William J. Bailey

A survey of methods for gas-lift optimization

This paper presents a survey of methods and techniques developed for the solution of the continuous gas-lift optimization problem over the last two decades. These range from isolated single-well analysis all the way to real-time multivariate optimization schemes encompassing all wells in a field. While some methods are clearly limited due to their neglect of treating the effects of interdependent wells with common flow lines, other methods are limited due to the efficacy and quality of the solution obtained when dealing with large-scale networks comprising hundreds of difficult to produce wells. The aim of this paper is to provide an insight into the approaches developed and to highlight the challenges that remain.

Valuing Future Information Under Uncertainty Using Polynomial Chaos

This paper estimates the value of information for highly uncertain projects whose decisions have long-term impacts. We present a mathematically consistent framework using decision trees, Bayesian updating, and Monte Carlo simulation to value future information today, even when that future information is imperfect. One drawback of Monte Carlo methods in multivariate cases is the large number of samples required, which may result in prohibitive run times when considerable computer time is required to obtain each sample, as it is in our example. A polynomial chaos approach suitable for black-box functions is used to reduce these computations to manageable proportions. To our knowledge, this is the first exposition of polynomial chaos in the valuation literature. In our example it provides a speed-up of more than two orders of magnitude. We demonstrate the approach with an oilfield example involving a future decision on where to place a new injection well relative to a fault. The example considers the value to the asset holder of a measurement, to be made in the future, that reveals the degree of reservoir compartmentalization caused by this fault. In conditions of high demand for rigs and other scarce capital-intensive oilfield equipment, it may be appropriate for the asset holder to agree to a forward contract for this critical measurement to be taken at a future date at some specified price. The service provider would be contractually bound to provide this measurement at this pre-agreed price within a specified time window, regardless of the prevailing price and availability of rigs and equipment. Despite the presence of financial uncertainty on future oil price and private uncertainty on reservoir variables that are largely unresolved by the measurement, our methodology provides a computationally efficient valuation framework, possibly leading to novel ways of setting up contract terms.

Forecast Optimization and Value of Information under Uncertainty

Optimization algorithms provide methods to explore complex solution spaces efficiently and accurately to achieve a desired outcome. Optimization problems are common in our daily lives. If planning to drive a car, one commonly decides on the best (optimum) route to the desired destination. For oil field exploration and development, optimization can take many forms, but essentially the goal is to maximize recovery, total production, or net monetary profit from the asset.

Large-scale expensive black-box function optimization

This paper presents the application of an adaptive radial basis function method to a computationally expensive black-box reservoir simulation model of many variables. An iterative proxy-based scheme is used to tune the control variables, distributed for finer control over a varying number of intervals covering the total simulation period, to maximize asset NPV. The method shows that large-scale simulation-based function optimization of several hundred variables is practical and effective.