Machine-learning methods have gained tremendous attention in the last decade. These methods are tackling and solving complex problems with impressive results in many different areas. Buzzwords such as “big data” and “deep learning” are now part of our everyday vocabulary. The underlying idea behind machine learning is that computers can identify patterns and learn from data with minimal human intervention. This is not very different from the notion of automatic history matching. Actually, the similarities are more profound: Both machine learning and history-matching methods use the same mathematical tools, such as linear algebra, optimization, Bayesian inference, and Monte Carlo sampling. In fact, I believe that machine learning is a natural research direction to improve our current history-matching methods.
At this point, some readers must be thinking about the risks of automated processes. They may very well claim that there are no surrogates for experience, a thorough understanding of the reservoir production mechanisms, and attention to the practical aspects and limitations of the models. After all, if machines can learn, humans can think. I am not trying to say otherwise. My only point is that we cannot ignore the recent developments in machine learning and we must not turn a blind eye to what computer-aided strategies can offer. If used responsibly, they can help us achieve better results in less time.
To conclude, I would like to point out that machine-learning methods may also find very interesting applications in data-driven production forecasts. In fact, data-driven methods are not new in the petroleum literature; see, for example, decline-curve analysis. These methods are used currently as complements (and often alternatives) to model-driven approaches. Machine-learning methods have much to offer in this regard, especially in cases with an abundance of data.
This Month's Technical Papers
Pattern-Based History Matching Maintains Consistency for Complex-Facies Reservoirs
Correlation-Based Localization Effective in Ensemble-Based History Matching
Novel Forward Model Quantifies Uncertainty of Fracture Networks
Recommended Additional Reading
SPE 191473 An Automatic History-Matching Work Flow for Unconventional Coupling MCMC and Nonintrusive EDFM Methods by Wei Yu, The University of Texas at Austin, et al.
SPE 191516 Robust Uncertainty Quantification Through Integration of Distributed Gauss-Newton Optimization With Gaussian Mixture Modeling and Parallelized Sampling Algorithms by Guohua Gao, Shell, et al.
SPE 191655 An Efficient Probabilistic Assisted History-Matching Tool Using Gaussian Process Proxy Models: Application to Coalbed Methane Reservoir by Sachin Rana, The Pennsylvania State University, et al.
| Alexandre Emerick, SPE, is a senior petroleum engineer at Petrobras Research Center in Rio de Janeiro. He has 16 years of experience in applied research in reservoir engineering. Emerick’s research interests include reservoir simulation, history matching, uncertainty quantification, and optimization. At Petrobras, he has worked as principal researcher and coordinator of projects on time-lapse seismic, smart fields, optimal well placement, history matching, and closed-loop reservoir management. Emerick holds BS and MS degrees in civil engineering from the University of Brasília, Brazil, and a PhD degree in petroleum engineering from The University of Tulsa. He is the author or coauthor of 36 technical papers, most about history matching. Emerick received the Outstanding Service Award as an SPE Journal technical editor in 2013 and 2014. He is a member of the JPT Editorial Committee. |
