Home // International Journal On Advances in Software, volume 14, numbers 1 and 2, 2021 // View article
Natural Language Processing Techniques for Enhancing Formation Evaluation
Authors:
Klemens Katterbauer
Rabeah Al Zaidy
Sara Abu Al Saud
Alberto Marsala
Keywords: reservoir formation evaluation; natural language processing; artificial intelligence; Petroleum industry
Abstract:
Formation evaluation literature and reports in the oil and gas industry are crucial in decision making and understanding of optimizing recovery. The literature provides a comprehensive summary of tools and interpretations, as well as use cases for individuals to learn and utilize the information for enhancing their formation evaluation interpretations and decision-making. A major challenge in practice is the abundance and heterogeneity of information available that leads to individuals facing enormous obstacles to retrieving the right information within an adequate timeframe. We present an overview of several approaches in natural language processing for creating an ontology framework of formation evaluation data and literature, as well as conversational AI tools to extract information for the users. The review outlines the challenges that are faced when categorizing data related to formation evaluation, as well as establishing correlations and connections between various information sources. Finally, the review will provide a summary of different conversational AI approaches and systems for assisting well log and formation evaluation interpretation, as well as the opportunities and challenges faced. In conclusion, we will dedicate the way forward for NLP-driven approaches for assisting formation evaluation interpretation in real-time, and the business impact it has in the oil and gas industry and relationship to other initiatives both in the oil and gas industry as well as beyond.
Pages: 71 to 78
Copyright: Copyright (c) to authors, 2021. Used with permission.
Publication date: December 31, 2021
Published in: journal
ISSN: 1942-2628