277712 Offshore Oil and Gas Field Infrastructure Planning Under Complex Fiscal Rules and Endogenous Uncertainties

Wednesday, October 31, 2012: 4:15 PM
325 (Convention Center )
Vijay Gupta, Department of Chemical Engineering, Carnegie Mellon University, Pittsburgh, PA and Ignacio E. Grossmann, Chemical Engineering Department, Carnegie Mellon University, Pittsburgh, PA

The planning of offshore oil and gas field infrastructure is a very critical problem as it involves huge investments which may lead to large profits or losses. This problem has received significant attention in recent years given the new discoveries of large oil and gas reserves in the last decade around the world. The optimization of investment and operations planning of offshore oil and gas field infrastructure is traditionally modeled using the net present value (NPV) as the objective function, without considering the effect of fiscal rules that are associated to these development sites [1-2]. These rules determine the share of the oil company and host government in the total oil produced in a given year. Therefore, the models with simple NPV objective functions may yield solutions that are optimistic, which can in fact be suboptimal after considering the impact of fiscal terms.

            The goal of this paper is to extend a mixed-integer nonlinear programming (MINLP) model for NPV-based oilfield development planning to include complex fiscal rules. In particular, we consider a recently proposed multi-field site strategic planning model [3] for offshore oil and gas fields as a basis to include generic fiscal rules with ring-fencing provisions. The proposed MINLP model is rather large as it includes many new discrete variables and additional constraints. We show that this model can be reduced to a variety of specific contracts models. Results on realistic instances show improved investment and operations decisions due to the explicit consideration of the fiscal terms during planning. Since the model becomes computationally very expensive to solve with the extension to fiscal rules, we provide several reformulation/approximation techniques and solution strategies that yield orders of magnitude reduction in the solution time [4].

In addition, uncertainty in the model parameters such as field size and its deliverability can have significant impact on investment and operations decisions. Therefore, we discuss the extension of the proposed model to handle endogenous uncertainties such as reservoir size and productivity using a framework based on two-stage and multi-sage stochastic programming approaches where timing of uncertainty realization depends on investment decisions. In particular, we first formulate the problem as a multistage stochastic programming model in the spirit of the work by Goel and Grossmann [5] and Tarhan et al. [6] in which special disjunctive constraints are considered as the conditional non-anticipativity constraints for endogenous uncertainties. Numerical results on several instances with uncertainty in the model parameters are reported as well as computational performance of the model with the proposed solution strategies [7]. In order to simplify the solution of this complex problem, we also consider a recursive two-stage programming model that is solved under a shrinking time horizon and present a comparison of the solution time and investment decisions with a multistage stochastic programming approach.


[1] Iyer, R. R.; Grossmann, I. E.; Vasantharajan, S.; Cullick, A. S. Optimal planning and scheduling offshore oilfield infrastructure investment and operations. Ind. Eng. Chem. Res. 1998, 37, 1380–1397.

[2] van den Heever, S. A.; Grossmann, I. E. An iterative aggregation/disaggregation approach for the solution of a mixed integer nonlinear oilfield infrastructure planning model. Ind. Eng. Chem. Res. 2000, 39, 1955–1971.

[3] Gupta V.; Grossmann I. E. An Efficient Multiperiod MINLP Model for Optimal Planning of Offshore Oil and Gas Field Infrastructure.  Ind. Eng. Chem. Res. 2012, DOI: 10.1021/ie202959w

[4] Gupta V.; Grossmann I. E. Modeling and Computational Strategies for Optimal Development Planning of Offshore Oilfields under Complex Fiscal Rules. To be Submitted.

[5] Goel, V.; Grossmann, I. E. A stochastic programming approach to planning of offshore gas field developments under uncertainty in reserves. Comput. Chem. Eng. 2004, 28 (8), 1409–1429.

 [6] Tarhan, B.; Grossmann, I.E.; Goel, V. Stochastic programming approach for the planning of offshore oil or gas field infrastructure under decision- dependent uncertainty. Ind. Eng. Chem. Res. 2009, 48(6), 3078–3097.

[7] Gupta, V.; Grossmann, I. E. Solution strategies for multistage stochastic programming with endogenous uncertainties. Comput. Chem. Eng. 2011, 35, 2235–2247.

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