Multi-attribute fusion thin reservoir prediction method, device, equipment and medium

Abstract

The invention belongs to the field of petroleum and natural gas exploration and development, and particularly discloses a multi-attribute fusion thin reservoir prediction method which comprises the following steps: S1, calibrating a well-to-seismic combined synthetic record, performing seismic structure interpretation on a near-well structure in combination with a through-well seismic section, and comparing the actual drilling depth of a seismic marker bed with the seismic structure interpretation horizon depth to obtain a well-to-seismic combined synthetic record; determining a well trajectory position; s2, seismic attributes reflecting reservoir features are extracted through seismic data; s3, compressing the seismic attribute space extracted in the step S2 by using a principal component analysis technology, and preferably selecting effective seismic attributes; s4, performing further optimization on the effective seismic attributes, and performing reservoir distribution prediction according to the optimized effective seismic attributes; and S5, analyzing whether a prediction result accords with geological expectation or not. The drilling efficiency, the well bore quality and the exploration benefits are improved, and the oil-gas exploration process is accelerated. The method is suitable for reservoir distribution prediction in seismic exploration.

Description

Technical Field

[0001] The present invention belongs to the field of oil and gas exploration and development, and specifically relates to a thin reservoir prediction method, device, equipment and medium with multi-attribute fusion. Background Art

[0002] With the deepening of seismic exploration and the development of geophysical exploration technology, especially the increasing complexity and difficulty of exploration targets, oil and gas exploration has developed from the original search for structural oil and gas reservoirs to lithologic and structural-lithologic composite oil and gas reservoirs. Taking the Maokou Formation in the Sichuan Basin as an example, the reservoir is thin, laterally discontinuous, and has strong spatial anisotropy. The rock physical response characteristics are significantly different from those of the surrounding rocks. However, due to the thin reservoir, the seismic response characteristics are not obvious, and the interpretation is difficult, which brings great challenges to the implementation of development wells.

[0003] In the integrated exploration mode of seismic exploration acquisition, processing and interpretation, thin reservoir prediction is an important part of this systematic project. The existing methods mainly use the results of seismic exploration to predict the underground reservoir distribution, but the prediction results are not fine enough and there are differences from the interpretation results of new wells. Summary of the Invention

[0004] The purpose of the present invention is to provide a thin reservoir prediction method, device, equipment and medium with multi-attribute fusion to improve the drilling efficiency and wellbore quality, accelerate the oil and gas exploration process, and improve the exploration efficiency.

[0005] In order to achieve the above purpose, the technical method adopted by the present invention is as follows: A thin reservoir prediction method with multi-attribute fusion includes the following steps: S1. Perform synthetic seismogram calibration based on seismic data and well logging data in the study area, combine the seismic cross-section passing through the well to conduct seismic structure interpretation of the structure beside the well, compare the actual drilling depth of the seismic marker bed with the depth of the seismic structure interpretation horizon, and determine the well trajectory position; S2. According to the well trajectory position, use seismic data to extract seismic attributes reflecting reservoir characteristics; S3. Use the principal component analysis technique to compress the seismic attribute space extracted in step S2, and optimize the effective seismic attributes that can reflect the reservoir spatial distribution characteristics and are independent of each other; S4. Further optimize the effective seismic attributes, and predict the reservoir distribution according to the optimized effective seismic attributes; S5. Analyze whether the prediction result meets the geological expectation.

[0006] As a limitation: the seismic attributes extracted in step S2 include peak amplitude attribute, thickness attribute and trough amplitude attribute; The peak amplitude attribute is the maximum energy value within the distribution range of the reservoir section calculated based on seismic data. The location with a larger peak amplitude attribute value is a favorable area for reservoir development; The thickness attribute is the thickness value of the area where the energy of the reservoir section is greater than 0 calculated based on seismic data. The location with a larger thickness attribute is a favorable area for reservoir development; The trough amplitude attribute is the minimum energy value less than 0 inside the rock formation calculated based on seismic data. The location with a smaller trough amplitude attribute is a favorable area for reservoir development.

[0007] As a limitation: In step S4, the further optimization of the effective seismic attributes specifically is: making a cross-plot of the reservoir thickness in the single-well logging interpretation in the logging data interpretation of the study area and the effective seismic attribute values of the rock formation in the study area, analyzing the correlation between the reservoir thickness and the effective seismic attributes, and optimizing the effective seismic attributes with higher correlation.

[0008] As a further limitation: In step S4, the reservoir distribution prediction specifically is: using the effective seismic attributes with higher correlation to predict the reservoir distribution, and the calculation formula is: A = peak amplitude attribute value / (trough amplitude attribute value × thickness attribute value), where A is the reservoir development probability, and the larger A is, the more developed the reservoir is.

[0009] As a further limitation: In step S5, specifically is: verifying the effect through the logging reservoir thickness value. When the logging reservoir thickness value is larger, calculating the reservoir development probability A at this logging reservoir thickness. If the reservoir development probability A is a larger value, the prediction result conforms to the geological expectation. If the reservoir development probability A is not a larger value, corresponding measures are taken to modify the well trajectory.

[0010] The present invention also discloses a thin reservoir prediction device for multi-attribute fusion, including: A well trajectory position determination module, which performs synthetic record calibration based on the seismic data and logging data in the study area, combines the seismic profile passing through the well to conduct seismic structure interpretation on the structure beside the well, and compares the actual drilling depth of the seismic marker layer with the depth of the seismic structure interpretation horizon to determine the well trajectory position; A seismic attribute extraction module, which extracts seismic attributes reflecting reservoir characteristics using seismic data according to the well trajectory position; A seismic attribute optimization module, which compresses the seismic attribute space extracted in step S2 using the principal component analysis technique, and optimizes the effective seismic attributes that can reflect the reservoir spatial distribution characteristics and are independent of each other; A reservoir distribution prediction module, which further optimizes the effective seismic attributes, and predicts the reservoir distribution according to the optimized effective seismic attributes; A prediction analysis module, which is used to analyze whether the prediction result conforms to the geological expectation.

[0011] The present invention also discloses an electronic device, which includes a memory, a processor, and a computer program stored on the memory and capable of running on the processor. When the processor executes the computer program, the above method is implemented.

[0012] The present invention also discloses a computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, the above method is implemented.

[0013] Due to the adoption of the above solution, the beneficial effects achieved by the present invention compared with the prior art are as follows: (1) A thin reservoir prediction method with multi-attribute fusion provided by the present invention uses the joint synthetic record calibration of seismic data and logging data, extracts seismic attributes reflecting reservoir characteristics based on seismic data, and uses the multi-attribute fusion technology to finely predict the development of thin reservoirs, accurately feedbacks well-seismic information to decision-makers, studies and processes new problems occurring during the actual drilling process, takes corresponding measures in a timely manner, accelerates the oil and gas exploration process, improves the exploration efficiency, predicts new situations that may occur next, greatly improves the working efficiency of drilling, and the wellbore quality; (2) The present invention also provides corresponding implementation devices, electronic devices, and readable storage media, further making the method more practical, and the devices, electronic devices, and readable storage media have corresponding advantages.

[0014] The present invention is applicable to the prediction of reservoir distribution in seismic exploration. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] The following further describes the present invention in detail with reference to the drawings and specific embodiments.

[0016] Figure 1 It is a flowchart of a thin reservoir prediction method with multi-attribute fusion according to Embodiment 1 of the present invention; Figure 2 It is a reservoir development prediction map predicted according to Embodiment 1 of the present invention; Figure 3 It is a structural block diagram of a thin reservoir prediction device with multi-attribute fusion according to Embodiment 2 of the present invention; Figure 4 It is a structural block diagram of an electronic device according to Embodiment 2 of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0017] The following further describes the present invention with reference to the embodiments. However, those skilled in the art should understand that the present invention is not limited to the following embodiments, and any improvements and equivalent changes made on the basis of the specific embodiments of the present invention are within the scope of protection of the claims of the present invention.

[0018] Embodiment 1 A Thin Reservoir Prediction Method with Multi-attribute Fusion A thin reservoir prediction method integrating multiple attributes, the process of which is as follows Figure 1 shown, including the following steps: S1. Perform synthetic seismogram calibration based on seismic data and logging data in the study area, combine the cross-well seismic profile to conduct seismic structure interpretation of the structure beside the well, compare the actual drilling depth of the seismic marker bed with the depth of the seismic structure interpretation horizon, and determine the well trajectory position; S2. According to the well trajectory position, use seismic data to extract seismic attributes reflecting reservoir characteristics, including peak amplitude attribute, thickness attribute, and trough amplitude attribute; The peak amplitude attribute is the maximum energy value within the distribution range of the reservoir section calculated based on seismic data. The position with a larger peak amplitude attribute value is a favorable area for reservoir development; The thickness attribute is the thickness value of the area where the energy of the reservoir section is greater than 0 calculated based on seismic data. The position with a larger thickness attribute is a favorable area for reservoir development; The trough amplitude attribute is the minimum energy value less than 0 inside the rock formation calculated based on seismic data. The position with a smaller trough amplitude attribute is a favorable area for reservoir development; S3. Use principal component analysis technology to compress the seismic attribute space extracted in step S2, and optimize the effective seismic attributes that can reflect the spatial distribution characteristics of the reservoir and are independent of each other; S4. Make a cross-plot of the reservoir thickness in the single-well logging interpretation in the logging data interpretation of the study area and the effective seismic attribute values of the rock formation in the study area, analyze the correlation between the reservoir thickness and the effective seismic attributes, optimize the effective seismic attributes with higher correlation, and use the effective seismic attributes with higher correlation to predict the reservoir distribution. The calculation formula is: A = peak amplitude attribute value / (trough amplitude attribute value × thickness attribute value), where A is the reservoir development probability, and the larger A is, the more developed the reservoir is; S5. Verify the effect through the logging reservoir thickness value. When the logging reservoir thickness value is large, calculate the reservoir development probability A at this logging reservoir thickness. If the reservoir development probability A is a large value, the prediction result meets the geological expectation. If the reservoir development probability A is not a large value, take corresponding measures to modify the well trajectory.

[0019] Using the method of this embodiment, the reservoirs of development wells with different lithologies and different depths in the study area were effectively predicted. The predicted reservoir development is as Figure 2 shown. According to the prediction results, a total of 6 well trajectory modification suggestions were put forward, and 2 of them have been drilled and obtained industrial gas flow, effectively improving the single-well productivity.

[0020] Embodiment 2 A thin reservoir prediction device, equipment, and medium integrating multiple attributes A thin reservoir prediction device integrating multiple attributes, the structural block diagram of which is as Figure 3 shown, including: The well trajectory position determination module performs synthetic seismogram calibration based on seismic data and logging data in the study area, conducts seismic structural interpretation of the structure beside the well in combination with the seismic section passing through the well, compares the actual drilling depth of the seismic marker bed with the depth of the seismic structural interpretation horizon, and determines the well trajectory position; The seismic attribute extraction module extracts seismic attributes reflecting reservoir characteristics from seismic data according to the well trajectory position; The seismic attribute optimization module compresses the seismic attribute space extracted in step S2 using principal component analysis technology, and optimizes effective seismic attributes that can reflect the spatial distribution characteristics of the reservoir and are independent of each other; The reservoir distribution prediction module further optimizes the effective seismic attributes, and predicts the reservoir distribution based on the optimized effective seismic attributes; The prediction analysis module is used to analyze whether the prediction result meets the geological expectation.

[0021] This embodiment also provides an electronic device, the structural block diagram of which is as Figure 4 shown, including a memory, a processor, and a computer program stored on the memory and capable of running on the processor. When the processor executes the computer program, the method described in Embodiment 1 is implemented.

[0022] This embodiment also provides a computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, the method described in Embodiment 1 is implemented.

Claims

1. A thin reservoir prediction method for multi-attribute fusion, characterized in that It includes the following steps: S1. Conduct synthetic seismogram calibration based on seismic data and logging data in the study area. Combine the cross-well seismic profile to conduct seismic structural interpretation of the structure beside the well. Compare the actual drilling depth of the seismic marker bed with the depth of the seismic structural interpretation horizon to determine the well trajectory position; S2. According to the well trajectory position, use seismic data to extract seismic attributes reflecting reservoir characteristics; S3. Use principal component analysis technology to compress the seismic attribute space extracted in step S2, and optimize the effective seismic attributes that can reflect the spatial distribution characteristics of the reservoir and are independent of each other; S4. Further optimize the effective seismic attributes, and conduct reservoir distribution prediction based on the optimized effective seismic attributes; S5. Analyze whether the prediction result meets the geological expectation.

2. The multi-attribute fusion-based thin reservoir prediction method according to claim 1, characterized in that The seismic attributes extracted in step S2 include peak amplitude attribute, thickness attribute, and trough amplitude attribute; The peak amplitude attribute is the maximum energy value calculated based on seismic data within the distribution range of the reservoir section. The location with a larger peak amplitude attribute value is a favorable area for reservoir development; The thickness attribute is the thickness value of the area where the energy of the reservoir section is greater than 0 calculated based on seismic data. The location with a larger thickness attribute is a favorable area for reservoir development; The trough amplitude attribute is the minimum energy value less than 0 calculated based on seismic data within the rock formation. The location with a smaller trough amplitude attribute is a favorable area for reservoir development.

3. A thin reservoir prediction method with multi-attribute fusion according to claim 1 or 2, characterized in that In step S4, the further optimization of the effective seismic attributes is specifically as follows: Make a cross-plot of the reservoir thickness interpreted by single-well logging in the logging data interpretation of the study area and the effective seismic attribute values of the rock formation in the study area, analyze the correlation between the reservoir thickness and the effective seismic attributes, and optimize the effective seismic attributes with higher correlation.

4. A method for predicting thin reservoirs with multi-attribute fusion according to claim 3, characterized in that, In step S4, the reservoir distribution prediction is specifically as follows: Use the effective seismic attributes with higher correlation to conduct reservoir distribution prediction. The calculation formula is: A = peak amplitude attribute value / (trough amplitude attribute value × thickness attribute value), where A is the reservoir development probability, and the larger A is, the more developed the reservoir is.

5. A method for predicting thin reservoirs with multi-attribute fusion according to claim 4, characterized in that, In step S5, it is specifically as follows: Verify the effect through the logging reservoir thickness value. When the logging reservoir thickness value is large, calculate the reservoir development probability A at the logging reservoir thickness. If the reservoir development probability A is a large value, the prediction result meets the geological expectation. If the reservoir development probability A is not a large value, take corresponding measures to modify the well trajectory.

6. A thin reservoir prediction device for multi-attribute fusion, characterized in that, It includes: Well trajectory position determination module, which conducts synthetic seismogram calibration based on seismic data and logging data in the study area, combines the cross-well seismic profile to conduct seismic structural interpretation of the structure beside the well, compares the actual drilling depth of the seismic marker bed with the depth of the seismic structural interpretation horizon, and determines the well trajectory position; Seismic attribute extraction module, which extracts seismic attributes reflecting reservoir characteristics using seismic data according to the well trajectory position; Seismic attribute optimization module, which uses principal component analysis technology to compress the seismic attribute space extracted in step S2, and optimizes the effective seismic attributes that can reflect the spatial distribution characteristics of the reservoir and are independent of each other; Reservoir distribution prediction module, which further optimizes the effective seismic attributes and conducts reservoir distribution prediction based on the optimized effective seismic attributes; Prediction analysis module, which is used to analyze whether the prediction result meets the geological expectation.

7. An electronic device, characterized in that, It includes a memory, a processor, and a computer program stored on the memory and capable of running on the processor. When the processor executes the computer program, the method described in any one of claims 1-5 is implemented.

8. A computer-readable storage medium, characterized in that, A computer program is stored on the computer-readable storage medium. When the computer program is executed by the processor, the method described in any one of claims 1-5 is implemented.

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