In-source compact carbonate reservoir favorable area prediction method based on ancient landform analysis

Abstract

The invention discloses an in-source compact carbonate reservoir favorable area prediction method based on ancient landform analysis, and relates to the technical field of oil-gas exploration, and the method comprises the steps: collecting large-area drilling and logging, logging and two-dimensional seismic survey line data; determining a stratigraphic division scheme by well-seismic combination, and counting key parameters of a research interval; determining the correlation between the crumb-generating limestone / stratum thickness and the stratum thickness and between the marlstone thickness / stratum thickness ratio and the stratum thickness; drawing a stratum thickness map, reconstructing an ancient landform in combination with the correlation characteristics, and dividing landform units; establishing and calculating grey correlation degrees y (C), y (phi) and y (G) of important reservoir parameters C, phi, G and H by using a grey correlation method; and calculating favorable degree values F of different landform units, and performing sorting so as to determine in-source compact carbonate reservoir favorable development landform units and regions. The method fills the blank of high-efficiency and low-cost prediction methods for quantitative evaluation of novel unconventional oil and gas reservoirs such as compact carbonate reservoirs in sources.

Description

Technical Field

[0001] The invention relates to the technical field of tight carbonate reservoir exploration, and in particular to a method for predicting favorable areas of tight carbonate reservoirs within a source based on paleo-geomorphological analysis. Background Art

[0002] Unconventional oil and gas reservoirs are currently the focus of increasing reserves and production in oil fields. In addition to deep-water marine shale, which is currently the main target of exploration and development, shallow-water marine carbonate rocks that are self-generated and self-reserved and formed in the source have also become important targets for the next step of exploration. At present, a large number of wells drilled in the Sichuan Basin have good oil and gas shows in this set of tight carbonate rocks, and some exploration wells have obtained industrial gas flow. However, the sedimentary environment, formation process and organic matter enrichment rules of carbonate rocks are quite different from those of marine shales, so the prediction method of favorable areas of marine shales cannot be directly applied. Exploration practice also shows that the existing prediction methods do not grasp the formation essence of tight carbonate reservoirs in the source, and cannot accurately select the most favorable blocks for the next step of exploration. For new blocks with less geological data, oil and gas evaluation will inevitably require large-scale coring in drilling, which greatly increases the exploration cost. Summary of the invention

[0003] In view of the above problems in the prior art, the present invention provides a method for predicting favorable areas of tight carbonate reservoirs within the source based on paleo-geomorphological analysis, which utilizes the existing logging data of old wells to evaluate favorable exploration areas in large regions, thereby solving the technical problem that the prediction methods of the prior art cannot accurately predict unconventional oil and gas reservoirs such as tight carbonate reservoirs within the source, and effectively reducing exploration costs.

[0004] The technical solution adopted by the present invention is as follows:

[0005] The method for predicting favorable areas of tight carbonate reservoirs in source areas based on paleo-geomorphological analysis includes the following steps:

[0006] S1. Collect large-area drilling and logging data, as well as existing 2D seismic line data;

[0007] S2. For the study layer, select wells with complete well-seismic data, conduct well-seismic stratigraphic framework comparison, establish a stratigraphic division scheme for the target layer, and calculate the reservoir thickness H, total organic carbon content C, porosity, and other parameters of each single well target layer. Key parameters such as gas content G, thickness of bioclastic limestone and marl;

[0008] S3. Calculate the ratio of bioclastic limestone to formation thickness A and the ratio of marlstone to formation thickness B, and conduct correlation analysis between A and B and formation thickness respectively;

[0009] S4, under the constraint of drilling thickness, combined with the correlation characteristics obtained in step S3, reconstruct the paleo-geomorphology of the large area, and divide it into highland (u), slope break (b), slope (s), slope foot (f), and depression (d);

[0010] S5. Use grey correlation analysis to establish important parameters, reservoir parameters C, The grey relational degree y(C) between G and H, y(G);

[0011] S6. According to the grey correlation degree calculated in step S5, using the favorable area prediction formula, Calculate each geomorphic unit, highland, slope break, slope, slope foot, and depression respectively u 、F b 、F s 、F f 、F d ;

[0012] S7, according to the F calculated in step S6, F u 、F b 、F s 、F f 、F d The favorable areas of tight carbonate reservoirs within the source are sorted and divided.

[0013] Furthermore, the method for reconstructing the paleo-geomorphology in step S4 is: according to the correlation between A, B and the stratum thickness obtained in step S3, if A is positively correlated with the stratum thickness and B is negatively correlated with the stratum thickness, the paleo-geomorphology in the region with thicker stratum thickness will be higher; if A is negatively correlated with the stratum thickness and B is positively correlated with the stratum thickness, the paleo-geomorphology in the region with thicker stratum thickness will be lower.

[0014] Furthermore, the calculation method of the grey relational degree in step S5 is:

[0015] (1) Determine the reservoir thickness H as the parent sequence, total organic carbon content C, porosity The gas content G is a subsequence,

[0016] They are H=[H(1),H(2),……,H(n)], C=[C(1),C(2),……,C(n)], G=[G(1),G(2),…,G(n)];

[0017] (2) Preprocess the data. Where X′(k) is the data in each sequence, is the single series average;

[0018] (3) Subtract the processed data of the parent sequence from each subsequence, take the absolute value, set it as |X(n)|, set a as the minimum value of the obtained data, and b as the maximum value of the obtained data;

[0019] (4) Then the grey correlation degree between each subsequence data and the corresponding parent sequence data is Y[X(k)]=(a+ρ×b) / (|X(n)|+ρ×b), where ρ is 0.5;

[0020] (5) The final grey correlation between each key parameter and reservoir thickness is the average value of the single sequence grey correlation, that is,

[0021] In summary, compared with the prior art, the present invention has the following advantages and beneficial effects:

[0022] 1. Use existing drilling and logging, well logging, and seismic data, without adding additional large-scale oil and gas evaluation costs;

[0023] 2. By combining well-seismic data with the reconstruction of regional paleo-geomorphology and the grey correlation analysis method, the gap in the efficient and low-cost prediction method for quantitative evaluation of new unconventional oil and gas reservoirs such as intra-source tight carbonate reservoirs is filled. BRIEF DESCRIPTION OF THE DRAWINGS

[0024] Figure 1 It is a schematic diagram of landform division of the present invention;

[0025] Figure 2 A typical single well stratigraphic division scheme for the study area of ​​the embodiment;

[0026] Figure 3 The embodiment is a graph showing the intersection of the ratio of bioclastic limestone to formation thickness A and formation thickness;

[0027] Figure 4 The embodiment is a graph showing the ratio B of marl to stratum thickness and the stratum thickness;

[0028] Among them, A is negatively correlated with the formation thickness, and B is positively correlated with the formation thickness;

[0029] Figure 5 This is a stratigraphic thickness map of the study layer section in the study area of ​​the embodiment;

[0030] Figure 6 Paleomorphological map of the study section in the study area for the example;

[0031] Figure 7 It is a scatter plot of F values ​​of different geomorphic units in the embodiment;

[0032] Figure 8 This is a diagram for dividing favorable areas of tight carbonate reservoirs within the source area of ​​the example study area. DETAILED DESCRIPTION

[0033] In order to make the purpose, technical solution and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the accompanying drawings and various embodiments and verification examples. It should be understood that the specific embodiments and verification examples described herein are only used to explain the present invention and are not used to limit the present invention, that is, the embodiments described are only part of the embodiments of the present invention, rather than all of the embodiments.

[0034] The word "embodiment" used here as an "exemplary" does not necessarily mean that any embodiment described is superior to or better than other embodiments. Unless otherwise specified, the performance index tests in the embodiments of this method adopt conventional test methods in the art. The terms described in the present invention are only for describing specific implementation methods and are not intended to limit the content disclosed in the present invention.

[0035] Unless otherwise specified, the technical and scientific terms used herein have the same meanings as commonly understood by ordinary technicians in the technical field to which the invention belongs; other raw materials, reagents, experimental methods and technical means not specifically specified in the present invention refer to the raw materials and reagents commonly used by ordinary technicians in the field, as well as the commonly used experimental methods and technical means.

[0036] The research target of this example is the limestone of the first member of the Maokou Formation of the Permian System in the eastern part of the Sichuan Basin, and the research is carried out according to the following steps:

[0037] S1. Collect drilling and logging data in the area, as well as existing 2D seismic line data;

[0038] S2. For the study layer, select wells with complete well-seismic data, conduct well-seismic stratigraphic framework comparison, and establish a stratigraphic division scheme for the target layer, such as Figure 2 , and statistics on the reservoir thickness H, total organic carbon content C, porosity of each single well target layer Gas content G, bioclastic limestone thickness, marlstone thickness;

[0039] S3. Calculate the ratio of bioclastic limestone to formation thickness A, and the ratio of marlstone to formation thickness B, and conduct correlation analysis between A and B and formation thickness, such as Figure 3 and Figure 4 .

[0040] S4. Based on the single well data and combined with the seismic change trend, draw the stratigraphic thickness map of the studied layer, such as Figure 5 , and combined with the correlation between A, B and the stratum thickness segment in step S3, the paleo-geomorphology of the large area is reconstructed, and the paleo-geomorphology of the large area is divided into highland (u), slope break (b), slope (s), slope foot (f), and depression (d), as shown in Figure 1 ; The geomorphic units of this embodiment are divided as follows Figure 6 ;

[0041] S5. Obtain important parameters reservoir parameters C according to the grey correlation method. The grey relational degree y(C) between G and H, y(G), calculate each geomorphic unit F; the favorable area prediction formula is

[0042] S6. According to the F calculated in step S5, u 、F b 、F s 、F f 、F d Sort by F u 、F b 、F s 、F f 、F d They correspond to high ground (u), slope break (b), slope (s), slope foot (f), and depression (d). Figure 7 , thus dividing the favorable areas of tight carbonate reservoirs in the source, such as Figure 8 .

[0043] The data results show that F b With F f , which is relatively high, and is the most favorable area for tight carbonate reservoirs within the source.

[0044] The above examples and verification examples only express the specific implementation methods of the present application, and their descriptions are relatively specific and detailed, but they cannot be understood as limiting the scope of protection of the present application. It should be pointed out that for ordinary technicians in this field, several variations and improvements can be made without departing from the technical solution concept of the present application, which all belong to the scope of protection of the present application.

Claims

1. A method for predicting favorable areas of tight carbonate reservoirs within the source based on paleo-geomorphological analysis, characterized in that: The steps include: S1. Collect large-area drilling and logging data, as well as existing 2D seismic line data; S2. For the study layer, select wells with complete well-seismic data, conduct well-seismic stratigraphic framework comparison, establish a stratigraphic division scheme for the target layer, and calculate the reservoir thickness H, total organic carbon content C, porosity, and other parameters of each single well target layer. Key parameters of gas content G, thickness of bioclastic limestone and marl; S3. Calculate the ratio of bioclastic limestone to formation thickness A and the ratio of marlstone to formation thickness B, and conduct correlation analysis between A and B and formation thickness respectively; S4. Under the constraint of drilling thickness, reconstruct the paleo-geomorphology of the region and divide it into highland u, slope break b, slope s, slope foot f, and depression d; S5. Use grey correlation analysis to establish important parameters, reservoir parameters C, The grey relational degree y(C) between G and H, y(G); S6. According to the grey correlation degree calculated in step S5, using the favorable area prediction formula, Calculate each geomorphic unit, highland, slope break, slope, slope foot, and depression respectively u 、F b 、F s 、F f 、F d ; S7, according to the F calculated in step S6, F u 、F b 、F s 、F f 、F d The favorable areas of tight carbonate reservoirs within the source are sorted and divided.

2. The method for predicting favorable areas of in-source tight carbonate reservoirs based on paleo-geomorphological analysis according to claim 1, characterized in that: The method for reconstructing the regional paleo-geomorphology in step S4 is: according to the correlation between A, B and the stratum thickness obtained in step S3, if A is positively correlated with the stratum thickness and B is negatively correlated with the stratum thickness, the paleo-geomorphology in the region with thicker stratum thickness is higher; if A is negatively correlated with the stratum thickness and B is positively correlated with the stratum thickness, the paleo-geomorphology in the region with thicker stratum thickness is lower.

3. The method for predicting favorable areas of in-source tight carbonate reservoirs based on paleo-geomorphological analysis according to claim 1, characterized in that: The calculation method of the grey relational degree in step S5 is: (1) Determine the reservoir thickness H as the parent sequence, and the total organic carbon content C, porosity φ, and gas content G as subsequences. They are H=[H(1),H(2),…,H(n)], C=[C(1),C(2),…,C(n)], φ=[φ(1),φ(2),…,φ(n)], G=[G(1),G(2),…,G(n)]; (2) Preprocess the data. Where X′(k) is the data in each sequence, is the single series average; (3) Subtract the processed data of the parent sequence from each subsequence, take the absolute value, set it as |X(n)|, set a as the minimum value of the obtained data, and b as the maximum value of the obtained data; (4) Then the grey correlation degree between each subsequence data and the corresponding parent sequence data is Y[X(k)]=(a+ρ×b) / (|X(n)|+ρ×b), where ρ is 0.5; (5) Finally, the grey correlation between each key parameter and reservoir thickness is the average value of the single sequence grey correlation, that is,

Images