The hydrocarbon PLUS FRACTIONs that comprise a significant portion of naturally occurring hydrocarbon fluids create major problems when determining the thermodynamic properties and the volumetric behavior of these fluids by equations of state. These problems arise due to the difficulty of properly characterizing the PLUS FRACTIONs (heavy ends). Proper characterization of the heavier components is important when cubic equations of state and/or solid formation thermodynamic models are used to describe complex phase behavior of reservoir fluids. The effect of heavy FRACTIONs characterization on thermodynamic modeling of wax precipitation has been investigated using different models including Won, Pan and Proposed Models. In order to characterize the PLUS FRACTION (heavier part) as a series of pseudocomponents, a probability model that expresses the mole FRACTION as a continuous function of the molecular weight has been used. The study has been conducted using several mixtures. Two different SCNs (Single Carbon Number), C7+ and C10+, were chosen. The chosen SCNs were distributed to multicomponents of five, six, and/or ten using continuous method. The results showed that the FRACTIONing is required to be able to predict wax precipitation. Distribution of C10+ using a proper distribution function has shown improvement in predictions of WAT and the amount of wax deposited in comparison with the characterization of C7+ using semi-continuous approach. In predicting WAT and the amount of wax build up the developed model showed superiority over the others.

Gas condensate reservoirs are characterized by a distinctive retrograde behavior and potential for condensate drop out during production and sampling. Efficient modeling of gas condensate reservoir requires careful phase behavior studies of samples collected prior to and during the production life of reservoir. In this work, an integrated characterization and tuning algorithm is proposed to analyze the pressure-volume-temperature (PVT) behavior of gas condensate samples. Each characterization and tuning scenario is described by a “ path” which specifies the class of fluid, splitting and lumping (if any), the type of correlation, and grouping strategy (static or dynamic). Different characterization approaches were tested for the effective description of heavy end. Meanwhile, dynamic and static strategies were implemented to tune the equation of state (EOS) through non-linear regression. The optimum combination of characterization and tuning approach was explored for each sample by a rigorous analysis of the results. It was found out that the exponential distribution function gives the best performance for heavy end characterization in a dynamic tuning strategy. Also, analyses indicate that using higher single carbon number may not necessarily make EOS tuning more accurate. In addition, the optimum step is reached in either the third or fourth step for most cases in a dynamic tuning approach, and is sensitive neither to the characterization path nor to the selected end carbon number.

The more clearly the clinician comprehends the processes of clinical perception and of linking the experience with explanation, the more thoughtfully clinical students can be guided to construct an accurate clinical memory of cases, and to construct a sound working knowledge structure from their personal examined experience. The structuring of the steps of clinical learning is intended as a check list to optimise their coverage, and as a framework for examining and codifying clinical working knowledge within which continuing education can be conducted.