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Big data analytics for seismic fracture identification using amplitude-based statistics

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Abstract

Present-day innovations in seismic acquisition tools and techniques have enabled the acquisition of detailed seismic datasets, which in many cases are extremely large (on the order of terabytes to petabytes). However, data analysis tools for extracting information on critical subsurface features such as fractures are still evolving. Traditional methods rely on time-consuming iterative workflows, which involve computing seismic attributes, de-noising, and expert interpretation. Additionally, with the increasingly widespread acquisition of time-lapse seismic surveys (4D), there is a heightened demand for reliable automated workflows to assist feature interpretation from seismic data. We present a novel data-driven tool for fast fracture identification in BIG post-stack seismic datasets, motivated by techniques developed for real-time face detection. The proposed algorithm computes spatio-temporal amplitude statistics using Haar-like bases, in order to characterize the seismic amplitude properties that correspond to fracture occurrence in a unit window or voxel. Under this approach, the amplitude data is decomposed into a collection of simple-to-calculate “mini-attributes,” which carry information on the amplitude gradient and curvature characteristics at varying locations and scales. These features then serve as inputs to a cascade of boosted classification tree models, which select and combine the most discriminative features to develop a probabilistic binary classification model. This overall approach helps to eliminate the computationally intensive and subjective use of ad hoc seismic attributes in existing approaches. We first demonstrate the viability of the proposed methodology for identifying discrete macro-fractures in a 2D synthetic seismic dataset. Next, we validate the approach using 3D post-stack seismic data from the Niobrara Shale interval within the Teapot Dome field. We show the applicability of the proposed framework for identifying sub-seismic fractures, by considering the amplitude profile adjacent to interpreted formation microimager (FMI) well log data. The upscaled spatial distribution of the predicted fractures shows agreement with existing geological studies and aligns with interpreted large-scale faults within the interval of interest.

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Abbreviations

A :

seismic amplitude

α m :

voting weight applied to weak classifier in AdaBoost model at iteration m

β :

split nodes in classification tree model

D ps :

minimum true positive ratio per stage in cascade training

F(s):

real-valued AdaBoost classifier summation

F ps :

maximum false positive ratio per stage in cascade training

f(s):

AdaBoost classifier categorical prediction

g m :

weak classification model fitted at iteration m

J :

number of terminal nodes in classification tree model

k :

Haar-like feature geometry index

m :

AdaBoost iteration index

M :

total number of AdaBoost iterations

N :

number of training windows

p :

total number of Haar-like feature combinations

s i j :

Haar-like feature score computed on window i based on unique feature combination j

t :

time index

Δ t :

height of Haar-like feature in time direction

V :

number of windows used in cascaded classifier model evaluation

x :

inline (or common depth point) index

Δ x :

width of Haar-like feature in inline (or common depth point) direction

y :

crossline index

Δ y :

length of Haar-like feature in crossline direction

γ i :

true designation of window i

𝜃 :

AdaBoost classification threshold

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Acknowledgments

The Niobrara fracture density log interpretations have been performed by Dr. Ahmed Ouenes (FracGeo) and his team. We are grateful for their support in making this data available for this study.

Funding

This research was supported by National Science Foundation Grant 1546553.

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Authors and Affiliations

  1. Department of Energy and Mineral Engineering, The Pennsylvania State University, 110 Hosler Building, University Park, State College, PA, 16802-5000, USA

    Egbadon Udegbe, Eugene Morgan & Sanjay Srinivasan

Authors
  1. Egbadon Udegbe
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  2. Eugene Morgan
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  3. Sanjay Srinivasan
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Corresponding author

Correspondence to Eugene Morgan.

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Appendices

Appendix 1

The following presents the pseudo-code for the AdaBoost classification scheme with a more complex base classifier (adapted from [9]):

figure a

where M is the total number of AdaBoost iterations, and I(⋅) is the indicator function.

Appendix 2

The following presents the pseudo-code for the cascade training procedure (adapted from [21]):

figure b

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Udegbe, E., Morgan, E. & Srinivasan, S. Big data analytics for seismic fracture identification using amplitude-based statistics. Comput Geosci 23, 1277–1291 (2019). https://doi.org/10.1007/s10596-019-09890-z

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