Are we at the cusp of major changes in reservoir and production system modelling? As we enter an era of unprecedented data availability, low cost computing, and broad application of data science, how do we evolve the practices that we have long trusted to support even better decision making?
Traditional guidance in production system modelling has been to keep models as simple as possible, allowing for only what complexity is needed to provide confident predictions supporting decisions. In green field development, models have often stayed simple; but, can we now augment or even replace these approaches to dramatically improve the efficiency and effectiveness of our designs? In brown field development, surveillance, and optimization, adding complexity can prove very tempting, particularly when we think that more detailed physics can deliver better predictability. However, systematic injection of complexity can also undermine efficiency, integration, collaboration and model re-use.
This workshop will illustrate the current challenges, best practices and emerging approaches for improving integration in reservoir and production system modelling, focusing on opportunities for better data utilization and efficiencies. Priority will be given to case studies that illustrate the value of such integration, the practical implementation of solutions, and the experience of improving human workflows driving modelling efforts.
Topics will include:
Consistency in geological/structural and dynamic modelling: making the 'big loop' work
Fluids characterization and fluid modelling approaches: consistent and fit-for-purpose
Multi-fidelity modelling approaches: consistency between decision-based models
The role of data-driven approaches and their integration in a modern, modelling workflow
Well design and surveillance using geologically consistent inflow
Production facility integration: better reservoir management through improved design and operations
Integrated production forecasting and development option assessments
Improved integration in field development
Integrated history matching and model calibration
Consistent handling of uncertainties
Attention! Training Course Added to This Event: Data-Driven Reservoir Modeling by Shahab D. Mohaghegh
This course will demonstrate the power of Artificial Intelligence and Machine Learning and the difference they can make for informed decision making when it comes to objectives such as infill location optimization and reservoir production and recovery optimization once domain expertise becomes the foundation of their use and application in the hydrocarbon reservoirs.