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Ahead of Print Abstract
DOI:10.1306/12162120181
Shale brittleness prediction using machine learning – A Middle East basin case study
Ayyaz Mustafa1 , Zeeshan Tariq2 , Abdulazeez Abdulraheem2 , Mohamed Mahmoud2 , Shams Kalam2 , and Rizwan Ahmed Khan2
1 Center for Integrative Petroleum Research (CIPR), College of Petroleum Engineering and Geosciences, King Fahd University of Petroleum and Minerals, Dhahran, Saudi Arabia
2 Petroleum Engineering Department, College of Petroleum Engineering and Geosciences, King Fahd University of Petroleum and Minerals, Dhahran, Saudi Arabia
Ahead of Print 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 (ML) 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 eastern 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. Feed-forward artificial neural network (FFANN) 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 utilized in this study. ANN was found to be better than ANFIS by giving high accuracy. The proposed model was then compared with widely used models in the industry such as Jarvie et al., (2007) and Rybacki et al., (2016) on a blind dataset. Results showed that the proposed model outperformed previous models by giving less error.
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