Connect the Dots: In Situ 4-D Seismic Monitoring of CO₂ Storage With Spatio-Temporal CNNs

Journal Article · Fri Oct 08 00:00:00 EDT 2021 · IEEE Transactions on Geoscience and Remote Sensing

DOI:https://doi.org/10.1109/tgrs.2021.3116618· OSTI ID:1827583

Feng, Shihang  ^[1]; Zhang, Xitong ^[2]; Wohlberg, Brendt Egon  ^[1]; Symons, Neill Philip  ^[1]; Lin, Youzuo  ^[1]

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1. Los Alamos National Lab. (LANL), Los Alamos, NM (United States)
2. Los Alamos National Lab. (LANL), Los Alamos, NM (United States); Michigan State Univ., East Lansing, MI (United States)

4-D seismic imaging has been widely used in CO₂ sequestration projects to monitor the fluid flow in the volumetric subsurface region that is not sampled by wells. Ideally, real-time monitoring and near-future forecasting would provide site operators with great insights to understand the dynamics of the subsurface reservoir and assess any potential risks. However, due to obstacles such as high deployment cost, availability of acquisition equipment, exclusion zones around surface structures, only very sparse seismic imaging data can be obtained during monitoring. That leads to an unavoidable and growing knowledge gap over time. The operator needs to understand the fluid flow throughout the project lifetime and the seismic data are only available at a limited number of times. This is insufficient for understanding reservoir behavior. To overcome those challenges, we have developed spatio-temporal neural-network-based models that can produce high-fidelity interpolated or extrapolated images effectively and efficiently. Specifically, our models are built on an autoencoder, and incorporate the long short-term memory (LSTM) structure with a new loss function regularized by optical flow. We validate the performance of our models using real 4-D post-stack seismic imaging data acquired at the Sleipner CO₂ sequestration field. We employ two different strategies in evaluating our models. Numerically, we compare our models with different baseline approaches using classic pixel-based metrics. We also conduct a blind survey and collect a total of 20 responses from domain experts to evaluate the quality of data generated by our models. Finally, via both numerical and expert evaluation, we conclude that our models can produce high-quality 2-D/3-D seismic imaging data at a reasonable cost, offering the possibility of real-time monitoring or even near-future forecasting of the CO₂ storage reservoir.

• Research Organization: Los Alamos National Lab. (LANL), Los Alamos, NM (United States)
• Sponsoring Organization: USDOE Laboratory Directed Research and Development (LDRD) Program
• Grant/Contract Number: 89233218CNA000001
• OSTI ID: 1827583
• Report Number(s): LA-UR-21-22295
• Journal Information: IEEE Transactions on Geoscience and Remote Sensing, Vol. 60; ISSN 0196-2892
• Publisher: IEEE
• Country of Publication: United States
• Language: English

Related Subjects

58 GEOSCIENCES
computer science
earth sciences
mathematics
4-D seismic imaging
CO2 sequestration
data interpolation and extrapolation
data scarcity
in situ monitoring
spatial-temporal analysis