Comprehensive Seismic Interpretation to Enhance Stratigraphy and Faults

Lacaze, S.; Durot, Benjamin; Devilliers, Amandine; Pauget, Fabien

doi:10.1190/sbgf2017-279

Cited by 3 publications

(2 citation statements)

References 7 publications

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“…An accurate RGT volume is thus helpful to well understand geologic structures of interest and qualify reservoir properties of continuity and morphology to interpret all associated geologic structures, and to build a subsurface model of a particular environment. It has shown great promise in many applications to improve horizon extraction (Stark, 2004; Fomel, 2010; X. Wu & Zhong, 2012), sedimentologic interpretation (Hongliu et al., 2012), stratigraphic interpretation (Alliez et al., 2007; Karimi & Fomel, 2015; Lacaze et al., 2017), geologic body detection (Prazuck et al., 2015), and structural implicit modeling (Grose et al., 2017; Irakarama et al., 2020; Laurent et al., 2016; Renaudeau et al., 2019).…”

Section: Introductionmentioning

confidence: 99%

“…It has shown great promise in many applications to improve horizon extraction (Stark, 2004;Fomel, 2010;X. Wu & Zhong, 2012), sedimentologic interpretation (Hongliu et al, 2012), stratigraphic interpretation (Alliez et al, 2007;Lacaze et al, 2017), geologic body detection (Prazuck et al, 2015), and structural implicit modeling (Grose et al, 2017;Irakarama et al, 2020;Laurent et al, 2016;Renaudeau et al, 2019).…”

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confidence: 99%

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Deep Relative Geologic Time: A Deep Learning Method for Simultaneously Interpreting 3‐D Seismic Horizons and Faults

Geng

et al. 2021

JGR Solid Earth

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Extracting horizons and detecting faults in a seismic image are basic steps for structural interpretation and important for many seismic processing schemes. A common ground of the two tasks is to analyze seismic structures and they are related to each other. However, previously proposed methods deal with the tasks independently, and challenge remains in each of them. We propose a volume‐to‐volume neural network to estimate a relative geologic time (RGT) volume from a seismic volume, and this RGT volume is further used to simultaneously interpret horizons and faults. The network uses U‐shaped framework with attention mechanism to systematically aggregate multi‐scale information and automatically highlight informative features, and achieves high prediction accuracy with affordable computational costs. To train the network, we build thousands of 3‐D noisy synthetic seismic volumes and corresponding RGT volumes with realistic and various structures. We introduce a loss function based on structure similarity to capture spatial dependencies among seismic samples for better optimizing the network, and use multiple reasonable assessments to evaluate the predicted results. Trained by using synthetic data, our network outperforms the conventional approaches in recognizing structural features in field data examples. Once obtaining an RGT volume, we can not only obtain seismic horizons by simply extracting RGT constant surfaces but also detect faults that are indicated by lateral RGT discontinuities. To be able to deal with large seismic volumes, we further propose a workflow to first estimate sub‐volumes of RGT and merge them to obtain a full RGT volume without boundary artifacts.

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Section: Introductionmentioning

confidence: 99%

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confidence: 99%

Deep Relative Geologic Time: A Deep Learning Method for Simultaneously Interpreting 3‐D Seismic Horizons and Faults

Geng

et al. 2021

JGR Solid Earth

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Geologic-time-based interpolation of borehole data for building high-resolution models: Methods and applications

et al. 2022

GEOPHYSICS

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Integrating borehole data into a uniformly sampled grid were an essential but challenging task for seismic data processing and reservoir characterization. A major effort was to improve well-log interpolation when reservoir properties were only directly observed via well logs at sparsely sampled locations. The subsurface property models obtained from existing interpolation approaches often did not consistently follow geologic structures, but they were essential in determining the distribution of reservoir properties. To solve this problem, we developed an alternative way to increase the borehole data coverage from the well locations for achieving accurate quantitative reservoir characterization. Based on a seismic image, we first computed a relative geologic time (RGT) volume that provided an implicit map of all the geologic structures in the seismic image. We then constructed an interpolated field from borehole data by following the constant RGT value that corresponded to the same geologic layer, and thus obtained a realistic and high-resolution model honoring seismic structures and well logs. This model could provide a low-frequency control for a learning or seismic inversion method to further correct the interpolation errors, or could used as a reliable initial background model to improve the performance of full-waveform inversion. We used synthetic and field data examples to demonstrate the effectiveness of our method even when reservoir properties were only observed at sparsely scattered locations. In comparison to the existing approaches, our method could produce a more geologically consistent subsurface model and robustly compensate for the low-frequency gap between seismic data and borehole measurements.

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3D relative geologic time estimation with deep learning

Geng

Gao

et al. 2020

SEG Technical Program Expanded Abstracts 2020

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Comprehensive Seismic Interpretation to Enhance Stratigraphy and Faults

Cited by 3 publications

References 7 publications

Deep Relative Geologic Time: A Deep Learning Method for Simultaneously Interpreting 3‐D Seismic Horizons and Faults

Deep Relative Geologic Time: A Deep Learning Method for Simultaneously Interpreting 3‐D Seismic Horizons and Faults

Geologic-time-based interpolation of borehole data for building high-resolution models: Methods and applications

3D relative geologic time estimation with deep learning

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