Quantitative Evaluation of Shale Brittleness Based on Brittle-Sensitive Index and Energy Evolution-Based Fuzzy Analytic Hierarchy Process

Xie, Jianyong; Zhang, Junjie; Fang, Yanping; Cao, Jian; Deng, Jixin

doi:10.1007/s00603-022-03213-y

Cited by 5 publications

(1 citation statement)

References 54 publications

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“…Gou et al [15] divided geological and construction parameters into six categories, screened and retained the parameters with the greatest correlation with production as the model input through grey clustering analysis, and established a reservoir quality evaluation model of fractured wells by using the normal distribution membership function. Xie et al [19] established a shale hydraulic fracturing brittleness evaluation model using the FAHP, taking into account factors such as shale brittle mineral content, porosity, and confining pressure. However, the above-mentioned studies directly analyze factors without considering the reduction in model accuracy caused by multicollinearity resulting from the strong correlation among production-influencing factors.…”

Section: Introductionmentioning

confidence: 99%

A Comprehensive Evaluation of Shale Oil Reservoir Quality

Tian,

Fu,

Liu

et al. 2024

Processes

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To enhance the accuracy of the comprehensive evaluation of reservoir quality in shale oil fractured horizontal wells, the Pearson correlation analysis method was employed to study the correlations between geological parameters and their relationship with production. Through principal component analysis, the original factors were linearly combined into principal components with clear and specific physical meanings, aiming to eliminate correlations among factors. Furthermore, Gaussian membership functions were applied to delineate fuzzy levels, and the entropy weight method was used to determine the weights of principal components, establishing a fuzzy comprehensive evaluation model for reservoir quality. Without using principal component analysis, the correlation coefficient between production and evaluation results for the 40 wells in the Cangdong shale oil field was only 0.7609. However, after applying principal component analysis, the correlation coefficient increased to 0.9132. Field application demonstrated that the average prediction accuracy for the cumulative oil production per kilometer of fractured length over 12 months for the 10 applied wells was 91.8%. The proposed comprehensive evaluation method for reservoir quality can guide the assessment of reservoir quality in shale oil horizontal wells.

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Section: Introductionmentioning

confidence: 99%