Uncertainty Reduction in Reservoir Geostatistical Description Using Distributed Temperature Sensing (DTS) Systems Data

Abstract 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 using data from production logging tools, for estimating the formation properties and production profile along the wellbore. Previous applications of history matching techniques using downhole temperature profile data have been mostly limited to simple reservoirs, and the quality of their results were unacceptable for more complex cases. In this work, we present the evaluation and application of temperature profile data from DTS systems for the purpose of uncertainty reduction in the reservoir description. We focus on the characterization of multi-layer multi-phase reservoir cases with a high degree of vertical heterogeneity. First, using 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 analysis, we also present an estimation for the expected reduction of uncertainty in the reservoir properties by assimilating the temperature data. Then, we apply the Ensemble Smoother with Multiple Data Assimilation (ES-MDA) algorithm to estimate the reservoir properties selected based on 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 using various combinations of these observed data for estimating the properties of a synthetic layered reservoir. Additionally, 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 significant amount of information about 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.