Shale brittleness prediction using machine learning-A Middle East basin case study

Ayyaz Mustafa, Zeeshan Tariq, Abdulazeez Abdulraheem, Mohamed Mahmoud, Shams Kalam, Rizwan Ahmed Khan

Research output: Contribution to journalArticlepeer-review

14 Scopus citations

Abstract

Brittleness index (BI) of rocks can help target the most suitable formation for the hydraulic fracturing stimulation in the shale reservoirs. The two most widely used approaches in the petroleum industry are based on mineralogical composition and elastic parameters for the BI estimation. However, these approaches may not be applied for all wells for BI determination due to the scarcity of mineralogical composition and shear wave slowness data. This paper presents a machine learning approach to predict the BI using readily available well logs.Well-log data were collected from three different wells that encompass a total of 2000-ft-thick interval of potential shale gas formation in one of the Middle East basins in the southeastern part of Saudi Arabia. Mineralogical composition of shale formations revealed that the shale intervals are composed of alternating high brittle and low brittle layers/zones and mainly composed of quartz, clay, feldspar, and mica. The feed-forward artificial neural network and adaptive neuro-fuzzy inference system (ANFIS) were employed to develop the predictive model for the BI using conventional well logs. The proposed model was tested and validated to check the consistency of the model. A total of 2007 data points were used in this study. The artificial neural network was found to be better than ANFIS by giving high accuracy. The proposed model was then compared to widely used models in the industry such as Jarvie et al. (2007) and Rybacki et al. (2016) on a blind data set. Results showed that the proposed model outperformed previous models by giving less error.
Original languageEnglish (US)
Pages (from-to)2275-2296
Number of pages22
JournalAAPG BULLETIN
Volume106
Issue number11
DOIs
StatePublished - 2022

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KAUST Repository Item: Exported on 2023-07-04

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