Identifying sweet spots and moving CBM reservoirs into production

C oalbed methane (CBM) is well recognised as an important natural gas resource. However, producing it commercially has proven to be a challenging process, primarily due to the complex nature of its gas storage and flow mechanisms. Although CBM reservoirs are naturally fractured due to the presence of face and butt cleats, gas stor­age and saturation depend on the adsorption capacity of the coal matrix. The ability to predict permeability variations and its evolu­tion in a CBM reservoir, which is affected by several dynamic CBM properties and operating conditions, also define the design, optimization, and analysis of the gas recovery process. As the permeability is naturally low, interventions are necessary in order to achieve commercially viable gas production rates. CBM are also dual-porosity media where the vast majority of the gas is stored in the low permeability coal matrix by sorp­tion. The flow to production wells, however, occurs through the coal’s natural fracture system, that stores relatively small amounts of gas, because coal matrix practically has no perme­ability. The result is that properties of the coal matrix have the greatest effect on the estimates of gas in place and recovery. It is well understood that the key parameter controlling gas flow in CBM reservoirs are cleat networks, gas storage, desorp­tion and permeability anisotropy (coal shrinkage and swelling). While the role of reservoir simulation is well under­stood for conventional oil and gas reservoirs and its related techniques for prediction and production optimisation, for unconventional reservoirs (CBM, Shale, etc.) current reservoir simulation software needs to be further developed and adapted to represent the performance of these types of fields and gener­ate more acceptable results. For example, the basic governing fluid flow regime in conventional reservoir simulations is limited to Darcy flow. For unconventional reservoirs, however, another flow regime term comes into the equation that reflects the flow of gas in the coal matrix and stands for diffusion. This new flow regime is not very well defined in the current simulators and needs further studies to appropriately address the production performance of a reservoir. Furthermore, the concept of multi-phase flow in reservoirs is well defined by the introduction of relative permeability equations in the flow equations. However, measuring relative permeability in coal is relatively complex (Ham and Kantas, 2008) due to its friability, heterogeneity, stress dependence and porous morphology. Young et al. (1992), for example, noticed that the derived relative permeability from a history match does not resemble the laboratory measurement. This phenomenon could be due to the change of matrix structure on phase flow in addition to wettability and capil­larity. In other words, both phases may flow through different passages over time due to heterogeneity. Thus dynamic rela­tive permeability (phase saturation) is required to accurately predict recovery from a CBM reservoir. As the focus on CBM has increased in the past decades, so has the quest for more effective technologies to help bring the reserves in such complicated conditions into production and to identify sweet spots. Reservoir modelling and reservoir simulation are technologies that are likely to play a key role in achieving this. Firstly, however, it is necessary to look at the challenges of CBM property variations.