Tight gas reservoirs provide almost one quarter of the current U.S. domestic gas production, with significant projected increases in the next several decades in both the U.S. and abroad. These reservoirs constitute an important play type, with opportunities for improved reservoir simulation & management, such as simulation model design, well placement. Our work develops robust and efficient strategies for improved tight gas reservoir simulation and management. Reservoir simulation models are usually acquired by upscaling the detailed 3D geologic models. Earlier studies of flow simulation have developed layer-based coarse reservoir simulation models, from the more detailed 3D geologic models. However, the layer-based approach cannot capture the essential sand and flow. We introduce and utilize the diffusive time of flight to understand the pressure continuity within the fluvial sands, and develop novel adaptive reservoir simulation grids to preserve the continuity of the reservoir sands. Combined with the high resolution transmissibility based upscaling of flow properties, and well index based upscaling of the well connections, we can build accurate simulation models with at least one order magnitude simulation speed up, but the predicted recoveries are almost indistinguishable from those of the geologic models. General practice of well placement usually requires reservoir simulation to predict the dynamic reservoir response. Numerous well placement scenarios require many reservoir simulation runs, which may have significant CPU demands. We propose a novel simulation-free screening approach to generate a quality map, based on a combination of static and dynamic reservoir properties. The geologic uncertainty is taken into consideration through an uncertainty map form the spatial connectivity analysis and variograms. Combining the quality map and uncertainty map, good infill well locations and drilling sequence can be determined for improved reservoir management. We apply this workflow to design the infill well drilling sequence and explore the impact of subsurface also, for a large-scale tight gas reservoir. Also, we evaluated an improved pressure approximation method, through the comparison with the leading order high frequency term of the asymptotic solution. The proposed pressure solution can better predict the heterogeneous reservoir depletion behavior, thus provide good opportunities for tight gas reservoir management.

The accurate description of fluid flow through porous media allows an engineer to properly analyze past behavior and predict future reservoir performance. In particular, appropriate mathematical models which describe fluid flow through porous media can be applied to well test and production data analysis. Such applications result in estimating important reservoir properties such as formation permeability, skin-factor, reservoir size, etc. "Real gas" flow problems (i.e., problems where the gas properties are specifically taken as implicit functions of pressure, temperature, and composition) are particularly challenging because the diffusivity equation for the "real gas" flow case is strongly non-linear. Whereas different methods exist which allow us to approximate the solution of the real gas diffusivity equation, all of these approximate methods have limitations. Whether in terms of limited applicability (say a specific pressure range), or due to the relative complexity (e.g., iterative character of the solution), each of the existing approximate solutions does have disadvantages. The purpose of this work is to provide a solution mechanism for the case of timedependent real gas flow which contains as few "limitations" as possible. In this work, we provide an approach which combines the so-called average pressure approximation, a convolution for the right-hand-side non-linearity, and the Laplace transformation (original concept was put forth by Mireles and Blasingame). Mireles and Blasingame used a similar scheme to solve the real gas flow problem conditioned by the constant rate inner boundary condition. In this work we provide solution schemes to solve the constant pressure inner boundary condition problem. Our new semi-analytical solution was developed and implemented in the form of a direct (non-iterative) numerical procedure and successfully verified against numerical simulation. Our work shows that while the validity of this approach does have its own assumptions (in particular, referencing the right-hand-side non-linearity to average reservoir pressure (similar to Mireles and Blasingame)), these assumptions are proved to be much less restrictive than those required by existing methods of solution for this problem. We believe that the accuracy of the proposed solution makes ituniversally applicable for gas reservoir engineering. This suggestion is based on the fact that no pseudotime formulation is used. We note that there are pseudotime implementations for this problem, but we also note that pseudotime requires a priori knowledge of the pressure distribution in the reservoir or iteration on gas-in-place. Our new approach has no such restrictions. In order to determine limits of validity of the proposed approach (i.e., the limitations imposed by the underlining assumptions), we discuss the nature of the average pressure approximation (which is the basis for this work). And, in order to prove the universal applicability of this approach, we have also applied this methodology to resolve the time-dependent inner boundary condition for real gas flow in reservoirs.

Rapid economic evaluations of investment alternatives in the oil and gas industry are typically contingent on fast and credible evaluations of reservoir models to make future forecasts. It is often important to also quantify inherent risks and uncertainties in these evaluations. These ideally require several full-scale numerical simulations which is time consuming, impractical, if not impossible to do with conventional (Finite Difference) simulators in real life situations. In this research, the aim will be to improve on the efficiencies associated with these tasks. This involved exploring the applications of Fast Marching Methods (FMM) in both conventional and unconventional reservoir characterization problems. In this work, we first applied the FMM for rapidly ranking multiple equi-probable geologic models. We demonstrated the suitability of drainage volume, efficiently calculated using FMM, as a surrogate parameter for field-wide cumulative oil production (FOPT). The probability distribution function (PDF) of the surrogate parameter was point-discretized to obtain 3 representative models for full simulations. Using the results from the simulations, the PDF of the reservoir performance parameter was constructed. Also, we investigated the applicability of a higher-order-moment-preserving approach which resulted in better uncertainty quantification over the traditional model selection methods. Next we applied the FMM for a hydraulically fractured tight oil reservoir model calibration problem. We specifically applied the FMM geometric pressure approximation as a proxy for rapidly evaluating model proposals in a two-stage Markov Chain Monte Carlo (MCMC) algorithm. Here, we demonstrated the FMM-based proxy as a suitable proxy for evaluating model proposals. We obtained results showing a significant improvement in the efficiency compared to conventional single stage MCMC algorithm. Also in this work, we investigated the possibility of enhancing the computational efficiency for calculating the pressure field for both conventional and unconventional reservoirs using FMM. Good approximations of the steady state pressure distributions were obtained for homogeneous conventional waterflood systems. In unconventional system, we also recorded slight improvement in computational efficiency using FMM pressure approximations as initial guess in pressure solvers.