New Model for Absolute Permeability Prediction in Coal Samples: Application of Modified Purcell Model to Mercury Injection Pressure and Nuclear Magnetic Resonance Data

The permeability of rocks is a criticalparameter in many subsurfacegeological applications, and pore properties measured on rock samples(including rock fragments) can be used to estimate rock permeability.A major use of MIP and NMR data is to assess the pore properties ofa rock in order to estimate the permeability based on empirical equations.Although sandstones have been extensively studied, permeability incoals has received less attention. Consequently, in order to obtainreliable predictions for coal permeability, a comprehensive studyof different permeability models was performed on coal samples havinga range of permeabilities from 0.003 to 1.26 mD. The model resultsshowed that the seepage pores in coals account for the bulk of thepermeability, while the contribution of adsorption pores to permeabilityis negligible. The models that only consider a single pore size pointon the mercury curve, such as the Pittman and Swanson model, or thosethat use the entire pore size distribution, like the Purcell and SDRmodel, are inadequate for predicting permeability in coals. This studymodifies the Purcell model to determine permeability from the seepagepores of coal, resulting in the enhancement of the predictive capability,with an increased R (2) and reduction inthe average absolute error by approximately 50% compared to the Purcellmodel. To apply the modified Purcell model to NMR data, a new modelwas developed that provides a high degree of predictive capability(similar to 0.1 mD). This new model can be used for cuttings, which couldlead to a new method for field permeability estimation.

Automated summary

The permeability of rocks, especially coal, is important in geological applications. Previous permeability models for coal have limitations in accuracy. This study modifies the Purcell model to improve its predictive capability for coal permeability. A new model based on NMR data is developed, providing high accuracy for field permeability estimation.