(614d) Computational Strategies for the Planning of Deepwater Oilfield Infrastructure Under Decision Dependent Uncertainty | AIChE

(614d) Computational Strategies for the Planning of Deepwater Oilfield Infrastructure Under Decision Dependent Uncertainty


Tarhan, B. - Presenter, Carnegie Mellon University
Grossmann, I. E. - Presenter, Carnegie Mellon University

Oilfield exploration and production operations consist of four major steps: exploration, appraisal, development and production. In each step, many decisions have to be made that affect the overall performance of the operation. In the beginning of exploration phase many uncertainties exist, and depending on the decisions made at each step, the uncertainty reduces gradually. Unfortunately, many crucial decisions related to planning of offshore oilfield infrastructure have to be made in the early steps where the uncertainty level is high. The quality of these decisions affects the overall profitability of the operation. Most of the available literature for exploration and planning of petroleum fields is based on deterministic models (Haugland et al. (1988), Iyer & Grossmann (1998), Van den Heever & Grossmann (2000), Lin & Floudas (2000), Carvalho et al. (2006)), while there are only few papers that deal with uncertainty (Jonsbraten (1998), Lund (2000), Begg et al. (2002), Cullick, Heath, Narayanan (2003), Aseeri, Gorman & Bagajewicz (2004), Goel & Grossmann (2004), Ulstein, Nygreen & Sagli (2007), Ozdogan & Horne (2006)).

In this paper, we consider a multistage stochastic programming approach for the design and planning of an oilfield infrastructure over a planning horizon under gradual uncertainty resolution. The oilfield infrastructure can be composed of Floating Production Storage and Offloading (FPSO) and/or Tension Leg Platform (TLP) facilities, sub-sea and/or TLP wells. The main uncertainties considered are in the initial maximum oil flowrate, size and water breakthrough time of the reservoir, which are represented by discrete distributions. Furthermore, it is assumed that these uncertainties are not immediately realized, but are gradually revealed as a function of design and operation decisions.

In order to account for these decision-dependent uncertainties, we propose a multistage stochastic programming model that captures the complex economic objectives and nonlinear reservoir behavior, and simultaneously optimizes the investment and operation decisions over the entire planning horizon. We propose special computational strategies for a solution algorithm that relies on a duality based branch and bound method (Tarhan and Grossmann, 2007) involving nonconvex mixed-integer nonlinear programming subproblems. The subgradient method has been used for solving the dual problem. During subgradient optimization, it is necessary to solve these nonconvex problems using global optimization in order to generate valid bounds. A variant of the algorithm in which a combination of global optimization and outer-approximation approach used during subgradient iterations is presented. The new approach yields great reductions in solution time and not necessarily sacrificing the quality of the solution. We describe results on several test problems involving nonlinear reservoir models to illustrate the capabilities of the proposed model. Also, we present comparisons of results obtained by using expected values to demonstrate the hedging effect of the proposed multistage programming approach.