Browsing by Subject "Optimization under uncertainty"

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    CO₂ EOR-storage design optimization under uncertainty
    (2013-05) Ettehadtavakkol, Amin; Lake, Larry W.; Sepehrnoori, Kamy, 1951-
    A partnership between oilfield operators and the federal government in the coupled CO₂ enhanced oil recovery (EOR) and storage projects brings long-term benefits for both. We quantify the win-win condition for this partnership in terms of an optimum storage tax credit. We describe the field-scale design optimization of coupled CO₂-EOR and storage operations from the viewpoint of oilfield operators. We introduce a CO₂ market model and investigate two special CO₂ market problems, namely a fixed storage requirement and an integrated asset optimization. The first problem follows an environmental objective by giving priority to the storage element of CO₂-EOR and storage; the second prioritizes the oil recovery and relies on the principles of a free market where CO₂ is a commodity and the commitment to storage is made based on the economic benefits. We investigate the CO₂ market sustainability conditions and quantitatively derive them for the fixed storage requirement and integrated asset optimization problems. Ultimately, we quantify the impact of storage tax credit on the operator benefits, the federal government benefits, and the optimum economic storage capacity of an oilfield. CO₂ EOR-storage projects are long-term and capital-intensive and therefore vulnerable to the risks of the CO₂ market. Two important uncertain economic parameters are investigated, the oil price and the storage tax credit. The government plays an important role in reducing the CO₂ market risks because it has the leverage to regulate the storage tax credit. The stochastic optimization results show that a transparent storage tax credit reinforces the sustainability of the CO₂ market and helps both the government and the oilfield operators boost their long-term benefits.
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    Optimization of production allocation under price uncertainty : relating price model assumptions to decisions
    (2011-08) Bukhari, Abdulwahab Abdullatif; Jablonowski, Christopher J.; Lasdon, Leon S.; Dyer, James S.
    Allocating production volumes across a portfolio of producing assets is a complex optimization problem. Each producing asset possesses different technical attributes (e.g. crude type), facility constraints, and costs. In addition, there are corporate objectives and constraints (e.g. contract delivery requirements). While complex, such a problem can be specified and solved using conventional deterministic optimization methods. However, there is often uncertainty in many of the inputs, and in these cases the appropriate approach is neither obvious nor straightforward. One of the major uncertainties in the oil and gas industry is the commodity price assumption(s). This paper investigates this problem in three major sections: (1) We specify an integrated stochastic optimization model that solves for the optimal production allocation for a portfolio of producing assets when there is uncertainty in commodity prices, (2) We then compare the solutions that result when different price models are used, and (3) We perform a value of information analysis to estimate the value of more accurate price models. The results show that the optimum production allocation is a function of the price model assumptions. However, the differences between models are minor, and thus the value of choosing the “correct” price model, or similarly of estimating a more accurate model, is small. This work falls in the emerging research area of decision-oriented assessments of information value.