The data assimilation process of adjusting variables in a reservoir simulation model to honor observations of field data is known as history matching and has been extensively studied for few decades. However, limited success has been achieved due to the high complexity of the problem and the large computational effort required by the practical applications. An automatic history matching module based on the ensemble Kalman filter is developed and validated in this dissertation. The ensemble Kalman filter has three steps: initial sampling, forecasting through a reservoir simulator, and assimilation. The initial random sampling is improved by the singular value decomposition, which properly selects the ensemble members with less dependence. In this way, the same level of accuracy is achieved through a smaller ensemble size. Four different schemes for the assimilation step are investigated and direct inverse and square root approaches are recommended. A modified ensemble Kalman filter algorithm, which addresses the preference to the ensemble members through a nonequally weighting factor, is proposed. This weighted ensemble Kalman filter generates better production matches and recovery forecasting than those from the conventional ensemble Kalman filter. The proposed method also has faster convergence at the early time period of history matching. Another variant, the singular evolutive interpolated Kalman filter, is also applied. The resampling step in this method appears to improve the filter stability and help the filter to deliver rapid convergence both in model and data domains. This method and the ensemble Kalman filter are effective for history matching and forecasting uncertainty quantification. The independence of the ensemble members during the forecasting step allows the benefit of high-performance computing for the ensemble Kalman filter implementation during automatic history matching. Two-level computation is adopted; distributing ensemble members simultaneously while simulating each member in a parallel style. Such computation yields a significant speedup. The developed module is integrated with reservoir simulators UTCHEM, GEM and ECLIPSE, and has been implemented in the framework Integrated Reservoir Simulation Platform (IRSP). The successful applications to two and three-dimensional cases using blackoil and compositional reservoir cases demonstrate the efficiency of the developed automatic history matching module.

Interpretation problems are commonly associated with calculating water saturation in nonhomogenous shaly sand reservoirs. Redefining petrophysical properties based on well logs in shaly sand reservoirs by using fundamental geologic attributes is an important tool in developing subsurface hydrocarbon resources. Studies of the electrical anisotropy of shaly sands have shown that the level of our understanding and our ability to correctly evaluate low resistivity and low contrast pay can be greatly improved. The model developed in this thesis is similar in form to the shaly sand Dual Water model by Clavier et al. (1984). It is an experiment based model designed to directly assess and quantify the mineralogical and electrical effects of clay minerals in heterogeneous reservoirs. Clay minerals usually have multiple effects on petrophysical properties obtained from geophysical well log measurements. The total expansible clay model evaluates these effects via direct measurement of independent mineralogy and conductivity of clay minerals within reservoir sands. This model integrates the following as an effective basis for characterizing shaly sand reservoirs: ??? Rietveld based Siroquant assay for quantitative X-ray diffraction, used in determining mineral percentages from standard XRD trace patterns, ??? Cation exchange capacity, used to determine the quantity of cations involved in the exchange at the shale-water interface, ??? Porosity, permeability, density and resistivity measurements, ??? Thin section petrography, used in identifying mineral patterns, visible porosity and reservoir quality. Overall, application of correlations drawn from the model yields improved results for water saturation which appeared consistent with those earlier calculated using known water saturation models (Clavier et al Dual Water model, 1984, Juhasz, 1981). A total of twenty three samples from two wells in the Cliff Head fIeld were analyzed for this study.

A significant percentage of computational time in compositional simulations is spent performing flash calculations to determine the equilibrium compositions of hydrocarbon phases in situ. Flash calculations must be done at each time step for each grid block; thus billions of such calculations are possible. It would be very important to reduce the computational time of flash calculations significantly so that more grid blocks or components may be used. In this dissertation, three different methods are developed that yield fast, robust and accurate phase behavior calculations useful for compositional simulation and other applications. The first approach is to express the mixing rule in equations-of-state (EOS) so that a flash calculation is at most a function of six variables, often referred to as reduced parameters, regardless of the number of pseudocomponents. This is done without sacrificing accuracy and with improved robustness compared with the conventional method. This approach is extended for flash calculations with three or more phases. The reduced method is also derived for use in stability analysis, yielding significant speedup. The second approach improves flash calculations when K-values are assumed constant. We developed a new continuous objective function with improved linearity and specified a small window in which the equilibrium compositions must lie. The calculation speed and robustness of the constant K-value flash are significantly improved. This new approach replaces the Rachford-Rice procedure that is embedded in the conventional flash calculations. In the last approach, a limited compositional model for ternary systems is developed using a novel transformation method. In this method, all tie lines in ternary systems are first transformed to a new compositional space where all tie lines are made parallel. The binodal curves in the transformed space are regressed with any accurate function. Equilibrium phase behavior calculations are then done in this transformed space non-iteratively. The compositions in the transformed space are translated back to the actual compositional space. The new method is very fast and robust because no iteration is required and thus always converges even at the critical point because it is a direct method. The implementation of some of these approaches into compositional simulators, for example UTCOMP or GPAS, shows that they are faster than conventional flash calculations, without sacrificing simulation accuracy. For example, the implementation of the transformation method into UTCOMP shows that the new method is more than ten times faster than conventional flash calculations.