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

Abstract

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 previous models where fiscal rules and uncertainty in the field parameters are considered separately, 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 is used based on our recent work, and 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 large instances, a Lagangean 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.

Supply chain networks servicing upstream operations in oil and gas fields after the shale revolution

“…required to move the oil and gas from wells to refineries. 16 Recent works have focused in the design of supply chains to exploit unconventional resources, 2,17,18 including the optimal planning of water acquisition and treatment 19,20 but not paying much attention to other material and service supplies.…”

“…Similar to Equation (7), the flow of recovered material between nodes k and k 0 is permitted provided that a reconditioning facility is installed at k and a storage facility is available at k 0 (Equation 16). Moreover, the storage facility at the receiving node k 0 should be able to manage material f, as imposed by Equation (17).…”

Resilient supply chain design and operations with decision‐dependent uncertainty using a data‐driven robust optimization approach

“…Although many real word applications involve uncertainties that are affected by decisions, these problems are inherently challenging to address from the computational perspective . In stochastic programming, a number of solution algorithms and applications involving decision‐dependent uncertainty have been proposed and solved in recent years . As for robust optimization, some recent literature considers specific cases of decision‐dependent uncertainty, where only binary decision variables are involved in the uncertainty sets and does not account for continuous decision variables, such as production capacity, in the uncertainty set, as to be addressed in this work.…”

Optimal Planning for Deepwater Oilfield Development Under Uncertainties of Crude Oil Price and Reservoir

“…As for ofshore ield infrastructure planning, Gupta and Grossmann [10] put forward a multiperiod mixed integer linear programming (MILP) model which involves oil-gas-water, three phases in reservoir. Based on the previous work, Gupta and Grossmann [11] took the production-sharing agreements and the endogenous uncertainties into consideration, making the proposed multistage model more accordant with practical situations. When building up the oilield development planning model, it is inevitable to consider uncertain factors such as reservoir behavior and crude oil price.…”