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.

A geomet rically based met hod for predict ing st ress-induced fract ure apert ure and flow in discret e fr… Giovanni Bert ot t i, Hamid Nick From invasion percolat ion t o flow in rock fract ure net works Falk Wit t el Download a PDF Pack of t he best relat ed papers 

Discrete Fracture Network (DFN) modelling and simulation is an active area of research in earth science due to the inability to observe detailed 3D structure of the subsurface fracture network. There are few software packages available for DFN modelling and simulation. However, they are mostly complex to use, commercial, and closed sourced. Thereby, precluding any form of adaptability by researchers to functionalities not included in these packages. This work introduces an easy to use, open source library, Y-Frac, for DFN modelling and analysis. Y-Frac is built upon the python APIs available on Rhinoceros 6. Hence, Y-Frac is fit for use on Rhinoceros software package. Y-Frac can model fracture networks containing circular, elliptical and regular polygonal fractures. This library is computationally cheap for DFN modelling and analysis. Some of the functionalities of this library for DFN analysis include fracture intersection analysis, cut-plane analysis, and percolation analysis. Algorithms for constructing an intersection matrix and determining percolation state of a fracture network are also included in this work. The output text file from this library containing modelled fracture networks’ parameters can serve as input for appropriate software packages to simulate flow and perform mechanical analysis in fracture networks. The practical applicability of Y-Frac was demonstrated by performing percolation threshold analysis of 3D fracture networks and comparing the results to published data.

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.