Development of a coalbed methane (CBM) field in its early stage is often plagued by the lack of well control and scarcity of geological data over a large geographical area. Therefore, constructing a representative static model to estimate the in-place-volume presents a formidable challenge. In this paper we proposed a workflow to overcome this challenge and applied it to a CBM field in the northern Bowen Basin of Australia.This workflow may be considered as a best practice for the following reasons. First, it makes use of data from various sources including cores, well logs, seismic interpretation, and topography. Second, it performs rigorous quality control on these data, such as depth shift and log normalization. Third, coal ply division and correlation and subsequent structural modeling are based on three types of correlation: well-to-well, well-to-seismic and, well-seismic-Graphic Information System. Fourth, it establishes the low, base and high trends for the most important reservoir properties. Fifth, it constructs a base case static model by combining the aforementioned structural and reservoir property models. Sixth, it uses sensitivity analysis, which varies one reservoir parameter at one time, to rank the impact of reservoir parameters on in-place-volume. Seventh, it uses uncertainty analysis which varies all reservoir parameters simultaneously to arrive at the P10, P50 and P90 in-place-volumes and their corresponding static models which can then be used for reservoir simulations to estimate the recoverable volumes.

Summary The development of a coalbed-methane (CBM) field in its early stage is often plagued by the lack of well control and the scarcity of geological data across a large geographical area. Therefore, constructing a representative static model to estimate the in-place volume presents a formidable challenge. In this paper, we propose a work flow to overcome this challenge and apply it to a CBM field in the northern Bowen basin of Australia. One may consider this work flow as a best practice for the following reasons. First, it makes use of data from various sources including cores, well logs, seismic interpretation, and topography. Second, it performs rigorous quality control on these data, such as depth shift and log normalization. Third, coal-ply division and correlation and subsequent structural modeling are based on three types of correlation: well-to-well, well-to-seismic, and well-seismic-geographic information system. Fourth, it establishes the low, base, and high trends for the most-important reservoir properties. Fifth, it constructs a base-case static model by combining the aforementioned structural and reservoir-property models. Sixth, it uses sensitivity analysis, which varies one reservoir parameter at a time, to rank the impact of reservoir parameters on in-place volume. Seventh, it uses uncertainty analysis that varies all reservoir parameters simultaneously to arrive at the P10, P50, and P90 in-place volumes and their corresponding static models that one can use for reservoir simulations to estimate the recoverable volumes.

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The study focused on the Permian aged Rangal coal measures within a 3D seismic volume in middle of Bowen basin. The Rangal Coal Measures (RCM) are a middle-rank coal seam gas (CSG) target with low permeability. Approximately 150 to 200 wells, with geophysical log data, are unevenly distributed in the seismic area of 51 km2. The effect of stress and related geomechanics on producing wells has been poorly understood. Petrophysical data, 3D seismic cube, geographic information system (GIS), borehole imager and well stress data were used to conduct the interpretative workflow. A novel four-step workflow was created to provide geomechanical insights in order to optimize future well designs for improved productivity and well integrity. The first step in the workflow was to finely delineate fracture distribution by using coherence and max curvature methods in the seismic volume. This was followed by determining the most appropriate number of coal-sensitive attributes using step-wise regression methods. The multi-attribute emerged density inversion was conducted utilizing probabilistic neural network training and predicting, which was derived from the density cube and predicted coal thickness map. The resulting density cubes included the overburden zone which was calculated by well data interpolation in consideration of GIS overlays. This was followed by 2D stress simulation that was created by analyzing a combination of maximum/ minimum stress, principal stress, Young's Modulus, and Possion's ratio in measured well data. Inputs such as fracture networks, coal thickness, density and velocity cubes were combined to process the simulation using designed fracture reservoir characterization software (FRS). The workflow was completed by using GeoPressure Analysis (GPA) software and data such as: stress, density and the velocity cubes to calculate the overburden pressure (Pov) and pore pressure (Pp). The overburden pressure was computed by integrating the density volume. The pore pressure volume was analyzed using the Fillippone formula. Vertical effective stress (VES) and the pore pressure coefficient were calculated using the principles of rock theory. The minimum / maximum horizontal principal stress (SHmin / SHmax) were calculated by utilizing the Zoback and Healy formulas and using the following input data: calculated Pp, Pov, measured effective stress ratio of the minimum/maximum horizontal principal stress and measured horizontal stress. Direct measured properties from geophysical logs such as principal stress showed good correlation with adjacent extracted values from the modeled principal stress. The well production properties indicated good correlation with geomechanical properties such as small fractures and stress. Single well average peak gas production and average peak gas production both decreased with the diversity factor of horizontal stress (DHSR) which was extrapolated from SHmax and SHmin. The originality of this study lies in the comprehensive integration of geological, geophysical and petrophysical data to create a functional geomechanical model. The results indicate that this geomechanical study will be useful for well planning and predicting production properties in each future study area.