Recent Advances and Practical Applications of Integrated Production Modeling at Jack Asset in Deepwater Gulf of Mexico

Abstract

This paper presents an integrated production model construction and forecasting workflow along with three practical real field applications from the Jack asset located in deepwater Gulf of Mexico. Integrated production modeling is a composite modeling strategy that couples subsurface (material balance or simulation) models to a surface network model via well-bore models. The objective of using an integrated production model (IPM) is to predict the reservoir performance, while honoring mechanical design constraints of the surface network. The integrated production model construction process consists of five steps, which are framing, modeling, static quality check, initialization, and dynamic quality check followed by forecasting. An IPM was built for Jack and used as the primary forecasting method forevaluation of artificial lift alternatives (gas lift, sea floor boosting, and electric submersible pumps)identifying key artificial lift design parameters using Experimental Design, andsupporting injection facility design. In all of the studies, a black oil flow simulation model was used as the subsurface model and coupled to a surface network model. Background Deepwater field developments bring unique challenges for engineering design and project economics. A field development plan consist of various focus (surface and subsurface) decisions which require multiple engineering disciplines to work on their sub-domains concurrently (Figure 1). Reservoir engineers focus on well count, placement, and recovery mechanism decisions whereas production/completion engineers seek out feasible artificial lift alternatives and well design. At the same time, facility/subsea engineers study the subsea lay-out, facility size, and topsides design. Although these surface and subsurface decisions appear separable, they are coupled via common design limitations. A reliable production/ economics forecast will rely on the physical limitations of the overall system. This motivates project teams to use a common forecasting platform to support inter-related field development decisions.

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