2009 Portland GSA Annual Meeting (18-21 October 2009)

Paper No. 6
Presentation Time: 3:00 PM

MODELING OF METHANE CONTROL IN COAL MINES: APPLICATIONS OF RESERVOIR ENGINEERING TECHNIQUES AND PREDICTIVE MODELS


KARACAN, C. Özgen, Pittsburgh Research Laboratory, CDC/NIOSH, 626 Cochrans Mill Road, PO Box 18070, Pittsburgh, PA 15236, cok6@cdc.gov

Methane control in underground coal mines, either by ventilation or degasification, is crucial to prevent possible fires and explosions due to excessive methane emissions. This presentation concentrates on the degasification techniques and demonstrates the application of coal bed reservoir engineering modeling techniques for optimizing and controlling methane emissions, as well as a practical tool to predict and control methane emissions.

The presentation shows examples from the NIOSH/PRL activities on detailed geomechanical and reservoir modeling, and degasification studies for longwall and continuous miner operations in Northern Appalachian Basin. Examples from the reservoir modeling studies that encompasses structural (faulted versus continuous), compositional (inertinite-rich versus vitrinite-rich) and various different reservoir characteristics of the coal beds (permeability, Langmuir parameters etc.), how they can be incorporated in detailed models and their influence on methane control are also presented and discussed. A brief discussion on the field reservoir studies follows as a complementary topic on gathering data for the current modeling activities to understand the strata behavior and reservoir properties that may affect methane emissions.

In the last part of the presentation a software suite that was developed and is being improved for longwall mines is presented as a predictive approach. This suite contains four main modules to predict dynamic elastic properties of coal-measure rock for better roof support and methane control; to predict gob gas venthole performance, to predict ventilation methane emission of longwall mines, and to select the best degasification choice using an expert system. These predictive models are presented as a proxy solution to more difficult reservoir engineering problems that are encountered in mining of coal seams.