This paper discusses the application of an efficient streamline-based inversion method to a large multi-well multi-tracer Partitioning Interwell Tracer Test (PITT) in the McClesky sandstone, Ranger Field, Texas, to characterize both permeability and oil saturation distribution. During a typical PITT, a conservative and a partitioning tracer are injected into the reservoir. The partitioning tracer gets partially absorbed into the oil phase, leading to a separation in the tracer responses that can be used to infer oil saturation distribution in the tracer-swept area. Our approach is extremely efficient because it relies on analytic computation of the sensitivity of the tracer response to reservoir parameters such as permeability and saturation using a single streamline simulation. We follow a two step procedure whereby we first match the conservative tracer response to determine the permeability distribution and then match the partitioning tracer response to obtain oil saturation distribution in the reservoir. The entire history matching took less than 6 hours in a PC as opposed to several months typically required for a manual history matching.

We compared our results to a manual history match obtained using a finite-difference simulator. Both the manual history matching and the streamline-based inversion identified similar large-scale trends in permeability and saturation distribution. However, well-specific matches were significantly improved over those obtained through the manual history matching. Our approach is much more efficient in terms of computation time and effort and the results are less sensitive to personal bias compared to manual history matching. Finally, we discuss a procedure to assess the results in terms of resolution of the estimates of permeability and saturation distribution.


Success of secondary and tertiary oil recovery projects targeting the remaining oil in mature or partially depleted reservoirs strongly depends on adequate description of reservoir heterogeneity and remaining oil distribution. Many field studies have reported successful application of conservative tracers to characterize inter-well communication, presence of flow barriers, and preferential flow paths to improve understanding of fluid movement in the reservoir.1–5 Also, single-well partitioning tracer tests have been widely used in the industry to estimate oil saturation in the vicinity of wells. Analysis of tracer tests typically requires the solution of an inverse problem. Todate, most of the work on inverse modeling associated with tracer data have been limited to estimating permeability distribution or transport parameters such as dispersivities or molecular diffusion. Inverse problems dealing with the estimation of spatial distribution of oil saturation has remained relatively unexplored.

During partitioning interwell tracer tests a suite of tracers with a range of oil-water partitioning coefficients are injected into the subsurface and are sampled at the producing wells.3–7 A conservative or non-partitioning tracer is also injected during the test. Because of the presence of oil, partitioning tracers are retarded compared to the non-partitioning tracer. The chromatographic separation between these tracers is utilized to estimate oil saturation in the reservoir.6–7 An excellent summary of the analytic methods for analysis of the PITT data in oil reservoirs is given by Tang.6 These analytic methods are simple and easy to apply; however, they only provide an estimate of average oil saturation. Potentially, every observed data point of a PITT may carry important information about reservoir properties. Thus, a direct match of the tracer history is desirable but is difficult because it involves the solution of a computationally intensive inverse problem.

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