Integration of expert and data-driven workflows to manage reservoir and well life cycle in Arctic conditions using innovative SICLO methodology

• Published in: IOP Conference Series: Materials Science and Engineering
• Main Authors: Antonic, M, Solesa, M, Zolotukhin, A B, Rakic, D, Aleksic, M
• Format: Article in Journal/Newspaper
• Language: unknown
• Published: IOP Publishing 2019
• Subjects: Arctic
• Online Access: http://dx.doi.org/10.1088/1757-899x/700/1/012056; https://iopscience.iop.org/article/10.1088/1757-899X/700/1/012056/pdf; https://iopscience.iop.org/article/10.1088/1757-899X/700/1/012056

Abstract SICLO (Source of data and information; Input data; Calculation/Analytic; Logic Analysis; Output/Value Delivery) methodology is an innovative concept for smart diagnostic, reservoir/well performance optimization and estimation of remaining reserves based on the integration of Petroleum Data Management System (PDMS) and expert rules. Implementation of SICLO methodology provides the best strategy on how to produce remaining reserves most profitably. PDMS is the foundation of SICLO methodology and provides structured and verified information that follows the Well Life Cycle. Within PDMS, data are organized and structured according to clearly defined principles and rules and filtered by different levels of quality control. Structured data allows integration of production and reservoir information with real-time data to achieve the maximum level of diagnosis of system operation performance according to reservoir and well potentials and system constraints. The built-in workflows and architecture of the whole process are automated and make the task accomplishment faster. SICLO methodology integrates expert-driven knowledge and pattern recognition tools improved by data-driven, artificial intelligence, neural network, and fuzzy logic technologies to deliver adaptive solutions for identifying locations of remaining reserves, optimizing oil and gas production, and minimizing associated operational costs.