As reservoirs mature, subsurface flow complexity and surface production operation challenges increase. This brings the necessity of making capital-intensive decisions to sustain or increase reservoir potential in an optimum way. However, subsurface uncertainties affect decision success. Reservoir surveillance, a process that involves data acquisition, validation, analysis, integration opportunity generation and execution, can mitigate the outcome of such decisions in the presence of uncertainties.
Although Value of Information (VOI) is a well-known process for justifying data acquisition, engineers struggle to extract the relevant information from historical data to apply Bayesian approach.
The objective of this paper is to illustrate a methodology for identifying the value of information in reservoir management, in particular for deriving the conditional probabilities of success when new and imperfect data are acquired. A methodology to assess the value of reservoir surveillance is supported by two cases.
In the first case, the incremental value of Real-Time Reservoir Characterization (RTRC) in underbalanced drilling (UBD) was nearly 100 times the cost of the services; in the second case, the incremental value of permananet downhole gauges (PDHG) was near 230 times the cost of installation and services. Reliability of facquired data, among other uncertainties, resulted to be a key success factor for both cases; however, in worst-case conditions, the incremental value was always positive.