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Gravity segregation in thick reservoirs, in particular, affects the distribution of hydrocarbon components in a fluid column. Given sufficient time, heavy molecules will migrate towards the bottom of the hydrocarbon column to form a mat of viscous oil. Compositional grading can be in the origin of miscalculation of reserves. In fields producing volatile oils with heavy ends, in particular, the oil formation volume factor (FVF) can vary significantly with depth. That can lead to overestimation or underestimation of the reserves depending upon fluid sampling depth. For that reason, subsurface sampling depth is critical and fluid sampling from different depths only can be considered as representative sampling. In this paper, assessment of compositional grading was explored using variation of reservoir fluid properties with depth. Different hydrocarbon column thickness have been tested to prove that grading will be enhanced for reservoir thickness exceeding 164 feet.

Pressure-volume-temperature (PVT) properties are critical to reservoir as well as production engineers, in particular. PVT properties could be determined experimentally. But, experiments are time consuming and costly. Moreover, laboratory PVT analysis does not consider the variations of PVT properties with respect to temperature since they are measured at reservoir temperature at the time of sampling. For that matter, the data is not benefitable. But, even if experimental analysis is done, it is difficult to obtain representative results to develop a new field. To tackle the above and other related problems, relying on sound PVT emperical correlations would be the ultimate solution. In this work, the intent is to develop stochastic models for PVT properties pertaining to Omani crude oils since it is believed that such correlations are scarce and not very precise. The empirical equations are developed for saturated Omani crude oils. The correlations are tested and validated. The empirical equations evaluation and assessment are done against existent experimental data and published correlations. 89

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