In this work, we demonstrate the application of OCTOPUS system to support the decision on drainage plan considering multiple reservoir cases (CARDOSO et al., 2017). Octopus employs a technology based on Artificial Intelligence to guide the process of high-VPL drainage plan. This work reveals OCTOPUS'S performance in supporting E&P experts by proposing drainage plan under reservoir uncertainty conditions. OCTOPUS interacts with a reservoir simulator to search the best well allocation scenario that maximizes the reservoir net present value (NPV). It uses mathematical methods to deal with constraints, and has a Genetic Algorithms (GA) optimizer to determine the optimal number, type (injector or producer), trajectory (vertical, horizontal or directional) and location of wells. Among the significant results, we highlight 2 main cases in which the increase in NPV achieved by OCTOPUS is over 4%. The first case is the optimization under geological uncertainty of an oil field during 35 years and the second case is a mesh densification study on a field under production for 16 years.

Keywords:
application, artificial intelligence, optimization, geological uncertainty, base case, reservoir development, scenario, reservoir, decision support system, injector
Subjects:
Information Management and Systems, Artificial intelligence
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2017. Offshore Technology Conference
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