Method for reconstructing well logging curve of emptying leakage well section of fractured-vuggy carbonate reservoir
A technology of carbonate rocks and well logging curves, applied in neural learning methods, seismology for well logging records, biological neural network models, etc., can solve the problems of large implementation costs, deviations in the accuracy of well logging curves, and data deviations and other issues to achieve the effect of less time-consuming learning and convenient operation
Inactive Publication Date: 2022-07-12
CHINA UNIV OF PETROLEUM (EAST CHINA)
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Problems solved by technology
First of all, the conventional empirical method predicts a large deviation in the accuracy of the logging curve. For traditional tight sandstone reservoirs, the deviation is relatively controllable. model, the disadvantages of the traditional conventional empirical method with large deviations are multiplied
Secondly, using imaging logging to obtain logging curve data is one of the mainstream methods at present. However, since the imaging logging technology needs to lower the test probe into the shock, this method has high testing accuracy and wide adaptability. The burial depth of fractured-vuggy reservoirs is often greater than 5000m, and it is difficult to run the probe in the imaging logging method, and the implementation cost is high, and the vent wells of fractured-vuggy reservoirs are often located in the m-level storage space. The data obtained by imaging logging often have certain deviations
Other known prior art also does not mention the solution to log curve reconstruction in the vent well section of carbonate fracture-cavity reservoirs
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[0062] see Figure 1-3 , a method for reconstructing a logging curve of a venting well in a carbonate fracture-cavity oil reservoir, which specifically includes the following steps:
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Abstract
The invention relates to a carbonate fractured-vuggy reservoir emptying leakage well section logging curve reconstruction method. The method comprises the following steps that firstly, a seismic inversion attribute body is resampled; a seismic attribute parameter geologic model is established, and then seismic attribute parameter curves are extracted from all drilling wells in a target stratum along a wellbore; taking the obtained key wells of all complete curves of the target layer as sample data, and constructing a deep neural network model of a plurality of hidden layers; and a neural network model is trained through multiple iterations, so that a nonlinear mapping relation between a seismic attribute parameter curve and a logging curve is established, and finally, seismic attribute parameters of well drilling without the logging curve serve as input, and a missing logging curve of a target well is generated by applying the trained neural network model. By means of the method, the problem that basic information of the fracture-vug type oil reservoir leakage anti-empty section is lost can be effectively solved.
Description
technical field [0001] The invention relates to the technical field of production and development of carbonate fracture-cavity oil reservoirs, in particular to a method for reconstructing logging curves of venting and leaking well sections in carbonate fracture-cavity oil reservoirs. Background technique [0002] The existing carbonate fracture-cave reservoirs rank among the top in the world in terms of oil and gas reserves and production, and are the research objects of great concern at home and abroad. According to statistics, more than 30% of the global carbonate reservoirs are fracture-cavity reservoirs. There are a large area of carbonate rock fracture-cave reservoirs in western my country. Such reservoirs have great production value and are an important position for China's oil production to increase reserves and production. Under the combined action of multi-stage tectonic movements and paleokarst, fracture-cavity reservoirs have formed dissolution pores and fractu...
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IPC IPC(8): G01V1/46G01V1/50G06F30/20G06K9/62G06N3/08G06F111/10
CPCG01V1/46G01V1/50G06N3/084G06F30/20G01V2210/6169G01V2210/6244G01V2210/6246G01V2210/624G06F2111/10
Inventor 孙致学宋文铜李吉康谭涛李小波谢爽
Owner CHINA UNIV OF PETROLEUM (EAST CHINA)
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