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.

This paper covers a super giant carbonate oilfield in the Middle East that has enjoyed pressure support and voidage maintenance, primarily with peripheral water injection and pattern development in some reservoir units over the last decades. However, premature and non-uniform water front advancement has been a great challenge, resulting in early and uncontrolled water breakthrough with some wells becoming inactive due to increasing watercut. This challenge is mostly attributed to reservoir heterogeneity and particularly to the presence of un-mapped high permeability streaks (greater than 1Darcy) in the carbonate reservoir, usually resulting in by-passed oil and high value of Remaning Oil Saturation with poorer sweep efficiency. As a result, aiming to reach the desired ultimate recovery factor has become a challenge. A multidisciplinary approach involving the integration of various datasets, including geology (core facies and core description), geophysics (seismic stratigraphy), petrophysics (open hole logs, cased hole saturation time-lapse logs, and cased hole production logs), reservoir and production engineering (actual wells performance), and drilling data (mud losses, pilot hole) etc, were used to identify the high permeability streaks aerially and vertically within the reservoir. These high permeability streaks were then tested in the 3D dynamic model with various sensitivities to assess the impact on the reservoir performance in order to improve the match with the actual performance. The preliminary results were further validated by acquiring more data and gaining deeper understanding from Pulse Neutron logs, Injection and Production Logging, Flow tests, Pressure Transient Analysis etc. In order to reactivate inactive wells, increase production performance, and improve the sweep efficiency, targeted water shut-off was carried out to isolate the watered out intervals. Injection and Production logging gave more insights to understanding injection conformance and reservoir performance with adequate measures taken to ensure optimal reservoir management. In addition, areas with by-passed oil were targeted with revised well completion, infill drilling and artificial lift strategies. This paper describes the approach used, challenges encountered, results obtained, and the way forward.