Abstract

Scope of this paper is to show how the proper definition of reservoir rock types, based on core data and integrated with nuclear magnetic resonance (NMR) log results, is able to provide a reliable strategic estimation of permeability in un-cored wells where only conventional logs are available. The target is to optimize the perforated intervals by means of a robust discrimination between movable and un-movable fluids and consequent detection of the reservoir zones characterized by the best gas deliverability potential.

Mercury injection capillary pressure measurements have been used to evaluate the core pore throat size distribution and to separate micro from macro porosity. The integration of these information with NMR, acquired on the same core, allows to calibrate the most efficient T2 cut-off, discriminating movable from the unmovable fluids.

The final outcome is a robust link between reservoir properties (defined and directly measured on core data) and log classification, giving a key driver for the definition of a synthetic permeability profile, rock type dependent, and applied for perforated interval optimization in wells where no cores are available. The blind test was a comparison between estimated permeability from the well production performance and permeability derived from NMR logs, showing a good match. The work has greatly increased the value of NMR acquisition in Gas industry, showing how a proper T2 Cutoff core /log calibration is a vital factor to get the benefit of NMR in Gas reservoirs permeability prediction, providing a useful driver for perforated interval optimization.

Introduction

At present time, permeability estimation still remains a great challenge due to its not scalar nature: different correlations have been defined in literature to link this parameter to conventional downhole measurements and petrophysical properties, anyway none of the current available open hole logs can deliver directly an indication of the estimated permeability. Even the most advanced NMR tool will fails if applied in a standalone approach. The result is that the conventional log analysis approach, where core data are used mainly for outcome validation, still leave some uncertainties in the permeability profile definition and related reservoir effective deliverability.

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