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Abstract
An analysis framework is presented that unifies the subsurface and surface disciplines involved with developing offshore projects. The method treats the complex interactions between surface and subsurface aspects of a production problem as a single system. The framework optimizes and assesses the risks and uncertainties associated with facility concept selection and sizing, with consideration of relevant subsurface and surface uncertainties and risks.

There is considerable uncertainty in the subsurface; this manifests itself in terms of a range of reservoir deliverability and reserves. In many projects, this subsurface uncertainty is not properly conveyed to the surface teams who are charged with selecting the best facilities design for the project. Often, the result of this disconnect yield suboptimal facilities followed by expensive retrofitting. The framework presented automates optimization of the concept design with rigorous reservoir and facilities modeling.

Two case studies are presented, both with multiple reservoirs connected through a common pipeline network. The first case is a typical deepwater U.S. Gulf of Mexico project. The second case is more typical of a medium water depth development in northern Europe. Full scenario analyses of multiple production platform alternatives and number of wells optimization is evaluated within a context of reservoir volumetric and drive mechanism uncertainties.

This framework accomplishes the following: it links physics-based reservoir deliverability and expert-system based facilities screening tools with the economics analysis in a unified workflow, and it applies advanced non-linear optimization to deliver better decisions regarding facilities selection and sizing.

Introduction
In a field development life cycle for new large offshore developments, a key decision occurs early in the life cycle, that is, selection of viable facility concepts that can be taken forward into pre-FEED (Front-End Engineering and Design). Selecting offshore facility concepts is a complex endeavor, associated with high risk. The facilities engineer must provide management with a high-return proposal for field development in a situation with large capital investment, often a billion dollars or more, given large subsurface, well, production, and surface uncertainties. The most common practice in the industry takes a linear, deterministic, case study approach, wherein the facility engineer considers a few concepts by analogy, given a range of reserves estimates from geology and reservoir engineering with considerations for drilling, water conditions, etc. The practice may lead to sub-optimal selections, as it tends to ignore the full range of alternative scenarios and uncertainties, leading to higher risk of over- or under-design.

We present an integrated decision framework anchored by a commercially available expert system that evaluates and then ranks facility concepts. Figure 1 a diagram of the integration of subsurface models with multiple reservoirs, wells, surface gathering network, production system, and economic evaluation. The key calculation components are an expert system for concept selection and costing, a coupled reservoir and network flow simulator, an uncertainty calculator, an optimizer, and an economics calculator.

In this work, for each case study we present two decision procedures which take advantage of the core components to integrate field development planning decisions with risk analysis. The approach has at its core a powerful expert system with an extensive worldwide database of offshore project metrics used for generating, ranking and then selecting concepts, based on a discounted cash flow analysis. The first procedure connects the expert system to a high-resolution uncertainty and optimization calculation engine which enables a more complete evaluation of risk in identifying and selecting optimal concepts. The first procedure provides a valuable “quick-look” capability, but it has some limitations, for example, the accuracy for production profile and reserves predictions and the granularity of economic analysis.

In the second procedure, we address the limitations by integrating the expert system and the uncertainty and optimization engines with high-resolution physics-based reservoir and network flow simulation and fiscal models.