The Crystal Ball Oil & Gas Forum drew more than 50 attendees from different segments of the Oil & Gas industry. David Meinert, petroleum engineer with Chevron, described a stochastic model of drilling schedules to evaluate the effects of slippage on the development of a deepwater asset. Having a sub-optimum number of wells at first oil will adversely impact production targets. Also, deterministic approaches to evaluating multi-year drilling schedules can produce an overly long time estimate. Chevron has therefore built a simple model in Crystal Ball that stochastically estimates the drilling schedule and predicts the number of producers available at first oil. Unforeseen events and non productive time are estimated to provide an ‘easily maintained drill queue model that stochastically reflects the uncertainties associated with drilling highly complex wells in a deepwater environment.’
Chris Hill described how Marathon is applying portfolio optimization to evaluate its acquisitions. Acquisition opportunities present a problem set which is reduced to manageable proportions by categorizing and ranking the unknowns. Modeling allows Marathon to distinguish between individual asset and overall portfolio performance and CrystalBall’s OptQuest is used to optimize the portfolio under selected scenarios.
Steve Hoye (Decisioneering) showed how Monte Carlo simulations of different granularity can be combined to provide a decision support environment for a multi project development portfolio. The process identifies those combinations of projects that minimize portfolio risk for a given return. Such projects are said to lie on the ‘efficient frontier.’ Efficient frontier projects can be further investigated in terms of systematic and unsystematic risks. CrystalBall’s ‘real options’ are used to describe strategic flexibility, the possibility of abandoning or expanding projects as more knowledge of their outcomes is available.
Speaking at the plenary Crystal Ball user group, John Schuyler (PetroSkills-OGCI and Decision Precision) used an enterprise model including the OptQuest tool to ‘optimize management’s levers’ as determined using the balanced scorecard (BSC) method. In general, the BSC method is a multi-criteria approach whereas Schuyler advocates a more focused metric of shareholder value. He proposes an executive information system built around a stochastic enterprise model that has shareholder value generation as the focus metric and BSC ‘centerpiece.’ According to the abstract, ‘This derives from forecasting free cash flow, converting to a distribution of net present value and calculating the expected monetary value. [...] The enterprise model is the core means for evaluating and optimizing alternate corporate strategies and for measuring performance.’ Read Schuyler’s paper on www.decisioneering.com/cbuc/2006/papers/cbuc06-schuyler.pdf.
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