The Information Content and Integration of Distributed-Temperature-Sensing Data for Near-Wellbore-Reservoir Characterization
- Bohan Xu (University of Tulsa) | Fahim Forouzanfar (University of Tulsa)
- Document ID
- Society of Petroleum Engineers
- SPE Reservoir Evaluation & Engineering
- Publication Date
- November 2017
- Document Type
- Journal Paper
- 906 - 923
- 2017.Society of Petroleum Engineers
- Distributed Temperature Sensing (DTS), Uncertainty Reduction, Information Content of Temperature Data, Ensemble Smoother with Multiple Data Assimilation, History Matching
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- 271 since 2007
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Downhole-temperature measurements provide valuable data for characterizing the flow between the reservoir and wellbore, which in turn is a function of the individual-reservoir-layer properties. In this paper, we investigate the use of downhole-temperature-profile data, provided from distributed-temperature-sensing (DTS) systems, as a cost-effective and robust alternative to other common approaches, such as use of data from production-logging tools, for estimating the formation properties and production profile along the wellbore. Previous applications of history-matching techniques by use of downhole-temperature-profile data have been mostly limited to simple reservoirs, and the quality of their results was unacceptable for more-complex cases. In this work, we present the evaluation and application of temperature-profile data from DTS systems for uncertainty reduction in the reservoir description. We focus on the characterization of multilayer multiphase reservoir cases with a high degree of vertical heterogeneity. First, by use of the principles of information theory, we investigate the information content of the temperature-profile data regarding various reservoir properties. By computing the mutual information between the reservoir parameters and the temperature-profile data, the reservoir properties with higher influence on the reservoir- and wellbore-temperature-profile data are identified. The associated uncertainty in these reservoir properties can be reduced by assimilating the downhole-temperature-profile data. Through these analyses, we also present an estimation for the expected reduction of uncertainty in the reservoir properties by assimilating the temperature data. Then, we apply the ensemblesmoother-with-multiple-data-assimilation (ES-MDA) algorithm to estimate the reservoir properties selected by use of our previous analysis. The set of observed data contains the wellbore-temperature profile, the temperature profile of the reservoir adjacent to the wellbore, and the flowing bottomhole pressure (BHP) of the well at a reference depth. We investigate the performance of the history-matching algorithm by use of various combinations of these observed data for estimating the properties of a synthetic layered reservoir. In addition, the implementation of a doubly stochastic model is also investigated to account for possible uncertainties in the prior mean of the reservoir properties. Our results show that the downhole-temperature-profile data contain a significant amount of information regarding the permeability and porosity of the reservoir layers. Moreover, the use of temperature-profile data within the ES-MDA history-matching algorithm is able to provide a good estimation of these properties and significantly reduce the uncertainty in the reservoir description.
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