Changing focus in modelling in the energy transition: Leveraging reservoir knowledge, multi-scenario modelling and artificial intelligence to robustly screen development options
As part of the energy transition value drivers and workflows in the Oil &Gas industry are changing significantly. In addition to the strong focus on de-carbonisation fast delivery of HCM projects is of utmost importance.
For the past years Petroleum Development Oman’s Hydrocarbon Maturation Centre (HMC) has consolidated reservoir knowledge in Formation specific thematic catalogues for PDO’s acreage. The objective of this multidisciplinary collection of data, experience and insights is to reduce FDP cycle times by providing users web-based access to standardised datasets, workflows and past development decisions and thereby leveraging the use of analogues for development decision making. However, despite the vast amount of analogue information specific development decisions such as optimal pattern type, well spacing, water throughput, etc. and corresponding recovery factors and production profiles commonly are still evaluated using reservoir simulation.
PDO is therefore developing a new web-based tool that leverages the extensive analogue knowledge, multi-scenario modelling, and machine learning to eliminate or significantly reduce the requirement for such time- consuming reservoir simulation studies.
For several reservoirs we generated 10,000’s of sector models covering all static and dynamic uncertainty ranges and simulated all plausible development concepts. The entire dataset is then used to train a ML model.
The generated sector model library is searchable and can be used to identify geological analogues. The AI webtool then can predict recovery factors, forecast ranges etc. for any analogue or new sector model for the available development concepts at a click of a button. This allows a fast & robust evaluation of subsurface development options with clarity on trade off’s, eliminates repetitive modelling & simulation requirements, and significantly accelerates simulation-based forecasting by 50% and more.
As part of the energy transition value drivers and workflows in the Oil &Gas industry are changing significantly. In addition to the strong focus on de-carbonisation fast delivery of HCM projects is of utmost importance.
For the past years Petroleum Development Oman’s Hydrocarbon Maturation Centre (HMC) has consolidated reservoir knowledge in Formation specific thematic catalogues for PDO’s acreage. The objective of this multidisciplinary collection of data, experience and insights is to reduce FDP cycle times by providing users web-based access to standardised datasets, workflows and past development decisions and thereby leveraging the use of analogues for development decision making. However, despite the vast amount of analogue information specific development decisions such as optimal pattern type, well spacing, water throughput, etc. and corresponding recovery factors and production profiles commonly are still evaluated using reservoir simulation.
PDO is therefore developing a new web-based tool that leverages the extensive analogue knowledge, multi-scenario modelling, and machine learning to eliminate or significantly reduce the requirement for such time- consuming reservoir simulation studies.
For several reservoirs we generated 10,000’s of sector models covering all static and dynamic uncertainty ranges and simulated all plausible development concepts. The entire dataset is then used to train a ML model.
The generated sector model library is searchable and can be used to identify geological analogues. The AI webtool then can predict recovery factors, forecast ranges etc. for any analogue or new sector model for the available development concepts at a click of a button. This allows a fast & robust evaluation of subsurface development options with clarity on trade off’s, eliminates repetitive modelling & simulation requirements, and significantly accelerates simulation-based forecasting by 50% and more.
