This paper (SPE 52981) is taken from an original manuscript "Streamline-Based Production-Data Integration into High-Resolution Reservoir Models," submitted at the invitation of the Journal of Petroleum Technology. This paper has not been peer reviewed.
Identifying the location and distribution of NAPL (Non Aqueous Phase Liquid) in the subsurface constitutes a vital step in the design and implementation of aquifer remediation schemes. In recent years, partitioning interwell tracer tests (PITT) have gained increasing popularity as a means to characterize NAPL distribution in-situ. In this method, a conservative and a partitioning tracer are injected into the contaminated site. The chromatographic separation between the conservative and the partitioning tracer can be used to infer NAPL distribution. The conventional approach to the analysis of the tracer response uses a first-order method of moments to compute average NAPL saturation. Such methods are limited due to the one-dimensional approximation and consequently can not provide detailed spatial distribution of the NAPL. In this paper we discuss a streamline-based inversion approach for partitioning tracer response to estimate spatial distribution of NAPL saturation in the subsurface. Our approach relies on an analytic sensitivity computation method that yields sensitivities of the partitioning tracer response to subsurface parameters such as porosity, permeability and NAPL saturation in a single streamline simulation. We can then use efficient techniques from geophysical inversion to match the field tracer response and estimate subsurface parameters. For characterizing NAPL saturation we follow a two-step procedure. First, we match the conservative tracer response that provides us with a permeability distribution. Next,the partitioning tracer response is matched by varying the NAPL saturation distribution in the subsurface. The non-uniqueness in the solution is addressed through the use of regularization techniques and/or prior information. The proposed technique has been applied to synthetic as well as field examples. The synthetic example is used to validate the procedure using tracer response from a 9-spot pattern. The field example is from the Hill Airforce Base, Utah where partitioning tracer tests were conducted in an isolated test cell with 4 injection wells, 3 extraction wells and 12 multi-level samplers. Tracer responses from 51 sampling locations are analyzed to determine permeability variations and NAPL saturation distribution in the test cell. Finally, a performance comparison with another commonly used inversion method viz. simulated annealing, shows that our proposed approach is faster by about three orders of magnitude.
A new approach that combines the use of static and dynamic data in fracture modeling has been developed. The approach provides the unique opportunity to constrain the fractured models to an effective permeability derived by automatically matching well performances using a streamline-based inverse approach. The effective permeability derived from well performances reflects the stress changes induced by fluid injection, and takes into account the increase of fracture conductivity along the present day maximum horizontal stress. Given the simplicity, robustness, and speed of the streamline based inversion, the estimation of effective permeability is proposed as an alternative to the tedious and often unreliable process of fracture modeling using stress measurements. Because of the availability of a true integrated fracture modeling framework, where any type of data could be used when deriving fracture models, the use of effective permeability derived by inversion is able to reduce the uncertainties by providing models with better fracture prediction capability, as illustrated on a carbonate reservoir. Introduction During the last two decades there have been major advances in the simulation of fractured reservoirs. Most current reservoir simulators are able to simulate dual porosity and dual permeability systems. The new generation of simulators1 are also able to account for the complex geometries created by faults that are commonly found in fractured reservoirs. However, the results of these reservoir simulators are only as good as the input of the reservoir rock properties. To the best of our knowledge, there is yet to be a well defined process for the estimation of 3D fracture permeability, fracture porosity and the shape factor for a dual porosity or dual permeability model. On the other hand, conventional reservoirs have the benefits of geostatistics that provide routinely reliable input for any single porosity model. Furthermore, uncertainties in modeling conventional reservoirs have been considerably reduced by integrating more and more data in the reservoir characterization process. Much effort has been spent on improving the description of the 100 feet surrounding the wellbore where no well will be drilled. Conversely, efforts to find a reliable fractured reservoir model in the interwell region (the one we are really interested in) are very rare. Ouenes et al.2 introduced a collection of artificial intelligence tools to model the interwell region of fractured reservoirs. The details of the method are explained in Ouenes3 and some new developments regarding its combination with DFN models are described by Ouenes and Hartley.4 The method was applied successfully using various static geologic and geophysical data, which are sufficient when the fractured reservoir is under primary production. The use of static data for fractured reservoir modeling is insufficient under secondary and tertiary flooding, which alters the reservoir permeability by increasing the conductivity of the fractures aligned along the present day maximum horizontal stress.5,6 Rather than focusing on stress measurement and stress modeling, we propose a new approach to incorporate the dynamic effects of fluid injection in the fractured reservoir modeling. The new approach uses inverse modeling to estimate the effective permeability, which has been affected by the stress changes due to the injection of fluids into the reservoir.
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