Modeling and Computational Strategies for Optimal Oilfield Development Planning under Fiscal Rules and Endogenous Uncertainties

<p>This dissertation proposes new mixed-integer optimization models and computational strategies for optimal offshore oil and gas field infrastructure planning under fiscal rules of the agreements with the host government, accounting for endogenous uncertainties in the field parameters using a stochastic programming framework. First, a multiperiod mixed-integer nonlinear programming (MINLP) model is proposed in Chapter 2 that incorporates field level investment and operating decisions, and maximizes the net present value (NPV). Two theoretical properties are proposed to remove the bilinear terms from the model, and further converting it to an MILP approximation to solve the problem to global optimality. Chapter 3 extends the basic deterministic model in Chapter 2 to include complex fiscal rules maximizing total contractor’s (oil company) share after paying royalties, profit share, etc. to the host government. The resulting model yields improved decisions and higher profit than the previous one. Due to the computational issues associated with the progressive (sliding scale) fiscal terms, a tighter formulation, a relaxation scheme, and an approximation technique are proposed. Chapter 4 presents a general multistage stochastic MILP model for endogenous uncertainty problems where decisions determine the timings of uncertainty realizations. To address the issue of exponential growth of non-anticipativity (NA) constraints in the model, a new theoretical property is identified. Moreover, three solution strategies, i.e. a k-stage constraint strategy; a NAC relaxation strategy; and a Lagrangean decomposition algorithm, are also proposed to solve the realistic instances and applied to process network examples. In Chapter 5, the deterministic formulations in Chapter 2 and 3 for oilfield development are extended to a multistage stochastic programming formulation to account for the endogenous uncertainties in field sizes, oil deliverabilities, water-oil-ratios and gas-oil-ratios. The Lagrangean decomposition approach from Chapter 4 is used to solve the problem, with parallel solutions of the scenarios. To improve the quality of the dual bound during this decomposition approach, a novel partial decomposition is proposed in Chapter 6. Chapter 7 presents a method to update the multipliers during the solution of a general twostage stochastic MILP model, combining the idea of dual decomposition and integer programming sensitivity analysis, and comparing it with the subgradient method. Finally, Chapter 8 summarizes the major findings of the dissertation and suggests future work on the subject.</p

Offshore Oilfield Development Planning under Uncertainty and Fiscal Considerations

The objective of this paper is to present a unified modeling framework to address the issues of uncertainty and complex fiscal rules in the development planning of offshore oil and gas fields which involve critical investment and operational decisions. In particular, the paper emphasizes the need to have as a basis an efficient deterministic model that can account for various alternatives in the decision making process for a multi-field site incorporating sufficient level of details in the model, while being computationally tractable for the large instances. Consequently, such a model can effectively be extended to include other complexities, for instance endogenous uncertainties and a production sharing agreements. Therefore, we present a new deterministic MINLP model followed by discussion on its extensions to incorporate generic fiscal rules, and uncertainties based on recent work on multistage stochastic programming. Numerical results on the development planning problem for deterministic as well as stochastic instances are discussed. A detailed literature review on the modeling and solution methods that are proposed for each class of the problems in this context is also presented.</p

Multistage stochastic programming approach for offshore oilfield infrastructure planning under production sharing agreements and endogenous uncertainties

<p>The paper presents a new optimization model and solution approach for the investment and operations planning of offshore oil and gas field infrastructure. As compared to the conventional models where either fiscal rules or uncertainty in the field parameters is considered, the proposed model is the first one in the literature that includes both of these complexities in an efficient manner. In particular, a tighter formulation for the production sharing agreements based on our recent work, and a perfect positive or negative correlation among the endogenous uncertain parameters (field size, oil deliverability, water–oil ratio and gas–oil ratio) is considered to reduce the total number of scenarios in the resulting multistage stochastic formulation. To solve the large instances of the problem, a Lagrangean decomposition approach allowing parallel solution of the scenario subproblems is implemented in the GAMS grid computing environment. Computational results on a variety of oilfield development planning examples are presented to illustrate the efficiency of the model and the proposed solution approach.</p

Optimal Development Planning of Offshore Oil and Gas Field Infrastructure under Complex Fiscal Rules

<p>The optimal development planning of offshore oil and gas fields has received significant attention in the recent years. In this paper, we present an efficient investment and operational planning model for this problem which is fairly generic and it is extended to include fiscal considerations. With the objective of maximizing total NPV for long-term planning horizon, the proposed non-convex multiperiod MINLP model involves decisions regarding facility installation and expansion, fieldfacility connections, well drilling schedule and production profiles of oil, water and gas in each time period. The model can be solved effectively with DICOPT for realistic instances and gives good quality solutions. Furthermore, it can be reformulated into an MILP after piecewise linearization and exact linearization techniques that can be solved globally in an efficient way. Solutions of the realistic instances are reported for the proposed models as well as the computational impact with consideration of the complex fiscal rules within development planning.</p

Assessing the benefits of production-distribution coordination in an industrial gases supply chain

<p>In this paper, we propose a multi-period MILP model for optimal enterprise-level planning of industrial gas operations with an objective to minimize the total cost of production and distribution. As compared to the fully sequential approach for optimizing production and then optimizing distribution, the proposed fully coordinated model simultaneously considers various trade-offs between production and distribution activities and yields optimal operational decisions for the supply chain as a whole. Through the continuum between fully sequential and fully coordinated approaches, different levels of coordination are investigated. The computational results for small instances show potential savings if a fully coordinated approach is implemented in place of a fully sequential one. This can be attributed to improved coordination of the production schedule with the distribution withdrawal schedule.</p

Simultaneous Production and Distribution of Industrial Gas Supply-Chains

In this paper, we propose a multi-period mixed-integer linear programming model for optimal enterprise-level planning of industrial gas operations. The objective is to minimize the total cost of production and distribution of liquid products by coordinating production decisions at multiple plants and distribution decisions at multiple depots. Production decisions include production modes and rates that determine power consumption. Distribution decisions involve source, destination, quantity, route, and time of each truck delivery. The selection of routes is a critical factor of the distribution cost. The main goal of this contribution is to assess the benefits of optimal coordination of production and distribution. The proposed methodology has been tested on small, medium, and large size examples. The results show that significant benefits can be obtained with higher coordination among plants/depots in order to fulfill a common set of shared customer demands. The application to real industrial size test cases is also discussed</p