@presentation {herrmann2024dt4gcs,
	title = {Digital Twins in the Era of Generative AI: Application to Geological CO 2  Storage},
	year = {2024},
	month = {2},
	abstract = {Our industry is experiencing significant changes due to AI and the challenges of the energy transition. While some view these changes as threats, recent advances in AI offer unique opportunities, especially in the context of Digital Twins for subsurface monitoring and control. IBM defines "A digital twin is a virtual representation of an object or system that spans its lifecycle, is updated from real-time data, and uses simulation, machine learning and reasoning to help decision-making." During this talk, I will explore these concepts and their significance in addressing the challenges of monitoring \& control of geological CO 2  storage projects. This talk also aims to illustrate how Digital Twins can serve as a platform to integrate the seemingly disparate and siloed fields of geophysics and reservoir engineering.},
	keywords = {Bayesian inference, CCS, deep learning, digital twin, Imaging, monitoring, SLIM, Uncertainty quantification},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/Halliburton/2024/herrmann2024dt4gcs},
	presentation = {https://slim.gatech.edu/Publications/Public/Conferences/Halliburton/2024/herrmann2024dt4gcs.pdf},
	author = {Abhinav Prakash Gahlot and Rafael Orozco and Haoyun Li and Huseyin Tuna Erdinc and Ziyi Yin and Mathias Louboutin and Felix J. Herrmann}
}
@unpublished {erdinc2024IMAGEggm,
	title = {Generative Geostatistical Modeling from Incomplete Well and Imaged Seismic Observations},
	year = {2024},
	month = {3},
	abstract = {Diffusion generative models are powerful frameworks for learning high-dimensional distributions and synthesizing high-fidelity images. However, their efficacy in training predominantly hinges on the availability of complete, high-quality training datasets, a condition that often proves unattainable, particularly in the domain of subsurface velocity-model generation. In this work, we propose to synthesize proxy subsurface velocities from incomplete well and imaged seismic observations by introducing additional corruptions to the observations during the training phase. In this context, proxy velocity models refer to random realizations of subsurface velocities that are close in distribution to the actual subsurface velocities. These proxy models can be used as priors to train neural networks with simulation-based inference. Our approach facilitates the generation of these proxy velocity samples by utilizing available datasets composed merely of seismic images and 5 (for now) wells per seismic image.After training, our foundation generative model permits the generation of velocity samples derived from unseen RTMs without the need of having access to wells.},
	keywords = {deep learning, diffusion, generative modeling, geostatistics, kriging, velocity model building},
	url = {https://slimgroup.github.io/IMAGE2024/erdinc2024SEG/abstract.html},
	author = {Huseyin Tuna Erdinc and Rafael Orozco and Felix J. Herrmann}
}
@article {yin2022TLEdgc,
	title = {Derisking geologic carbon storage from high-resolution time-lapse seismic to explainable leakage detection},
	journal = {The Leading Edge},
	volume = {42},
	number = {1},
	year = {2023},
	note = {(The Leading Edge)},
	month = {01},
	pages = {69-76},
	abstract = {Geological carbon storage represents one of the few truly scalable technologies capable of reducing the CO$_2$ 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 CO$_2$ 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 CO$_2$ 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 CO$_2$ leakage automatically on unseen data and with a reasonable accuracy.},
	keywords = {CAM, CCS, classification, explainability, JRM, resolution, seismic imaging, time-lapse},
	doi = {10.1190/tle42010069.1},
	url = {https://slim.gatech.edu/Publications/Public/Journals/TheLeadingEdge/2022/yin2022TLEdgc/paper.html},
	software = {https://github.com/slimgroup/GCS-CAM},
	author = {Ziyi Yin and Huseyin Tuna Erdinc and Abhinav Prakash Gahlot and Mathias Louboutin and Felix J. Herrmann}
}
@conference {herrmann2023CCUSdgs,
	title = {Derisking geological storage with simulation-based seismic monitoring design and machine learning},
	booktitle = {Carbon, Capture, Utilization, and Storage},
	year = {2023},
	note = {(CCUS, Houston)},
	month = {04},
	keywords = {CAM, CCS, CCUS, classification, explainability, JRM, seismic imaging},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/CCUS/2023/herrmann2023CCUSdgs/CCUS2023.pdf},
	url2 = {https://slim.gatech.edu/Publications/Public/Conferences/CCUS/2023/herrmann2023CCUSdgs},
	author = {Felix J. Herrmann and Huseyin Tuna Erdinc and Abhinav Prakash Gahlot and Ziyi Yin and Mathias Louboutin}
}
@conference {erdinc2023IMAGEecl,
	title = {Enhancing CO2 Leakage Detectability via Dataset Augmentation},
	booktitle = {International Meeting for Applied Geoscience and Energy},
	year = {2023},
	note = {(IMAGE, Houston)},
	month = {08},
	abstract = {Previous work showed that neural classifiers can be trained to detect CO2 leakage from time-lapse seismic images. While this result is crucial to the global deployment of geological carbon storage (GCS), its success depends on relatively dense non-replicated time-lapse data acquisition. In this study, we show that by augmenting the training set with various coarse receiver samplings and corresponding seismic images, we can improve the leakage detection capabilities and accuracy while increasing the robustness with respect to low-cost coarse receiver samplings, e.g. ocean bottom nodes (OBNs).},
	keywords = {CCS, classifier, deep learning, leakage detection, SEG},
	url = {https://slimgroup.github.io/IMAGE2023/DetectabilityWithVision/abstract.html},
	presentation = {https://slim.gatech.edu/Publications/Public/Conferences/SEG/2023/erdinc2023IMAGEecl},
	author = {Huseyin Tuna Erdinc and Abhinav Prakash Gahlot and Mathias Louboutin and Felix J. Herrmann}
}
@presentation {erdinc2023ML4SEISMICias,
	title = {Improved automatic seismic CO2 leakage detection via dataset augmentation},
	year = {2023},
	month = {11},
	abstract = {Previous works showed that neural classifiers can be trained to detect CO2 leakage from time-lapse seismic images. While this result is crucial to the global deployment of geological carbon storage (GCS), its success depends on relatively dense non-replicated time-lapse data acquisition. In this study, we present an approach to enhance the detection accuracy and robustness of CO2 leakage detection by augmenting the training dataset with a variety of coarsely sampled receiver data and their corresponding receiver numbers. This augmentation strategy is particularly beneficial for scenarios where low-cost coarse receiver samplings, such as with ocean bottom nodes (OBNs), are utilized. Furthermore, we explore interpretability of the classifier{\textquoteright}s decisions by generating saliency maps for further analysis.},
	keywords = {CCS, classifier, deep learning, leakage detection, ML4SEISMIC, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/ML4SEISMIC/2023/erdinc2023ML4SEISMICias},
	author = {Huseyin Tuna Erdinc and Abhinav Prakash Gahlot and Mathias Louboutin and Felix J. Herrmann}
}
@conference {gahlot2023NIPSWSifp,
	title = {Inference of CO2 flow patterns {\textendash} a feasibility study},
	booktitle = {Neural Information Processing Systems (NeurIPS)},
	year = {2023},
	note = {(NeurIPS 2023 Workshop - Tackling Climate Change with Machine Learning (Spotlight))},
	month = {10},
	abstract = {As the global deployment of carbon capture and sequestration (CCS) technology intensifies in the fight against climate change, it becomes increasingly imperative to establish robust monitoring and detection mechanisms for potential underground CO$_2$ leakage, particularly through pre-existing or induced faults in the storage reservoir{\textquoteright}s seals. While techniques such as history matching and time-lapse seismic monitoring of CO$_2$ storage have been used successfully in tracking the evolution of CO$_2$ plumes in the subsurface, these methods lack principled approaches to characterize uncertainties related to the CO$_2$ plumes{\textquoteright} behavior. Inclusion of systematic assessment of uncertainties is essential for risk mitigation for the following reasons: (i) CO$_2$ plume-induced changes are small and seismic data is noisy; (ii) changes between regular and irregular (e.g., caused by leakage) flow patterns are small; and (iii) the reservoir properties that control the flow are strongly heterogeneous and typically only available as distributions. To arrive at a formulation capable of inferring flow patterns for regular and irregular flow from well and seismic data, the performance of conditional normalizing flow will be analyzed on a series of carefully designed numerical experiments. While the inferences presented are preliminary in the context of an early CO$_2$ leakage detection system, the results do indicate that inferences with conditional normalizing flows can produce high-fidelity estimates for CO$_2$ plumes with or without leakage. We are also confident that the inferred uncertainty is reasonable because it correlates well with the observed errors. This uncertainty stems from noise in the seismic data and from the lack of precise knowledge of the reservoir{\textquoteright}s fluid flow properties.},
	keywords = {Amortized Variational Inference, Bayesian inference, CCS, conditional normalizing flows, geological carbon storage, monitoring, NIPS, Summary Statistics},
	doi = {10.48550/arXiv.2311.00290},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/NIPS/2023/gahlot2023NIPSWSifp/paper.pdf},
	presentation = {https://slim.gatech.edu/Publications/Public/Conferences/NIPS/2023/gahlot2023NIPSWSifp/poster.pdf},
	author = {Abhinav Prakash Gahlot and Huseyin Tuna Erdinc and Rafael Orozco and Ziyi Yin and Felix J. Herrmann}
}
@conference {herrmann2022EAGEcvm,
	title = {Capturing velocity-model uncertainty and two-phase flow with Fourier Neural Operators},
	booktitle = {EAGE Annual Conference Proceedings},
	year = {2022},
	note = {(EAGE, Madrid)},
	month = {06},
	pages = {AI in Geoscience and Geophysics: Current Trends and Future Prospects (Dedicated Session)},
	keywords = {CCS, EAGE, Fourier neural operators, JRM, seismic imaging, Uncertainty quantification},
	presentation = {https://slim.gatech.edu/Publications/Public/Conferences/EAGE/2022/herrmann2022EAGEcvm/index.html},
	author = {Ali Siahkoohi and Thomas J. Grady II and Abhinav Prakash Gahlot and Huseyin Tuna Erdinc and Felix J. Herrmann}
}
@conference {erdinc2022AAAIdcc,
	title = {De-risking Carbon Capture and Sequestration with Explainable CO$_2$ Leakage Detection in Time-lapse Seismic Monitoring Images},
	booktitle = {AAAI 2022 Fall Symposium: The Role of AI in Responding to Climate Challenges},
	year = {2022},
	note = {(AAAI 2022 Fall Symposium: The Role of AI in Responding to Climate Challenges, Arlington)},
	month = {11},
	abstract = {With the growing global deployment of carbon capture and sequestration technology to combat climate change, monitoring and detection of potential CO$_2$ leakage through existing or storage induced faults are critical to the safe and long-term viability of the technology. Recent work on time-lapse seismic monitoring of CO$_2$ storage has shown promising results in its ability to monitor the growth of the CO$_2$ plume from surface recorded seismic data. However, due to the low sensitivity of seismic imaging to CO$_2$ concentration, additional developments are required to efficiently interpret the seismic images for leakage. In this work, we introduce a binary classification of time-lapse seismic images to delineate CO$_2$ plumes (leakage) using state-of-the-art deep learning models. Additionally, we localize the leakage region of CO$_2$ plumes by leveraging Class Activation Mapping (CAM) methods.},
	keywords = {AAAI, CAM, CCS, classification, explainability, JRM, seismic imaging},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/AAAI/2022/erdinc2022AAAIdcc/erdinc2022AAAIdcc.pdf},
	presentation = {https://slim.gatech.edu/Publications/Public/Conferences/AAAI/2022/erdinc2022AAAIdcc/index.html},
	author = {Huseyin Tuna Erdinc and Abhinav Prakash Gahlot and Ziyi Yin and Mathias Louboutin and Felix J. Herrmann}
}
@presentation {erdinc2022ML4SEISMICdgp,
	title = {De-risking GCS projects with explainable CO$_2$ leakage detection in time-lapse seismic images},
	year = {2022},
	month = {11},
	abstract = {With the global deployment of Carbon, capture and storage (CCS) technology to combat climate change, there is an associated risk of contamination with CO2 leaking back to the atmosphere. Thus, it requires continuous monitoring of CO2 after the injection stops at the storage site. In this work, we generated synthetic CO2 plume development data with both leakage and no leakage scenarios. We trained a convolutional neural network (CNN) discriminative classifier and also a generative classifier and compared their performances in CO2 leakage detection. The accuracy of our discriminative classifier on the test data is 85\% and that of the generative classifier is 90\%. The Class Activation Mapping (CAM) results of the discriminative classifier and the latent space representation of our dataset in the case of generative classifier strengthens our claims about trustworthy leakage classification.},
	keywords = {CAM, CCS, classification, explainability, GCS, Imaging, JRM, ML4SEISMIC, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/ML4SEISMIC/2022/erdinc2022ML4SEISMICdgp/index.html},
	author = {Huseyin Tuna Erdinc and Abhinav Prakash Gahlot and Ziyi Yin and Mathias Louboutin and Felix J. Herrmann}
}
@presentation {yin2022ML4SEISMICsfg,
	title = {Simulation-based framework for geological carbon storage monitoring},
	year = {2022},
	month = {11},
	abstract = {While various monitoring modalities exist to track the behavior of CO2 plumes to ensure safe operations and compliance with regulatory requirements, active 3D time-lapse seismic monitoring has proven superior but costly. At SLIM, we aim to reduce the operating costs by optimizing acquisition design, to help drive innovations in seismic monitoring acquisition design and imaging, and to test novel time-lapse acquisition and imaging technologies in silico at scale. In this talk, we will introduce our open-source software platform simulation-based monitoring design framework. We demonstrate how to make use of proxy models for seismic properties derived from real 3D imaged seismic and well data to conduct realistic synthetic geological carbon storage projects. Furthermore, we discuss our proposed sparse non-replicated seismic acquisition and cutting-edge methodology to recover the dense data or to directly image the sparse non-replicated via joint recovery model. This automatic workflow ends with deep neural classifiers to detect potential CO2 leakage over seal through pressure-induced fault openings. We envisage the development of an automatic workflow to handle the large number of continuously monitored CO2 injection sites needed to help combat climate change.},
	keywords = {CAM, CCS, classification, deep learning, explainability, GCS, Imaging, JRM, machine learning, ML4SEISMIC, SLIM, time-lapse},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/ML4SEISMIC/2022/yin2022ML4SEISMICsfg/index.html},
	author = {Ziyi Yin and Huseyin Tuna Erdinc and Abhinav Prakash Gahlot and Mathias Louboutin and Felix J. Herrmann}
}
