Abstract

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.