Reservoir Performance and Monitoring
“If you have one watch, you know the time. If you have two, you will never be sure.”
This was a quote that my professor in reservoir engineering, T.D. van Golf-Racht, once stated when he tried to make us students reflect upon the availability and the quality of the data given for a specific exercise. This was in the late 1980s, and the amount of data was rather limited compared with today’s vast amount of data available. The cost of new data is increasing; most operators are cutting cost, and the cost/benefit exercise is getting more and more challenging. Actionable information is even more important, and payback time is the crucial number to know.
When you have obtained the necessary funding for acquiring data, make sure that you spend it wisely by collecting the data the right way and understanding the data you have gathered. Paper SPE 169539, related to diagnostic fracture injection tests, highlights the pitfalls when acquiring data to prepare for a hydraulic-fracture treatment.
Understanding well and reservoir performance is the ultimate goal of data gathering, and this gives the necessary foundation for making decisions. Regardless of the amount of data available and supercomputers at hand, analytical methods still play an important role in today’s analysis of reservoir performance. Another featured paper describes the use of modern analytical tools in an excellent way.
Data are of particular importance when you try to establish fundamental knowledge, whether it is drainage strategy for a new discovery or (even more fundamental) recovery processes for new types of hydrocarbons such as hydrates, described in paper OTC 25328.
We, the engineers, need to prepare for tougher times ahead. We need to make sure that key data are collected and be prepared to skip the rest. We need to make sure that more time is spent understanding the data than gathering them. We need to make sure that we perform cost/benefit analysis both before and after data acquisition.
This Month's Technical Papers
Recommended Additional Reading
SPE 163811 Enabling Numerical Simulation and Real-Time Production Data To Monitor Waterflooding Indicators by A. Al-Jasmi, Kuwait Oil Company, et al.
SPE 169219 Wireless Retrofit Solutions To Replace Failed Permanent Downhole Gauges: Case Study in a Gas Well by Annabel Green, Tendeka, et al.
SPE 165431 An Integrated Practical Approach to Forecasting Multiwell SAGD Production Using Analog, Analytical, and Numerical Modeling Techniques by Yomi Adesimi, Suncor Energy, et al.
SPE 169485 Development of a Fully Coupled Two-Phase-Flow-Based Capacitance/Resistance Model by Fei Cao, The University of Texas at Austin, et al.
|Erik Vikane, SPE, is vice president of operations for the Oseberg East platform at Statoil, Bergen. He has 23 years of diverse experience in the upstream business. Starting out at as a well-intervention engineer, Vikane then moved to reservoir engineering and has held various positions within reservoir management, early phase development, exploration, and business development in Norway, London, and Houston. His areas of interest include asset management (both subsurface and topside), reservoir performance and monitoring, and integrated uncertainty studies. Vikane holds an MS degree in petroleum engineering from the Norwegian University of Science and Technology. He serves on the JPT Editorial Committee.|
Reservoir Performance and Monitoring
Erik Vikane, SPE, Vice President of Operations, Statoil
01 September 2014
Ghawar vs. Permian Basin: Is There Even a Comparison?
While some try to put the two enormous oil producers toe-to-toe, the best thing to do might be to understand why they are different.
Correlation-Based Localization Effective in Ensemble-Based History Matching
To enhance the applicability of localization to various history-matching problems, the authors adopt an adaptive localization scheme that exploits the correlations between model variables and observations.
Pattern-Based History Matching Maintains Consistency for Complex-Facies Reservoirs
A challenging problem of automated history-matching work flows is ensuring that, after applying updates to previous models, the resulting history-matched models remain consistent geologically.
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