A Simple Machine-Learning Approach for the Discovery of Digital Subsurface Geoscience Analog Data

The need for accelerating and improving the quality of opportunities in the asset maturation life cycle encouraged us to develop a digital solution to help geoscientists extract hidden value in their structured datasets. The focus was on creating an unsupervised machine-learning (ML) algorithm that can be trained on a structured dataset to enable the geoscientist to be presented systematically with a ranked list of analogs that meet a predefined set of weighted criteria. This has time-saving and quality-improving implications for prospect risk and volume screening, benchmarking, quality assurance and subsurface insights. The ML-assisted analytics workflow will result in more confident estimates of volumes and risk, and a list of similar reservoirs that can provide insights and new interpretation scenarios.