Saudi Aramco (Saudi Arabia)

ABSTRACT A detailed chemical kinetic mechanism has been developed to describe the oxidation of small hydrocarbon and oxygenated hydrocarbon species. The reactivity of these small fuels and intermediates is of critical importance in understanding and accurately describing the combustion characteristics, such as ignition delay time, flame speed, and emissions of practical fuels. The chosen rate expressions have been assembled through critical evaluation of the literature, with minimum optimization performed. The mechanism has been validated over a wide range of initial conditions and experimental devices, including flow reactor, shock tube, jet‐stirred reactor, and flame studies. The current mechanism contains accurate kinetic descriptions for saturated and unsaturated hydrocarbons, namely methane, ethane, ethylene, and acetylene, and oxygenated species; formaldehyde, methanol, acetaldehyde, and ethanol.

Large-scale carbon fixation requires high-volume chemicals production from carbon dioxide. Dry reforming of methane could provide an economically feasible route if coke- and sintering-resistant catalysts were developed. Here, we report a molybdenum-doped nickel nanocatalyst that is stabilized at the edges of a single-crystalline magnesium oxide (MgO) support and show quantitative production of synthesis gas from dry reforming of methane. The catalyst runs more than 850 hours of continuous operation under 60 liters per unit mass of catalyst per hour reactive gas flow with no detectable coking. Synchrotron studies also show no sintering and reveal that during activation, 2.9 nanometers as synthesized crystallites move to combine into stable 17-nanometer grains at the edges of MgO crystals above the Tammann temperature. Our findings enable an industrially and economically viable path for carbon reclamation, and the "Nanocatalysts On Single Crystal Edges" technique could lead to stable catalyst designs for many challenging reactions.

Twenty-micrometer-thick single-crystal methylammonium lead triiodide (MAPbI3) perovskite (as an absorber layer) grown on a charge-selective contact using a solution space-limited inverse-temperature crystal growth method yields solar cells with power conversion efficiencies reaching 21.09% and fill factors of up to 84.3%. These devices set a new record for perovskite single-crystal solar cells and open an avenue for achieving high fill factors in perovskite solar cells.

The current study aimed at evaluating the capabilities of seven advanced machine learning techniques (MLTs), including, Support Vector Machine (SVM), Random Forest (RF), Multivariate Adaptive Regression Spline (MARS), Artificial Neural Network (ANN), Quadratic Discriminant Analysis (QDA), Linear Discriminant Analysis (LDA), and Naive Bayes (NB), for landslide susceptibility modeling and comparison of their performances. Coupling machine learning algorithms with spatial data types for landslide susceptibility mapping is a vitally important issue. This study was carried out using GIS and R open source software at Abha Basin, Asir Region, Saudi Arabia. First, a total of 243 landslide locations were identified at Abha Basin to prepare the landslide inventory map using different data sources. All the landslide areas were randomly separated into two groups with a ratio of 70% for training and 30% for validating purposes. Twelve landslide-variables were generated for landslide susceptibility modeling, which include altitude, lithology, distance to faults, normalized difference vegetation index (NDVI), landuse/landcover (LULC), distance to roads, slope angle, distance to streams, profile curvature, plan curvature, slope length (LS), and slope-aspect. The area under curve (AUC-ROC) approach has been applied to evaluate, validate, and compare the MLTs performance. The results indicated that AUC values for seven MLTs range from 89.0% for QDA to 95.1% for RF. Our findings showed that the RF (AUC ​= ​95.1%) and LDA (AUC ​= ​941.7%) have produced the best performances in comparison to other MLTs. The outcome of this study and the landslide susceptibility maps would be useful for environmental protection.

Abstract One of the most accepted geologic models is the relation between reflector curvature and the presence of open and closed fractures. Such fractures, as well as other small discontinuities, are relatively small and below the imaging range of conventional seismic data. Depending on the tectonic regime, structural geologists link open fractures to either Gaussian curvature or to curvature in the dip or strike directions. Reflector curvature is fractal in nature, with different tectonic and lithologic effects being illuminated at the 50-m and 1000-m scales. Until now, such curvature estimates have been limited to the analysis of picked horizons. We have developed what we feel to be the first volumetric spectral estimates of reflector curvature. We find that the most positive and negative curvatures are the most valuable in the conventional mapping of lineations — including faults, folds, and flexures. Curvature is mathematically independent of, and interpretatively complementary to, the well-established coherence geometric attribute. We find the long spectral wavelength curvature estimates to be of particular value in extracting subtle, broad features in the seismic data such as folds, flexures, collapse features, fault drags, and under- and overmigrated fault terminations. We illustrate the value of these spectral curvature estimates and compare them to other attributes through application to two land data sets — a salt dome from the onshore Louisiana Gulf Coast and a fractured/karsted data volume from Fort Worth basin of North Texas.

Bilirubin, a breakdown product of heme, is normally glucuronidated and excreted by the liver into bile. Failure of this system can lead to a buildup of conjugated bilirubin in the blood, resulting in jaundice. The mechanistic basis of bilirubin excretion and hyperbilirubinemia syndromes is largely understood, but that of Rotor syndrome, an autosomal recessive disorder characterized by conjugated hyperbilirubinemia, coproporphyrinuria, and near-absent hepatic uptake of anionic diagnostics, has remained enigmatic. Here, we analyzed 8 Rotor-syndrome families and found that Rotor syndrome was linked to mutations predicted to cause complete and simultaneous deficiencies of the organic anion transporting polypeptides OATP1B1 and OATP1B3. These important detoxification-limiting proteins mediate uptake and clearance of countless drugs and drug conjugates across the sinusoidal hepatocyte membrane. OATP1B1 polymorphisms have previously been linked to drug hypersensitivities. Using mice deficient in Oatp1a/1b and in the multispecific sinusoidal export pump Abcc3, we found that Abcc3 secretes bilirubin conjugates into the blood, while Oatp1a/1b transporters mediate their hepatic reuptake. Transgenic expression of human OATP1B1 or OATP1B3 restored the function of this detoxification-enhancing liver-blood shuttle in Oatp1a/1b-deficient mice. Within liver lobules, this shuttle may allow flexible transfer of bilirubin conjugates (and probably also drug conjugates) formed in upstream hepatocytes to downstream hepatocytes, thereby preventing local saturation of further detoxification processes and hepatocyte toxic injury. Thus, disruption of hepatic reuptake of bilirubin glucuronide due to coexisting OATP1B1 and OATP1B3 deficiencies explains Rotor-type hyperbilirubinemia. Moreover, OATP1B1 and OATP1B3 null mutations may confer substantial drug toxicity risks.

Abstract In conventional seismic processing, velocity analysis is performed by using the normal moveout (NMO) equation which is based on the assumption of flat, horizontal reflectors. Imaging by migration (either before or after stack) is done normally in a subsequent step using these velocities. In this paper, velocity analysis and imaging are combined in one step, and migration itself is used as a velocity indicator. Because, unlike NMO, migration can be formulated for any velocity function, migration-based velocity analysis methods are capable of handling arbitrary structures, i.e., those with lateral velocity variations.In the proposed scheme, each shot gather (profile) is migrated with an initial depth-velocity model. Profile migration is implemented in the (x, omega ) domain, but the actual implementation of profile migration is not critical, as long as it is not done in a spatial-wavenumber domain, which would preclude handling of lateral velocity variations. After migration with an initial velocity model, the velocity error is estimated, and the initial velocity model is updated; the process is repeated until convergence is achieved. The velocity analysis is based on the principle that after prestack migration with the correct velocity model, an image in a common-receiver gather (CRG) is aligned horizontally regardless of structure. The deviation from horizontal alignment is therefore a measure of the error in velocity. If the migration velocity is lower than the velocity of the medium, events curve upward, whereas if the migration velocity is higher than the velocity of the medium, events curve downward.

The main function of traditional proppants is to provide and maintain conductive fractures during well production where proppants should meet closure stress requirement and show resistance to diagenesis under downhole conditions. Many different proppants have been developed in the oil & gas industry, with various types, sizes, shapes, and applications. While most proppants are simply made of silica or ceramics, advanced proppants like ultra-lightweight proppant is also desirable since it reduces proppant settling and requires low viscosity fluids to transport. Additionally, multifunctional proppants may be used as a crude way to detect hydraulic fracture geometry or as matrices to slowly release downhole chemical additives, besides their basic function of maintaining conductive hydraulic fractures. Different from the conventional approach where proppant is pumped downhole in frac fluids, a revolutionary way to generate in-situ spherical proppants has been reported recently. This paper presents a comprehensive review of over 100 papers published in the past several decades on the subject. The objectives of this review study are to provide an overview of current proppant technologies, including different types, compositions, and shapes of proppants, new technologies to pump and organize proppants downhole such as channel fracturing, and also in-situ proppant generation. Finally, the paper sheds light on the current challenges and emphasizes needs for new proppant development for unconventional resources.

The possibility to fulfill the increasing market demand and producers' needs in processing straightforwardly crude oil, a cheap and universally available feedstock, to produce petrochemicals appears to be a very attractive strategy.

Fiber–optic sensors have been widely deployed in various applications, and their use has gradually increased since the 1980 s. Distributed fiber–optic sensors, which enable continuous and real–time measurements along the entire length of an optical fiber cable, have undergone significant improvements in underlying industries. In the oil and gas industry, distributed fiber–optic sensors can provide significantly valuable information throughout the life cycle of a well and can monitor pipelines transporting hydrocarbons over great distances. Here, we review the deployment of fiber–optic Rayleigh–based distributed acoustic sensing (DAS), Raman–based distributed temperature sensing (DTS), and Brillouin–based distributed temperature and strain sensing (DTSS) in the oil and gas industry. In particular, we describe the operation principle and basic experimental setups of the DAS, DTS, and DTSS, highlighting their applications in the upstream, midstream, and downstream sectors of the oil and gas industry. We further developed a prototype of a fiber–optic hybrid DAS–DTS system that simultaneously measures vibration and temperature along a multimode fiber (MMF). The reported hybrid sensing system was tested in an operational oil well. This work also discusses the challenges that might hinder the growth of the distributed fiber–optic sensing market in the petroleum industry, and we further point out the future directions of related research.

Mapping fault planes using seismic images is a crucial and time-consuming step in hydrocarbon prospecting. Conventionally, this requires significant manual efforts that normally go through several iterations to optimize how the different fault planes connect with each other. Many techniques have been developed to automate this process, such as seismic coherence estimation, edge detection, and ant-tracking, to name a few. However, these techniques do not take advantage of the valuable experience accumulated by the interpreters. We have developed a method that uses the convolutional neural network (CNN) to automatically detect and map fault zones using 3D seismic images in a similar fashion to the way done by interpreters. This new technique is implemented in two steps: training and prediction. In the training step, a CNN model is trained with annotated seismic image cubes of field data, where every point in the seismic image is labeled as fault or nonfault. In the prediction step, the trained model is applied to compute fault probabilities at every location in other seismic image cubes. Unlike reported methods in the literature, our technique does not require precomputed attributes to predict the faults. We verified our approach on the synthetic and field data sets. We clearly determined that the CNN-computed fault probability outperformed that obtained using the coherence technique in terms of exhibiting clearer discontinuities. With the capability of emulating human experience and evolving through training using new field data sets, deep-learning tools manifest huge potential in automating and advancing seismic fault mapping.

The potential impacts of machine learning and artificial intelligence (AI) on society are receiving increased attention owing to the rapid growth of these technologies during the fourth industrial revolution. Thus, a detailed analysis of the positive implications and drawbacks of AI technology in human society is necessary. The development of AI technology has created new markets and employment opportunities in vital industries, including transportation, health, education, and the environment. According to experts, the rapidly increasing improvements in AI will continue. As part of humankind's continual efforts to create more prosperous technological growth, automation and AI are changing people's lives and are widely considered to be game-changers in a variety of industries. This study presents a review of how automation and AI may affect businesses and jobs. To determine some of the prospective long-term consequences of AI on human civilisation, this study investigates a variety of connected primary impacting potentials, including job losses, employees' well-being, dehumanisation of jobs, fear of AI, and examples of autonomous technology developments, such as autonomous-vehicle challenges. A diverse methodology of narrative review and thematic pattern was used to add to transdisciplinary or multidisciplinary work, particularly in the theoretical development of AI technologies.

An artificial solid electrolyte interphase on aluminum enables aqueous batteries with high specific energy and good reversibility.

With decades of successful application of model predictive control (MPC) to industrial processes, practitioners are now focused on ease of commissioning, monitoring, and automation of maintenance. Many industries do not necessarily need better algorithms, but rather improved usability of existing technologies to allow a limited workforce of varying expertise to easily commission, use, and maintain these valued applications. Continuous performance monitoring, and automated model reidentification are being used as vendors work to deliver automated adaptive MPC. This paper examines industrial practice and emerging research trends towards providing sustained MPC performance.

Abstract Magnetic field sensors are an integral part of many industrial and biomedical applications, and their utilization continues to grow at a high rate. The development is driven both by new use cases and demand like internet of things as well as by new technologies and capabilities like flexible and stretchable devices. Magnetic field sensors exploit different physical principles for their operation, resulting in different specifications with respect to sensitivity, linearity, field range, power consumption, costs etc. In this review, we will focus on solid state magnetic field sensors that enable miniaturization and are suitable for integrated approaches to satisfy the needs of growing application areas like biosensors, ubiquitous sensor networks, wearables, smart things etc. Such applications require a high sensitivity, low power consumption, flexible substrates and miniaturization. Hence, the sensor types covered in this review are Hall Effect, Giant Magnetoresistance, Tunnel Magnetoresistance, Anisotropic Magnetoresistance and Giant Magnetoimpedance.

This systematic literature review explores the digital transformation (DT) and cybersecurity implications for achieving business resilience. DT involves transitioning organizational processes to IT solutions, which can result in significant changes across various aspects of an organization. However, emerging technologies such as artificial intelligence, big data and analytics, blockchain, and cloud computing drive digital transformation worldwide while increasing cybersecurity risks for businesses undergoing this process. This literature survey article highlights the importance of comprehensive knowledge of cybersecurity threats during DT implementation to prevent interruptions due to malicious activities or unauthorized access by attackers aiming at sensitive information alteration, destruction, or extortion from users. Cybersecurity is essential to DT as it protects digital assets from cyber threats. We conducted a systematic literature review using the PRISMA methodology in this research. Our literature review found that DT has increased efficiency and productivity but poses new challenges related to cybersecurity risks, such as data breaches and cyber-attacks. We conclude by discussing future vulnerabilities associated with DT implementation and provide recommendations on how organizations can mitigate these risks through effective cybersecurity measures. The paper recommends a staged cybersecurity readiness framework for business organizations to be prepared to pursue digital transformation.

Lead halide perovskite solar cells (PSCs) have advanced rapidly in performance over the past decade. Single-crystal PSCs based on micrometers-thick grain-boundary-free films with long charge carrier diffusion lengths and enhanced light absorption (relative to polycrystalline films) have recently emerged as candidates for advancing PSCs further toward their theoretical limit. To date, the preferred method to grow MAPbI3 single-crystal films for PSCs involves solution processing at temperatures ≳120 °C, which adversely affects the films’ crystalline quality, especially at the surface, primarily because of methylammonium iodide loss at such high temperatures. Here we devise a solvent-engineering approach to reduce the crystallization temperature of MAPbI3 single-crystal films (<90 °C), yielding better quality films with longer carrier lifetimes. Single-crystal MAPbI3 inverted PSCs fabricated with this strategy show markedly enhanced open-circuit voltages (1.15 V vs 1.08 V for controls), leading to power conversion efficiencies of up to 21.9%, which are among the highest reported for MAPbI3-based devices.

Colonization of the land by plants most likely occurred in a stepwise fashion starting in the Mid-Ordovician. The earliest flora of bryophyte-like plants appears to have been cosmopolitan and dominated the planet, relatively unchanged, for some 30 million years. It is represented by fossilized dispersed cryptospores and fragmentary plant remains. In the Early Silurian, cryptospore abundance and diversity diminished abruptly as trilete spores appeared, became abundant, and underwent rapid diversification. This change coincides approximately with the appearance of vascular plant megafossils and probably represents the origin and adaptive radiation of vascular plants. We have obtained a diverse trilete spore occurrence from the Late Ordovician that suggests that vascular plants originated and diversified earlier than previously hypothesized, in Gondwana, before migrating elsewhere and secondarily diversifying.

The translation of "next-generation" sequencing directly to the clinic is still being assessed but has the potential for genetic diseases to reduce costs, advance accuracy, and point to unsuspected yet treatable conditions. To study its capability in the clinic, we performed whole-exome sequencing in 118 probands with a diagnosis of a pediatric-onset neurodevelopmental disease in which most known causes had been excluded. Twenty-two genes not previously identified as disease-causing were identified in this study (19% of cohort), further establishing exome sequencing as a useful tool for gene discovery. New genes identified included EXOC8 in Joubert syndrome and GFM2 in a patient with microcephaly, simplified gyral pattern, and insulin-dependent diabetes. Exome sequencing uncovered 10 probands (8% of cohort) with mutations in genes known to cause a disease different from the initial diagnosis. Upon further medical evaluation, these mutations were found to account for each proband's disease, leading to a change in diagnosis, some of which led to changes in patient management. Our data provide proof of principle that genomic strategies are useful in clarifying diagnosis in a proportion of patients with neurodevelopmental disorders.

Transformation of carbon dioxide into various chemicals including methanol is a top priority field of study owing to the association of CO2 with global warming. There is a need for renewable and sustainable energy sources and replacement of fossil fuel with a fuel having comparable energy density. Electrochemical reduction is a unique approach to convert CO2 to methanol by employing alternative energy sources where electrocatalyst plays a crucial role. A lot of effort is made to understand and increase the efficiency of electrocatalysts. Unadulterated metals, metal oxide, composite materials, and metal–organic frameworks (MOFs) are employed for the electrochemical reduction of CO2 to methanol. However, MOFs engrossed the enormous consideration due to simplicity, higher surface area, and unique structural features. In recent years, MOFs and their derivatives find significant applications in the electrocatalysis of oxygen and hydrogen evolution, oxygen, hydrogen, and CO2 reduction. The primary emphasis of the current review is the electroreduction of CO2 to methanol by coalescing the vantages of non-MOFs, MOFs, and their composite materials. The challenges to achieve electrocatalyst with higher efficiency and better selectivity for the electroreduction of CO2 are analyzed. Several research directions are proposed for MOF electrocatalysts to enhance the catalytic efficiency in methanol production. This review substantiates the efforts to develop new MOFs with superior efficiency, chemical stability, and conductivity.