The optimization process in the oil industry requires a number of technical applications as well as significant manpower, expertise, and skills to develop robust reservoir and production engineering workflows. Several studies have reported that up to 70% of an engineer's time is spent in gathering, formatting, translating, and parsing data among applications. Often, engineers are focused on isolated problems and deliver results that are optimized for that element of study. The optimization process frequently involves time-consuming iterations of input variables building output profiles, from which engineers will choose the optimum result. This optimized output data is then taken to the next step in the chain to optimize the subsequent study. These intermittent optimization choices may not deliver suitable input parameters on suboptimal output of the subsequent analyses. Because these sensitivities magnify through the optimization processes, ultimate recovery profiles and field economics are often suboptimal. Even with today's highly integrated databases and enhanced computing power, the traditional working processes will deliver suboptimal outputs. This paper discusses new toolkits that can model a full chain of application driven data manipulations to assess the ultimate economic effect of engineering decisions such that the best system-wide (holistic) choices can be exploited; the whole of the system is more than the sum of its parts. The holistically optimum result produces the practical natural optimum of the actual operational process. We will demonstrate how holistic optimization processes can greatly enhance workflows for production and reservoir management, fracture design, water conformance, and field development planning.
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