No abstract

In recent years, with the steep drop and increased volatility in oil price, there is an urgency for making our field (re-development) plans more dynamic and efficient with faster payback and with particular emphasis on robustness against uncertainties. This paper describes a root cause analysis and a methodology to achieve up to ~30% improvement in field development planning project cycle and developing a better-integrated reservoir understanding. A comprehensive integrated analysis of available data is a key success criterion for robust decision-making. A detailed value stream mapping and a timeline analysis for data analysis in the hydrocarbon maturation process revealed that our process cycle efficiency is only 16% with a significant room for improvement. Any improvement can be directly translated to man-hour cost saving and acceleration of oil delivery. Effective use of technology and digitalization for knowledge management, standardized ways of working and easy access to historical data, analysis and diagnostics were identified as key focus areas to improve delivery. An innovative process and web based digital platform, iResDAT, is developed for accelerating data analysis. It mines from volumes of petro-technical databases and translates data into standardized diagnostics using latest data analytics and visualization technologies. It has already reduced dramatically the time to mine critical subsurface data and prepare required integrated diagnostics that are auditable and can be re-created in a few seconds. Based on the early pilot studies the cycle time reduction in the data analysis phase is close to 30% with improved quality and standardization of the integrated analysis. It has already transformed the ways of working where the subsurface discussion can happen across disciplines using a single platform that enforces early integration for reservoir understanding and associated uncertainty characterization. It is a web-based platform where the diagnostic dashboards are crowd sourced; sustained and enhanced by the business to ensure the relevance and sustainability with the Corporate Data Management and IT functions. It is a building block towards quality controlled and auditable data analysis and interpreted dataset, which may form the backbone for any advanced analytics in future to enable digitally enabled hydrocarbon maturation.