Technology Focus: History Matching and Forecasting (April 2012)
Authors: Romeu Regis Kruel 
Published: February 2015

Technology Focus
Many reservoir engineers dislike the very idea of automatic history matching applied to real full-field studies. They believe there is no artificial substitute for experienced reasoning, deep understanding of the reservoir mechanisms, and attention to real-life practical aspects of the problem. Some use terms such as art and intuition. For them, even if computers long ago learned to play chess, computers will never be able to perform real-case history matching on their own or at least they are still too far from this achievement. Very often, during technical sessions, immediately following an advanced mathematical presentation on history matching, someone in the audience makes his or her point about the limits of automatic approaches. To avoid disputes, experienced speakers prefer less pretentious expressions such as assisted or semiautomatic history matching.
Indeed, history matching can be seen as a two-step iterative process, normally requiring many cycles to be completed. Broadly speaking, the first step is about analysis and setting the problem parameters, and the second step is about searching for and computing solutions. We start our discussion with the second part, which has a more obvious algorithmic nature. There has being a great deal of research and progress in this area. The ensemble Kalman filter is dominating the scene, but gradient-based methods and global-optimization stochastic methods are attracting merited attention. Most published contributions come from universities, and, typically, papers include examples to demonstrate successful algorithm application. These examples can be simple synthetic or somewhat-more-realistic cases, but the discussion is naturally focused on the solution method and not on the entire problem as found in the field.
The first part of the problem is less mathematized, for now, and involves essential tasks such as to be clear about the practical purposes and requirements in the particular context; to have a full understanding of the quality of the reservoir model and the production data; to design or redesign well-justified objective functions; to set adequate parameterization, considering the main uncertainties and their effect on the simulation results; to represent properly and sample the uncertainty space; and to evaluate results from the previous steps of the history-matching process judiciously. Unfortunately, the strategies used to consider this part of the problem are much less discussed and documented. In fact, many of these tasks are open to further formalization and, ultimately, can be automated also. We definitely need more papers illuminating these other aspects of the reservoir-engineering problem, instead of relying on intuition.
Recommended additional reading at OnePetro: www.onepetro.org.
SPE 146292 Practical Assisted History Matching and Probabilistic Forecasting Procedure: A West Africa Case Study by B.O. Dujardin, SPE, Chevron, et al.
SPE 141929 An Ensemble Smoother for Assisted History Matching by J.-A. Skjervheim, Statoil, et al.
SPE 142497 Advanced History-Matching Techniques Reviewed by R. Rwechungura, Norwegian University of Science and Technology, et al.
SPE 143067 History Matching and Uncertainty Quantification: Multiobjective Particle-Swarm-Optimization Approach by Linah Mohamed, SPE, Heriot-Watt University, et al.