We present a consistent and functional methodology for conducting value-of-information (VoI) analysis on proposed well tests in the presence of multiple critical uncertainties associated with the reservoir and the measurement/interpretation. Such uncertainties can impact asset economics acutely when they influence recoverable hydrocarbons. A key feature of this analysis is that the value of the measurement is determined before the measurement is made. Our demonstration uses an example of a marginal offshore asset where severe compartmentalization is possible. In this example, the VoI is a critical deciding factor because it is not obvious a priori that this value will justify the cost of the test in this marginal asset.
Our analysis uses a novel approach to standard decision trees by introducing new decision tree nodes in the form of "uncertainty" and "transformation uncertainty" nodes that can accommodate single or numerous continuous and discrete uncertainties. Without such novel tree components, a standard decision tree would become far too cumbersome for practical purposes and may even result in sub-optimal (uneconomic) development decisions from being made because of the discretized nature of the existing decision tree constructs.
We also consider, in addition to the reservoir uncertainties, the uncertainty in the well test measurement and/or the interpretation itself. We present expected asset net present value as a function of measurement uncertainty. Since well-test uncertainty can be related to the well-test duration, we show how the optimum well-test duration can be determined by identifying the time at which the difference between VoI and test cost is maximized. The cost of running the test longer than this optimal duration will only serve to reduce the net present value of the asset.
The problem of establishing meaningful value-of-information (VoI) metrics for a well test when faced with multiple significant uncertainties is examined. Such uncertainties can impact asset economics acutely when they influence recoverable hydrocarbons. We examine this VoI through an example of a small, marginal, offshore asset where the primary reservoir uncertainties relate to the degree of possible compartmentalization as well as permeability and porosity. The underlying driver of this study was to devise and test a consistent and functional methodology that can compute meaningful VoI for a well test such that all significant uncertainties are considered, including measurement uncertainty.