Day 1: Monday, 1st February
Session 1: DBM Current Best Practices – What Can be Enhanced?
Decision based integrated Modeling (DBM) continues to play a pivotal role in the oil and gas industry by ensuring that multi-disciplinary data integration produces the desired outcome for the most effective and actionable solutions. This session will examine present-day DBM technical workflows and best practices, along with potential enhancements through the implementation of emerging technology, particularly in big data analytics.
Session 2: Multi-Disciplinary Data Integration – New Approaches
Subsurface data integration is one of the main motivations to build reservoir models, but needs to fill the gaps between geological, geophysical, petrophysical, geomechanical and reservoir engineering perspectives. In this session, we welcome presentations on recent and new approaches to help practitioners and asset teams to reduce these gaps, including new theories, workflows, methods and organizations. Talks in this session are open to promising methods, case studies using new approaches and lessons learned. We expect thought-provoking presentations which challenge the current siloed reservoir modeling practices, show concrete ideas to make progress and promote discussions among the workshop attendance.
Day 2: Tuesday, 2nd February
Session 3: Machine Learning and Artificial Intelligence – Practical Applications, Recent Innovations and Automation
Saturation estimation and reservoir production are as good as we are able to provide better geological model. Reservoirs are complex and integrating all available data within few kilometers is becoming a critical part to provide a realistic model that is representative of the current reservoir and how it will behave in the next coming years of production. In other words, our way of constructing such models will need to evolve from the traditional techniques. The use of artificial intelligence (AI) and machine learning (ML) techniques to compensate the thousands of simulation runs is the new way of working. Like in many applications now, AI have proven to be powerful data processing tools and it is becoming an efficient tool for detecting complex relationship between inputs and outputs. Multiple researchers have already used it to better understand fluid flow in porous media and the current workshop is looking for contribution of AI in practical applications to enhance and facilitate decision based integrated reservoir modeling.
Session 4: Case Studies: Successes and Pitfalls
In this final session, case studies showcasing the important role of integration in reservoir modeling will be presented. A variety of topics will be discussed including the use of new technologies alongside well-established workflows for solving key reservoir management decisions. Valuable lessons learned will be shared with success stories and drawback to avoid.