Recent Submissions

  • CDAnet: A Physics-Informed Deep Neural Network for Downscaling Fluid Flows

    Hammoud, Mohamad Abed ElRahman; Titi, Edriss S.; Hoteit, Ibrahim; Knio, Omar (Journal of Advances in Modeling Earth Systems, American Geophysical Union (AGU), 2022-11-29) [Article]
    Generating high-resolution flow fields is of paramount importance for various applications in engineering and climate sciences. This is typically achieved by solving the governing dynamical equations on high-resolution meshes, suitably nudged towards available coarse-scale data. To alleviate the computational cost of such downscaling process, we develop a physics-informed deep neural network (PI-DNN) that mimics the mapping of coarse-scale information into their fine-scale counterparts of continuous data assimilation (CDA). Specifically, the PI-DNN is trained within the theoretical framework described by Foias et al. (2014) to generate a surrogate of the theorized determining form map from the coarse-resolution data to the fine-resolution solution. We demonstrate the PI-DNN methodology through application to 2D Rayleigh-Bénard convection, and assess its performance by contrasting its predictions against those obtained by dynamical downscaling using CDA. The analysis suggests that the surrogate is constrained by similar conditions, in terms of spatio-temporal resolution of the input, as the ones required by the theoretical determining form map. The numerical results also suggest that the surrogate’s downscaled fields are of comparable accuracy to those obtained by dynamically downscaling using CDA. Consistent with the analysis of Farhat, Jolly, and Titi (2015), temperature observations are not needed for the PI-DNN to predict the fine-scale velocity, pressure and temperature fields.
  • An Overview of the Oil+Brine Two-Phase System in the Presence of Carbon Dioxide, Methane, and Their Mixture

    Nair, Arun Kumar Narayanan; Che Ruslan, Mohd Fuad Anwari; Cui, Ronghao; Sun, Shuyu (Industrial & Engineering Chemistry Research, American Chemical Society (ACS), 2022-11-29) [Article]
    An overview of the molecular simulation studies of the oil+brine two-phase system in the presence of CO2, CH4, and their mixture at geological conditions is presented. The simulation results agreed well with the experimental results and the density gradient theory predictions on the basis of the cubic–plus–association equation of state (CPA EoS) (withDebye–Hückel electrostatic term) and the perturbed chain statistical associating fluid theory (PC-SAFT) EoS. The interfacial tension (IFT) of the alkane+H2O system showed almost a linear increase with an increasing number of carbon atoms in the alkane molecule. These IFTs are approximately equal for linear, branched, and cyclic alkanes. Here, the negative surface excess of the alkanes might explain the increase in the IFTs with an increase in the pressure. The surface excesses of the alkanes increased with decreasing temperature. This may explain the decrease of the slopes in the IFT versus pressure plot with a decrease in the temperature. The IFT behavior of the alkane+water+CH4/CO2 system was found to be similar to that observed for the alkane+water system. The addition of CO2 had a more significant influence on the IFT than the addition of CH4. Here, CH4 and CO2 exhibited a positive surface excess. The negative surface excess of the salt ions probably explains the increase in the IFTs of the alkane+brine system with increasing salt content. The solubilities of CH4 and/or CO2 in the H2O-rich phase of the alkane+brine+CH4/CO2 system increased with decreasing salt content (salting-out effect). The IFT of the aromatic hydrocarbon+H2O system is much lower than that of the alkane+H2O system. The surface excess followed the order o-xylene > ethylbenzene > toluene > benzene for the aromatic hydrocarbon+H2O system. This trend has a direct correlation with the aromatic–aromatic interaction.
  • Mechanism Analysis of Shale Gas Adsorption under Carbon Dioxide–Moisture Conditions: A Molecular Dynamic Study

    Liu, Jie; Zhang, Tao; Sun, Shuyu (Energy & Fuels, American Chemical Society (ACS), 2022-11-23) [Article]
    In recent decades, shale gas, which has been regarded as a source of clean energy, is gradually replacing conventional energy. Shale gas adsorption in carbon dioxide (CO2)–moisture systems has been discussed in many previous studies; however, the intrinsic mechanism has not been clarified yet. In this work, the molecular dynamic (MD) method is adopted to study the adsorption behaviors of shale gas adsorption in the realistic kerogen nanoslit. The spatial density distributions of shale gas and different components have strong inhomogeneity. To reveal the heterogeneous adsorption mechanism, the potential of mean force (PMF) distributions of shale gas components are calculated on different target positions for the first time. The water (H2O) component prefers to adsorb on the oxygen-enriched position, as a result of the strong molecular polarity and hydrogen bond interactions. The CO2 component tends to adsorb on the carbon-rich site, which is the result of combining the van der Waals interaction and molecular polarity with kerogen walls. The potential energy contours are computed to verify the affinities between different components and the kerogen surface, and the potential energy difference can be observed between the bulk phase and adsorbed phase, which corresponds to the density and PMF analyses. The sensitivity analysis is also carried out to verify the above mechanism explanation. Higher temperature facilitates the desorption of shale gas, and higher pressure leads to more adsorption quantity. In the larger pore space, because of more content of H2O and CO2 molecules, the adsorption amount of methane (CH4) decreases. More content of CO2 is conducive to the desorption of shale gas, verified by cases in various component proportions.
  • Interfacial Properties of the Hexane+Carbon Dioxide+Water System in the Presence of Hydrophilic Silica

    Yang, Yafan; Che Ruslan, Mohd Fuad Anwari; Nair, Arun Kumar Narayanan; Qiao, Rui; Sun, Shuyu (The Journal of Chemical Physics, AIP Publishing, 2022-11-22) [Article]
    Molecular dynamics simulations were conducted to study the interfacial behavior of the CO2+H2O and hexane+CO2+H2O systems in the presence of hydrophilic silica at geological conditions. Simulation results for the CO2+H2O and hexane+CO2+H2O systems are in reasonable agreement with the theoretical predictions based on the density functional theory. In general, the interfacial tension (IFT) of the CO2+H2O system exponentially (linearly) decreased with increasing pressure (temperature). The IFTs of the hexane+CO2+H2O (two-phase) system decreased with increasing mole fraction of CO2 in the hexane/CO2-rich phase xCO2. Here the negative surface excesses of hexane lead to a general increase in the IFTs with increasing pressure. The effect of pressure on these IFTs decreased with increasing xCO2 due to the positive surface excesses of carbon dioxide. The simulated water contact angles of the CO2+H2O+silica system fall in the range from 43.8 to 76.0o, which is in reasonable agreement with the experimental results. These contact angles increased with pressure and decreased with temperature. Here the adhesion tensions are influenced by variations in fluid-fluid IFT and contact angle. The simulated water contact angles of the hexane+H2O+silica system fall in the range from 58.0 to 77.0o and are not much affected by the addition of CO2. These contact angles increased with pressure and the pressure effect was less pronounced at lower temperatures. Here the adhesion tensions are mostly influenced by variations in fluid-fluid IFTs. In all studied cases, CO2 molecules could penetrate into the interfacial region between the water droplet and the silica surface.
  • An efficient bound-preserving and energy stable algorithm for compressible gas flow in porous media

    Kou, Jisheng; Wang, Xiuhua; Chen, Huangxin; Sun, Shuyu (Journal of Computational Physics, Elsevier BV, 2022-11-11) [Article]
    Due to the great significance of the natural gas and shale gas, it is becoming increasingly important to simulate compressible gas flow in porous media. The model obeys an energy dissipation law as well as molar density must be positive and bounded in terms of the equation of state. For the purpose of eliminating nonphysical solutions as well as improving the stability in practical simulation, preservation of these properties is essential for a promising numerical method, but it is actually challenging due to the strong nonlinearity and complexity of the model. In this paper, we propose an efficient linearized numerical scheme that inherits the energy dissipation law as well as preserves the boundedness of molar density. Specifically, to treat the logarithmic type Helmholtz free energy density determined by the Peng-Robinson equation of state, we propose a novel adaptive stabilization approach involving the second derivatives of the convex energy terms. At each time step, the stabilization parameter is adaptively updated by a simple and explicit formula to ensure the energy dissipation law. The stabilized and linearized chemical potential allows to formulate the local mass conservation equation as an equivalent convection-diffusion form, and from this, an adaptive time step strategy is proposed to preserve the positivity and boundedness of molar density. The calculation of the time step size is fully explicit and easy to implement. Additionally, the fully discrete scheme is constructed using the conservative cell-centered finite difference method with the upwind strategy, and thus, it enjoys the local mass conservation. Numerical results are also presented to demonstrate the excellent performance of the proposed scheme.
  • MLReal: Bridging the gap between training on synthetic data and real data applications in machine learning

    Alkhalifah, Tariq Ali; Wang, Hanchen; Ovcharenko, Oleg (Artificial Intelligence in Geosciences, Elsevier BV, 2022-11-11) [Article]
    Among the biggest challenges we face in utilizing neural networks trained on waveform (i.e., seismic, electromagnetic, or ultrasound) data is its application to real data. The requirement for accurate labels often forces us to train our networks using synthetic data, where labels are readily available. However, synthetic data often fail to capture the reality of the field/real experiment, and we end up with poor performance of the trained neural networks (NNs) at the inference stage. This is because synthetic data lack many of the realistic features embedded in real data, including an accurate waveform source signature, realistic noise, and accurate reflectivity. In other words, the real data set is far from being a sample from the distribution of the synthetic training set. Thus, we describe a novel approach to enhance our supervised neural network (NN) training on synthetic data with real data features (domain adaptation). Specifically, for tasks in which the absolute values of the vertical axis (time or depth) of the input section are not crucial to the prediction, like classification, or can be corrected after the prediction, like velocity model building using a well, we suggest a series of linear operations on the input to the network data so that the training and application data have similar distributions. This is accomplished by applying two operations on the input data to the NN, whether the input is from the synthetic or real data subset domain: (1) The crosscorrelation of the input data section (i.e., shot gather, seismic image, etc.) with a fixed-location reference trace from the input data section. (2) The convolution of the resulting data with the mean (or a random sample) of the autocorrelated sections from the other subset domain. In the training stage, the input data are from the synthetic subset domain and the auto-corrected (we crosscorrelate each trace with itself) sections are from the real subset domain, and the random selection of sections from the real data is implemented at every epoch of the training. In the inference/application stage, the input data are from the real subset domain and the mean of the autocorrelated sections are from the synthetic data subset domain. Example applications on passive seismic data for microseismic event source location determination and on active seismic data for predicting low frequencies are used to demonstrate the power of this approach in improving the applicability of our trained NNs to real data.
  • Assimilation of global positioning system radio occultation refractivity for the enhanced prediction of extreme rainfall events in southern India

    Boyaj, Alugula; Dasari, Hari Prasad; Rao, Y. V. Rama; Ashok, Karumuri; Hoteit, Ibrahim (Meteorological Applications, Wiley, 2022-11-08) [Article]
    Here, we investigated the impact of assimilating the satellite-based product of Global Positioning System (GPS) radio occultation (RO) refractivity profiles data on the simulation of selected extreme rainfall events in three states of southern India: Tamil Nadu, Telangana, and Kerala. We assimilated the GPS RO data into the weather research and forecasting model using a 3DVar assimilation technique and evaluated the results against unassimilated (control) simulations. Various observations (e.g., rainfall measurements from AWS/rain-gauge) and observation-based gridded rainfall were used. The assimilation of the data yielded improved prediction of the spatial distributions of extreme rainfall regions and the amounts of rainfall. The analysis of the simulated dynamical and thermodynamic processes indicated that the assimilation of the data enabled the model to simulate significantly deep convection, high instability, and strong vertical motions. A vorticity budget analysis confirmed the marginally strengthened low-level convergence. The vertical motions because of assimilation facilitated an increased vertical advection of vorticity, which enhanced the extreme conditions in storms. Moreover, the assimilation of the data resulted in enhanced water vapor condensation and high levels of ice, cloud, and rain water in clouds, all of which contributed to extreme rainfall.
  • A Review of El Niño Southern Oscillation Linkage to Strong Volcanic Eruptions and Post-Volcanic Winter Warming

    Dogar, Muhammad Mubashar; Hermanson, Leon; Scaife, Adam A.; Visioni, Daniele; Zhao, Ming; Hoteit, Ibrahim; Graf, Hans-F.; Dogar, Muhammad Ahmad; Almazroui, Mansour; Fujiwara, Masatomo (Earth Systems and Environment, Springer Science and Business Media LLC, 2022-11-07) [Article]
    Understanding the influence of volcanism on ENSO and associated climatic impacts is of great scientific and social importance. Although many studies on the volcano–ENSO nexus are available, a thorough review of ENSO sensitivity to explosive eruptions is still missing. Therefore, this study aims to provide an in-depth assessment of the ENSO response to volcanism. Most past studies suggest an emerging consensus in models, with the vast majority showing an El Niño-like SST response during the eruption year and a La Niña-like response a few years later. RCP8.5-based climate model projections also suggest strong El Niño conditions and significant monsoonal rainfall reduction following strong tropical volcanism. However, some studies involving climate reconstructions and model simulations still raise concerns about the ENSO–volcano link and suggest a weak ENSO response to volcanism. This happens because ENSO response to volcanism seems very sensitive to reconstruction methods, ENSO preconditioning, eruption timing, position and amplitude. We noticed that some response mechanisms are still unclear, for instance, how the tropical volcanic forcing with nearly uniform radiative cooling projects onto ENSO when coincidental ENSO events are underway. Moreover, there are very less observational and proxy records for assessing the extratropical volcanism impact on ENSO. Nevertheless, model-based studies suggest that Northern (Southern) Hemispheric extratropical eruptions may lead to an El Niño (La Niña)-like response. We further noticed that the origin of post-eruption winter warming is still elusive; however, recent findings suggest that the large-scale circulation changes concurrently occurring during volcanism are the potential source of high-latitude winter warming. Existing uncertainties in the simulated ENSO response to volcanism could be reduced by considering a synchronized modeling approach with large ensembles.
  • Continuous and Discrete Data Assimilation with Noisy Observations for the Rayleigh-Benard Convection: A Computational Study

    Hammoud, Mohamad Abed El Rahman; LeMaitre, Olivier; Titi, Edriss S.; Hoteit, Ibrahim; Knio, Omar (arXiv, 2022-11-05) [Preprint]
    Obtaining accurate high-resolution representations of model outputs is essential to describe the system dynamics. In general, however, only spatially- and temporally-coarse observations of the system states are available. These observations can also be corrupted by noise. Downscaling is a process/scheme in which one uses coarse scale observations to reconstruct the high-resolution solution of the system states. Continuous Data Assimilation (CDA) is a recently introduced downscaling algorithm that constructs an increasingly accurate representation of the system states by continuously nudging the large scales using the coarse observations. We introduce a Discrete Data Assimilation (DDA) algorithm as a downscaling algorithm based on CDA with discrete-in-time nudging. We then investigate the performance of the CDA and DDA algorithms for downscaling noisy observations of the Rayleigh-Bénard convection system in the chaotic regime. In this computational study, a set of noisy observations was generated by perturbing a reference solution with Gaussian noise before downscaling them. The downscaled fields are then assessed using various error- and ensemble-based skill scores. The CDA solution was shown to converge towards the reference solution faster than that of DDA but at the cost of a higher asymptotic error. The numerical results also suggest a quadratic relationship between the ℓ2 error and the noise level for both CDA and DDA. Cubic and quadratic dependences of the DDA and CDA expected errors on the spatial resolution of the observations were obtained, respectively.
  • Machine Learning-Based Accelerated Approaches to Infer Breakdown Pressure of Several Unconventional Rock Types

    Tariq, Zeeshan; Yan, Bicheng; Sun, Shuyu; Gudala, Manojkumar; Aljawad, Murtada Saleh; Murtaza, Mobeen; Mahmoud, Mohamad (ACS Omega, American Chemical Society (ACS), 2022-11-04) [Article]
    Unconventional oil and gas reservoirs are usually classified by extremely low porosity and permeability values. The most economical way to produce hydrocarbons from such reservoirs is by creating artificially induced channels. To effectively design hydraulic fracturing jobs, accurate values of rock breakdown pressure are needed. Conducting hydraulic fracturing experiments in the laboratory is a very expensive and time-consuming process. Therefore, in this study, different machine learning (ML) models were efficiently utilized to predict the breakdown pressure of tight rocks. In the first part of the study, to measure the breakdown pressures, a comprehensive hydraulic fracturing experimental study was conducted on various rock specimens. A total of 130 experiments were conducted on different rock types such as shales, sandstone, tight carbonates, and synthetic samples. Rock mechanical properties such as Young’s modulus (E), Poisson’s ratio (ν), unconfined compressive strength, and indirect tensile strength (σt) were measured before conducting hydraulic fracturing tests. ML models were used to correlate the breakdown pressure of the rock as a function of fracturing experimental conditions and rock properties. In the ML model, we considered experimental conditions, including the injection rate, overburden pressures, and fracturing fluid viscosity, and rock properties including Young’s modulus (E), Poisson’s ratio (ν), UCS, and indirect tensile strength (σt), porosity, permeability, and bulk density. ML models include artificial neural networks (ANNs), random forests, decision trees, and the K-nearest neighbor. During training of ML models, the model hyperparameters were optimized by the grid-search optimization approach. With the optimal setting of the ML models, the breakdown pressure of the unconventional formation was predicted with an accuracy of 95%. The accuracy of all ML techniques was quite similar; however, an explicit empirical correlation from the ANN technique is proposed. The empirical correlation is the function of all input features and can be used as a standalone package in any software. The proposed methodology to predict the breakdown pressure of unconventional rocks can minimize the laboratory experimental cost of measuring fracture parameters and can be used as a quick assessment tool to evaluate the development prospect of unconventional tight rocks.
  • Upwind, No More: Flexible Traveltime Solutions using Physics-Informed Neural Networks

    Taufik, Mohammad Hasyim; Waheed, Umair bin; Alkhalifah, Tariq Ali (IEEE Transactions on Geoscience and Remote Sensing, Institute of Electrical and Electronics Engineers (IEEE), 2022-11-01) [Article]
    The eikonal equation plays an important role across multidisciplinary branches of science and engineering. In geophysics, the eikonal equation, and its characteristics, are used in addressing two fundamental questions pertaining to seismic waves: what paths do the seismic waves take (its spreading)? and how long do they take? There have been numerous attempts to solve the eikonal equation, which can be broadly categorized as finite-difference and physics informed neural network (PINN) based approaches. While the former has been developed and optimized over the years, it still inherits some numerical inaccuracies and also the cost scales exponentially with the velocity model size. More importantly, it requires upwind calculations to satisfy the viscosity solution. PINNs, on the other hand, have shown great promise due to several features allowing for higher accuracy and scalability than conventional approaches. In this paper, we demonstrate another unique feature of PINN solutions, specifically its flexibility resulting from the global nature of its NN functional optimization, allowing for functional gradients referred to as automatic differentiation. This feature allows us to overcome the inability of conventional methods to handle large areas of missing information (gap) in the velocity model. We find empirically that the PINNs interpolation-extrapolation inherent capability enables us to circumvent a scenario when traveltime modelling is performed on velocity models containing gaps. Such a capability is crucial when performing traveltime modelling using the global tomographic Earth velocity model.
  • Comprehensive assessment of PM10 and PM2.5 pollution in the west side of Saudi Arabia using CMAQ and WRF-Chem models

    Montealegre, Juan Sebastian (2022-11) [Thesis]
    Advisor: Stenchikov, Georgiy L.
    Committee members: Jones, Burton; Sun, Shuyu
    This work is aimed to study the capabilities of CMAQ and WRF-Chem models for predicting the PM10 and PM2.5 pollution in the west side of Saudi Arabia. To do this fairly, one-month simulations (April, 2021) are done in both models using same initial and boundary conditions, meteorology and anthropogenic emissions. Unique configurations in both models allow to compare differences in the chemical processes and natural emissions estimation of each model. Simulated PM (PM10 and PM2.5) surface concentrations and AOD are compared with available observations to assess models’ performance. Moreover, CMAQ is used to study a real air pollution episode generated by a fire in the Rabigh Electricity Power Station between April 8 and April 11, 2021.
  • Rapid NMR T2 Extraction from Micro-CT Images Using Machine Learning

    Li, Yiteng; He, Xupeng; Alsinan, Marwa; Kwak, Hyung; Hoteit, Hussein (SPE, 2022-10-31) [Conference Paper]
    Nuclear magnetic resonance (NMR) is an important tool for characterizing pore size distributions of reservoir rocks. Pore-scale simulations from digital rocks (micro-CT images) provide deep insights into the correlation between pore structures and NMR relaxation processes. Conventional NMR simulations using the random walk method could be computationally expensive at high image resolution and particle numbers. This work introduces a novel machine-learning-based approach as an alternative to conventional random walk simulation for rapid estimation of NMR magnetization signals. This work aims to establish a "value-to-value" model using artificial neural networks to create a nonlinear mapping between the input of Minkowski functionals and surface relaxivity, and NMR magnetization signals as the output. The proposed workflow includes three main steps. The first step is to extract subvolumes from digital rock duplicates and characterize their pore geometry using Minkowski functionals. Then random walk simulations are performed to generate the output of the training dataset. An optimized artificial neural network is created using the Bayesian optimization algorithm. Numerical results show that the proposed model, with fewer inputs and simpler network architecture than the referenced model, achieves an excellent prediction accuracy of 99.9% even for the testing dataset. Proper data preprocessing significantly improves training efficiency and accuracy. Moreover, the inputs of the proposed model are more pertinent to NMR relaxation than the referenced model that used twenty-one textural features as input. This works offers an accurate and efficient approach for the rapid estimation of NMR magnetization signals.
  • An Effective Method of Estimating Nuclear Magnetic Resonance Based Porosity Using Deep Learning Approach

    Tariq, Zeeshan; Gudala, Manojkumar; Xu, Zhen; Yan, Bicheng; Sun, Shuyu; Mahmoud, Mohamed (SPE, 2022-10-31) [Conference Paper]
    Carbonate rocks are very heterogeneous and have very complex pores structure due to the presence of intra-particle and inter-particle porosities. This makes the characterization and evaluation of the petrophysical data, and the interpretation of the carbonate rocks a big challenge. Porosity in complex lithologies, particularly carbonate reservoirs, is difficult to measure using conventional (Quad-Combo) well logs. Nuclear Magnetic Resonance (NMR) derived porosity is considered the total porosity "gold standard", as it is measured exclusive of matrix and mineralogy. However, due to NMR tools existing as relatively new technology, and the extra expense in logging runs and rig time, most wells lack these data. Most of the existing approaches to predict the rock porosity was developed on the Neutron-density porosity logs that usually are resulted in inaccurate estimation, especially in the fractured zone and highly dolomitized rocks. In this study, deep learning model was efficiently utilized to predict the Nuclear Magnetic Resonance based effective porosity in carbonate rocks. The petrophysical well logs such as bulk density, gamma-ray, neutron porosity, photoelectric log, and caliper log were used as predictors. A total of 3800 data points were obtained from several wells located in a carbonate reservoir. A comprehensive data exploratory analysis tools (EDA) was utilized to evaluate the quality of the dataset which led to removing the extreme values and outliers. A fully connected Deep Neural Network (DNN) was trained to predict NMR based effective porosity. The hyperparameters of DNN model such as number of hidden layers, number of neurons, activation functions, and learning algorithms were varied using a grid search optimization approach. The K-fold cross-validation criteria were used to enhance the generalization capabilities of ML models. The evaluation of ML models was assessed by the coefficient of determination (R2), root means square error (RMSE), and. average absolute percentage error (AAPE). The results showed that the DNN resulted in a significantly low error and high R2 between actual and predicted values. An accuracy of 87% was recorded between actual and predicted NMR values. The new model to predict the NMR porosity is trained on the NMR-determined porosity. NMR porosity is based on the number of hydrogen nuclei in the pore spaces that are independent of the rock minerals and related to the pore spaces only.
  • A Machine Learning Based Accelerated Approach to Infer the Breakdown Pressure of the Tight Rocks

    Tariq, Zeeshan; Yan, Bicheng; Sun, Shuyu; Gudala, Manojkumar; Mahmoud, Mohamed (SPE, 2022-10-31) [Conference Paper]
    Unconventional oil reservoirs are usually classified by extremely low porosity and permeability values. The most economical way to produce hydrocarbons from such reservoirs is by creating artificially induced fractures. To design the hydraulic fracturing jobs, true values of rock breakdown pressure is required. Conducting hydraulic fracturing experiments in the laboratory is a very expensive and time consuming process. Therefore, in this study, different machine learning models were efficiently utilized to predict the breakdown pressure of the tight rocks. In the first part of the study, a comprehensive hydraulic fracturing experimental study was conducted on various rock specimens, to measure the breakdown pressure. A total of 130 experiments were conducted on different rock types such as shales, sandstone, tight carbonates, and synthetic cement samples. Rock mechanical properties such as Young's Modulus E, Poisson's ratio, Unconfined Compressive strength (UCS), and indirect tensile strength sigma_t were measured before conducting hydraulic fracturing tests. Machine learning models were used to correlate the breakdown pressure of the rock as a function of fracturing experimental conditions and rock properties. In the machine learning model, we considered experimental conditions including injection rate, overburden pressures, and fracturing fluid viscosity, and rock properties including Young's Modulus, Poisson's ratio, Unconfined Compressive strength (UCS), and indirect tensile strength, porosity, permeability, and bulk density. Machine learning models include Random Forest (RF), Decision Trees (DT), and K-Nearest Neighbor (KNN). During training of ML models, the model hyper-parameters were optimized by grid search optimization approach. With the optimal setting of the ML models, the breakdown pressure of the unconventional formation were predicted with an accuracy of 95%. The proposed methodology to predict the breakdown pressure of unconventional rocks can minimize the laboratory experimental cost of measuring fracture parameters and can be used as a quick assessment tool to evaluate the development prospect of unconventional tight rocks.
  • A Lagrangian model-based physical connectivity atlas of the Red Sea coral reefs

    Wang, Yixin; Raitsos, Dionysios E.; Krokos, Georgios; Zhan, Peng; Hoteit, Ibrahim (Frontiers in Marine Science, Frontiers Media SA, 2022-10-28) [Article]
    Connectivity, the exchange of individuals and genes among geographically separated marine populations, plays a key role in coral reef biodiversity and resilience. The Red Sea is a semi-enclosed basin with dynamic circulation and abundant coral reefs, making it a natural laboratory for coral reef connectivity research. Previous studies broadly investigated Red Sea connectivity, but were spatially restricted to regional or sparsely-distributed reef sites. Here, using hydrodynamic and particle tracking models, a high-resolution circulation-driven physical connectivity atlas covering every Red Sea coral reef, including seasonality, was simulated and further validated against available in-situ genetic datasets. The simulation was conducted without incorporating larval traits to isolate and quantify the connectivity contributed by circulation. Our validation experiment suggests the importance of circulation in shaping the genetic structure of Red Sea reef species, supporting the Isolation By Circulation (IBC) theory in the Red Sea seascape genetics. The simulated atlas reveals that reefs in the northern Red Sea are better sources and destinations than those in the southern basin, regardless of season. The east-west connections between the southern reefs are identified to be weak. Complex circulation dynamics drive a regional-specific seasonality, e.g., the Farasan Islands reefs are better sources during summer while the nearby Bab-Al-Mandeb strait reefs are better sources during winter. The west-coast reefs are generally winter-intensified sources whereas the east-coast reefs are generally summer-intensified sources. The revealed seasonality of physical connectivity is important for larval dispersal processes as reef species may spawn in different seasons. This physical connectivity atlas provides a reference for designing marine conservation strategies from a circulation perspective and easy-to-access physical connectivity datasets for the future Red Sea seascape genetic studies.
  • Tritium and radiocarbon in the water column of the Red Sea

    Povinec, P. P.; Papadopoulos, V. P.; Krokos, Georgios; Abualnaja, Yasser; Pavlidou, A.; Kontuľ, I.; Kaizer, J.; Cherkinsky, A.; Molnár, A.; Molnár, M.; Palcsu, L.; Al Ghamdi, A. S.; Anber, H. A.; Al Othman, A. S.; Hoteit, Ibrahim (Journal of Environmental Radioactivity, Elsevier BV, 2022-10-27) [Article]
    Despite being the busiest transient sea in the world due to the Suez Canal, radionuclide distribution studies in seawater and sediment of the Red Sea remain rare. A sampling expedition in the Red Sea was conducted from June 9 to July 6, 2021, visiting a transect of several deep sampling stations located along the central axis of the basin from the Gulf of Aqaba to the southern Red Sea (near Farasan Island, Saudi Arabia). The collected seawater profile samples were analyzed for tritium, radiocarbon and oxygen-18. The observed tritium levels in surface waters of the Red Sea peaked at 0.3–0.4 TU, similar to the values observed in the western Arabian Sea (decay corrected). The values observed at waters below 150 m were around 0.2 TU, however, at depths of 450 and 750 m, tritium minima (<0.2 TU) were observed, which could be associated with a partial return flow of bottom waters from the southern to the northern Red Sea. At two stations at the depth of about 550 m, deep Δ14C minima were observed as well (−4‰ and −10‰), documenting ongoing transport of carbon in the water column, important for sink of anthropogenic carbon.
  • High-resolution climate characteristics of the Arabian Gulf based on a validated regional reanalysis

    Dasari, Hari Prasad; Vijaya Kumari, K.; Langodan, Sabique; Abualnaja, Yasser; Desamsetti, Srinivas; Vankayalapati, Koteswararao; Thang, Luong; Hoteit, Ibrahim (Meteorological Applications, Wiley, 2022-10-26) [Article]
    The regional climate of the Arabian Gulf (AG) and its variability are examined based on a 40-year (1980–2019), 5-km regional reanalysis of the Arabian Peninsula (AP reanalysis). The AP reanalysis fields were first validated against the available observations over the AG, suggesting that this high-resolution reanalysis well reproduces the spatio-temporal features of the AG atmospheric circulations. The validated AP reanalysis fields were then analysed to examine the climatic characteristics over the AG including the monthly mean, maximum and minimum temperatures, and the seasonal variations in winds, relative humidity and rainfall over the AG. The AG climate is mostly dry between May and October, and experiences moderate rainfall between December and January. The higher (lower) pressure difference between the northwest and southeast AG during summer (winter) generates the northwesterly Shamal winds over the north (central) AG. The mean Shamal winds are relatively stronger (weaker) and prolonged (shorter) during summer (winter); however, the short lived Shamal jet events in winter can be occasionally stronger than summer. In terms of interannual variability, the Shamal winds are stronger and more persistent in summer during El Niño years and in winter during La Niña years. These differences are mainly associated with changes in temperature gradients between the eastern AG and northwestern AP.
  • Wavefield reconstruction inversion modelling of Marchenko focusing functions

    Hajjaj, Ruhul F.; Ridder, Sjoerd A. L. de; Livermore, Philip W.; Ravasi, Matteo (arXiv, 2022-10-26) [Preprint]
    Marchenko focusing functions are in their essence wavefields that satisfy the wave equation subject to a set of boundary, initial, and focusing conditions. Here, we show how Marchenko focusing functions can be modeled by finding the solution to a wavefield reconstruction inversion problem. Our solution yields all elements of the focusing function including evanescent, refracted, and trapped waves (tunneling). Our examples indicate that focusing function solutions in higher dimensions are however challenging to compute by numerical means due to the appearance of strong evanescent waves.
  • Wavefield reconstruction inversion modelling of Marchenko focusing functions

    Hajjaj, Ruhul F.; Ridder, Sjoerd A. L. de; Livermore, Philip W.; Ravasi, Matteo (arXiv, 2022-10-26) [Preprint]
    Marchenko focusing functions are in their essence wavefields that satisfy the wave equation subject to a set of boundary, initial, and focusing conditions. Here, we show how Marchenko focusing functions can be modeled by finding the solution to a wavefield reconstruction inversion problem. Our solution yields all elements of the focusing function including evanescent, refracted, and trapped waves (tunneling). Our examples indicate that focusing function solutions in higher dimensions are however challenging to compute by numerical means due to the appearance of strong evanescent waves.

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