Empirical evaluation of reservoir characteristics for cost and performance optimisation in oilfield operations

Proper cost management and performance improvement in oilfield production are deeply context-dependent and necessitate the understanding of Reservoir knowledge modelling and their impact on the operational efficiency and resource distribution. A KCMPO (Knowledge-Driven Cost Management and Performance Optimisation) framework is proposed which combines reservoir knowledge modelling, activity-based cost analysis, and data-driven decision intelligence. The use of machine learning techniques such as clustering and pattern recognition helps to discover cost-driving activities and to build dynamic links between reservoir properties, production behaviour, and monetary results. An ICT-enabled knowledge management framework that allows for the support of transparency, traceability, and continual learning across the various layers of decision-making is also included. The empirical assessment shows that the developed framework increases both cost efficiency and productive performance, thus establishing a data-supported decision-making process in oilfields that is systematic and sustainable.