Field-Scale Characterization of Permeability and Saturation Distribution Using Partitioning Tracer Tests: The Ranger Field, Texas
- Pavel A. Illiassov (Texas A&M U.) | Akhil Datta-Gupta (Texas A&M U.)
- Document ID
- Society of Petroleum Engineers
- SPE Journal
- Publication Date
- December 2002
- Document Type
- Journal Paper
- 409 - 422
- 2002. Society of Petroleum Engineers
- 7.2.2 Risk Management Systems, 5.3.1 Flow in Porous Media, 4.1.5 Processing Equipment, 5.2.1 Phase Behavior and PVT Measurements, 5.5.7 Streamline Simulation, 5.5 Reservoir Simulation, 7.6.2 Data Integration, 5.1 Reservoir Characterisation, 5.6.4 Drillstem/Well Testing, 5.1.1 Exploration, Development, Structural Geology, 4.1.2 Separation and Treating, 5.1.5 Geologic Modeling, 5.6.5 Tracers, 5.5.8 History Matching, 5.3.2 Multiphase Flow, 5.1.8 Seismic Modelling, 4.3.4 Scale
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This paper discusses the application of an efficient streamlinebased inversion method to a large multiwell multitracer Partitioning Interwell Tracer Test (PITT) in the McClesky sandstone, the 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 on 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 largescale 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 interwell 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.6,7 Analysis of tracer tests typically requires the solution of an inverse problem. To date, 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 have 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.3-5,8,9 A conservative or nonpartitioning tracer is also injected during the test. Because of the presence of oil, partitioning tracers are retarded compared to the nonpartitioning tracer. The chromatographic separation between these tracers is utilized to estimate oil saturation in the reservoir.8,9 An excellent summary of the analytic methods for analysis of the PITT data in oil reservoirs is given by Tang.8 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.
Manual history matching is a common practice wherein one attempts to find a combination of permeability, porosity, and oil saturation that would lead to an adequate match of both conservative and partitioning tracer data.4,5 Such manual history matching is a time-consuming process that relies heavily on personal judgments and trial and error. Past attempts to solve the inverse problem by automated history matching using finite-difference simulators have shown that despite ever-increasing computational speed, finite-difference simulators often require prohibitively large computation time. Recently, streamline simulation has received wide-spread attention because of its high-speed performance.10,12 Use of streamline simulation in analyzing field tracer data can be beneficial in several ways. Apart from significantly faster forward simulations, another major advantage is that parameter sensitivities can be computed analytically in a single forward simulation. Such sensitivities quantify the relationship between small perturbations in reservoir properties, such as permeability or saturation, and changes in observed tracer response.
Vasco et al.13 presented a streamline-based production data integration approach that exploits an analogy between streamlines and seismic ray tracing. Yoon et al.14 demonstrated the utility of the streamline-based inversion method for estimating nonaqueous phase liquid (NAPL) saturation in an aquifer from PITT data and applied the method to a set of tracer tests carried out in a test cell at the Hill Air Force Base. In this paper, we show how a field-scale PITT data can be analyzed using the streamline-based inverse method. Our approach is fast, eliminates much of the trial-anderror associated with manual history matching, and provides an estimate of the spatial distribution of oil saturation in the reservoir. We also discuss and illustrate a procedure to assess the solution to the inverse problem using a resolution analysis. To our knowledge, this is the first time inverse modeling has been applied for fieldscale estimation of saturation distribution.
|File Size||16 MB||Number of Pages||14|