Optimal producer well placement and production planning in an oil reservoir

• Published in: Computers & chemical engineering 2013-08, Vol.55, p.109-125
• Main Authors: Tavallali, M.S., Karimi, I.A., Teo, K.M., Baxendale, D., Ayatollahi, Sh
• Format: Article
• Language: English
• Subjects: Applied sciences; Crude oil, natural gas and petroleum products; Drilling. Casing. Preparing wells for production; Dynamic optimization; Energy; Exact sciences and technology; Fuels; General. Spacing of wells. Planning of development. Legislation; Inventory control, production control. Distribution; Mathematical programming; Oil production planning; Oil reservoir modeling; Operational research and scientific management; Operational research. Management science; Optimal well placement; Prospecting and production of crude oil, natural gas, oil shales and tar sands
• ISSN: 0098-1354; 1873-4375
• DOI: 10.1016/j.compchemeng.2013.04.002

•MINLP model for simultaneous well placement and production planning in an oil reservoir with water injection.•To our knowledge, the first mathematical programming model to embed rigorous subsurface multiphase flow dynamics.•Our model gives new/infill producer wells, their locations, and production/injection rates of all wells.•Enhanced outer-approximation algorithm (Grossmann and co-workers) for this complex, dynamic, and nonconvex MINLP. Most of the available literature on optimal well placement has employed numerical simulators in a black box manner linked to an external search engine. In this work, we formulate the contents of that box inside a mixed integer nonlinear programming model for optimal well placement. We provide a unified model that integrates the subsurface, wells, and surface levels of an upstream production project. It links the production plan with the aforementioned elements, and economics and market. This results in a complex spatiotemporal mixed integer nonlinear model, for whose solution we modify and augment an existing outer approximation algorithm. The model solution provides the optimal number of new producers, their locations, and optimal production plan over a given planning horizon. To our knowledge, this is the first contribution that uses mathematical programming in a real dynamic sense by honoring the constituent partial differential equations.