Dynamic optimization of capacity management under the uncertainty of reservoir properties and market volatility

"We are running out of hydrocarbon", is what we have been hearing for the last few decades. This kind of information can pressure major hydrocarbon producers to invest more in exploration. Recently, exploration activities, especially for gas, have increased in the US and the Middle-East. Moreover, a significant amount of optimization research studies focus on field developments (i.e. well placements and configurations) that maximizes profits or recovery. It appears that most of the major hydrocarbon producers and researchers have not addressed the issue of Capacity Management (CM) and its potential of adding reserves with respect to time and delaying development projects without any significant additional cost. Managing production capacity across a portfolio of producing assets is a complex optimization problem. There are field level constraints including technical attributes (e.g. crude type and reservoir properties), facility limitations, and costs. Moreover, there are corporate level constraints (e.g. supply and spare capacity commitments). While complex, such a problem can be specified and solved deterministically; however, the presence of uncertainty, in reservoir performance and the hydrocarbon markets, makes the stochastic approach more realistic. This paper investigates this problem in three sections: (1) We specify an integrated stochastic optimization model that solves for the optimal production allocation for a portfolio of producing assets under the uncertainties of the markets and reservoir performances, (2) We then perform a sensitivity analysis for different market and reservoir models, and (3) We perform a value of information analysis to estimate the value of more accurate reservoir model. CM can significantly affect the production allocation of a portfolio of producing assets and hence affect the profit; however, it is shocking that little research has addressed it. Here, we address this issue by specifying an integrated stochastic optimization model that simulates reservoir performance and decision-maker behavior. Using this model, we compare and contrast the various production allocation decisions that result from different economic and reservoir models with the goal of understanding the impact of these model assumptions on decision-making. The results show that the optimum production allocation is a function of market and reservoir (i.e. reservoir maturity, size, and quality) parameters.