Preemptive Detection of High Water-Cut Wells in Delaware Basin using a
Joint Unsupervised and Supervised Learning Approach
Abstract
High water cut has been an issue in the Delaware basin for many years
now. Volume of produced water continue to increase, resulting in adverse
environmental impacts and higher reservoir-management costs. To address
these problems, a data-driven workflow has been developed to
pre-emptively identify the high water-cut wells. The workflow uses
unsupervised pseudo-rock typing followed by supervised classification
trained on well logs from 17 wells in the Delaware basin. The workflow
requires a suite of 5 well logs from a 500-ft depth interval surrounding
the kick-off points of these wells, which includes 200 ft above and 300
ft below the KOP. First, the well logs are clustered into 5 pseudo-rock
types using multi-level clustering. Using statistical features extracted
from these 5 pseudo-rock types, 3 supervised classifiers, namely
K-nearest neighbor, support vector machine, and logistic regression, are
trained and tested to detect the high water-cut wells. Over 100 cross
validations, the 3 classifiers perform at a median Matthew’s Correlation
Coefficient (MCC) of 0.90. The kurtosis of the neutron porosity log
response of the pseudo-rock type A0, interpreted as a shale lithology,
is the most The submitted paper is currently under review. Dr. Sid Misra
is the lead investigator on this topic. informative/relevant signature
associated with high water cut. Next, the presence of pseudo-rock type
A1, interpreted as high-permeability lithology, is an informative
signature of low water-cut wells. The kurtosis of the density porosity
log response of the pseudo-rock type B0, interpreted as carbonate
lithology, and the presence of pseudo-rock type B1, interpreted as a
tight sandstone lithology, serve as informative signatures for
differentiating high water cut wells from low water cut wells.