Shale gas well staged fracturing effect evaluation and yield prediction method based on random forest
A random forest and shale gas well technology, applied in prediction, computer parts, character and pattern recognition, etc., can solve problems such as heavy workload, unsatisfactory real complex shale reservoirs, complex calculations, etc.
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[0069] Based on the fracturing construction data and stage production of 196 fracturing stages in a shale gas field in eastern Sichuan, the original sample set A is formed. The 11 frac construction factors used are shown in the table below:
[0070] Table 1
[0071]
[0072] 1) Determine the main factors affecting the fracturing effect and production
[0073] a. Level 1 dimensionality reduction - Pearson correlation coefficient
[0074] The Pearson correlation coefficients among the 11 influencing factors were calculated, and the correlation coefficients among the 11 influencing factors were all lower than 0.9. Therefore, these 11 influencing factors enter the subsequent second-level dimensionality reduction. The total sample set B at this time is the same as the original sample set A.
[0075] b. Level 2 dimensionality reduction - recursive feature elimination method based on support vector machine
[0076] The results of the recursive feature elimination method based...
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