In project management, there is a model known as the quality triangle, which states that a project is constrained by three aspects: quality, time, and cost. Usually, one of these constraints is fixed. For example, if the cost is fixed, the quality of the project will depend heavily on the time available for the conclusion. Of course, this is an idealized and incomplete model of reality, but it has its merits.
In the course of my career, I have had the opportunity to work on several history-matching cases. Every time I think about these cases, the quality triangle comes to mind. Most history-matching studies have fixed resources—that is, the team of engineers and geoscientists is predetermined. (So, the cost vertex of the triangle is settled.) Moreover, the deadlines are always very strict. This constrained scenario often leads to an unfortunate result: The quality of the study suffers. In some cases, this quality reduction comes in the form of a reduction in scope. For example, instead of matching data from all wells of the reservoir, we limit the analysis to the wells near the area of interest of the project. However, we must remember that history matching is never the end of the history. Creeping the quality in a reservoir study can result in very serious consequences for a project by harming the decision-making, which surely can cost a lot of money.
So, what is the way around this problem? The answer is twofold: people and technology. Training is important, and integration is paramount. A talented and engaged team is the main component for success. Technology is the other component. Recent advances in seismic inversion, geostatistics, reservoir simulation, assisted history matching, and high-performance computing (just to mention a few) should always be on the table. In good hands, they can make a difference.
As usual, we had a nice collection of papers on history matching and forecasting published this past year. This feature summarizes some good examples, but you can find many other interesting papers on the OnePetro.org online library. I hope you enjoy the reading.
This Month's Technical Papers
Model Error Estimation Improves Forecasting
Rapid Forecast Calibration Using Nonlinear Simulation Regression With Localization
Multilevel Strategies Improve History Matching of Complex Reservoir Models
Recommended Additional Reading
IPTC 19128 Benchmarking of State-of-the-Art Assisted History-Matching Methods Under Reservoir Uncertainty on a Complex Waterflooding Process by Marko Maucec, Saudi Aramco, et al.
SPE 195549 A Proper Data Comparison for Seismic History-Matching Processes by Alessandra Davolio, University of Campinas, et al.
SPE 195837 Methods for Probabilistic Uncertainty Quantification With Reliable Subsurface Assessment and Robust Decision-Making by Shusei Tanaka, Chevron, et al.
| Alexandre Emerick, SPE, is a technical consultant at Petrobras Research Center in Rio de Janeiro. He has 17 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 38 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. |
