Miguel A. Corrales

Hello everyone and welcome to my personal website !

I am a Ph.D. student in the Earth Science and Engineering Program at King Abdullah University of Science and Technology (KAUST). My research focuses on reservoir characterization with the help of deep generative modeling for subsurface imaging applications. Hopefully, I will try to incorporate uncertainty quantification along those lines.

My research aims to tackle the challenges to bridge deep learning and subsurface imaging. My knowledge comprehends deep learning, deep generative modeling, uncertainty quantification , fluid flow in porous media, percolation theory, and inverse problems.

news

Oct 25, 2022	Welcome to my personal website. I am currently working on it . Plese be patient with the updates.
Oct 9, 2022	Hackathon KAUST NVIDIA 2022 Winners Accelerating Scientific applications using GPU’s.
Jun 7, 2022	BEST IN SHOW, Hackathon Winners - Explainable A.I., EAGE ANNUAL 2022, Madrid-Spain

selected publications

A Wasserstein GAN with Gradient Penalty for 3D Porous Media Generation.

M. Corrales, M. Izzatullah, H. Hoteit, and M. Ravasi

Nov 2022

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Linking the pore-scale and reservoir-scale subsurface fluid flow remains an open challenge in areas such as oil recovery and Carbon Capture and Storage (CCS). One of the main factors hindering our knowledge of such a process is the scarcity of physical samples from geological areas of interest. One way to tackle this issue is by creating accurate, digital representations of the available rock samples to perform numerical fluid flow simulations. Recent advancements in Machine Learning and Deep Generative Modeling open up a new promising avenue for generating realistic digital rock samples at low cost. This is particularly the case for Generative Adversarial Networks (GANs) due to their ability to learn complex high-dimensional distributions and produce high-quality samples. The present study introduces a novel Wasserstein GAN with gradient penalty (WGAN-GP) to generate 3D high-quality porous media samples. Moreover, a comprehensive set of evaluation metrics inspired by the geometry and topology of the structure and the fluid flow properties is established to assess the quality of the generative process.
Plug and Play Post-Stack Seismic Inversion with CNN-Based Denoisers

J. Romero, M. Corrales, N. Luiken, and M. Ravasi

Nov 2022

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Seismic inversion is the prime method to estimate subsurface properties from seismic data. However, such inversion is a notoriously ill-posed inverse problem due to the band-limited and noisy nature of the data. Consequently, the data misfit term must be augmented with appropriate regularization that incorporates prior information about the sought-after solution. Conventionally, model-based regularization terms are problem-dependent and hand-crafted; this can limit the modeling capability of the inverse problem. Recently, a new framework has emerged under the name of Plug-and-Play (PnP) regularization, which suggests reinterpreting the effect of the regularizer as a denoising problem. Convolutional neural networks-based denoisers are state-of-the-art methods for image denoising: their adoption in the PnP framework has led to algorithms with improved capabilities over classical regularization in computer vision and medical imaging applications. In this work, we present a comparison between standard model-based and data-driven regularization techniques in post-stack seismic inversion and give some insights into the optimization and denoiser-related parameters tuning. The results on synthetic seismic data indicate that PnP regularization using a bias-free CNN-based denoiser with an additional noise map as input can outperform standard model-based methods.
Bayesian RockAVO: Direct petrophysical inversion with hierarchical conditional GANs

M. Corrales, M. Izzatullah, M. Ravasi, and H. Hoteit

Aug 2022

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Reservoir characterization is a critical component in any oil and gas, geothermal, and CO2 sequestration project. A fundamental step in the process of characterizing the subsurface is represented by the inversion of petrophysical parameters from seismic data. However, this problem suffers from various uncertainty sources originating from inaccuracies in the measurements, modeling errors, and complex geological processes. Moreover, the non-linearity of the rock-physics model and Zoeppritz equation that constitute the modelling operator, further complicates the inversion process. In this work, we propose a novel data-driven approach where well-log information is used to obtain optimal basis functions that link band-limited petrophysical reflectivities to pre-stack seismic data. Subsequently, the inversion of such band-limited reflectivities for petrophysical parameters is framed in a Bayesian framework where a generative adversarial network is used to produce a geologically realistic prior distribution. The trained prior distribution is updated using the Stein Variational Gradient Descent and a set of representative solutions is produced that is consistent with the uncertainties in the data and the nonlinear operators.
Data-Driven, Direct Rock-Physics Inversion of Pre-Stack Seismic Data

M. Corrales, M. Ravasi, and H. Hoteit

Jun 2022

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The inversion of petrophysical parameters from seismic data represents a fundamental step in the reservoir characterization framework. However, the non-linearity of the rock-physics models that relate petrophysical properties to seismic pre-stack amplitudes makes such an inversion challenging. We propose a hybrid approach, where data-driven basis functions are learned from well-logs to directly link band-limited petrophysical reflectivities to pre-stack seismic data. Petrophysical parameters are subsequently obtained by means of regularized post-stack seismic inversion. By performing two modeling steps at training time and a single inversion step at inference time, our method aims to be more efficient and robust than conventional two-step inversion workflows. The proposed method is tested on a synthetic dataset from the Smeaheia reservoir model. Numerical results show that porosity is the best-inverted rock property, followed by water saturation and clay content. Moreover, the method is shown to be also applicable in the context of reservoir monitoring to invert time-lapse, pre-stack seismic data for water saturation changes.
The Potential for Underground CO2 Disposal Near Riyadh

M. Corrales, S. Mantilla, A. Tasianas, H. Hoteit, and A. Afifi

Feb 2022

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Carbon Capture Storage (CCS) and Carbon Capture Utilization and Storage (CCUS) have recently gained global attention as promising techniques to mitigate net CO2 emissions. Within this framework, the Saudi Arabian 2030 vision targets the large-scale deployment of CCS and CCUS projects to promote its circular carbon economy. This study evaluates the potential for underground sequestration of CO2 emitted from industrial sources near Riyadh, Saudi Arabia, which emit 46 Mton/year.A deterministic geologic model corresponding to the Unayzah Formation was constructed using published data incorporating sedimentary facies distribution, porosity, permeability, and connectivity. Compositional simulations were performed to assess the CO2 plume flow in the presence of conduits, barriers, and baffles. Similarly, injectivity and injection rate effects on solubility and residual trapping were evaluated. A sensitivity analysis and an uncertainty quantification study were carried out to obtain a probabilistic assessment of the total storage capacity and trapping contributions.The geological evaluation indicates that the area under Riyadh is unsuitable because the Triassic sandstones are too shallow, and the Paleozoic section was entirely removed by erosion during the Carboniferous. Alternatively, the Hawtah area, at 150 km south of Riyadh, is deemed suitable for CO2 sequestration. These sandstones are porous, permeable, tightly sealed, and correspond to hydrocarbon reservoirs in anticlinal structures along the Hawtah, Nuayyim, and Dilam trends. They are favorable for CO2 disposal outside oil and gas fields due to lateral and vertical permeability barriers and up-dip pinch-out against the Batin arch. Simulation results, fifty years after CO2 injection and two hundred fifty years of monitoring, show that the Unayzah Formation satisfies the conditions of capacity, injectivity, and seal efficiency required for technical feasibility.Furthermore, lower injection rates promote higher solubility and residual trapping due to gravity-controlled flow exceeding viscous and capillary forces. Residual trapping contributes 50\% to the storage, while solubility adds 10\%. The variables that have a higher impact on secure trapping are residual gas saturation, water salinity, and permeability. The current CO2 storage capacity in the area evaluated exceeds 300 Megatons (Mt), and the assessment is still ongoing, with no vertical leakage through the caprocks of the Khuff and Sudair Formations.Overall, the novelty in this research focuses on the unprecedented use of public domain data to construct a detailed geological model of the Unayzah Formation in the Hawtah and Nuayyim area that allowed a better understanding of CO2 flow mechanisms in the reservoir and its capacity to store CO2. This study concludes that the Unayzah reservoir in the Kharj-Hawtah area is a viable candidate for secure CO2 disposal from industrial sources in Riyadh.