History Matching and Forecasting

The initial work on computer techniques for assisted history matching date back to the 1960s. However, it was a long journey between the development of early methods and operational use. Initially, these methods were referred to as “automatic history matching,” giving the wrong impression that it was something we could delegate to a computer. Fortunately, industry and academia soon realized that “assisted” was a more accurate term than “automatic.”

Nowadays, there is an impressive amount of literature and a large number of assisted-history-matching methods. The diversity is so vast that it is challenging to divide these methods into categories. For example, there are several flavors of methods based on sensitivity and gradient-optimization algorithms. There are also methods grounded on stochastic optimization, evolutionary algorithms, design of experiments, proxy modeling, streamline simulation, and Kalman filters. This is just to mention a few, and the list is still growing.

Regardless of the specific method of your choice, I believe the main development in history matching is the recognition that it can be formulated as a Bayesian inference problem. Bayes’ rule provides an elegant framework to formalize the process of learning from data to update our beliefs. The beauty is in the fact that Bayes’ rule gave to history matching the correct meaning. History matching is no longer a searching process to find the best model. Instead, history matching is a process of mitigating uncertainty in light of new information.

The papers summarized in this feature and the ones indicated in the additional-reading list are excellent examples of recent developments and applications of assisted-history-matching techniques. All are aligned with the modern Bayesian interpretation. I hope you enjoy the reading.

This Month's Technical Papers

Uncertainty Quantification for History-Matching Problems

Field-Scale Assisted History Matching Using a Systematic Ensemble Kalman Smoother

Drill and Learn: A Decision-Making Work Flow To Quantify Value of Learning

Recommended additional reading

SPE 179549 Streamline-Based Rapid History Matching of Bottomhole Pressure and Three-Phase Production Data by Dongjae Kam, Texas A&M University, et al.

SPE 182684 Generation of a Proposal Distribution for Efficient MCMC Characterization of Uncertainty in Reservoir Description and Forecasting by Xin Li, The University of Tulsa, et al.

SPE 182693 A Robust Iterative Ensemble-Smoother Method for Efficient History Matching and Uncertainty Quantification by Xiang Ma, ExxonMobil, et al.

Alexandre Emerick, SPE, is a reservoir engineer at Petrobras Research Center (CENPES) in Rio de Janeiro. He has 14 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 Brasilia, Brazil, and a PhD degree in petroleum engineering from The University of Tulsa. He is the author/coauthor of 26 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 and can be reached at

History Matching and Forecasting

Alexandre Emerick, SPE, Reservoir Engineer, Petrobras Research Center

01 April 2017

Volume: 69 | Issue: 4