Abstract

Reliable permeability and mineralogy estimates in carbonate formations can significantly improve the prediction of acid fracture conductivity in these challenging reservoirs. Well logs are good candidates to provide information about petrophysical properties of the formation with the required resolution for prediction of acid fracture conductivity. The assessment of permeability and mineralogy from well logs, however, has been highly dependent on core measurements in carbonate formations. Conventional well-log-based permeability assessment techniques including porosity-permeability correlations are not reliable in the case of carbonate formations. High spatial heterogeneity, variable lithology, and complex pore structure result in a poor correlation between permeability, porosity, and irreducible water saturation in carbonates. Rock typing has been suggested in the literature to be used to improve permeability assessment in carbonates. Most of the introduced rock typing methods are dependent on core measurements. However, core data are generally sparse and not available with the sampling rate required for prediction of acid fracture conductivity.

In this paper we propose an iterative permeability assessment technique based on well logs, which takes into account rock types. We also introduce three rock classification techniques based on conventional well logs including (a) a log-derived analytical factor, (b) unsupervised artificial neural network, and (c) supervised artificial neural network. The first two techniques are independent of core measurements for rock classification. However, the third technique is highly dependent on core measurements in the field. We successfully applied the proposed techniques in two carbonate formations, Happy Spraberry oil field and Hugoton gas field. The petrophysical rock classification is in a good agreement with identified core-derived rock classes. The results show approximately 54% improvement in permeability assessment compared to conventional permeability assessment technique, which can significantly improve prediction of acid stimulation jobs.

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