the identified lithology is laminar shale; Determine whether the target layer segment has a first predetermined color feature on the electrical imaging image, whether it has a layered or blocky structure on the image, and whether the corresponding high-resolution resistivity on the lithology identification plate is within a third predetermined range, if yes, the identified lithology is siltstone; Determine whether the electrical imaging image of the target layer has a second predetermined color feature, whether the image has a layered or blocky structure, and whether the corresponding high-resolution resistivity on the lithology identification plate is greater than a predetermined value. If yes, the identified lithology is shell limestone; It is determined whether the electrical imaging of the target layer segment has a second predetermined color feature, whether the image has a lens feature, and whether the corresponding high-resolution resistivity on the lithology identification plate is within a fourth predetermined range. If so, the identified lithology is dolomite.

6. The shale oil reservoir lithology identification method based on imaging high-resolution resistivity according to claim 5 is characterized in that: The method for identifying typical structures and structural features on the electrical imaging image comprises: Determining the number of layers per meter in the stratum on the electrical imaging image; If the number of beddings per meter is greater than a first predetermined number of bars, it is classified as a laminated structure; if the number of beddings per meter is less than or equal to the first number of bars and greater than or equal to the second number of bars, it is classified as a layered structure; if the number of beddings per meter is less than the second number of bars, it is classified as a blocky structure.

7. The shale oil reservoir lithology identification method based on imaging high-resolution resistivity according to claim 6 is characterized by: 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; The fourth predetermined range is: 200-500Ω·m.

8. A shale oil reservoir lithology identification device based on imaging high-resolution resistivity, characterized in that: include: An acquisition unit is used to acquire well logging curves and electrical imaging data of target layer sections in the work area; A preprocessing unit, used for preprocessing the electrical imaging data to obtain an electrical imaging image and a high-resolution resistivity curve; A well logging curve optimization unit, used for optimizing the well logging curve with the highest lithology correlation among all the well logging curves; The reservoir lithology identification unit is used to combine the well logging curve with the highest correlation with lithology, the high-resolution resistivity curve and the electrical imaging image to identify the reservoir lithology of the target layer.

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