De-risking geological carbon storage from high resolution time-lapse seismic to explainable leakage detection

• URL: http://arxiv.org/abs/2211.03527v1
• Date: Fri, 7 Oct 2022 12:27:18 GMT
• Title: De-risking geological carbon storage from high resolution time-lapse seismic to explainable leakage detection
• Authors: Ziyi Yin, Huseyin Tuna Erdinc, Abhinav Prakash Gahlot, Mathias Louboutin, Felix J. Herrmann
• Abstract summary: Geological carbon storage represents one of the few truly scalable technologies capable of reducing the CO2 concentration in the atmosphere. An important aspect of risk mitigation concerns assurances that the injected CO2 remains within the storage complex. We present a methodology where time-lapse images are created by inverting non-replicated time-lapse monitoring data jointly.
• Score: 2.021175152213487
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Geological carbon storage represents one of the few truly scalable technologies capable of reducing the CO2 concentration in the atmosphere. While this technology has the potential to scale, its success hinges on our ability to mitigate its risks. An important aspect of risk mitigation concerns assurances that the injected CO2 remains within the storage complex. Amongst the different monitoring modalities, seismic imaging stands out with its ability to attain high resolution and high fidelity images. However, these superior features come, unfortunately, at prohibitive costs and time-intensive efforts potentially rendering extensive seismic monitoring undesirable. To overcome this shortcoming, we present a methodology where time-lapse images are created by inverting non-replicated time-lapse monitoring data jointly. By no longer insisting on replication of the surveys to obtain high fidelity time-lapse images and differences, extreme costs and time-consuming labor are averted. To demonstrate our approach, hundreds of noisy time-lapse seismic datasets are simulated that contain imprints of regular CO2 plumes and irregular plumes that leak. These time-lapse datasets are subsequently inverted to produce time-lapse difference images used to train a deep neural classifier. The testing results show that the classifier is capable of detecting CO2 leakage automatically on unseen data and with a reasonable accuracy.

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