Summary
Spurred by improvements in reliability, cost, and accuracy, sensors offer a means of increasing expected ultimate hydrocarbon recovery in producing assets as well as in planned and prospective projects. Ultimate hydrocarbon recoveries larger than those currently achieved are possible, especially when sensors are used with advanced recovery methods. However, it is often unclear if the incremental recovery justifies the cost of installing the sensors. This paper proposes a method for estimating incremental values attributable to real-time sensors and provides a demonstration of the method for several production technologies and reservoir settings. The method offers a transparent and practical means of making value of information (VOI) computations to be implemented readily by project teams. An additional benefit of this method is that the process of specifying the inputs to the analysis facilitates a systematic discussion of strengths and weaknesses, and builds consensus regarding assumptions. The method is applied to four scenarios developed by a panel of industry experts to represent generic, but yet realistic reservoirs. The results for these scenarios indicate the value of sensors depends on the market price for product and the type of reservoir and production technology. The greatest absolute economic value for the use of sensors is obtained for a deepwater reservoir, while the greatest economic value per equivalent barrel of oil produced is obtained for a mature onshore reservoir. These expected economic values are intended to be compared to the cost required to implement the sensors to assess whether or not there is an expected net benefit.
Introduction
Formal methods of valuing information (sometimes called monetizing information) have existed in the research and professional literature for many years. Most publications on VOI have appeared in financial, economic, operations research, or decision analysis journals (Roberts and Weitzman 1981); little has appeared in engineering publications, especially petroleum engineering publications. Recently, a review of VOI in the oil and gas industry was presented by Bratvold et al. (2007).
VOI methods are simple at the highest conceptual level: the values for courses of action with and without sensors are estimated and compared. The difference between the expected values with and without sensors is the expected value of the sensors and therefore represents the maximum willingness to pay (WTP) for the sensors. If the WTP for the sensors is greater than the cost of installation (e.g., sensor cost, installation costs, and deferred production) and operation of the sensors, their installation is expected to provide a net benefit.
VOI assessments have the following components:They account for uncertainty in the outcome of decisions. The existence of uncertainty is the reason the valuation is based on expected values.They capture the ability of the sensors to change a decision. Typical decisions are an optimization of the current technology, immediate changes in technology, or the nature and timing of future technology changes.They allow for the sensors to change the monetary outcome of a course of action even when a decision is not changed by the information.
This paper proposes a method for VOI assessment of real-time sensors and demonstrates the method for four different combinations of hydrocarbon recovery technologies and reservoir settings:CO2 injection in mature oil reservoirs,steam-assisted gravity drainage in heavy oil reservoirs,hydraulic fracturing in tight gas reservoirs, andwaterflooding in deepwater sandstone reservoirs.
Drawing on industry experts, significant effort was made to make the cases as realistic as possible so the results can be used to inform the development of project- and corporate-level plans regarding the use of sensors. But, because of project and portfolio idiosyncrasies, the results are not to be viewed as definitive or totally generalizable.
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