Development of applied data-driven modeling approaches for SAGD operations design

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Steam assisted gravity drainage (SAGD) has been employed extensively and considered as a viable and effective method for commercial in-situ production of oil sands reserves. This extraction method, however, requires generation of excessive amount of steam by combustion of natural gas. The water consumption, energy use, and carbon emissions associated with steam generation are costly and have adverse environmental impacts, threatening the sustainability of oil sands development projects in Canada. In order to enhance the competitiveness of Canada's unconventional oil and gas industries, it is extremely critical that the technology be operated at optimal conditions to achieve a cleaner exploitation of the valuable resources. Present workflow that entails static reservoir modeling and numerical flow simulations usually provide only approximate solutions to recovery responses, as numerous simplifications and assumptions must be invoked. The modeling process itself is also quite cumbersome and time-consuming, limiting its application in real-time optimization. The proposed research aims to develop improved and applied data-driven modeling approaches by means of statistical analysis and artificial intelligence techniques that would provide practical tools suitable for performance prediction, design, and real-time decision-making for SAGD operations. These tools will be used for the purposes of reservoir characterization, recovery performance prediction, uncertainty assessment, quantitative ranking and evaluation of SAGD operations, and design of optimal development or production strategies. The outcomes of this research would improve our ability to understand and predict SAGD reservoir performance. New modeling procedures, detailed workflow guidelines, case study examples involving field data, and numerical algorithms explored through this study would find immediate application in enhancing efficiencies in field-scale well planning and plant operations, while reducing carbon footprint of current and future oil sands projects.