Machine Learning in Automating Carbon Storage Site Assessment

Abstract

AbstractTo store CO2 in depleted oil and gas fields or saline aquifers, a detailed site assessment is typically done manually, which is time-consuming and costly, as there are large number of older wells with poor quality records. The study presented here will leverage cloud computing and artificial intelligence (AI) tools like Optical Character Recognition (OCR) and Natural Language Processing (NLP) to automate the legacy well assessment for efficient decision-making in storage site selection, thus reducing human effort. Results from our preliminary tests show that with this approach one can extract 80% of the desired information from various data sources including hand-written well reports and analyze information to accelerate CO2 storage risk level estimation.