2019 AAPG Annual Convention and Exhibition:

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Challenges and Solution to AI Application in E&P Decision-Making


E&P companies strive to organize data, information and knowledge consistently to facilitate comparison, to learn lessons from the past and to better plan for the future. However, the lessons from past investments are seldom fully known or used due to lack of knowledge standards, changes in personnel, strategic priorities, cost controls and simply pressure on time.

Artificial Intelligence (AI) including machine learning could be applied readily in many stages of E&P lifecycle. However, machine learning algorithms are best applied to structured and regularized data to gain meaningful results. Data preparation, regularization and standardization represent 90% of the efforts in many AI applications. To analyze and solve more complex subsurface problems at asset or portfolio level using AI, a large amount of effort would have to be made to standardize field and reservoir knowledge.

We have conducted in-depth analysis and systematic documentation of the world’s most important fields and reservoirs and have established a comprehensive knowledge classification system to regularize reservoir knowledge for decision-making using AI tools. The regularized reservoir knowledge covers every known type of reservoir in all types of petroliferous basin around the world. Each documented field report details how the field was discovered followed by basin genesis and source rock, structure and trap definition, reservoir characteristics and fluid properties all the way to resources and recovery insights, including development strategy, reservoir management and improved recovery techniques applied and their outcomes. A comprehensive knowledge model, with 450 geological and reservoir engineering attributes, has been established at both reservoir and field level. Each attribute has been consistently defined and contains a set of standardized values following a pioneering classification system. Rigorous standards, consistent rules and clear guidelines have been applied to capture reservoir and field knowledge to form a global knowledge base.

To facilitate translation of this knowledge base into real-time intelligence and insight, a software platform with a robust search engine and powerful set of analytics has been developed for searching, retrieving, characterizing and benchmarking E&P assets against global analogs. Our industry-leading knowledge base provides a solid foundation for the application of AI and machine learning technologies to optimize the E&P decision-making.