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Willigers B.J.A.,Palantir Economic Solutions Ltd. | Begg S.H.,University of Adelaide | Bratvold R.B.,University of Stavanger
Society of Petroleum Engineers - SPE Asia Pacific Oil and Gas Conference and Exhibition 2010, APOGCE 2010 | Year: 2010

Natural gas and electricity are commonly traded through swing contracts that enable the buyer to exploit changes in market price or market demand by varying the quantity they receive from the producer (seller). The producer is assured of selling a minimum quantity at a fixed price, but must be able to meet the variable demand from the buyer. The flexibility of such contracts enables both parties to mitigate the risks and exploit the opportunities that arise from uncertainty in production, demand, price, etc. But how valuable are they? Traditional Net Present Value, based on expected values, cannot value this flexibility, and the traditional options-valuation techniques could not model the complexity of the terms of such contracts. Taking gas contracts as an example, this paper seeks to (a) raise awareness of how flexibility creates value for both parties and (b) show how Least-Squares Monte Carlo Simulation can be used to quantify its value in dollar terms, from the perspective of both producer and buyer. Since the value of flexibility arises from the ability it gives to respond to fluctuations (such as in commodity prices), a useful model of swing contracts needs to reflect the nature of these fluctuations. Copyright 2010, Society of Petroleum Engineers.

Willigers B.J.A.,Palantir Economic Solutions Ltd. | Majou F.,Palantir Economic Solutions Ltd.
SPE Hydrocarbon Economics and Evaluation Symposium | Year: 2010

The process of portfolio optimization provides guidance to decision makers on how to manage an asset base given corporate objectives, market conditions, and organizational capability. Many applications in the oil and gas industry are based upon Markowitz's efficient portfolio theory. In the standard implementation of this framework, an efficient portfolio is defined as one that yields the highest value given a specific degree of risk. A corporate decision maker will aim, however, to select a portfolio that meets several often competing objectives, i.e., maximize portfolio value while minimizing capital expenditure. The optimal portfolio choice given one constraint is typically not optimal given one of the competing constraints. This requires the portfolio manager to identify and select those portfolios that best meet all corporate constraints. Deciding which portfolio to develop is often compounded by there being several portfolios having similar economic characteristics. However, these portfolios can generally be differentiated by strategy, which may depend on non-financial attributes such as the geographic location of the assets or on geological settings that might require different engineering expertise. In this study, a large set of exploration portfolios and their attributes have been simulated. Through applying a series of simple and transparent filters, a few portfolios can be identified that meet all the corporate constraints. After a shortlist has been created, the portfolios can easily be characterized by strategy and the tradeoffs between them can be assessed.

Willigers B.J.A.,Palantir Economic Solutions Ltd.
Proceedings - SPE Annual Technical Conference and Exhibition | Year: 2011

Many unconventional gas developments provide only marginal economic returns. Realizing a commercial success with such projects depends on the identification of an efficient development program. Significant project value can be unlocked by, for example, optimizing the drilling program and the gas export infrastructure development. The optimization of an unconventional green-field gas development is subject to a multitude of objectives, constraints, and tradeoffs. Decision making is further complicated by multiple sources of uncertainty. Tools like decision trees and Monte Carlo simulations can help assess exploration and early-stage development projects. However, these methods are impractical for large numbers of options and uncertainties, which are associated with many mid-stage development projects. Many decision situations are in fact complex optimization problems and practical techniques are required to enhance decision making policies. The genetic algorithm is a powerful method to optimize complex non-linear systems for a given set of constraints. However, many real-life situations involve sequential decision making, which is likely to cause preferences to change when uncertainties are progressively resolved. The value of managerial flexibility associated with such projects cannot be quantified in a single execution of a genetic algorithm. However by a series of iterative calculations of a genetic algorithm that is conditioned with Bayesian logic the optimization procedure can be significantly improved. Copyright 2011, Society of Petroleum Engineers.

Willigers B.J.A.,Palantir Economic Solutions Ltd. | Prendergast K.,Palantir Economic Solutions Ltd. | Muslumov Z.,Palantir Economic Solutions Ltd.
SPE Hydrocarbon Economics and Evaluation Symposium | Year: 2010

The future profitability and the ultimate hydrocarbon recovery of North Sea fields are largely dictated by the management of the infrastructure used to process and transport hydrocarbons. Optimization of this infrastructure is therefore an important strategic objective for most North Sea players. In the North Sea, a common offshore infrastructure design consists of a regional hub that collects hydrocarbons from the fields located in the catchment area. This collective production is exported by a pipeline leaving the hub. Hubs, pipelines and oil-and-gas receiving terminals are typically paid a tariff for their services. Once tariff receipts become insufficient to cover the costs of those services, many commercial agreements default to cost-sharing arrangements, where the operating costs of the facility are distributed over its user-fields 1. This cost sharing generally translates into higher operating costs for these fields and may cause certain user-fields to become uneconomical. As one user-field ceases production, the infrastructure facility's operating costs are distributed over fewer fields. This may translate into a "domino-effect" scenario, in which more and more fields become uneconomical, thereby cutting short the economic life of the infrastructure facility. In this paper, the economic interdependencies between a hub and its user-fields are illustrated first by a generic example, then by an analysis of the Bruce hub and its user-fields in the UK North Sea. The results highlight that accurate modeling of hub and user-fields interactions can increase understanding and mitigate the risks of potential "domino-effects", in which the economics of transportation and processing facilities may deteriorate rapidly and turn large segments of the North Sea uneconomical.

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