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Data-Driven Tool Uses Amplitude-Based Statistics To Identify Seismic Fractures

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 miniattributes, 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.

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.
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Data-Driven Tool Uses Amplitude-Based Statistics To Identify Seismic Fractures

01 October 2019

Volume: 71 | Issue: 10

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