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This paper presents a study on the impact of well integrity failure on coalbed methane (also known as coal seam gas) production and potential shallow water contamination using numerical simulations with a finite-difference method. Two connection types and 12 cases were simulated: Type A – leakage through cement sheath and Type B – impaired zonal isolation at the aquifer interval. The effect of the distance between the aquifer and the coal seam, drainage area and desorption time on gas and water production was also inspected. Results show that both connection types have strong effects on the gas and water production; the cumulative water and gas production increases with increasing drainage radius; the distance between aquifer and the coal seam has a negative effect on the water production but a negligible effect on the gas production; desorption time, ranging from 5 to 30 days, has a negligible effect on the water and gas production. Connection Type A yields a potential water contamination but connection Type B does not. Gas concentration in the shallow aquifer decreases sharply with an increase of distance away from the producer and the unsafe area are within an area with a radius ranging from approximately 50 m to 90 m away from the producer in this study.

This paper presents an integrated approach of discrete fracture network modelling for a naturally fractured buried-hill carbonate reservoir in the Jingbei Oilfield by using a 3D seismic survey, conventional well logs, and core data. The ant tracking attribute, extracted from 3D seismic data, is used to detect the faults and large-scale fractures. Fracture density and dip angle are evaluated by observing drilling cores of seven wells. The fracture density distribution in spatiality was predicted in four steps; firstly, the ant tracking attribute was extracted as a geophysical log; then an artificial neural network model was built by relating the fracture density with logs, e.g., acoustic, gamma ray, compensated neutron, density, and ant tracking; then 3D distribution models of acoustic, gamma ray, compensated neutron and density were generated by using a Gaussian random function simulation; and, finally, the fracture density distribution in 3D was predicted by using the generated artificial neural network model. Then, different methods were used to build the discrete fracture network model for different types of fractures of which large-scale fractures were modelled deterministically and small-scale fractures were modelled stochastically. The results show that the workflow presented in this study is effective for building discrete fracture network models for naturally fractured reservoirs.

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