Data-Driven Tool Uses Amplitude-Based Statistics To Identify Seismic Fractures
Authors: Carpenter Chris 
Published: September 2019

This article, written by JPT Technology Editor Chris Carpenter, contains highlights of paper SPE 191668, “Big Data Analytics for Seismic Fracture Identification Using Amplitude-Based Statistics,” by Egbadon Udegbe, SPE, Eugene Morgan, SPE, and Sanjay Srinivasan, SPE, Pennsylvania State University, prepared for the 2018 SPE Annual Technical Conference and Exhibition, Dallas, 24–26 September. The paper has not been peer reviewed.
Data-analysis tools for extracting information about critical subsurface features such as fractures are still evolving. Traditional methods rely on time-consuming iterative work flows, which involve computing seismic attributes, denoising, and expert interpretation. Additionally, the increasingly widespread acquisition of time-lapse seismic surveys has led to heightened demand for reliable automated work flows to assist in deriving feature interpretation from seismic data. The authors present a novel data-driven tool for fast fracture identification in post-stack seismic data•sets.
Introduction
The paper develops an automated work flow for fast and robust fracture identification that directly uses seismic amplitude data as input. Adapted from real-time face-detection methods, the proposed algorithm computes spatiotemporal amplitude statistics using Haar-like bases to characterize the seismic amplitude properties that correspond to fracture occurrence in a unit window. In this approach, the amplitude data are decomposed into a lower-dimensional collection of simple-to-calculate mini-attributes, which contain gradient and curvature characteristics at varying locations and scales. These features serve as inputs to a cascade of boosted classification trees, which select and combine the most-discriminative features to develop a probabilistic binary classification model. This approach helps to eliminate the computationally intensive and subjective use of seismic attributes in existing approaches.
Approach
The proposed methodology uses a supervised learning approach. This involves specifying examples of seismic amplitude regions that either contain or do not contain fractures and subsequently presenting these examples to a binary classification scheme that develops a set of rules to distinguish between both fracture and nonfracture windows. Finally, using this trained classification model, any arbitrary amplitude section or volume can be scanned to determine the location of fractures on the basis of the rules defined within the specified window.
Training-Window Selection. Careful consideration is required in defining the dimensions of the unit window. In 2D, this unit window is a rectangular region parallel to time and space axes. In 3D, the unit window refers to a cuboidal region with one time and two space axes. The dimensions of the window in the vertical (time) direction are influenced by the time-sampling interval and the source wavelet frequency. In the lateral directions, window dimensions are influenced by the spatial sampling interval, which is a result of the survey acquisition geometry. Overall, smaller unit windows lead to higher resolution in identifying fracture presence. This, however, is achieved at the expense of fewer amplitude features to characterize the window, as well as slower scan speeds, because more regions must be visited to make predictions.
Once a seismic amplitude data set with known fracture locations and a unit window with specified dimensions has been established, multiple locations within the data set may be visited and amplitude samples extracted from both fractured and nonfractured regions.