Field Performance Prediction Under Uncertainty – Thriving on Automation Field Performance Prediction Under Uncertainty – Thriving on Automation
2021 SPE Distinguished Lecturer
Ralf Schulze-Riegert works at the Schlumberger Norwegian Technology Center, with a focus on optimization and uncertainty management applications. He holds a PhD in theoretical physics from the University of Hanover, Germany and worked in research and consulting positions in the Oil & Gas and automotive industries for 25 years.
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About the event
The petroleum industry invested in subsurface uncertainty management technology for a several years. Driven by lower economic margins, rigorous subsurface uncertainty assessments enter field development submission and approval processes.
Subsurface uncertainty modelling covers workflow processes from static geological description to dynamic reservoir performance assessment. Ensemble-based modelling concepts are available to assess the impact of alternative geological scenarios on reservoir production forecasting. While multiple-model generation becomes technically manageable, model validation and result interpretation continue to be a challenge for field development concept selection.
Solution designs integrate digital innovations, scalable computation services as well as data analytics for managing large number of reservoir models. Automation is a promise and a need for extracting value for decision processes. Simulated well production profiles cannot be visually inspected for 100s or 1000s of reservoir models. Consequently, objective performance criteria need to be well defined to enable analytics and machine learning techniques.
To benefit from digital technologies, uncertainty management in subsurface modelling requires standardized workflows and automation for faster execution loops. At the same time, heavy oil fields with 1000+ wells in Colombia require a different solution design compared to oil fields in Malaysia with long production history or gas-condensate fields in Western Siberia producing from extreme tight rock formations. This presentation discusses examples, solutions and new challenges created in uncertainty management for field development planning.