Thin interbed type shale oil layer selection judgment method and system

Abstract

The invention relates to a thin interbed type shale oil layer selection judgment method and system, and belongs to the field of oil-gas exploration and development. Obtaining a logging curve of each single well in the target area, determining an RD-AC amplitude difference curve and a GR-AC amplitude difference curve of each single well according to the logging curve, and judging the shale oil development condition of each single well interval by integrating the RD-AC amplitude difference curve of each single well and the corresponding GR-AC amplitude difference curve. The method is simple to operate and easy to implement, and the shale oil development condition of the single well interval can be well judged.

Description

Technical Field

[0001] The invention relates to a thin interbedded shale oil layer selection and discrimination method and system, belonging to the field of oil and gas exploration and development. Background Art

[0003] Continental sedimentary water bodies can be divided into saltwater lake basins, brackish water lake basins and freshwater lake basins according to the salinity of the water body. The development characteristics of sediments in each water environment are similar, so it is crucial to identify the selection standards of continental shale oil under different water salinities. At present, some shale oil has been found in saltwater lake basins, brackish water lake basins and freshwater lake basins in China. For example, the Shengli Jiyang Shale Oil National Demonstration Zone of Sinopec, which has already made a breakthrough, belongs to saltwater shale oil, but its depression area is large, the thickness of the shale oil single layer is also large, and the selection of shale oil is often easier to determine. In a saltwater environment, a series of small depressions are often developed. The depression area is small and the single-layer thickness of shale oil is also small. For thin interlayer shale oil in a saltwater environment, the existing technology superimposes natural gamma curves and resistivity curves on clay rocks, and evaluates the oil abundance of shale by the size of the envelope area of ​​the two curves. This method only evaluates shale oil in clay rocks and cannot evaluate shale oil in the entire layer of a single well. Summary of the invention

[0004] The purpose of the present invention is to provide a thin interbedded shale oil layer selection and discrimination method and system, so as to provide a layer selection and evaluation method for single well thin interbedded shale oil in a saltwater environment.

[0005] To achieve the above object, the solution of the present invention includes:

[0006] A thin interbedded shale oil layer selection and discrimination method of the present invention comprises the following steps:

[0007] 1) Obtaining the logging curves of each single well in the target area, wherein the logging curves include an acoustic time difference AC curve, a resistivity RD curve and a natural gamma ray GR curve;

[0008] 2) Determine the RD-AC amplitude difference curve and GR-AC amplitude difference curve of each single well based on the logging curve, and combine the RD-AC amplitude difference curve of each single well with the corresponding GR-AC amplitude difference curve to determine the shale oil development status of each single well layer.

[0009] Beneficial effect: The thin interbedded shale oil layer selection and discrimination method of the present invention obtains the RD-AC amplitude difference curve and the GR-AC amplitude difference curve of each single well by acquiring the logging curve data of each single well, and uses the RD-AC amplitude difference curve and the GR-AC amplitude difference curve of each single well to judge the shale oil development of the single well. The method can realize the discrimination of thin interbedded shale oil in a simple way, and the discrimination is accurate, which further promotes the development of shale oil exploration and development technology.

[0010] Furthermore, in the step 2), a superimposed product identification curve is obtained by multiplying the single well RD-AC amplitude difference curve and the GR-AC amplitude difference curve, and the preferred layer section is obtained according to the superimposed product identification curve.

[0011] Beneficial effects: The present invention obtains a superimposed product identification curve based on the product of the RD-AC amplitude difference curve and the GR-AC amplitude difference curve, and identifies the shale oil development layer according to the superimposed product identification curve. The identification is more intuitive and accurate.

[0012] Furthermore, the discrimination threshold of the corresponding well is determined according to the size of the superimposed product discrimination curve of each well, and the layer section in the discrimination curve that is larger than the discrimination threshold is taken as the preferred layer section.

[0013] Furthermore, the discrimination threshold is the average value of the maximum value and the minimum value of the superimposed product discrimination curve or the average value of the superimposed product discrimination curve.

[0014] Beneficial effect: For the layer section larger than the single well threshold, shale oil is well developed, so the layer section larger than the threshold is selected.

[0015] Furthermore, the method also includes the step of further screening the preferred layer sections, and selecting the layer sections with thickness greater than a thickness threshold from the preferred layer sections as the best layer sections.

[0016] Beneficial effect: The present invention selects a layer section with a thickness greater than a single well threshold and a shale oil thickness greater than a thickness threshold as the optimal layer section. The shale oil content in the selected layer section is higher and has greater mining value.

[0017] A thin interbedded shale oil layer selection and discrimination system of the present invention comprises a processor, wherein the processor is used to execute program instructions to implement the thin interbedded shale oil layer selection and discrimination method as described in any one of the above claims.

[0018] Beneficial effects: The thin interbedded shale oil layer selection and discrimination system of the present invention has a simple structure and includes a processor, through which the thin interbedded shale oil layer selection and discrimination method can be better implemented. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] Figure 1 is a schematic diagram of a technical route in a method embodiment of the present invention;

[0020] Figure 2 It is a schematic diagram of the division of different types of lithofacies bodies of key single wells W318, W79-13, and W248 in the method embodiment of the present invention;

[0021] Figure 3 This is a schematic diagram of the matching analysis of the relationship between the main lithofacies and organic carbon in the method embodiment of the present invention;

[0022] Figure 4 This is a schematic diagram of the matching analysis of the relationship between organic carbon and oil content in the method embodiment of the present invention;

[0023] Figure 5 Schematic diagram of the comprehensive logging curve of shale oil in Well W318 in the method embodiment of the present invention;

[0024] Figure 6 It is a preferred schematic diagram of the shale oil development layer section of Well W318 in the method embodiment of the present invention. DETAILED DESCRIPTION

[0025] The present invention will be further described in detail below in conjunction with the accompanying drawings.

[0026] Embodiment of the method for distinguishing thin interbedded shale oil layers:

[0027] In this embodiment, a thin interbedded shale oil layer selection and discrimination method is firstly used to obtain the logging curves of each single well in the target area, and determine the RD-AC amplitude difference curve and GR-AC amplitude difference curve of each single well according to the logging curves, and then combine the RD-AC amplitude difference curve of each single well with the corresponding GR-AC amplitude difference curve to discriminate the shale oil development of each single well segment. This scheme is obtained by studying a large amount of logging data and coring segment data, and by obtaining data from typical single wells, such as the key coring wells W318, W79-13, and W248 in the Dongpu Depression of the continental saline lake, and conducting research. The research ideas are as follows:

[0028] like Figure 1 As shown in the figure, firstly, the lithofacies body is divided, and then the relationship between the lithofacies body and the oil content is discussed. On the basis of the determination of the lithofacies body with high oil content, the corresponding relationship between the logging curve and it is established through the electrical logging curve method; through the internal relationship of the amplitude difference of the logging curves of multiple wells, the vertical distribution layer section of the lithofacies body with high oil content is determined; then through the internal relationship of the amplitude difference of the logging curves of multiple wells, the logging data of different single wells are first obtained, and by analyzing the core sampling layer section, the different types of lithofacies bodies existing in the single well are divided according to the percentage of carbonate rock, clay and felsic content, and the main lithofacies body type is determined according to the quantity distribution.

[0029] In this example, the key core wells W318, W79-13, and W248 in the Dongpu Depression of the continental saline lake are used as examples for analysis. Based on the logging data of W318, W79-13, and W248 wells, the lithofacies of a single well are divided into 12 lithofacies according to the percentage of carbonate rock, clay, and felsic content using three-terminal method, such as Figure 2 As shown, it includes carbonate facies ①, clay carbonate facies ②, felsic carbonate facies ③, clay facies ④, carbonate clay facies ⑤, felsic clay facies ⑥, felsic facies ⑦, carbonate felsic facies ⑧, clay felsic facies ⑨, carbonate mixed facies ⑩, clay mixed facies felsic mixed rock facies

[0030] TOC test (organic carbon test) was carried out on samples in each of the 12 lithofacies identified, and the arithmetic mean method was used to obtain the organic carbon test value of each lithofacies, such as Figure 3 As shown in the figure, the matching analysis of various lithofacies and corresponding organic carbon is carried out. Through the analysis, the intrinsic relationship between organic carbon (TOC) and oil content (S1) can be obtained.

[0031] Specifically, through a large number of organic carbon test analyses, it is known that Figure 4 As shown in Table 1, there is a positive linear correlation between the organic carbon (TOC) and oil content (S1) of different types of lithofacies, and the relationship between the two is y = 0.9026x-0.0885; where x is the organic carbon content (TOC) and y is the oil content (S1). The distribution characteristics of organic carbon (TOC) and oil content (S1) of different types of lithofacies are obtained, as shown in Table 1:

[0032] Table 1

[0033]

[0034]

[0035] According to oil and gas geochemical research, the oil generation of lithofacies with TOC ≥ 1.0% plays a major role in oil and gas accumulation, and can reach a contribution ratio of more than 80%. Therefore, this embodiment uses organic carbon content (TOC) = 1.0% as the threshold value and oil content (S1) as 0.8 mg / g as the threshold value, and regards lithofacies with organic carbon content exceeding 1.0% and oil content (S1) exceeding 0.8 mg / g as high-oil-content lithofacies.

[0036] As shown in Table 1, the high oil content lithofacies in this embodiment include: carbonate lithofacies, clay carbonate lithofacies, carbonate mixed lithofacies, clay mixed lithofacies, felsic mixed lithofacies and carbonate clay lithofacies.

[0037] Through the electric logging curve method, the oil content of high oil content lithofacies (the high oil content lithofacies in this embodiment are carbonate lithofacies, clay carbonate lithofacies, carbonate mixed lithofacies, clay mixed lithofacies, felsic mixed lithofacies and carbonate clay lithofacies) is established with the logging curve. Among them, the oil content includes organic carbon content (TOC) and oil content (S1). By performing TOC test (organic carbon test) on key exploration wells W318, W79-13, and W248 and fitting different types of logging curves, it can be seen that the amplitude difference between the oil content (S1) and the electric logging curve acoustic wave time difference (AC)-electric logging curve resistivity (RD) (abbreviated as RD-AC amplitude difference) has a good fitting. For example, for the part with high measured value of oil content (S1), the amplitude difference between the electric logging curve acoustic wave time difference (AC) and the electric logging curve resistivity (RD) of the corresponding depth section is large, and vice versa.

[0038] For continental shale oil, high mud content is its main characteristic, which is reflected in the electrical logging curve, as the natural gamma (GR) value of the electrical logging curve is relatively large. The acoustic time difference (AC) curve of the electrical logging curve can well reflect the porosity of various types of lithology. Therefore, the acoustic time difference (AC) of the electrical logging curve and the natural gamma (GR) value of the electrical logging curve can well reflect the porosity of different types of lithofacies of continental shale oil. When the amplitude difference (GR-AC amplitude difference) of the acoustic time difference (AC) of a certain type of electrical logging curve-the natural gamma (GR) of the electrical logging curve is larger, the porosity of the shale oil of this type is larger, and vice versa.

[0039] Based on the above analysis, the relationship between the oil content and the RD-AC amplitude difference curve and the relationship between the GR-AC amplitude difference curve and the porosity can be obtained. Based on the relationship between the oil content and the RD-AC amplitude difference curve and the relationship between the GR-AC amplitude difference curve and the porosity, the scheme in this embodiment can be obtained. The specific implementation plan is as follows:

[0040] The logging curves obtained in this embodiment include the acoustic time difference AC curve, the resistivity RD curve and the natural gamma GR curve. The RD-AC amplitude difference curve and the GR-AC amplitude difference curve of each single well are determined according to the logging curves. Figure 5 The 14th column of , and then determine the vertical distribution of high oil content lithofacies. Figure 5As shown in the 15th and 16th columns of , the vertical distribution layer of the high porosity lithofacies is determined. The RD-AC amplitude difference curve of a single well is multiplied or added with the corresponding GR-AC amplitude difference curve to obtain a superimposed product identification curve. In this embodiment, the superimposed product identification curve is obtained by multiplying the RD-AC amplitude difference curve of a single well with the corresponding GR-AC amplitude difference curve, as shown in FIG. Figure 5 As shown in the 17th column of .

[0041] According to the superimposed product discrimination curve of a single well, the discrimination threshold of the single well is determined, wherein the discrimination threshold can be the average value of all values ​​of the superimposed product discrimination curve, or the average value of the maximum and minimum values ​​in the discrimination curve. In this embodiment, the average value of the maximum and minimum values ​​in the superimposed product discrimination curve is used as the discrimination threshold of the single well, such as Figure 5 As shown, the maximum value is 4, the minimum value is 0, and the average value is 2, that is, the threshold value of the single well is 2. According to the threshold value of the single well, the layer segment that is greater than the discrimination threshold is preferably used as the preferred layer segment. In this embodiment, the step of further screening the preferred layer segment is also included, and the layer segment with a thickness greater than the thickness threshold is selected from the preferred layer segment as the optimal layer segment. When the single well threshold is determined, the layer segment greater than the single well threshold and with a thickness greater than the thickness threshold is preferably used as the optimal layer segment.

[0042] According to the determined threshold, the portion greater than the threshold is analyzed, and the oil-bearing layer interval is optimized according to the accumulated oil thickness, for example Figure 6 As shown in the figure, three development sections are found, namely layer 1, layer 2 and layer 3. The cumulative thickness of layer 1 and layer 2, which are greater than the threshold, are relatively small, while layer 3 has the largest thickness. Therefore, according to the principle of thickness greater than the threshold, layer 3 of this single well is selected as the best shale oil development section.

[0043] Example of thin interbedded shale oil layer selection and discrimination system:

[0044] A thin interbedded shale oil layer selection discrimination system in this embodiment includes a memory, a processor and an internal bus, and the processor and the memory communicate and exchange data with each other through the internal bus. The memory includes at least one memory capable of storing well logging data and well logging curve data, and the processor executes various functional applications and data processing by running software programs and modules stored in the memory to implement the thin interbedded shale oil layer selection discrimination method described in the method embodiment of the present invention.

[0045] That is to say, the method in the above method embodiment should be understood as a process of a thin interbedded shale oil layer selection discrimination method that can be implemented by computer program instructions. These computer program instructions can be provided to a processor so that the processor executes these instructions to generate functions specified in the above method flow.

[0046] The processor may be a microprocessor MCU, a programmable logic device FPGA or other processing device.

[0047] The memory may be various memories that use electrical energy to store information, such as RAM, ROM, etc.; it may also be various memories that use magnetic energy to store information, such as hard disks, floppy disks, magnetic tapes, magnetic core memories, bubble memories, USB flash drives, etc.; it may also be various memories that use optical methods to store information, such as CDs, DVDs, etc.; of course, it may also be other types of memories, such as quantum memories, graphene memories, etc.

[0048] Specific implementation methods are given above, but the present invention is not limited to the described implementation methods. The basic idea of ​​the present invention lies in the above basic scheme. For ordinary technicians in this field, it does not take creative work to design various deformed models, formulas, and parameters according to the teachings of the present invention. Changes, modifications, substitutions, and variations of the implementation methods without departing from the principles and spirit of the present invention still fall within the scope of protection of the present invention.

Claims

1. A method for distinguishing thin interbedded shale oil layers, characterized in that: The following steps are involved: 1) Obtaining the logging curves of each single well in the target area, wherein the logging curves include an acoustic time difference AC curve, a resistivity RD curve and a natural gamma ray GR curve; 2) Determine the RD-AC amplitude difference curve and GR-AC amplitude difference curve of each single well based on the logging curve, and combine the RD-AC amplitude difference curve of each single well with the corresponding GR-AC amplitude difference curve to judge the shale oil development of each single well layer.

2. The thin interbedded shale oil layer selection and discrimination method according to claim 1 is characterized in that: In the step 2), a superimposed product identification curve is obtained by multiplying the single well RD-AC amplitude difference curve and the GR-AC amplitude difference curve, and the preferred layer section is obtained according to the superimposed product identification curve.

3. The thin interbedded shale oil layer selection and discrimination method according to claim 2 is characterized in that: The discrimination threshold of the corresponding well is determined according to the size of the superimposed product discrimination curve of each well, and the layer section in the discrimination curve that is larger than the discrimination threshold is taken as the preferred layer section.

4. The thin interbedded shale oil layer selection and discrimination method according to claim 3 is characterized in that: The discrimination threshold is the average value of the maximum value and the minimum value of the superimposed product discrimination curve or the average value of the superimposed product discrimination curve.

5. The thin interbedded shale oil layer selection and discrimination method according to claim 2 or 3, characterized in that: The method also includes the step of further screening the preferred layer sections, and selecting the layer sections with thickness greater than a thickness threshold from the preferred layer sections as the best layer sections.

6. A thin interbedded shale oil layer selection and discrimination system, comprising a processor, characterized in that: The processor is used to execute program instructions to implement the thin interbedded shale oil layer selection and discrimination method as described in any one of claims 1-5.

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