Data-driven optimization of brittleness index for hydraulic fracturing

نویسندگان

چکیده

Evaluation of brittleness index (BI) is a fundamental principle hydraulic fracturing design. A wide variety BI calculations often baffle field engineers. The traditional value comparison may also not make the best BI. Moreover, it mixed up with fracability in applications, thus causing concerns. We, therefore, redefine as pressure under certain rock mechanical (mainly brittleness), geological and injecting conditions to clarify confusion. Then, we propose data-driven workflow optimize BIs by controlling conditions. machine learning (ML) employed predict (fracturing pressure) based on measurement. Three representative ML algorithms are applied average prediction, aiming restrict interference algorithm performances. contribution pressure/fracability prediction error analysis (rather than method BI-value comparison) proposed new criterion for optimization. Six classic correlations (mineral-, logging- elastic-based) evaluated, three which optimized derivation using backward elimination strategy. stress ratio (ratio minimum maximum horizontal principal stress), representing feature, introduced into derived calculation independent variable analysis. reliability verified analyses data eight stages from seven different wells. Approximately 40%–50% errors reduced differences among performances significantly restrained. provides more reliable option evaluating formation. proposes promising application scenario fracturing, makes efficient broader usages compared comparison.

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ژورنال

عنوان ژورنال: International Journal of Rock Mechanics and Mining Sciences

سال: 2022

ISSN: ['1873-4545', '1365-1609']

DOI: https://doi.org/10.1016/j.ijrmms.2022.105207