It is key for the success of the Paleozoic marine shale gas exploration and development in South China to search for the sweet spots or shale gas-rich zones within the shale gas reservoir. Since it directly impacts the shale gas enrichment degree and hydro-fracturing effect of the shale gas reservoir, the development degree of shale gas reservoir fractures is the important factor for the prediction and evaluation of the sweet spots or shale gas-rich zones. This paper proposes a seismic fracture facies evaluation workflow for shale gas reservoirs and its application through the utilization of 100 km2 wide-azimuth 3D seismic data post stack attributes in the Block MA shale gas exploration area. We focus especially on using neural network techniques to analyze the 3D seismic attribute data for fracture prediction. The examples show that using the technique could play an important role in the prediction and assessment of the gas-rich zones in shale gas reservoirs.

Keywords:
Artificial Intelligence, shale gas, machine learning, complex reservoir, Reservoir Characterization, neural network, gas-rich zone, prediction, shale gas exploration, azimuth
Subjects:
Reservoir Characterization, Unconventional and Complex Reservoirs, Seismic processing and interpretation, Shale gas
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2015. Unconventional Resources Technology Conference
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