Reservoir characterization

History Matching and Forecasting-2014

In the past decade, there has been a great deal of research and progress in the development of computational methods to assist reservoir engineers in the arduous task of history matching their models.

In the past decade, there has been a great deal of research and progress in the development of computational methods to assist reservoir engineers in the arduous task of history matching their models. Developments in computer hardware and software and the use of geostatistics, optimization, and Monte Carlo methods are among the reasons for such progress. However, the requirements for a good history match also have been increasing. If, in the past, we were satisfied with a good data fit, this is definitely not the case anymore. Modern history matching is a much more comprehensive discipline. It entails geological modeling, geostatistics, reservoir simulation, scale issues, data analysis, deep understanding of reservoir mechanisms, interdisciplinary approaches, optimization methods, statistics, and inverse-problems theory.

Nevertheless, we have to recognize that history matching in itself is not the goal. The goal is to generate models for production forecasting aiding the decision-making process involved in the development and management of petroleum reservoirs. One can definitely argue that a good history match does not ensure a reliable forecast. On the other hand, forecasting from models without a reasonable history match is most certainly temerarious (and probably meaningless). In fact, forecasting is an inherently uncertain process. In this sense, the goal of history matching is to mitigate this uncertainty. However, we have to keep in mind that uncertainty will never be removed completely. First, models are only representations of the actual physical phenomena; there will always be approximation errors. Second, data are insufficient and have measurement errors; hence, it is impossible to fully determine all unknown model parameters. Finally, uncertainty quantification has a high degree of subjectivity. Hence, bad forecasting often will be related to wrong assumptions and misinterpretations.

Among current research trends in history matching and forecasting, it is worth mentioning uncertainty quantification, methods based on ensembles, assimilation of time-lapse seismic data, and history matching of models with complex physics (e.g., fractured reservoirs and thermal recovery processes). However, one particular topic has caught the attention of many researchers: the development of history matching workflows that fully integrate the geological modeling in the loop. This is often called the “big-loop approach.”

Despite the developments in methods, software, and computers, human intervention and good engineering judgment will continue to be vital. Therefore, it is important to keep up to date in the literature. The papers summarized in this feature and those indicated in the additional-reading list are good examples of recent developments in, and field applications of, history matching and forecasting.

This Month's Technical Papers

History Matching the Norne Full-Field Model Using an Iterative Ensemble Smoother

Time-Lapse Seismic Data Integration Incorporates Pressure and Saturation Effects

Assessing Production Forecast Accuracy: A West Africa Field Case History

Recommended Additional Reading

SPE 163638 Estimation of Mutual Information and Conditional Entropy for Surveillance Optimization by Duc H. Le, The University of Tulsa, et al.

SPE 167150 Prior Information Enhances Uncertainty Quantification in Shale-Gas Decline-Curve Forecasts by Raul Gonzalez, Texas A&M University, et al.

SPE 163604 Preventing Ensemble Collapse and Honoring Multipoint Geostatistics With the Subspace EnKF/EnS and Kernel PCA Parameterization by Pallav Sarma, Chevron ETC, et al.

Alexandre Emerick, SPE, is a reservoir engineer at Petrobras Research Center in Rio de Janeiro. He has 11 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. To date, he has published 16 technical papers, most about history matching. Emerick received the Outstanding Service award as an SPE Journal technical editor in 2013. He is a member of the JPT Editorial Committee.