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Abstract. The multiphase flow through wellhead restrictions of an offshore oil field in Iran is investigated and two sets of new correlations are presented for high flow rate and water cut conditions. The both correlations are developed by using 748 actual data points, corresponding to critical flow conditions of gas-liquid mixtures through wellhead chokes. The first set of correlations is a modified Gilbert equation and predicts liquid flow rates as a function of flowing wellhead pressure, gas-liquid ratio and surface wellhead choke size. To minimize error in such condition, in the second correlation, free water, sediment and emulsion (BS & W) is also considered as an effective parameter. The predicted oil flow rates by the new sets of correlations are in the excellent agreement with the measured ones. These results are found to be statistically superior to those predicted by other relevant published correlations. The both proposed correlations exhibit more accuracy (only 2.95% and 2.0% average error, respectively) than the existent correlations. These results should encourage the production engineer which works at such condition to utilize the proposed correlations for future practical answers when a lack of available information, time, and calculation capabilities arises.

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Due to the computational simplicity and time efficiency, pore network and morphological techniques are two practical approaches for characterization of pore-scale microstructures. The methods are quasi-static and exploit pore space spatial statistics to simulate pore invasions. Here, both procedures are evaluated applying the workflows to pore-level micro-scale subdomains of Sandstone, Carbonate and Shale formations. A statistical approach is also utilized to improve the accuracy of Shale characterization by spatial restoration of fragmentary parts of organic matter. Post-processing results include relative permeability and capillary pressure curves, absolute permeability, formation factor, and thermal connectivity. The results appear to suggest that the accuracy of pore network modeling in the characterization of subdomains of micro-CT images is compromised by the presence of limited number of network elements, ignoring the resistance of pore elements, multi-scale structures, and tight/weak connections represented by an inadequate number of voxels. Pore network extraction negatively affects the accuracy of petrophysical predictions and ignores solid matrix and its thermal and electrical properties. The pore morphological approach accurately reproduces the fluid occupancies, efficiently deals with a variety of rock configurations and resolutions, and preserves connectivity and details of original images having more geometrical features than the pore network modeling. However, it predicts limited step-wised data points and realizations sourcing from its voxel-based nature. In addition, direct simulations confirm that stochastic conditional reconstruction of organic matter inside shale sub-volumes remarkably boosts the pore space connectivity and improves the accuracy of predictions.