E&P companies strive to organise 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 to analyse and address complicated subsurface issues. Machine learning algorithms and AI tools are best applied to structured and regularised data to gain more meaningful results, but a large amount of effort needs to be made to standardise field and reservoir knowledge.
At C&C Reservoirs, 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 regularise reservoir knowledge for decision making using AI tools. 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, DAKS, C&C Reservoirs secure cloud-based, asset centred, knowledge platform, has been developed for searching, retrieving, characterising and benchmarking E&P assets against global analogues. Our industry-leading knowledge base provides a solid foundation for the application of AI and machine learning technologies to optimise the E&P decision-making.