Interpretation of Distributed Fluid Temperature Logging in a Producer with Gradient Optimization and Uncertainty Analysis

Abstract

Summary The paper provides an approach for interpreting downhole distributed temperature sensing (DTS) and the results of its application in cases of synthetic and real production data. The outcome of such interpretation is a profile of fluid flows from reservoir layers. The given problem, however, is ambiguous, that is why the suggested approach consists of three steps: formulation of the inverse problem based on minimization of the constructed functional with the developed fast gradient optimization method, massive parallel inversions to collect a set of different interpretations and Bayesian inference of the most probable flow profiles incorporating uncertainty. All three issues are discussed in detail. Modifications of gradient optimizer making it fast and robust are described along with regularization allowing us to approach global functional minimum for synthetic data (illustration is provided) and decrease the ambiguity for real data. Explanation and example of how statistical analysis turns a set of interpretations into the most probable flow profiles and corresponding uncertainty with EM-clustering using Dirichlet distribution are included. All in all, the developed approach for effective evaluation of flow profiles and their statistical analysis can become a useful tool in oil and gas industry automating a big part of DTS interpretation process.