Estimation of rocks' failure parameters from drilling data by using artificial neural network.

ABSTRACT: Comprehensive and precise knowledge about rocks' mechanical properties facilitate the drilling performance optimization, and hydraulic fracturing design and reduces the risk of wellbore-related problems. This paper is concerned with the failure parameters, namely, cohesion and friction angle which are conventionally estimated using Mohr's cycles that are drawn using compressional tests on rock samples. The availability, continuity and representability, and cost of acquiring those samples are major concerns. The objective of this paper is to investigate an alternative technique to estimate these parameters from the drilling data. In this work, more than 2200 data points were used to develop and test the correlations built by the artificial neural network. Each data point comprises the failu

SUBMITTER: Siddig O 

PROVIDER: S-EPMC9950081 | biostudies-literature | 2023 Feb

REPOSITORIES: biostudies-literature