Streamline-based models have shown great potential in reconciling high resolution geologic models to production data. In this work we extend the streamline-based production data integration technique to naturally fractured reservoirs. We use a dualporosity streamline model for fracture flow simulation by treating the fracture and matrix as separate continua that are connected through a transfer function. Next, we analytically compute the sensitivities that define the relationship between the reservoir properties and the production response in fractured reservoirs. Finally, production data integration is carried out via the Generalized Travel Time inversion (GTT). We also apply the streamline-derived sensitivities in conjunction with a dual porosity finite difference simulator to combine the efficiency of the streamline approach with the versatility of the finite difference approach. This significantly broadens the applicability of the streamlinebased approach in terms of incorporating compressibility effects and complex physics. The number of reservoir parameters to be estimated is commonly orders of magnitude larger than the observation data, leading to non-uniqueness and uncertainty in reservoir parameter estimate. Such uncertainty is passed to reservoir response forecast which needs to be quantified in economic and operational risk analysis. In this work we sample parameter uncertainty using a new two-stage Markov Chain Monte Carlo (MCMC) that is very fast and overcomes much of its current limitations. The computational efficiency comes through a substantial increase in the acceptance rate during MCMC by using a fast linearized approximation to the flow simulation and the likelihood function, the critical link between the reservoir model and production data. The Gradual Deformation Method (GDM) provides a useful framework to preserve geologic structure. Current dynamic data integration methods using GDM are inefficient due to the use of numerical sensitivity calculations which limits the method to deforming two or three models at a time. In this work, we derived streamline-based analytical sensitivities for the GDM that can be obtained from a single simulation run for any number of basis models. The new Generalized Travel Time GDM (GTT-GDM) is highly efficient and achieved a performance close to regular GTT inversion while preserving the geologic structure.

Integration of dynamic data is critical for reliable reservoir description and has been an outstanding challenge for the petroleum industry. This work develops practical dynamic data integration techniques using streamline approaches to condition static geological models to various kinds of dynamic data, including two-phase production history, interference pressure observations and primary production data. The proposed techniques are computationally efficient and robust, and thus well-suited for large-scale field applications. We can account for realistic field conditions, such as gravity, and changing field conditions, arising from infill drilling, pattern conversion, and recompletion, etc., during the integration of two-phase production data. Our approach is fast and exhibits rapid convergence even when the initial model is far from the solution. The power and practical applicability of the proposed techniques are demonstrated with a variety of field examples. To integrate two-phase production data, a travel-time inversion analogous to seismic inversion is adopted. We extend the method via a 'generalized travel-time' inversion to ensure matching of the entire production response rather than just a single time point while retaining most of the quasi-linear property of travel-time inversion. To integrate the interference pressure data, we propose an alternating procedure of travel-time inversion and peak amplitude inversion or pressure inversion to improve the overall matching of the pressure response. A key component of the proposed techniques is the efficient computation of the sensitivities of dynamic responses with respect to reservoir parameters. These sensitivities are calculated analytically using a single forward simulation. Thus, our methods can be orders of magnitude faster than finite-difference based numerical approaches that require multiple forward simulations. Streamline approach has also been extended to identify reservoir compartmentalization and flow barriers using primary production data in conjunction with decline type-curve analysis. The streamline 'diffusive' time of flight provides an effective way to calculate the drainage volume in 3D heterogeneous reservoirs. The flow barriers and reservoir compartmentalization are inferred based on the matching of drainage volumes from streamline-based calculation and decline type-curve analysis. The proposed approach is well-suited for application in the early stages of field development with limited well data and has been illustrated using a field example from the Gulf of Mexico.

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This study proposes novel optimization methodologies for the optimization of problems that reveal non-physical step discontinuities. More specifically, it is proposed to use gradient-only techniques that do not use any zeroth order information at all for step discontinuous problems. A step discontinuous problem of note is the shape optimization problem in the presence of remeshing strategies, since changes in mesh topologies may - and normally do - introduce non-physical step discontinuities. These discontinuities may in turn manifest themselves as non-physical local minima in which optimization algorithms may become trapped. Conventional optimization approaches for step discontinuous problems include evolutionary strategies, and design of experiment (DoE) techniques. These conventional approaches typically rely on the exclusive use of zeroth order information to overcome the discontinuities, but are characterized by two important shortcomings: Firstly, the computational demands of zero order methods may be very high, since many function values are in general required. Secondly, the use of zero order information only does not necessarily guarantee that the algorithms will not terminate in highly unfit local minima. In contrast, the methodologies proposed herein use only first order information, rather than only zeroth order information. The motivation for this approach is that associated gradient information in the presence of remeshing remains accurately and uniquely computable, notwithstanding the presence of discontinuities. From a computational effort point of view, a gradient-only approach is of course comparable to conventional gradient based techniques. In addition, the step discontinuities do not manifest themselves as local minima. / Thesis (PhD)--University of Pretoria, 2010. / Mechanical and Aeronautical Engineering / unrestricted