Benoit Couet

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.

A practical sequential lexicographic approach for derivative-free black-box constrained optimization

Many engineering optimization problems involve models that might not exhibit the necessary smoothness to warrant efficient use of gradient algorithms. Many of these problems are also subject to constraints that might be simulation-based and as costly to compute as the objective function. Traditionally, such problems are solved using either penalty methods or lexicographic ordering that evaluates aggregate constraints prior to computing objective values. This study describes a cost-effective approach to performing such optimizations. After classifying all constraints depending on their computational cost, points not satisfying linear constraints are feasibilized, and a suitable penalty term constructed. A sequential lexicographic ordering is then applied in which inexpensive nonlinear constraints take precedence over expensive ones, which in turn take precedence over objective function values. The performance advantage of the proposed method over traditional ones is demonstrated with a set of analytical test problems, and with oilfield-production optimization examples that use ‘black-box’ simulators.

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.

Optimal Stimulation of Oil Production

A single thin oil-bearing stratum has constant thickness and is surrounded by a circular impermeable boundary. This circle C contains a domain D o which is filled with oil, the rest is filled with water. The permeabilities for oil and for water are taken to be the same.

Benchmarking time-dependent neutron problems with Monte Carlo codes

Abstract Many nuclear logging tools measure the time dependence of a neutron flux in a geological formation to infer important properties of the formation. The complex geometry of the tool and the borehole within the formation does not permit an exact deterministic modelling of the neutron flux behaviour. While this exact simulation is possible with Monte Carlo methods the computation time does not facilitate quick turnaround of results useful for design and diagnostic purposes. Nonetheless a simple model based on the diffusion-decay equation for the flux of neutrons of a single energy group can be useful in this situation. A combination approach where a Monte Carlo calculation benchmarks a deterministic model in terms of the diffusion constants of the neutrons propagating in the media and their flux depletion rates thus offers the possibility of quick calculation with assurance as to accuracy. We exemplify this approach with the Monte Carlo benchmarking of a logging tool problem, showing standoff and bedding response.