Advanced Techniques for Reservoir Simulation and Modeling of Non-Conventional Wells

This project targets the development of (1) advanced reservoir simulation techniques for modeling non-conventional wells; (2) improved techniques for computing well productivity (for use in reservoir engineering calculations) and well index (for use in simulation models), including the effects of wellbore flow; and (3) accurate approaches to account for heterogeneity in the near-well region

The sedimentation rate of disordered suspensions

An explicit expression for the sedimentation velocity at low particle Reynolds number in a concentrated suspension is derived and evaluated for two different approximations to the hydrodynamic interactions: a strict pairwise additive approximation and a far-field, or Rotne–Prager, approximation. It is shown that the simple Rotne–Prager approximation gives a very accurate prediction for the sedimentation velocity of random suspensions from the dilute limit all the way up to close packing. The pairwise additive approximation, however, fails completely, predicting an aphysical negative sedimentation velocity above a volume fraction φ ≈ 0.23. The explanation for these different behaviors is shown to be linked to the "effective medium" behavior of the suspensions. It is shown analytically and by Stokesian dynamics simulation that a suspension of neutrally buoyant particles may be modeled as a homogeneous fluid with an effective viscosity, but a sedimenting suspension cannot. As a result, the Rotne–Prager approximation actually captures the correct features of the many-body interactions in sedimentation. An analytical expression for the sedimentation rate, which is in good agreement with experiment, is obtained using the Percus–Yevick hard-sphere distribution function

Triangle based TVD schemes for hyperbolic conservation laws

A triangle based total variation diminishing (TVD) scheme for the numerical approximation of hyperbolic conservation laws in two space dimensions is constructed. The novelty of the scheme lies in the nature of the preprocessing of the cell averaged data, which is accomplished via a nearest neighbor linear interpolation followed by a slope limiting procedures. Two such limiting procedures are suggested. The resulting method is considerably more simple than other triangle based non-oscillatory approximations which, like this scheme, approximate the flux up to second order accuracy. Numerical results for linear advection and Burgers' equation are presented

Surrogate Model for Geological CO2 Storage and Its Use in MCMC-based History Matching

Deep-learning-based surrogate models show great promise for use in geological carbon storage operations. In this work we target an important application - the history matching of storage systems characterized by a high degree of (prior) geological uncertainty. Toward this goal, we extend the recently introduced recurrent R-U-Net surrogate model to treat geomodel realizations drawn from a wide range of geological scenarios. These scenarios are defined by a set of metaparameters, which include the mean and standard deviation of log-permeability, permeability anisotropy ratio, horizontal correlation length, etc. An infinite number of realizations can be generated for each set of metaparameters, so the range of prior uncertainty is large. The surrogate model is trained with flow simulation results, generated using the open-source simulator GEOS, for 2000 random realizations. The flow problems involve four wells, each injecting 1 Mt CO2/year, for 30 years. The trained surrogate model is shown to provide accurate predictions for new realizations over the full range of geological scenarios, with median relative error of 1.3% in pressure and 4.5% in saturation. The surrogate model is incorporated into a Markov chain Monte Carlo history matching workflow, where the goal is to generate history matched realizations and posterior estimates of the metaparameters. We show that, using observed data from monitoring wells in synthetic `true' models, geological uncertainty is reduced substantially. This leads to posterior 3D pressure and saturation fields that display much closer agreement with the true-model responses than do prior predictions

Optimization of carbon-capture-enabled coal-gas-solar power generation

a b s t r a c t Computational optimization is used to determine the optimal design and time-varying operations of a carbon dioxide capture retrofit to a coal-fired power plant. The retrofit consists of an amine-based temperature-swing absorption system, to which process steam is supplied from an auxiliary unit. Two candidate auxiliary heat sources are explored: natural gas and solar thermal. The NPV (net present value) of the retrofitted facility is maximized to determine which auxiliary system is preferable, under a variety of economic conditions. Optimized NPV is found to be most sensitive to the price of natural gas and the electricity price. At an 8% real discount rate, without renewable energy incentives, natural gas prices must be high (in excess of 10 USD/GJ) for a solar thermal design to be preferable, and electricity prices must reach z55 USD/MWh in order for solar-thermal-based designs to have a positive NPV. Incentives such as investment tax credits and solar power purchase agreements can make solar-thermal-based designs preferable to natural-gas-based designs under certain circumstances