8 research outputs found
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Adaptive forward-inverse modeling of reservoir fluids away from wellbores
This Final Report contains the deliverables of the DeepLook Phase I project entitled, ''Adaptive Forward-Inverse Modeling of Reservoir Fluids Away from Wellbores''. The deliverables are: (i) a description of 2-D test problem results, analyses, and technical descriptions of the techniques used, (ii) a listing of program setup commands that construct and execute the codes for selected test problems (these commands are in mathematical terminology, which reinforces technical descriptions in the text), and (iii) an evaluation and recommendation regarding continuance of this project, including considerations of possible extensions to 3-D codes, additional technical scope, and budget for the out-years. The far-market objective in this project is to develop advanced technologies that can help locate and enhance the recovery of oil from heterogeneous rock formations. The specific technical objective in Phase I was to develop proof-of-concept of new forward and inverse (F-I) modeling techniques [Gelinas et al, 1998] that seek to enhance estimates (images) of formation permeability distributions and fluid motion away from wellbore volumes. This goes to the heart of improving industry's ability to jointly image reservoir permeability and flow predictions of trapped and recovered oil versus time. The estimation of formation permeability away from borehole measurements is an ''inverse'' problem. It is an inseparable part of modeling fluid flows throughout the reservoir in efforts to increase the efficiency of oil recovery at minimum cost. Classic issues of non-uniqueness, mathematical instability, noise effects, and inadequate numerical solution techniques have historically impeded progress in reservoir parameter estimations. Because information pertaining to fluid and rock properties is always sampled sparsely by wellbore measurements, a successful method for interpolating permeability and fluid data between the measurements must be: (i) physics-based, (ii) conditioned by signal-processing tenets, and (iii) solved with sufficiently rigorous mathematical and numerical techniques. Such a methodology is applied in this project, as we extend the F-I modeling methods developed at LLNL for ground water remediation to DeepLook reservoir problems involving transient multiphase flows. The results obtained at this juncture are encouraging; and the proposed objectives of Phase I have been achieved
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A new breed of innovative ground water modeling
Sparse data is a critical obstacle in every ground water remediation project. Lack of data necessitates non-unique interpolations that can distort modeled distributions of contaminants and essential physical properties (e.g., permeability, porosity). These properties largely determine the rates and paths that contaminants may take in migrating from sources to receptor locations. We apply both forward and inverse model estimates to resolve this problem because coupled modeling provides the only way to obtain constitutive property distributions that simultaneously simulate the flow and transport behavior observed in borehole measurements. Innovations in multidimensional modeling are a key to achieving more effective subsurface characterizations, remedial designs, risk assessments, and compliance monitoring in efforts to accelerate cleanup and reduce costs in national environmental remediations. Fundamentally new modeling concepts and novel software have emerged recently from two decades of research on self-adaptive solvers of partial differential equations (PDEs). We have tested a revolutionary software product, PDEase, applying it to coupled forward and inverse flow problems. In the Superfund cleanup effort at Lawrence Livermore National Laboratory`s (LLNL) Livermore Site, the new modeling paradigm of PDEase enables ground water professionals to simply provide the flow equations, site geometry, sources, sinks, constitutive parameters, and boundary conditions. Its symbolic processors then construct the actual numerical solution code and solve it automatically. Powerful grid refinements that conform adaptively to evolving flow features are executed dynamically with iterative finite-element solutions that minimize numerical errors to user-specified limits. Numerical solution accuracy can be tested easily with the diagnostic information and interactive graphical displays that appear as the solutions are generated