Cyclic Solvent Process (CSP) is a non-thermal solvent-based heavy oil recovery technology that was invented and developed by Imperial Oil Resources Limited through a multi-year integrated research program. The commercial viability of potential development concepts and their associated uncertainties are also an active area of investigation. A key input to an economic model is the global (or development level) flow stream. The conventional approach of developing the global flow stream involves the determination of well schedule through a well prioritization algorithm that adheres to a set of flow stream capacity constraints. The resulting flow streams can then be passed to an economic tool to evaluate a set of KPIs (Key Performance Indicators) in an uncoupled manner. One of the main challenges encountered in this approach is that it is difficult to optimize the overall economic performance due to (1) the absence of well-defined objective function, (2) the decoupling of the flow stream generation and the economic calculations, (3) the pre-defined characteristics of the well prioritization algorithm.

The main objective of this study is to develop a mathematical optimization model for CSP commercial projects. A two-stage optimization framework, which integrates Genetic Algorithm (GA) as master optimizer and Mixed Integer Linear Programming (MILP) as sub-optimizer, is described. A conceptual commercial scenario is simulated as a case study and economic uplift is demonstrated.

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