Deep Learning for Characterizing Paleokarst Collapse Features in 3‐D Seismic Images

Abstract

Paleokarst systems are found extensively in carbonate-prone basins worldwide. They can form large reservoirs and provide efficient pathways for hydrocarbon migration, but they can also create serious engineering geohazards. The full delineation of potentially buried paleokarst systems plays an important role for reservoir characterization, oil and gas production, and other engineering tasks. We propose a supervised convolutional neural network (CNN) to automatically and accurately characterize paleokarst and associated collapse features from 3-D seismic images. To avoid time-consuming manual labeling for training the CNN, we propose an efficient workflow to automatically generate numerous 3-D training image pairs including synthetic seismic images and the corresponding label images of the collapsed paleokarst features simulated in the seismic images. With this workflow, we are able to simulate realistic and diverse geologic structures and collapsed paleokarst features in the training images from which the CNN can effectively learn to recognize the collapsed paleokarst features in real field seismic images. Two field examples from the Fort Worth Basin demonstrate that our CNN-based method is superior to conventional automatic methods in delineating paleokarst collapse features from seismic images. From the CNN-based paleokarst characterization, we can further automatically extract 3-D collapsed paleokarst systems and quantitatively measure their geometric parameters. Our CNN-based method is highly efficient and takes only seconds to classify collapsed paleokarst features a 3-D seismic image with 320 × 1, 024 × 1, 024 samples (approximately 268 km 2) by using one graphics processing unit. We propose a convolutional neural network (CNN) to fully detect paleokarst collapse features in 3-D seismic images and extract 3-D paleokarst chimney tubes all at once, which would provide a clear 3-D perspective of

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“…When manually labeled ground truth for geophysical images is not available, synthetic images and corresponding labeled solutions are used for large-scale three-dimensional seismic image interpretation (Bi et al, 2021;X. Wu et al, 2020), for microseismicity locationing (Q.…”

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“…Sufficient and reliable training data are the keys to solve problems using supervised machine learning. Both labelling faults and karst in real seismic data and generating synthetic labels and data can provide reliable training data for training a neural network (Wu, Yan, et al, 2020;Wu, Geng, et al, 2020;S. Wei et al, 2022;Yan et al, 2022).…”

Deep learning based on self-supervised pre-training: Application on sandstone content prediction

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