The continuous rise in global energy demand requires the production of
oil and gas from unconventional shale resources. One major concern has
been the large volumes of produced water associated with the production
of hydrocarbon from the shale resources. We developed a data-driven
workflow for identifying potentially high water-producing wells drilled
in unconventional shale formation. To that end, we applied unsupervised
learning followed by supervised learning to process five conventional
well logs, namely shallow and deep resistivity logs, density porosity
logs, neutron porosity logs, and gamma ray logs, from a well drilled in
an unconventional shale formation. A novelty of our study is the use of
clustering methods to generate pseudo-lithology that is fed into a
classifier for the desired identification of the excess water producing
wells. The data-driven workflow was tested on 23 wells in Gulf coast
basin and 29 wells in Fort Worth basin. Fort Worth and Gulf Coast basins
in the U.S. are highly productive shale basins that produce 380 million
cubic feet of gas and 1.74 million barrels of crude oil every day.
Additionally, we identified geophysical signatures that explain the
excess water production from the wells drilled in unconventional shale
reservoirs. For future work, molecular simulation, core analysis, and
advanced well logs studies need to be incorporated for a better
explanation of the causes of excess water production in unconventional
reservoirs.