Reservoir Rock and Fluid Characterization Using Distributed Temperature Sensing DTS Systems Data

Abstract

Abstract The variation of temperature profile of the sandface and wellbore offers important indicators for the occurrence of various downhole phenomena, as functions of reservoir formation and fluid properties. This paper aims to investigate the assimilation of temperature and production data for robust and efficient estimation of reservoir formation properties and fluid characterization. Very few previous work in the literature used Distributed Temperature Sensing (DTS) data for the characterization of the reservoir fluid and geostatistical description. The main contribution of this work lies in the proposed methodology for integration of temperature data from DTS systems to obtain a robust estimation of the reservoir fluid description, as well as introducing the integration of temperature data as an alternative and/or complementary data for integrative reservoir characterization. Two synthetic reservoir examples are investigated. In the first example, we focus on resolving the geostatistical description of a 3D multi-phase reservoir under water flooding. An ensemble-based data assimilation algorithm is used to compare the characterization of reservoir formation porosity and permeability by integration of temperature profile and well production data as opposed to the integration of production logging tool (PLT) data. Our results show that the DTS temperature data shows good potential as an alternative to the PLT data for reservoir rock characterization, although there may be a risk of model overfitting due to the large amount of data. In the second example, we attempt to estimate the original composition of the reservoir fluid as well as reservoir rock properties using downhole temperature profile data. A relatively simple multi-layer radial reservoir model is considered in this case. However, in order to honor the physical bounds on the various model parameters, a constrained ensemble-based data assimilation algorithm based on Lagrange multipliers is adopted. The computational results show that the original reservoir fluid composition could be excellently estimated for a case with known reservoir properties. Presence of uncertainty in the reservoir formation properties resulted in an poor estimation of the original reservoir fluid composition.