Face-Detection Algorithm Handles Big Data To Help Identify Candidates for Restimulation
Authors: Wilson Adam 
Published: October 2018

This article, written by Special Publications Editor Adam Wilson, contains highlights of paper SPE 187328, “From Face Detection to Fractured-Reservoir Characterization: Big Data Analytics for Restimulation-Candidate Selection,” by Egbadon Udegbe, SPE, Eugene Morgan, SPE, and Sanjay Srinivasan, SPE, The Pennsylvania State University, prepared for the 2017 SPE Annual Technical Conference and Exhibition, San Antonio, Texas, USA, 9–11 October. The paper has not been peer reviewed.
The recent proliferation of subsurface data from instrumented wells has created significant challenges for traditional production-data-analysis methods to extract useful information for reservoir management. This paper demonstrates the viability of a production-data-classification approach adapted from real-time face detection for identifying restimulation candidates. The approach has the potential to be used as a big-data analytic tool for long-duration production-data analysis to serve as a screening tool for selection of restimulation candidates.
Introduction
Restimulation treatments in producing shale wells have the potential to improve economic performance by increasing the conductivity of existing fractures or enhancing their contact with the formation. The influence of matrix and fracture characteristics on the success of restimulation, however, is not completely understood, which has led to uncertainty in determining favorable candidate wells. Several methods to select restimulation candidates have been proposed. These methods, however, are time-consuming and tend to require detailed input data or exhibit a lack of generality for other reservoir settings.
This paper aims to address these challenges with a new methodology for fast and robust analysis of production data from hydraulically fractured wells. A dual-permeability forward-flow modeling approach is used to generate multiple realizations of production-rate profiles by modifying fracture and other parameters. Using this data, pattern-recognition tools are applied to help uncover trends associated with favorable and unfavorable restimulation candidates. This is achieved using a binary classification framework adapted from real-time face detection, which uses simple numerical criteria computed directly from raw flow-rate data, thus eliminating the need for detailed information and promoting computational efficiency. The algorithm also provides probabilistic predictions, which serve as a means to rank candidate wells. While the process of training the classifier has the potential to be computationally intensive, the application of the trained classifier on the observed data is extremely fast, making the method useful for real-time classification of well performance.
Face-Detection Overview
The Viola-Jones face-detection algorithm is a binary classification scheme that takes labeled examples of images containing faces and nonfaces and develops a set of numerical criteria for distinguishing between both categories. Using these rules, an arbitrary test image can be analyzed and classified as a face or a nonface.