Abstract

Defining pressure dependent permeability (PDP) behaviour in coalbed methane (CBM) or coal seam gas (CSG) reservoirs using reservoir simulation is non-unique based on the uncertainty in coal properties and input parameters. A diagnostic fracture injection test (DFIT) can be used to investigate bulk permeability at a reservoir level and at lowered net effective stress conditions. As coal has minimal matrix porosity and under DFIT conditions cleat porosity is fluid saturated with reasonably definable total compressibility values, the DFIT data can provide insight into PDP parameters. At pressures above the fissure opening pressure, pressure dependent leak off (PDL) behaviour increases exponentially with increasing pressure. Many authors have noted that with decreasing pressure PDP declines exponentially with increasing net effective stress. Thus, PDP behaviour can be defined by PDL.

In this paper, we show how combined analyses, using typically collected field data, can be used to better define and constrain the modelling of PDP. We illustrate this process based on a well case study that includes the following data: fracture fabric and porosity reasonably defined from image log and areal core studies; DFIT data acquired under initial saturation conditions; hydraulic fracturing data; and longer term production data. These analyses will be integrated and used to constrain the parameters required to obtain a rate and pressure history-match from the post-frac well production data.

This workflow has application in other coal seam gas cases by identifying key variables where hydraulic fracturing performance has been unable to overcome limitations based on pressure or stress dependent behaviours and often accompanied by low reservoir permeability values. While this is purposely targeting areas where only typically collected field data is available, this workflow can include coal testing data for matrix swelling/shrinkage properties or other production data analysis techniques.

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