Track: Data Analytics and Big Data

Abstract

Shale gas has been regarded as one of the most promising energy sources to sustain the world’s energy demand. However, its exploration is still underdeveloped in several countries due to a lack of methods and technology implementation compared to conventional hydrocarbon exploration. In addition, the technology, methods, and data available in various oil and gas companies are currently still concentrated on conventional hydrocarbon exploration. The purpose of this study is to propose a new comprehensive method in shale gas exploration by utilizing the existing conventional hydrocarbon exploration data using data science and decision analysis approaches. The methods used in this study are K-Mean Clustering to cluster the similar rock characters (TOC, Porosity, Water Saturation, and Poisson Ratio) then continued by Multi-Criteria Decision Analysis to determine the best rock cluster for shale gas exploration. The study takes Banuwati Shale Formation in Asri Basin as a case which is well known as one of the promising source rocks in Indonesia. Based on this study, the rocks in the study area can be classified into three clusters. Cluster 1 is determined as “High Fractability Cluster”, Cluster 2 is determined as “Water Saturated Cluster” and Cluster 3 is determined as “High Organic Content Cluster” based on its physical and chemical properties. Meanwhile, Cluster 3 is determined as the best cluster with 10212 ft – 10412 ft (3113 m – 3174 m) depth interval preferred as the sweet spot for Shale Gas exploration based on Multi-Criteria Decision Analysis result.

Keywords: Shale Gas, Data Science, K-Mean Clustering, Decision Analysis, Asri Basin