In unconventional reservoir sweet-spot identification, brittleness is an important proxy used as a measure of easiness for oil and gas production. Production from this low permeability reservoir is realized by hydraulic fracturing, which depends on how brittle the rock is—as it opens natural fractures and also creates new fractures. An estimate of brittleness, brittleness index, is obtained at well locations through a mathematical combination of elastic logs. In practice, problems arise to predict brittleness because of the limited availability of elastic logs and sparsity of wells to understand the lateral variation of brittleness. To address this problem, machine learning techniques are adopted to predict brittleness at well locations from readily available geophysical logs and spatially using continuous 3D seismic data. This study tests machine learning algorithms to forecast reservoir brittleness throughout the entire reservoir interval using well logs and 3D seismic in a shale gas field of central Appalachian Basin. Our results show the effectiveness of using gradient boosting to predict brittleness from gamma ray, density, and neutron logs with a training and testing R2 score of 0.95 and 0.85, respectively. We demonstrate a novel application of seismic texture as an indicator for brittleness through the qualitative agreement of the inversion output with the blind well and also the fracture attribute.
Supervised machine learning to predict brittleness using well logs and seismic signal attributes: Methods and application in an unconventional reservoir
Ore, Tobi, and Dengliang Gao. "Supervised machine learning to predict brittleness using well logs and seismic signal attributes: Methods and application in an unconventional reservoir." Paper presented at the SEG/AAPG/SEPM First International Meeting for Applied Geoscience & Energy, Denver, Colorado, USA and online, September 2021. doi: https://doi.org/10.1190/segam2021-3594773.1
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