Reservoir Uncertainty Analysis: The Trends from Probability to Algorithms and Machine Learning
- Maureen Ani (Robert Gordon University) | Gbenga Oluyemi (Robert Gordon University) | Andrei Petrovski (Robert Gordon University) | Sina Rezaei-Gomari (Teesside University)
- Document ID
- Society of Petroleum Engineers
- SPE Intelligent Energy International Conference and Exhibition, 6-8 September, Aberdeen, Scotland, UK
- Publication Date
- Document Type
- Conference Paper
- 2016. Society of Petroleum Engineers
- 7.2.3 Decision-making Processes, 5.5.5 Evaluation of uncertainties, 5 Reservoir Desciption & Dynamics, 7.6 Information Management and Systems, 7.6.4 Data Mining, 7.6.6 Artificial Intelligence, 5.5 Reservoir Simulation, 7 Management and Information
- Reservoir Modelling, Artificial Intelligence, Uncertainty Modelling, Algorithms, Reservoir Uncertainty
- 217 in the last 30 days
- 607 since 2007
- Show more detail
- View rights & permissions
|SPE Member Price:||USD 8.50|
|SPE Non-Member Price:||USD 25.00|
For over fifty years, reservoir development around the world has covered different reservoir types and environments with vast technology, expertise and a growing variety of approaches. However, the predominant challenge from which a myriad of other field development issues arise has been on how to accurately characterise reservoir parameters because the obtained results are largely associated with uncertainties due to subsurface geological complexities.
This paper focuses on the evolving advances and current practices in reservoir uncertainty modelling and gives insight into the future trends. This work critically examines the foremost statistical reservoir uncertainty analysis approaches, the current probabilistic and stochastic uncertainty modelling workflows which are typically based on various numerical models, and the very recent development of embedding some artificial intelligence algorithms (which include genetic algorithms, artificial neural networks, Bayesian networks amongst others) in reservoir uncertainty modelling, which now points to a future of using more sophisticated machine learning systems for achieving reservoir models and parameters with higher confidence.
These evolving trends and approaches are discussed in more detail in this paper; with an in-depth analysis of the associated workflows, fundamental principles, strengths, weaknesses, robustness and economics of each approach. Also, reconciliation between the statistical, probabilistic, stochastic and artificial intelligence methods present a deep insight into the prospects of using artificial intelligence for optimising the modelling of reservoir uncertainties beyond the capabilities of conventional methods. Thus saving time and cost by quantifying the uncertainties in reservoir properties as well as regenerating new best-fit reservoir attributes using the robust uncertainty analysis networks and the pattern-recognition ability of machine learning networks.
Hence, this paper presents a comprehensive review of the various uncertainty analysis methods, and also analyses the confidence of artificial intelligence applications which are increasingly pushing the frontiers to improved uncertainty modelling.
|File Size||1 MB||Number of Pages||12|
Abdulraheem, A., Ahmed, M., Vantala, A. and Parvez, T., 2009, "Prediction of Rock Mechanical Parameters for Hydrocarbon Reservoirs Using Different Artificial Intelligence Techniques", SPE Saudi Arabia Section Technical Symposium and Exhibition held in AlKhobar, Saudi Arabia, Society of Petroleum Engineers, 09–11 May.
Baroni, G. and Tarantola, S., 2012, "A General Probabilistic Framework for Uncertainty and Sensitivity Analysis of Deterministic Models", 2012 International Congress on Environmental Modelling and Software Managing Resources of a Limited Planet, Sixth Biennial Meeting held in Leipzig, Germany, International Environmental Modelling and Software Society, Leipzig, 1-5 July.
Caers, J., Park, K. and Scheidt, C., 2010. Modeling Uncertainty of Complex Earth Systems in Metric Space. Handbook of Geomathematics, Springer, pp. 877–901. URL http://books.google.com/books?id=nPqzpCs7k5EC
Elphick, R., Gillis, G., Bryant, I., Rottenberg, R. and Ryer, T., 2016, Schlumberger Oilfield Glossary, [Online]. Available: http://www.glossary.oilfield.slb.com/en/Terms/u/uncertainty.aspx [2016, March 4].
Meddaugh, W. S., Champenoy, N., Osterloh, W. T. and Tang, H., 2011, "Reservoir Forecast Optimism - Impact of Geostatistics, Reservoir Modeling, Heterogeneity, and Uncertainty.", SPE Annual Technical Conference and Exhibition, Denver, Colorado, USA, Society of Petroleum Engineers, 30 October-2 November.
Schlumberger, Schlumberger Oilfield Glossary. Available: http://www.glossary.oilfield.slb.com [2016, January 15].
Shahkarami, A., Mohaghegh, S.D., Gholami, V. and Bromhal, G., 2015, "Application of Artificial Intelligence and Data Mining Techniques for Fast Track Modeling of Geologic Sequestration of Carbon Dioxide – Case Study of SACROC Unit", SPE Digital Energy Conference and Exhibition held in The Woodlands, Texas, Society of Petroleum Engineers, March 3-5.
Sheldon, J.W., Harris, C.D., and Bavly, D., 1960, "A Method for General Reservoir Behavior Simulation on Digital Computers", 35th Annual Fall Meeting of the Society of Petroleum Engineers of the AIME (American Institute of Mining, Metallurgical and Petroleum Engineers), Society of Petroleum Engineers, Denver, Colorado, October 2-5.
Yang, C., Nghiem, L., Erdle, J., Moinfar, A., Fedutenko, E., Li, H., and Mirzabozorg, A., 2015, "An Efficient and Practical Workflow for Probabilistic Forecasting of Brown Fields Constrained by Historical Data", SPE Annual Technical Conference and Exhibition held in Houston, Texas, USA, Society of Petroleum Engineers, 28-30 September.