Micritization alters carbonate grain textures through repeated dissolution-reprecipitation cycles. Although microbial activity influences micritization in shallow marine carbonates, few studies integrate sedimentological and microbial analyses in modern settings. Here, we examined how bacterial communities vary along 80-cm sediment cores collected seasonally over 1 year from Al Kharrar Lagoon (Eastern Red Sea, Saudi Arabia). Micritized grains with microborings and cryptocrystalline aragonite fillings-often associated with extracellular polymeric substances-occurred in ~60% of samples, especially in surface sediments. Bacterial communities were structured primarily by sediment depth rather than season. Key candidates potentially involved in carbonate cycling were inferred from taxonomy and prioritized by abundance, cultured representatives and expected tolerance to lagoon conditions. Surface layers were enriched in aerobic and sulfur-oxidizing taxa (Thiogranum, Thioalkalispira, Thiotrichales), whereas deeper layers contained mainly fermentative lineages (Aminicenantales, Anaerolineae). Carbonate precipitation was associated with nitrate reducers (Fulvivirga) in surface sediments and sulfate reducers (Desulfosarcina, Desulfatiglans) at depth. Versatile taxa such as Cyanobacteria, Tropicimonas, Ruegeria and Woeseia in surface layers and Dehalococcoidia and Anaerolineaceae in deeper layers, may contribute to both dissolution and precipitation processes. These results indicate that, alongside abiotic processes, bacterial communities can significantly influence carbonate cycling and reshape sediment textures in shallow Red Sea environments.

Underground hydrogen storage (UHS) is a promising solution for managing energy supply variability and enabling large-scale, long-term energy storage. By storing surplus hydrogen during periods of low demand and retrieving it when needed, UHS enhances energy diversity and operational flexibility, supporting a more resilient and adaptable energy system. However, injecting hydrogen into geological formations triggers a complex interplay of biological and geochemical interactions that extends far beyond conventional considerations of physical containment and deliverability. This review systematically examines subsurface microbial activity and the mechanisms governing microbial and geochemical processes during UHS, including methanogenesis, sulfate reduction, acetogenesis, iron reduction, and their coupled interactions with reservoir mineralogy. We then present a comprehensive synthesis of experimental investigations, ranging from batch reactors and microfluidic platforms to field-scale trials, complemented by computational studies encompassing batch geochemical simulations (e.g., PHREEQC), reservoir-scale reactive transport modeling (e.g., DuMux, CMG-GEM, Eclipse), and molecular dynamics simulations of interfacial and wetting behavior. Field observations from historical town gas storage sites and ongoing UHS pilot projects worldwide are critically evaluated to bridge the gap between laboratory findings and real-world subsurface behavior. By integrating findings across disciplines, we identify persistent knowledge gaps, particularly regarding the kinetics of long-term microbial and geochemical reactions, spatiotemporal variability, scale-up from laboratory to field conditions, and feedback mechanisms under dynamic operational cycles. We conclude with a critical appraisal of risk mitigation strategies, including microbial control, site selection criteria, and adaptive monitoring frameworks, offering a forward-looking perspective on the design and optimization of future UHS systems across diverse geological settings.

Micritization of marine carbonate grains is an early diagenetic process involving the alteration of the original carbonate fabric and the formation of cryptocrystalline textures through cycles of dissolution and reprecipitation. Microorganisms play a significant role in this process, actively contributing to the formation of constructive and destructive aragonite and high Mg-calcite envelopes. However, the complex interdependencies among sedimentological, geochemical and microbiological processes governing micritization in modern shallow-marine settings remain poorly understood. To address this knowledge gap, four sediment cores were collected across an inlet to the Al-Kharrar Lagoon on the Red Sea coast of Saudi Arabia. A comprehensive multidisciplinary analysis of the Holocene sedimentary sequences from these cores (<1 m depth) was conducted, combining petrographic, mineralogical, geochemical and microbial ecology analyses. Scanning electron microscopy displayed carbonate sediments covered by mud-sized aragonite needles resulting from the breakdown of coarser carbonate grains and/or microbial activity. Additionally, the presence of Mg-rich calcite, mini-micrite crystals (<1 mu m) and biofilms on the grains' surface indicated microbial-mediated precipitation. Microborings and carbonate fillings were also found beneath the surfaces. Besides the clear signature of biological residues on grains, DNA-based analysis confirmed the presence of bacteria typically associated with carbonate precipitation and microboring, including cyanobacteria (potential endoliths), aerobic heterotrophs and sulphate-reducing bacteria (both potential precipitation inducers). These bacteria were widely distributed throughout the cores, with the highest abundance observed in the upper sediment layers. Overall, the results confirmed the active processes of dissolution and micrite formation within the carbonate sediments of the Al Kharrar lagoon, positioning the site as a valuable modern analogue for Mesozoic micritic limestones. These results advance the understanding of the microbial communities involved in the micritization process and provide useful insights into the evolution of microporosity, a defining characteristic of many Middle Eastern reservoirs.

Microbial impacts on early carbonate diagenesis, particularly the formation of Mg-carbonates at low temperatures, have long eluded scientists. Our breakthrough laboratory experiments with two species of halophilic aerobic bacteria and marine carbonate grains reveal that these bacteria created a distinctive protodolomite (disordered dolomite) rim around the grains. Scanning Electron Microscopy (SEM) and X-ray Diffraction (XRD) confirmed the protodolomite formation, while solid-state nuclear magnetic resonance (NMR) revealed bacterial interactions with carboxylated organic matter, such as extracellular polymeric substances (EPS). We observed a significant carbon isotope fractionation (average δ13C = 11.3‰) and notable changes in Mg/Ca ratios throughout the experiments. Initial medium δ13C was − 18‰, sterile sediments were at 2‰ (n = 12), bacterial-altered sediments were − 6.8‰ (n = 12), and final medium δ13C was − 4.7‰. These results highlight the role of bacteria in driving organic carbon sequestration into Mg-rich carbonates and demonstrate the utility of NMR as a tool for detecting microbial biosignatures. This has significant implications for understanding carbonate diagenesis (dissolution and reprecipitation), climate science, and extraterrestrial research.

In numerous Middle Eastern carbonate reservoirs, peloidal wackestone, packstone, and grainstone facies deposited in shallow-marine environments are rock types with excellent pore storage potential in microporous and micritized grains. While the origin of microporosity has been studied extensively, the process of early marine micritization remains unclear. One hypothesis suggests that early marine micritization first alters carbonate microtextures, which then facilitates the formation of micro spar and micropores in the micritized sediments during later subsurface diagenesis. Therefore, a better understanding of the origin and spatial distribution of micritized sediments is essential for accurately predicting the distribution of microporosity in limestones. This study examines micritization products in shallow-marine carbonate sediments from four lagoons on the Arabian plate coast: the Red Sea, the Arabian Sea, and the Arabian/Persian Gulf. Micritized grains are identified and characterized using optical and backscattered scanning electron microscopy. Petrographic observations are compared and correlated with oceanographic and environmental parameters to identify micritization styles and environmental conditions at a regional scale. The findings present several key insights: i) cryptocrystalline micritic microtextures are heterogeneous, characterized by a combinations of microborings and various microborings infill materials, ii) Red Sea and Arabian Gulf sediments are primarily made up of micritized grains, with about 60% of the grains being micritized grains or peloids, whereas Arabian Sea coast sediments are mainly microbored with minimal infill of endolithic tunnels and rare cryptocrystalline microtextures, and iii) the arid climate and warm, restricted oligotrophic seawater of the Red Sea and Arabian Gulf promote micritization. Conversely, the cooler seawater of the Arabian Sea relative to Red Sea and Arabian Gulf, largely open to the Indian Ocean and influenced by the southeast Asian monsoon and associated upwelling currents, promotes intense endolithic activity but limited boring infilling (incomplete micritization). Hence, we show for the first time that the early marine and microbial diagenetic process of micritization relate to a welldefined set of parameters of a regional environmental and oceanographic settings, corresponding to those that also promote the tropical-biochemical carbonate factory.

Microbial carbonates, and stromatolites in particular, represent the earliest geological record of life on Earth, which dominated the planet as the sole biotic carbonate factory for almost 3 b.y., from the Archean to the late Proterozoic. Rare and sparsely scattered across the globe in the present day, modern “living” stromatolites are typically relegated to extreme environmental niches, remaining as vestiges of a prodigious microbial past. Here, we report the first discovery of living shallow-marine stromatolites in the Middle East, on Sheybarah Island, Al Wajh carbonate platform, on the NE Red Sea shelf (Saudi Arabia). We detail their regional distribution and describe their environmental conditions, internal structures, and microbial diversity. We also report the first discovery of reticulated filaments in a photic setting, associated with these stromatolites. The Sheybarah stromatolites occur in the intertidal to shallow subtidal zones along the seaward-facing beach in three depth-dependent growth forms. Their inner layers were formed by microbially mediated accretion and differential lithification of sediment grains. Compositional microbial analysis revealed the presence of a wide range of microbial life forms.

Micritization is an early diagenetic process that gradually alters primary carbonate sediment grains through cycles of dissolution and reprecipitation of microcrystalline calcite (micrite). Typically observed in modern shallow marine environments, micritic textures have been recognized as a vital component of storage and flow in hydrocarbon reservoirs, attracting scientific and economic interests. Due to their endolithic activity and the ability to promote nucleation and reprecipitation of carbonate crystals, microorganisms have progressively been shown to be key players in micritization, placing this process at the boundary between the geological and biological realms. However, published research is mainly based on geological and geochemical perspectives, overlooking the biological and ecological complexity of microbial communities of micritized sediments. In this paper, we summarize the state-of-the-art and research gaps in micritization from a microbial ecology perspective. Since a growing body of literature successfully applies in vitro and in situ 'fishing' strategies to unveil elusive microorganisms and expand our knowledge of microbial diversity, we encourage their application to the study of micritization. By employing these strategies in micritization research, we advocate promoting an interdisciplinary approach/perspective to identify and understand the overlooked/neglected microbial players and key pathways governing this phenomenon and their ecology/dynamics, reshaping our comprehension of this process.

Abstract In numerous carbonate reservoirs in the Middle East, peloidal packstone‐grainstones are rock types with excellent pore storage potential in micritised microporous grains. However, the origin of the micro‐porosity and associated micro‐spar remains unclear, and one hypothesis is that both micro‐spar and porosity originate from early marine micritisation and were later altered during subsequent diagenesis (i.e. cementation recrystallisation). The south‐eastern coast of the Arabian Gulf is recognised as a modern, albeit miniature, depositional setting analogue to Mesozoic carbonate sequences that form the supergiant reservoirs of the Middle East. Using optical microscopy, backscattered scanning electron microscopy and carbon and oxygen stable isotope analysis the present study aims to document the nature of internal microstructures of micritic envelopes and peloids from the surface sediments of various sub‐environments of the Abu Dhabi Lagoon. Results highlight a high degree of diversity and heterogeneities of most micritic envelopes and peloids observed across the sub‐environments. First, carbonate grains from ooid and bioclastic shoals show the simpler micritic envelopes. Here, micritic envelopes and peloids show sparse microborings filled with banded radial aragonite cement, a pattern of production of cryptocrystalline texture (e.g. micritisation) that is similar to the sequence of micritisation observed in the modern sediment of the Great Bahama Bank. Conversely, in the subtidal and intertidal zones with mangroves or seagrass, the micritic envelopes and peloids are much more complex and show multiple generations of microborings that are either empty or filled with carbonate materials of varying types (i.e. various cements, fragments, etc.).

Biogenic carbonate structures such as rhodoliths and foraminiferal-algal nodules are a significant part of marine carbonate production and are being increasingly used as paleoenvironmental indicators for predictive modeling of the global carbon cycle and ocean acidification research. However, traditional methods to characterize and quantify the carbonate production of biogenic nodules are typically limited to two-dimensional analysis using optical and electron microscopy. While micro-computed tomography (µCT) is an excellent tool for 3D analysis of inner structures of geomaterials, the trade-off between sample size and image resolution is often a limiting factor. In this study, we address these challenges by using a novel multi-scale µCT image analysis methodology combined with electron microscopy, to visualize and quantify the carbonate volumes in a biogenic calcareous nodule. We applied our methodology to a foraminiferal-algal nodule collected from the Red Sea along the coast of NEOM, Saudi Arabia. Integrated µCT and SEM image analyses revealed the main biogenic carbonate components of this nodule to be encrusting foraminifera (EF) and crustose coralline algae (CCA). We developed a multi-scale µCT analysis approach for this study, involving a hybrid thresholding and machine-learning based image segmentation. We utilized a high resolution µCT scan from the sample as a ground-truth to improve the segmentation of the lower resolution full volume µCT scan which provided reliable volumetric quantification of the EF and CCA layers. Together, the EF and CCA layers contribute to approximately 65.5 % of the studied FAN volume, corresponding to 69.01 cm3 and 73.32 cm3 respectively, and the rest is comprised of sediment infill, voids and other minor components. Moreover, volumetric quantification results in conjunction with CT density values, indicate that the CCA layers are associated with the highest amount of carbonate production within this foraminiferal-algal nodule. The methodology developed for this study is suitable for analyzing biogenic carbonate structures for a wide array of applications including quantification of carbonate production and studying the impact of ocean acidification on skeletal structures of marine calcifying organisms. In particular, the hybrid µCT image analysis we adopted in this study proved to be advantageous for the analysis of biogenic structures in which the textures and components of the internal layers are distinctly visible despite having an overlap in the range of CT density values.

Microporosity hosts a significant portion of the total hydrocarbon volume in the Middle Eastern carbonate reservoirs. An improved understanding of microcrystals' morphology that hosts micropores, their impact on reservoir properties, and their spatial distributions will contribute to a more accurate reservoir quality prediction. This study proposes a methodology that utilizes machine learning approaches for microporosity characterization and integrates a multi-scale dataset ranging from micrometer-scale SEM images to meter-scale seismic attributes to build reservoir porosity models. We tested the methodology on an outcrop in Riyadh, Saudi Arabia, which exposes the upper part of the Jubaila Formation equivalent to the lower part of the Arab-D reservoir. We acquired a 35 m-long core and a 600 m-long 2D seismic line behind the outcrop. Laboratory-scale petrophysical measurements, including porosity, permeability, acoustic velocity, bulk and grain density, and x-ray diffraction, were performed over 106 horizontal core plugs drilled from the core. We quantitatively characterized the morphology and microtextures of micrite crystals using SEM images by performing Random Forest classifications trained on SEM image features. We performed unsupervised classification using Self-Organizing Map (SOM) to all lab-measured properties for data clustering. We investigated potential correlations between SOM clusters with micrite morphology, which resulted in a predictable relationship following the granularity and sphericity of microcrystals with porosity, permeability, and acoustic velocity. It was possible to represent multiple lithofacies with a single log-linear porosity-permeability relationship and a single value of equivalent differential effective medium (DEM) aspect ratio in velocity-porosity space. The inter-relationship between micrite morphology and microporosity in the well was then propagated to reservoir-grid scale using inverse differential effective medium (DEM) of acoustic impedance from inverted seismic data. The methodology developed in this study thus provides a practical way to integrate key sub-grid scale micro-and macro-heterogeneities into reservoir scale property models.

Microporous carbonates host a significant portion of the remaining oil-in-place in the giant carbonate reservoirs of the Middle East. Carbonates host wide range of pore sizes, however the key element influencing hydrocarbon flow is pore interconnectivity. We evaluate the use of confocal microscopy to image fluorescent epoxy pore casts of microporous carbonates. The acquired high-resolution 3D confocal images are used to gain invaluable insights on the interconnectivity between macroporosity and microporosity. We analyzed the sensitivity of quality of epoxy pore cast images to: fluorochrome selection, objective lens, and imaging medium by imaging standard fluorescent spherical beads. Guided by the sensitivity results, we acquired 3D images of the multi-modal pore space in an Indiana limestone sample with lateral- and axial-resolution of 0.36 µm and 2 µm, respectively. And we were able to identify the multi-scale pore types in the studied carbonate sample and highlight their interconnectivity.

Petrographic analysis of thin sections is one of the most important and routinely used methods in a wide range of energy, earth and environmental science applications. There has been a growing motivation for computer-aided automated analysis of thin sections due to an inherent subjectivity of interpretation by geologists and the ever-increasing need for analysis of new and legacy thin section petrography data. Particularly in carbonate reservoirs, Dunham texture classification and depositional facies prediction based on thin sections are crucial for accurate description and modeling of the reservoir, but these two tasks are labor and time-intensive and their accuracy is highly dependent on the experience and the ability of the interpreter. This study addresses these challenges by exploring deep learning methods in two case studies to predict Dunham textures and depositional facies using thin sections. The proposed methodologies were applied to 1038 thin sections collected from outcrop analogues of the Upper Jurassic Hanifa Formation. To accelerate the learning speed, transfer learning was applied based on various pre-trained models provided by PyTorch. We used a pre-trained DenseNet model to classify Dunham textures from RGB images of thin sections. For the prediction of depositional facies we applied an integrated methodology consisting of two VGG models and a U-Net model. First, one VGG model was used to classify thin sections into three groups, namely, grain-dominated, mud-dominated, and stromatoporoid facies. Then the U-Net model was used to identify oncoids to further classify oncoidal facies from grain-dominated facies. Finally, another VGG model was applied to classify the peloid-rich facies from the left grain-dominated facies. The Dunham classification model resulted in a prediction accuracy of 89%. The most frequent misclassification was mainly from the identification of packstone, particularly mud-supported packstone, which the model misclassified as wackestone. The depositional facies prediction model achieved final accuracy of 86% where the misclassification was often due to oncoids identification. Overall, the results of this study demonstrate the potential of our proposed workflow to obtain automated prediction of petrographic features from a large number of thin sections. Deep learning offers a promising way forward for rapid and accurate prediction of petrographic features for geoscience applications, while minimizing bias associated with manual interpretation.

Spontaneous imbibition is a fundamental fluid flow mechanismthatplays a significant role in various applications of multiphase fluidflow in porous media, including oil extraction from subsurface reservoirsand underground carbon dioxide storage. Understanding the dynamicsof imbibition, driven by capillary forces across multilayered systems,is essential for designing and optimizing field applications. Laboratoryexperiments with the traditional Amott cell, commonly used to quantifythe imbibition performance by immersing an oil-saturated core plugin water and measuring the extracted oil, do not fully replicate actualreservoir conditions. Under reservoir conditions, imbibition occurswithin the porous formations across different rock types, while inthe Amott cell, imbibition occurs between the rock and the open surroundingwater medium. This misrepresentation of field conditions may not replicatethe true potential of imbibition. In this study, we use micro-CT anddynamic pore-scale imaging as an alternative approach to visualizeand quantify rock-to-rock imbibition within heterogeneous porous media,which cannot be achieved with traditional methods. This work aimsat introducing a new concept to evaluate the imbibition mechanismacross different porous formations, reflecting the conditions of multilayersystems in the subsurface.

Abstract This study focuses on monitoring the velocity of carbonate rocks saturated with brine and supercritical CO2 using a triaxial apparatus. The main objective is to investigate the effects of these fluids on the geophysical properties of carbonate rocks. The specimens were obtained from the Late Jurassic Arab-D formation outcropping in Wadi Daqlah, north of Riyadh, central KSA. This outcrop serves as an analog for the prolific Arab-D subsurface hydrocarbon reservoirs. Ultrasonic transducers were utilized to monitor P and S wave velocities across various stages, including isotropic loading, brine injection, CO2 heating, and the transition from brine to supercritical CO2 fluids. We used coda wave analysis to obtain more accurate first arrival times of ultrasonic data. P and S wave velocities increase with effective stress following a power function. Temperature decreases elastic moduli more than the reduction of bulk mass density. This results in slower P and S wave velocities. P wave velocity drops rapidly for brine-saturated rock that is invaded by supercritical CO2, due to a sudden reduction of elastic properties. Our study demonstrates the complexity of velocity changes resulting from variable fluid saturations, mass densities and elastic moduli.

The presence of hydraulically conductive fracture networks fundamentally affects subsurface fluid flow, the recovery factor and productivity in hydrocarbon carbonate reservoirs. However, methods to directly detect and map the 3D distribution and intensity of fracture networks in the subsurface is difficult. We present a new workflow and methodologies to overcome these limitations utilizing 3D digital photogrammetry on a prominent outcrop of the Late Jurassic (Kimmeridgian) Jubaila Formation near Wadi Laban (Riyadh, Saudi Arabia) as an example. Subsurface equivalents of the Upper Jubaila Formation host major oil and gas accumulations on the Arabian platform. The workflow includes mapping of fracture intensity, distribution and orientation along a 750 m long exposure transect using a high-resolution 3D Digital Outcrop Model (DOM). Results show two main fracture trends, which were E-W and NNE-SSW oriented. The techniques enable computation of fracture intensity directly on the 3D DOM, and quantification of lateral and vertical fracture intensity variability and fracture corridors. The new methodology of 3D DOM analysis, provides a reliable characterization of fractures and fracture intensity that are critical for enhancing 3D reservoir models.

Summary Since the advent of digital rock analysis, there has been a growing need for machine learning methods to analyze multi-scale and multi-modal image data and integrate with traditional formation evaluation methods. In this study, we applied feature augmentation machine learning models for facies identification using well log and multi-scale image data from whole cores. Our main goal is to determine geological and petrophysical facies, and fill in the missing information in their estimation using digitally derived parameters. Incorporation of digitally derived data from whole core CT and thin section petrographs improved the accuracy of the model by up to 80% compared to using conventional well data alone. We apply the model defined on a subset to the entire well, which can further be extended to multiple wells. This study thus provides a systematic workflow for facies prediction that can handle large datasets, including multi-scale image data and conventional well logs, to improve reservoir characterization studies.

Stromatolites are the vestige of first life on earth and were the dominating carbonate forming marine biota in the Archean and Proterozoic. During the course of the Phanerozoic their importance in producing carbonates has been reduced to niche occurrences usually found in challenging environments, such as hypersaline marine settings and alkaline lakes. Most recently, the discovery in 2010 of a new chlorophyll type - chlorophyll f - from stromatolites in Hamelin Pool in Shark Bay, Western Australia has sparked much additional interest in the genesis and composition of modern stromatolites. We report the discovery of stromatolites in the NE Red Sea on Sheybara Island, Al Wajh carbonate platform, KSA. Based on satellite and drone surveys calibrated by site surveys, the Red Sea stromatolites are distributed over an area of about 50,000 m2 in an intertidal to very shallow subtidal setting on a paleo-reef flat facing the open sea. Two principal growth shapes are recognized: (i) elongated rhomboidal structures 10-100 cm in length, up to 5-50 cm in width and up to 10 cm in height and (ii) low relief (height <3 cm) irregular shaped tabular sheets in the shallow subtidal environment. The rhomboidal intertidal stromatolites are pustular on the outside and laminated internally. X-ray CT scanning of the stromatolite samples showed moderately well laminated, millimeter scale, lithified layers potentially representing alternating modes of sedimentation and growth. Scanning Electron Microscopy (SEM) revealed that laminae consist of heavily bored carbonate grains, calcified tubes of filamentous cyanobacteria, mucoid sheets and spider-web like organic matter of likely dehydrated extracellular polymeric substance (EPS). Carbonate precipitates of sub-micron size equant crystals and elongated aragonite needles, either occurring as single rods or in mashes, were also apparent from SEM. Molecular analysis of bacteria diversity show that cyanobacteria dominate the stromatolite surface, while heterotrophic bacteria are the main component in deeper layers. During a sampling campaign in March 2021 salinity, pH and dissolved oxygen have been measured with average values at 42ppt, 7.8±0.1 and 5.9±0.5mg/L, respectively, typical for coastal Red Sea surface marine waters. Water temperatures range from 18°C in the winter to 29°C in the summer. During exposure at low tides surface temperatures over the tidal flats may fall as low as 12°C in the winter exceeding 43°C in the summer. Large numbers of cerithid gastropods were found grazing on the stromatolite surfaces apparently not affecting their growth. Hence, the setting and conditions are overall similar to some of the stromatolites found on the Exuma Islands in the Bahamas, the only other known occurrence of stromatolites in normal marine waters. Research is continuing on the environmental conditions, the aerial distribution, the microbial diversity and chemical composition of these modern stromatolites to determine why they form in this particular location and if they are similar or not to other reported occurrences of stromatolites.

&lt;p&gt;Fracture networks are responsible for channeling flow in subsurface reservoirs (hydrocarbon or geothermal) and markedly impact well productivity and ultimate recovery. Yet, methods to provide fracture (network) distribution at sufficiently high resolution are still lacking &amp;#8211; mainly because subsurface data do not adequately capture natural fractures at the mesoscale (cm to m in size) beyond the well bore. In this study we utilize an outcrop analogue to bridge this scale gap.&amp;#160; Over the last decades 3D digital photogrammetry drastically improved in terms of measurement amount and quality enabling the collection of large data sets over wide outcrops. Such data provide critical insights on depositional and structural heterogeneities that may then be utilized for reservoir analogue simulations. Subject of this study is an outcrop in Wadi Laban located in SW Riyadh, Saudi Arabia, along the Mecca-Riyadh highway. We constructed a reliable 3D Digital Outcrop Model (DOMs) at high resolution of the Late Jurassic (Kimmeridgian) Upper Jubaila Formation following a ~800m long escarpment without any occlusion or bias. In particular we reconstruct a colorized dense point cloud using the high-quality setting of Agisoft Metashape&amp;#169; software. We investigated DOMs with CloudCompare&amp;#169; software (CloudCompare, 2021) to map the visible fractures 3D exposure and infer general fractures pattern. Four fracture sets are evident in the data: the predominant sets 1 and 2 are roughly E-W oriented, while sets 3 and 4 are roughly NNE-SSW oriented. Most fractures are strata bound and sub-vertical in nature. Fracture intensity (P21) analysis along the entire outcrop enables us to describe and quantify lateral and vertical variability. Laterally natural fractures are concentrated in corridors with a spacing of few tens of meters. Vertically, fracture intensity is heterogeneous. Furthermore, we found a strong correspondence between fracture intensity on the outcrop and a porosity log acquired on core samples from a well drilled only a few meters behind the outcrop. The outcome of this study provides a step forward for the comparison of outcrop and subsurface fractures, and expand the application of outcrop data to generate high resolution and fidelity reservoir analogue models.&lt;/p&gt;

Studies on the effects of global marine plastic pollution have largely focused on physiological responses of few organism groups (e.g., corals, fishes). Here, we report the first observation of polymer nanoparticles being incorporated into the calcite skeleton of a large benthic foraminifera (LBF), a significant contributor to global carbonate production. While previous work on LBF has documented selectivity in feeding behaviour and a high degree of specialization regarding skeletal formation, in this study, abundant cases of nanoplastic encrustation into the calcite tests were observed. Nanoplastic incorporation was associated with formation of new chambers, in conjunction with rapid nanoplastic ingestion and subsequent incomplete egestion. Microalgae presence in nanoplastic treatments significantly increased the initial feeding response after 1 day, but regardless of microalgae presence, nanoplastic ingestion was similar after 6 weeks of chronic exposure. While ~ 40% of ingesting LBF expelled all nanoplastics from their cytoplasm, nanoplastics were still attached to the test surface and subsequently encrusted by calcite. These findings highlight the need for further investigation regarding plastic pollution impacts on calcifying organisms, e.g., the function of LBF as potential plastic sinks and alterations in structural integrity of LBF tests that will likely have larger ecosystem-level impacts on sediment production.

Petrographic analysis is one of the most important and routinely used methods in a wide range of earth and environmental science applications. There has been a growing motivation for computer-aided automated analysis of thin sections due to an inherent subjectivity of interpretation by geologists and the ever-increasing need for analysis of new and legacy thin section petrography data. In this study, we propose a workflow for petrographic features prediction of carbonate reservoir including Dunham textures classification and depositional facies prediction based on thin sections using deep-learning. A pre-trained DenseNet model is applied for auto-classification of Dunham textures. An integrated scheme consists of two VGG models and one U-Net model is used for depositional facies prediction. First, one VGG model was used to classify thin sections into three groups, namely, grain-dominated, mud-dominated, and stromatoporoid facies. Then the U-Net model was used to identify oncoids to further classify oncoidal facies from grain-dominated facies. Finally, another VGG model was applied to classify the peloid-rich facies from the left grain-dominated facies. We used 1042 thin sections collected from the Upper Jurassic Hanifa reservoir analog formation, Saudi Arabia, to demonstrate our proposed workflow and the prediction results show that our proposed method performs well in automatically petrographic features prediction. The Dunham classification model resulted in a prediction accuracy of 89%, and the depositional facies prediction model achieved final accuracy of 86%.