Data Driven Modeling and Optimization of Oil and Gas Assets
By R. Pallav Sarma, Chief Scientist, Tachyus
Thursday, May 17th 2018 @ 11:30 AM
Petroleum Club 12th Floor - 5060 California Avenue, Bakersfield
Speaker: Pallav Sarma, Tachyus, San Francisco
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In traditional reservoir management, various types of predictive models have been applied over the years for either qualitative or quantitative optimization of various reservoir management decisions. Such models range from the very simple analytical models (type-curves, etc.) to the very complex reservoir simulation models. Simulation models attempt to model detailed behavior of reservoir physics and integrate all kinds of measured data, and can therefore be used for quantitative optimization. However, many issues such as the significant time and effort required to build and calibrate these models, computational complexity, etc. generally prohibit their practical use for closed-loop quantitative optimization.
Additionally, there have also been many attempts at the application of traditional machine learning approaches for predictive modeling of production performance. While such models can be built very efficiently and are very fast to evaluate, however, due to spatial sparsity of data, combined with poor measurement quality, and the absence of the underlying physics in such models, such purely data-driven approaches have only had limited success. Key reservoir management decisions such as infill drilling locations are hard to predict with such models as new wells do not have any historical data.
In short, there is a significant opportunity to enhance traditional reservoir management with new quantitative tools and technologies that allow integration of all kinds to data to create accurate predictive models while significantly reducing the cycle time from data to decisions. Such models can then be used to efficiently optimize key reservoir management decisions and improve ultimate recovery, NPV etc. over the life of the field.
This talk describes a unique modeling approach termed Data Physics. Data Physics is the amalgamation of the state-of-the-art in machine learning and the same underlying physics present in reservoir simulators. These models can be created as efficiently as machine learning models, integrate all kinds of data, and can be evaluated orders of magnitude faster than full scale simulation models, and since they include similar underlying physics as simulators, they have good long term predictive capacity and can even be used to predict performance of new wells without any historical data. We present applications of Data Physics models to real steamflood and waterflood optimizations with thousands of wells, wherein, the injectant is redistributed to maximize/minimize multiple objectives. A significant increase in actual incremental oil production and reduction in operational cost is demonstrated.
Pallav Sarma is Chief Scientist at Tachyus leading the development of the modeling and optimization technologies underlying the Tachyus platform. His expertise is in the general area of closed-loop reservoir management, with multiple patents and numerous papers on the subject. He has over 12 years of experience working for Chevron and Schlumberger. As a Staff Research Scientist for Chevron, he was responsible for their key data assimilation and optimization technologies. He received many awards including the Dantzig Dissertation award from INFORMS, Ramey and Miller Fellowships and Stanford, and Chevron’s Excellence in Reservoir Management award. He loves to actively participate in oil industry organizations, and is currently in the committees of the SPE Reservoir Symposium Conference, the European Conference on the Mathematics of Oil Recovery (ECMOR), and Stanford Petroleum Investment Committee (PIC). Pallav holds a Ph.D. in Petroleum Engineering and a Ph.D. Minor in Operations Research from Stanford University, and a B.Tech from Indian School of Mines.