(640a) Oil Spill Response Planning with Consideration of Oil Transport and Weathering Process: A Multi-Objective Mixed-Integer Dynamic Optimization Approach

The recent Deepwater Horizon/BP oil spill in the Gulf of Mexico has become the focus of public attention due to its significant ecological, economic and social impacts [1-2]. This incident, coupled with previous catastrophic oil spills, has demonstrated the importance of developing responsive and effective oil spill response planning strategies for the government and for the oil exploration and production industries [3]. Oil spill response usually occurs within a complex environment that requires timely decisions to balance the response cost and responsiveness and to address such issues as weathering and movement of the oil slick, selection of response/cleanup methods, coordination of coastal protection activities, availability of cleanup facilities, regulation constraints, variational operational windows and performance degradation. This is usually a nontrivial task for decision makers (incident commanders), who must coordinate considerable resources and must plan many operations. For instance, in the response to the Deepwater Horizon oil spill, over 39,000 personnel, 5,000 vessels, and 110 aircraft were involved, over 700 kilometer boom has been deployed, 275 controlled burns have been carried out, approximately 27 million gallons of oily liquid has been recovered by skimmers, and more than 1.5 million gallons of chemical dispersant (Corexit) have been used as of July 1, 2010. Although a few models have been developed for oil spill response planning, response operations and the oil weathering process are usually considered separately [4-9]. Yet significant interactions between them exist throughout the response [9-11]. Oil spill cleanup activities change the volume and area of the oil slick and in turn affect the oil transport and weathering process, which also affects coastal protection activities and cleanup operations (e.g., performance degradation and operational window of cleanup facilities). Therefore, it is critical to integrate the response planning model with the oil transport and weathering model, although this integration has not been addressed in the existing literature to the best of our knowledge. Besides, none of the existing oil spill response planning models, however, has taken into account coastal protection planning, which is usually required for massive oil spills. Moreover, only a single objective is used in the existing literature; and the time span of the entire response operations, which is the measure of the responsiveness, has not been considered by the existing optimization models.

In this work, we develop a bi-criterion optimization approach for seamlessly integrating the planning of oil-spill response operations with the oil transport and weathering process under the economic and responsiveness criteria. The economic criterion is measured by the total response cost, and responsiveness is measured by the time span of the entire response operations. A mixed-integer dynamic optimization (MIDO) model is proposed that simultaneously predicts the time trajectories of the oil volume and slick area and the optimal response cleanup schedule and coastal protection plan, by taking into account the time-dependent oil physiochemical properties, spilled amount, hydrodynamics, weather conditions, facility availability, performance degradation, variational operational window, and regulatory constraints [12]. To solve the MIDO problem, we reformulated it as a mixed-integer nonlinear programming (MINLP) problem using orthogonal collocation on finite elements. We also developed a mixed-integer linear programming (MILP) model [13] to obtain a good starting point for solving the nonconvex MINLP problem. ε-constraint method is used for solving the multi-objective optimization problem and it produces a Pareto-optimal curve that reveals how the optimal total cost, oil spill cleanup operations, and coastal protection plans change under different specifications of the response time span. The application of the proposed optimization approach is illustrated through two examples based on the incidents of Deepwater Horizon/BP oil spill and the Argo Merchant oil spill.

References:

[1] http://www.deepwaterhorizonresponse.com/

[2] http://www.restorethegulf.gov/

[3] NOAA Oil spill case histories 1967-1991: summaries of significant U.S. and international spills; Seattle, Washington, 1992.

[4] Wilhelm WE, Srinivasa AV. Prescribing tactical response for oil spill cleanup operations. Management Science. 1997; 43:386-402.

[5] Psaraftis HN, Ziogas B. A tactical decision algorithm for the optimal dispatching of oil spill cleanup equipment. Management Science. 1985; 31:1475-1491.

[6] Iakovou E, Douligeris C, Korde A. A synthesis of decision analysis, assessment, and planning for oil spill models for contingency incidents. Omega. 1994; 22:457-470.

[7] Srinivasa AV, Wilhelm WE. A procedure for optimizing tactical response in oil spill clean up operations. European Journal of Operational Research. 1997; 102:554-574.

[8] Reed M, Johansen I, Brandvik PJ, Daling P, Alun Lewisà, Fioccoà R, Mackay D, Prentki R. Oil spill modeling towards the close of the 20th century: overview of the state of the art. Spill Science & Technology Bulletin. 1999; 5:3-16.

[9] Brebbia CA. Oil spill modeling and processes. WIT Press: UK, 2001.

[10] Fingas M. The basics of oil spill cleanup. Lewis: New York, 2001.

[11] Ornitz B, Champ M. Oil spills first principles: prevention and best response. Elsevier: Netherlands, 2003.

[12] You F., Leyffer S. Mixed-Integer Dynamic Optimization for Oil Spill Response Planning with Integration of An Oil Transport and Weathering Model. AIChE Journal, Submitted. Preprint: ANL/MCS-P1794-1010

[13] Zhong Z., You F. Oil Spill Response Planning with Consideration of Physicochemical Evolution of the Oil Slick: A Multi-objective Optimization Approach. Computers & Chemical Engineering, Submitted. Preprint: ANL/MCS-P1786-0810