Core Samples Research Articles

Conceptual framework for data-driven reservoir characterization: Integrating machine learning in petrophysical analysis

Reservoir characterization plays a critical role in optimizing oil and gas production by accurately determining subsurface properties such as porosity, permeability, and fluid saturation. Traditionally, this process has relied on the interpretation of well logs, seismic data, and core samples using manual or physics-based methods. However, these approaches often face limitations when dealing with complex datasets and heterogeneous reservoir properties. This review develops a conceptual framework for integrating machine learning (ML) into petrophysical analysis to revolutionize reservoir characterization. By utilizing advanced ML algorithms such as supervised learning, unsupervised learning, and deep learning, this model demonstrates the potential of data-driven methods to improve the accuracy and efficiency of reservoir analysis. The framework focuses on how ML techniques can automate the interpretation of well logs, enhance seismic data processing, and predict rock properties from core samples, leading to more accurate identification of reservoir boundaries and productive zones. This approach not only reduces interpretation errors but also accelerates decision-making in reservoir management. Moreover, machine learning can handle the vast amounts of data generated in reservoir studies, enabling real-time analysis and adaptive decision support. The study also addresses challenges such as data quality, model interpretability, and computational scalability, offering insights into future applications of ML in reservoir characterization. By combining petrophysical expertise with machine learning capabilities, this framework aims to significantly advance the field of reservoir characterization and contribute to more efficient resource management in the oil and gas industry.

High-precision digital rock construction and electrical property upscaling in tight sandstone

The lithology and rock physical properties of tight sandstone reservoirs become increasingly complex, resulting in a more challenging reservoir evaluation process. Conventional rock physics experiments are too restrictive to adequately investigate formation properties. The emerging digital rock physics (DRP) technology in recent years effectively overcomes these constraints. Nevertheless, the current DRP faces two major challenges: the integration of multiple-resolution images and the upscaling of rock physics properties (electricity). In this paper, we propose an innovative method for assigning electrical characteristics to voxel units, enabling the integration of multi-resolution images, as well as a novel approach for establishing a new saturation model to achieve electrical property upscaling. Initially, core samples are selected and drilled based on logging data and lithology analysis, followed by multi-resolution scanning experiments, including X-ray CT, QEMSCAN, and MAPS. After that, mineral components are segmented, and multi-component digital rocks are constructed. Considering mineral voxel as the analytical units, pore characteristics within each mineral, which are extracted from MAPS, are used to generate 3-D pores by QSGS method. These generated 3-D pores are then merged with their respective mineral voxel. Subsequently, the electrical properties of the merged models are simulated and assigned to multi-component digital rock. This process enables the integration of multi-resolution images, leading to the construction of high-precision digital rocks. Additionally, high-precision digital rock-based electrical modeling is conducted, and a new saturation model is then established by combining digital rock physics, experiment rock physics, and theoretical rock physics. Finally, the new saturation model is applied to calculate saturation, accomplishing electrical property upscaling. Application results show the new saturation model improves the accuracy of saturation evaluation, demonstrating the feasibility of the upscaling method for electrical properties.

Sustainable application of recycled plastics in asphalt pavement: Case study of a trial in Newtonville, Ontario, Canada

Global research on using plastic waste in asphalt roads highlights its benefits: recycling waste, reducing greenhouse gas emissions, and providing an alternative to non-renewable asphalt binders. This study aims to bridge the gap between laboratory results and field performance of recycled plastic-modified asphalt in cold regions, promoting broader adoption. Laboratory analysis of core samples was performed as part of a quality assurance program. High-temperature rutting resistance was evaluated using the Hamburg wheel tracking test, while low-temperature cracking resistance was assessed through semi-circular bending testing. Results show that the impact of recycled plastics and fibers on rutting resistance varies with temperature, with the greatest benefit at the highest temperatures. For low-temperature cracking resistance, polyethylene terephthalate (PET) fibers outperform mixed plastics by delaying crack propagation. Low-temperature conditioning can induce thermal shrinkage in the asphalt mixture, slightly moderating the effects of recycled plastics and fibers.

Role of macroscopic on-site evaluation of endoscopic ultrasound-guided fine-needle aspiration/biopsy: Results of a multicentric prospective study

BACKGROUND The concept of macroscopic on-site evaluation (MOSE) was introduced in 2015 when the endoscopist observed better diagnostic yield when the macroscopically visible core on MOSE was superior to 4 mm. Recent studies suggest that MOSE by the endoscopist may be an excellent alternative to rapid on-site evaluation, and some classifications have been published. Few studies have assessed the adequacy of histologic cores in MOSE during endoscopic ultrasound-guided fine-needle aspiration/biopsy (EUS-FNA/FNB). AIM To evaluate the performance of MOSE during EUS-FNA/FNB. METHODS This multicentric prospective study was conducted in 16 centers in 3 countries (Egypt, Iraq, and Morocco) and included 1108 patients with pancreatic, biliary, or gastrointestinal pathology who were referred for EUS examination. We prospectively analyzed the MOSE in 1008 patients with available histopathological reports according to 2 classifications to determine the adequacy of the histological core samples. Data management and analysis were performed using a Statistical Package for Social Sciences (SPSS) version 27. RESULTS A total of 1074 solid lesions were biopsied in 1008 patients with available cytopathological reports. Mean age was 59 years, and 509 patients (50.5%) were male. The mean lesion size was 38 mm. The most frequently utilized needles were FNB-Franseen (74.5%) and 22 G (93.4%), with a median of 2 passes. According to 2 classifications, 618 non-bloody cores (61.3%) and 964 good samples (95.6%) were adequate for histological evaluation. The overall diagnostic yield of cytopathology was 95.5%. The cytological examination confirmed the diagnosis of malignancy in 861 patients (85.4%), while 45 samples (4.5%) were inconclusive. Post-procedural adverse events occurred in 33 patients (3.3%). Statistical analysis showed a difference between needle types (P = 0.035) with a high sensitivity of FNB (97%). The analysis of the relationship between the MOSE-score and the final diagnosis showed a significant difference between the different scores of the MOSE (P < 0.001). CONCLUSION MOSE is a simple method that allows endoscopists to increase needle passes to improve sample quality. There is significantly higher FNB sensitivity and cytopathology diagnostic yield with good MOSE cores.

Repurposing of PSMA-targeted diagnostic and therapeutic agents for the detection and treatment of giant cell tumors of bone

Giant cell tumor of bone (GCTB) is a rare bone tumor often necessitating surgical intervention, radiation therapy, or treatment with bisphosphonates or denosumab. 99mTc-MDP bone scintigraphy for GCTB has limited specificity, and the relatively high uptake of 18F-FDG in GCTB makes it challenging to differentiate it from other benign bone tumors. More specific detection and treatment modalities for GCTB are needed to enhance patient monitoring and outcomes. Prostate Specific Membrane Antigen (PSMA) is present in the neovasculature of various tumors, yet unexplored in GCTB. PSMA-targeted imaging and radiotherapeutic agents Locametz and Pluvicto are a powerful theranostic pair for detecting and treating PSMA-positive metastatic tumors, including those in bone, and thus have considerable potential to be repurposed for GCTB. This study aimed to determine if the vasculature of GCTB was PSMA-positive and whether targeting it with PSMA-specific agents was feasible. Using bone core samples from 28 GCTB patients and 9 negative controls, we present the first robust detection of PSMA on the tumor vasculature of GCTB. To demonstrate the potential repurposed use of PSMA-targeted agents in detecting and treating GCTB, we used a PSMA-specific fluorescent probe (FAM-C6-1298) as a model for these radiopharmaceutical agents. Incubation of fresh GCTB tissue samples with FAM-C6-1298 showed increased fluorescence intensity compared to controls, indicating successful targeting of PSMA in GCTB tissue. In conclusion, our data established that PSMA is not only present in the tumor vasculature of GCTB patient tissue but can be effectively targeted with repurposed PSMA-specific radiopharmaceuticals for diagnosis and therapy.

IMPROVING THE RELIABILITY OF THE THREE-DIMENSIONAL GEOLOGICAL BASIS OF COMPLEX DEVELOPMENT TARGETS

This article proposes a new method for identifying different groups of reservoirs when constructing a three-dimensional geological and hydrodynamic model of the target. Productive formations of the Bajocian and Bathonian stages of the Jurassic system of the Kalamkas field were selected as the object of study. This field is tectonically associated with the northern part of the North Buzachinsky arch, located at the northwestern end of the Turan plate. It was noted that these productive deposits have a complex geological structure, due in lithological terms to uneven alternation of sand-siltstone and clay rocks. In addition, four different reservoir classes are identified within each tier, which are characterized by their own permeability-porosity correlation relationships. As a result, for reliable construction of the filtration field, it is necessary to determine the distribution zones of each of the identified reservoir classes within the reservoir. This is due to the fact that due to the different filtration field, the development of reserves from each group of reservoirs will occur unevenly both in area and in section. In this regard, the integrated Flow Zone Indicator (FZI) was evaluated using the results of laboratory core studies. In order to obtain a continuous FZI parameter for wells without core sampling and laboratory analysis, the results of laboratory studies were compared with the results of well geophysical studies (well logging). As a result, analytical dependence of FZI and log data was obtained. Based on this analytical dependence, new three-dimensional models were constructed and zones of distribution of different groups of reservoirs were identified. To verify the obtained results, the synthetic FZI curve calculated from the obtained analytical relationship was compared with the FZI estimate based on the results of laboratory core studies. It is concluded that the synthetic FZI curve is consistent with the FZI estimate based on the results of laboratory core studies and, therefore, the proposed approach can be used when performing work related to 3D modeling.

EPCO-55. SPATIAL HETEROGENEITY IN THE TUMOR CORE AND INFILTRATING MARGIN OFIDHWT GLIOBLASTOMA

Abstract We integrated single-cell RNA-sequencing, Visium spatial transcriptomic, and Xenium in situ transcriptomic data to identify differences in cell composition between the tumor core and infiltrating edge in IDHWT glioblastoma (GBM). Using CellTrek, an algorithm that maps single cells to their most-likely locations in spatial transcriptomic data, we identified extensive spatial organization in GBM. Cell mapping revealed distinct regions enriched for tumor, immune, endothelial, neuronal, and glial cells. These were associated with the location of the tissue sample – edge regions were enriched for neurons and glia (as expected), while core regions were enriched for tumor cells and infiltrating immune cells, particularly macrophages. Within the core samples, the tumor and immune cells tended to occupy distinct spatial niches. Spatial clustering of mapped tumor cells across all samples revealed five spatially segregated tumor cell states, each associated with specific biological processes, including (1) histone methylation, (2) proliferation, (3) wound-healing, EMT, and antigen presentation, (4) hypoxia response, and (5) stem cell differentiation. These tumor cell states exhibited preferential colocalization with specific non-malignant cell types. For instance, antigen-presenting/wound-healing tumor cells tended to colocalize with diverse immune cells, while stem-like tumor cells tended to reside in neuron-rich regions at the tumor edge. Next, to identify ligand-receptor interactions governing this spatial organization, we analyzed known ligand-receptor pairs within all three data types. This revealed known and novel mediators of tumor cell interactions with vasculature, lymphoid cells, myeloid cells, neurons, and glia. While some interactions were shared across all tumor cell states (e.g. all tumor cell states interacted with myeloid cells via APP/CD74 interactions), others were specific to particular tumor cell states (e.g. wound-healing/antigen-presenting tumor cells interacted with macrophages via CSF1/CSF1R, while proliferating and stem-like tumor cells interacted with neurons through multiple known NRXN/NLGN interactions). These interactions suggest potential targets for clinical disruption of the tumor ecosystem.

EPCO-07. INTEGRATING SPATIALLY RESOLVED MULTI-OMICS DATA TO UNCOVER DYSFUNCTIONAL METABOLISM DRIVEN NETWORKS THAT ENHANCE INFILTRATION OF DIFFUSE GLIOMAS

Abstract BACKGROUND Diffuse infiltration is an aggressive feature of high-grade gliomas with survival implications. The contribution of crosstalk between non-neoplastic and neoplastic cells to tumor infiltration remains largely understudied due to the lack of profiling techniques that retain spatial information. Spatial multi-omic profiling is a promising approach to comprehensively analyze transcript-omics, prote-omics and metabol-omics on the same tissue section while preserving information about the spatial organization of cells. Integration of these spatial studies allows for inferring the consequences of complex cell-cell communication underlying tumor infiltration. We hypothesize that the glioma edge is enriched with pro-infiltration ligands-receptors driving tumor infiltration. Strategies that disrupt these ligand-receptor networks may suppress glioma infiltration and improve clinical outcomes. METHODS We utilized a glioma tissue micro-array (TMA) to establish the neoplastic and non-neoplastic heterogeneity in the GBM infiltration edge. Each TMA slide consists of pathologist annotated tumor core and edge samples of Glioblastoma (IDH Wildtype, WHO Grade 4; n=10), Diffuse Astrocytoma (IDH Mutant, WHO Grade 3; n=3), Oligodendroglioma (IDH mutant, WHO Grade 2; n=5) and 2 non-brain control samples. We performed spatial multi-omics profiling on adjacent sections of the TMA using 10x Xenium spatial transcriptomics, imaging mass cytometry (IMC) and mass spectrometry imaging (MSI). Integration, visualization, and quantification of the spatial data was done on VisioPharm. RESULTS In GBM, we identified candidate mRNA transcripts and proteins for ligands enriched at the infiltrating edge (compared to tumor core; p<0.0001) that correlated with a poor progression free survival (PFS; r2=0.22). These candidate ligands were also significantly enriched at the edge of oligodendroglioma WHO Grade 2 and astrocytoma WHO Grade 3. CONCLUSIONS Spatial multi-omics profiling on a TMA consisting of Glioma WHO grades 2-4 identified differentially expressed and targetable pro-tumor infiltration ligands associated with lower PFS in GBM. Functional studies to uncover the role of metabolites enriched at the glioma edge for the expression of the identified pro-infiltration ligands are ongoing.

NEW ASPECTS OF CORE MICROTOMOGRAPHY

Compared to many other technologies of laboratory studies of core samples, the modern «digital core» technology is a rather young scientific and industrial direction, but for decades it has attracted close attention of a wide range of scientists and specialists of the oil and gas industry (geologists, sedimentologists, lithologists, geophysicists, developers, geological modelers, hydrodynamicists, physicists). For many specialists of practical sphere new methodical techniques and methods of such researches remain little known. If earlier computer X-ray microtomographs in the Russian Federation were the prerogative of only academic structures or service organizations, nowadays many subsoil users have started to equip their own laboratory and research centers with the corresponding specialized equipment, understanding that without these data it is hardly possible to build high-quality geological, petrophysical, tectonic-physical, rheological, hydrodynamic and geological-field models of the studied sediments, as well as to prepare progragmatic models of the studied sediments. In this article the authors made an attempt to study and analyze the current state of development of «digital core» technology.Using extensive factual material the accumulated experience of special studies in the field of full-size core tomography is summarized, estimates of absolute permeability of low-permeability complex sediments are made, results of studying cores in frozen and unfrozen state are given, detailed analysis of fracturing at different stages of sample preparation and ongoing studies is carried out, and specific problems for which a solution using the technology is expected in the near future are formulated In this article the positive and negative sides of poroset and direct methods of modeling of filtration properties by digital core model are studied. Particular attention is paid to the need for large-scale studies to obtain statistically reliable data samples, which can be used to select the most qualitative methods of research and application of algorithms for processing of initial geological and geophysical information.

Conceptual advances in petrophysical inversion techniques: The synergy of machine learning and traditional inversion models

Petrophysical inversion techniques are essential for estimating reservoir properties such as porosity, permeability, and fluid saturation, which are critical for optimizing hydrocarbon recovery and reservoir management. Traditionally, deterministic and stochastic inversion methods have relied on well log data, core samples, and seismic information to generate models of subsurface properties. However, these techniques face limitations in handling large datasets, nonlinear relationships, and uncertainties in complex reservoir environments. Recent advances in machine learning (ML) offer an opportunity to enhance these traditional methods by providing a data-driven approach capable of learning patterns and refining inversion models. This review explores the potential synergy between traditional petrophysical inversion techniques and machine learning algorithms to create a new paradigm for reservoir parameter estimation and analysis. Machine learning’s ability to process large volumes of diverse data and capture nonlinear relationships complements the physics-based constraints of traditional inversion methods. By combining these two approaches, hybrid models can provide improved accuracy in predicting reservoir properties and handling data gaps or uncertainties. The review discusses specific case studies where machine learning-assisted inversion models have been successfully applied, such as the estimation of porosity and permeability, seismic inversion for lithology identification, and fluid saturation modeling. These hybrid models demonstrate enhanced predictive accuracy, faster computational efficiency, and real-time adaptability, transforming reservoir characterization and management. The integration of machine learning with traditional inversion techniques represents a major advancement in petrophysical analysis. This synergy holds the potential to revolutionize reservoir parameter estimation, making it more accurate, robust, and responsive to real-time data, ultimately improving decision-making in reservoir engineering and maximizing hydrocarbon recovery. Keywords: Petrophysical Inversion, Machine Learning, Traditional Inversion, Models.

Unveiling the influence of tumor and immune signatures on immune checkpoint therapy in advanced lung cancer.

This study investigates the variability among patients with non-small cell lung cancer (NSCLC) in their responses to immune checkpoint inhibitors (ICIs). Recognizing that patients with advanced-stage NSCLC rarely qualify for surgical interventions, it becomes crucial to identify biomarkers that influence responses to ICI therapy. We conducted an analysis of single-cell transcriptomes from 33 lung cancer biopsy samples, with a particular focus on 14 core samples taken before the initiation of palliative ICI treatment. Our objective was to link tumor and immune cell profiles with patient responses to ICI. We discovered that ICI non-responders exhibited a higher presence of CD4+ regulatory T cells, resident memory T cells, and TH17 cells. This contrasts with the diverse activated CD8+ T cells found in responders. Furthermore, tumor cells in non-responders frequently showed heightened transcriptional activity in the NF-kB and STAT3 pathways, suggesting a potential inherent resistance to ICI therapy. Through the integration of immune cell profiles and tumor molecular signatures, we achieved an discriminative power (area under the curve [AUC]) exceeding 95% in identifying patient responses to ICI treatment. These results underscore the crucial importance of the interplay between tumor and immune microenvironment, including within metastatic sites, in affecting the effectiveness of ICIs in NSCLC.

Unveiling the influence of tumor and immune signatures on immune checkpoint therapy in advanced lung cancer

This study investigates the variability among patients with non-small cell lung cancer (NSCLC) in their responses to immune checkpoint inhibitors (ICIs). Recognizing that patients with advanced-stage NSCLC rarely qualify for surgical interventions, it becomes crucial to identify biomarkers that influence responses to ICI therapy. We conducted an analysis of single-cell transcriptomes from 33 lung cancer biopsy samples, with a particular focus on 14 core samples taken before the initiation of palliative ICI treatment. Our objective was to link tumor and immune cell profiles with patient responses to ICI. We discovered that ICI non-responders exhibited a higher presence of CD4+ regulatory T cells, resident memory T cells, and TH17 cells. This contrasts with the diverse activated CD8+ T cells found in responders. Furthermore, tumor cells in non-responders frequently showed heightened transcriptional activity in the NF-kB and STAT3 pathways, suggesting a potential inherent resistance to ICI therapy. Through the integration of immune cell profiles and tumor molecular signatures, we achieved an discriminative power (area under the curve [AUC]) exceeding 95% in identifying patient responses to ICI treatment. These results underscore the crucial importance of the interplay between tumor and immune microenvironment, including within metastatic sites, in affecting the effectiveness of ICIs in NSCLC.

Delineation of the reservoir petrophysical parameters from well logs validated by the core samples case study Sitra field, Western Desert, Egypt.

In the northern section of the Western Desert, there are many extremely profitable petroleum and natural gas deposits in the Abu EL-Gharadig Basin. This study aims to highlight the hydrocarbon potential of Abu Roash F Formation, which stands for high organic content unconventional tight reservoirs, and Abu Roash G Formation which stands for conventional sand reservoirs, in Sitra field located in the central-western part of the Abu EL-Gharadig Basin. The research employed well-log data from four wells to ascertain petrophysical properties combined with core samples of two wells for a comprehensive examination and description of lithology. Initially, we commenced the execution of petrophysical analysis, encompassing log quality control procedures. Subsequently, we identified and revealed zones of interest and hydrocarbon indicators in both formations. Additionally, we ascertained the three most influential parameters, shale Volume, effective Porosity, and water saturation, which serve as defining factors for reservoir quality. Subsequently, an examination of the core samples, which encompassed lithologic description, lithofacies analysis, paleoenvironmental interpretation, petrographic analysis, and porosity assessment is conducted. For the sake of a more accurate interpretation, we conclude our research with cartographic maps created to evaluate the geographical distribution of hydrocarbon potential based on petrophysical characteristics, Distribution of the net-to-gross ratio among wells by correlating the litho-saturation models (rock models) for the four wells. The foregoing results declare that The Abu Roash F carbonate-rich rocks are a contender for unconventional tight oil reservoir potential with thin secondary porosity and high organic content, which normally requires a kind of hydraulic fracturing for prospective oil extraction, Furthermore, the upper section of Abu Roash G formation, particularly in well sitra8-03, has highly favorable conventional reservoir characteristics.

Exploring frictional properties of upper plate fault reactivation in subduction zones: The Atacama Fault System in northern Chile

The Maule 2010 and Tohoku-Oki 2011 earthquakes demonstrated how dormant upper plate faults can be reactivated as normal faults by plate margin relaxation following megathrust slip. However, the reactivation mechanisms of these types of faults are yet unexplored. To provide a better understanding of these mechanisms, we collected fault core samples from fault segments of the Atacama Fault System in northern Chile. The sampled fault segments have clear morphological evidence of Quaternary reactivation as normal fault. We performed laboratory experiments to measure the fault strength, stability and dynamic weakening. We consider low-velocity tests for exploring the frictional strength and velocity dependence of friction via a double-direct shear apparatus and ii) high-velocity tests for investigating the frictional properties at seismic velocities via a rotary shear apparatus. The experiments revealed that fault cores have low frictional strength, velocity-strengthening behaviour and strong dynamic weakening. Additionally, a novel experimental procedure that simulates stress relaxation by stepwise reducing of the normal stress on the sample assembly showed: 1. Accelerating creep towards dynamic weakening in chlorite-rich gouges and 2. oscillatory sliding in fault gouges enriched in illite. By extrapolating our experimental observations to natural conditions, we conclude that stable sliding is favoured during the interseismic phase of the subduction earthquake cycle, whereas unstable sliding is favoured during the coseismic and postseismic phases. The latter occurs via normal stress reduction during the shift from interseismic compression to co- and postseismic tension at the plate margin.

Radiocarbon ageing of Indigenous culturally‐significant trees for bushfire management on Minjerribah (North Stradbroke Island), southeast Queensland

SummaryPartnerships between Indigenous People and governments for joint management of ancestral lands, designated as protected conservation areas for biodiversity and cultural heritage, provide an opportunity to leverage accumulated traditional land management knowledge with emerging science. In Australia, veteran or large old trees in natural landscapes are of significant ecological importance, and among the Indigenous communities, they are living monuments to historical cultural practices, but their survival, endurance and long lives are rarely acknowledged to afford them protection. In sub‐tropical Australia, the lack of consistent annual growth rings in stem wood makes it difficult to estimate the age of large trees using methods such as the periodic diameter increment. Ngugi et al., 2020 published radiocarbon (14C) dates using wood core (‘pith‐wood’) samples from 12 Indigenous culturally‐significant trees covering five species on Minjerribah (North Stradbroke Island). Due to the imprecise calendar age results of the single samples used in the original study (Ngugi et al., 2020), subsequent radiocarbon dating of an additional three wood core samples from each tree was undertaken to age the trees more precisely. The revised tree ages ranged from 63 to 531 years and suggest an important role of past Indigenous land management practices into protecting Bugari (Cypress Pine, Callitris columellaris F. Muell.) from deadly crown scorching fires. These results underscore the importance of incorporating Indigenous practices in current fire management strategies and plans. Estimating tree ages based on the periodic diameter increment method overestimated the age relative to that derived from radiocarbon dating. New bias correction factors were developed for adjusting the recorded periodic tree diameter increments.

Unlocking underground hydrogen storage potential: Geochemical characterization of North Dakota's geological formation

Underground hydrogen storage offers a promising solution for sustainable energy, with geologic formations such as salt caverns, aquifers, and depleted hydrocarbon reservoirs identified as suitable options for safely storing hydrogen. Some studies have been carried out to investigate hydrogen-rock-fluid interactions. However, none have simultaneously combined experimental analysis and geochemical modeling of the equilibrium and kinetic reactions using a well-characterized North Dakota carbonate formation as a case study to investigate its geochemical characterization and implications. This research explores the mineralogical and fluid composition of specific depleted hydrocarbon reservoirs in North Dakota while investigating the intricate geochemical interplays between hydrogen gas, fluid, and rock formations. This study focused on the Red River Formation, a primary hydrocarbon-producing entity in North Dakota, to unveil its potential for underground hydrogen storage (UHS). This formation's stratigraphic complexity and extensive hydrocarbon yield underscores its significance. We conducted experiments combining analytical tests and geochemical modeling using core samples from the Red River Formation. X-ray diffraction (XRD), X-ray fluorescence (XRF) spectroscopy, and scanning electron microscopy (SEM) techniques were employed to decipher mineralogical and elemental constituents. This study covers areas such as the Bicentennial, Coyote Creek, Horse Creek, and Medicine Poll Hills Fields and identified common minerals such as calcite, dolomite, anhydrite, and quartz, along with essential elements such as oxygen, magnesium, aluminum, and silicon. Geochemical simulations were also performed to evaluate the equilibrium and kinetic reactions expected during hydrogen gas injection into this formation. Our findings revealed stable mineral phases, the absence of detrimental environmental impacts, promising prospects for hydrogen storage in the studied carbonate reservoirs without geochemical alterations and promising a sustainable pathway toward net-zero carbon emissions.

Prostate-specific antigen, digital rectal examination, and prostate cancer detection: A study based on more than 7000 transrectal ultrasound-guided prostate biopsies in Ghana

Purpose of the studyThis study aims to determine the role of serum prostate-specific antigen (PSA) levels and digital rectal examination (DRE) in predicting the histological outcomes of prostate biopsies by analyzing a database of over 7000 patients who underwent transrectal ultrasound (TRUS)-guided prostate biopsies. MethodsWe conducted a retrospective analysis of men who underwent TRUS-guided prostate biopsies at Korle Bu Teaching Hospital, a tertiary referral center in Accra, Ghana, from July 2005 to December 2022. The biopsies, which included 10 to 12 core samples, were prompted by PSA levels greater than 4.0 ng/mL, abnormal DRE findings, or both. We then correlated histopathology results with PSA and DRE findings. ResultsOut of 7,338 patients who presented for biopsy, 76.3% were between the ages of 60 and 79. Histology reports were available for 5,289 patients, of whom 2,564 (48.5%) were diagnosed with prostate cancer. Cancer detection rates based on PSA levels were as follows: 21.6% for PSA <4 ng/mL, 21.7% for PSA 4-10 ng/mL, 32.7% for PSA 10-20 ng/mL, 53.0% for PSA 20-50 ng/mL, 71.5% for PSA 50-100 ng/mL, and 92.0% for PSA >100 ng/mL. When DRE findings were classified according to the 2016 TNM System (AJCC 8th Edition) as T1, T2, T3, and T4, cancer detection rates were 26.8%, 51.8%, 87.6%, and 95.7%, respectively. The overall cancer detection rate was significantly higher with abnormal DRE findings (64.6% vs. 26.7%, p < 0.001). Additionally, 78.2% of the detected cancers were high-grade (Gleason score of 7 or more). ConclusionThis extensive study of Ghanaian men undergoing TRUS biopsies reveals a high prostate cancer detection rate, with nearly 80% of the detected cancers being high-grade. These findings underscore the importance of PSA and DRE in the early detection of prostate cancer and should be considered in patient counseling and discussions regarding the implementation of prostate cancer screening programs in this population.

Analyzing the Mineralogy and Space Weathering Characteristics of the Finest Fraction in Apollo Core Sample 73002

AbstractApollo 17 core sample 73001/2 was recently made available to researchers for analysis using state‐of‐the‐art techniques in the framework of a modern understanding of lunar surface processes. In this work, we employ transmission electron microscopic analysis to observe the mineralogy, microstructural, and chemical characteristics of space weathering and solar energetic particle (SEP) track distribution in soil grains in the &lt;20 μm size fraction in core sample 73002. The modal mineralogy and stratigraphic space weathered grain abundance suggests that a geologically recent mixing event affected the top 3 cm of 73002. Surface exposure age distributions derived from SEP tracks demonstrate that individual regolith grains rarely reside on the surface for longer than ∼4 million years. The abundance of surface exposed monomineralic fragments with respect to depth correlates well with bulk measurements of space weathered soils using other techniques, such as ferromagnetic resonance. Exposure age distributions suggest the presence of two unique in situ reworking zones spanning the top 8 cm of the core and median exposure ages decrease with increasing depth for both reworking zones, albeit at different rates. These rates were compared to reworking models and suggest a relationship between median exposure age and reworking rate with respect to depth. Applications of modern transmission electron microscopy to core sample 73001/2 have proven useful in understanding lunar regolith evolution both within the context of the Apollo 17 field site and more broadly via in situ reworking.

Image-driven prediction system: Automatic extraction of aggregate gradation of pavement core samples integrating deep learning and interactive image processing framework

Aggregate gradation plays a crucial role in determining the asphalt pavement performance, necessitating assessment post-construction. Additionally, efficient gradation measurement is vital for the design and construction of reclaimed pavement. This study proposes a methodology for predicting gradation based on imagery. Initially, a scanning device is devised to capture unfolded side surface images of pavement core samples. Subsequently, an interactive software named PyImageJ is developed for image post-processing and acquiring aggregate feature information. A morphological model is then introduced to determine the mass ratios among various grading levels of coarse aggregates with nominal particle sizes greater than 2.36 mm. Finally, sequence prediction models are established to forecast the mass ratios among different grading levels of fine aggregates with nominal particle sizes smaller than 2.36 mm. Simple conversion of the mass ratios can yield the gradation. Experimental results indicate that the Long Short-Term Memory (LSTM) network offers more accurate predictions for the mass ratios of fine aggregates in stone mastic asphalt (SMA-13), with an R-squared value of 0.8985. Conversely, one-dimensional convolutional neural networks (1D CNN) are better suited for predicting the fine aggregate mass ratios in asphalt concrete (AC-20 and AC-25), with R-squared values of 0.9010 and 0.8824, respectively. Visualization results demonstrate that the predicted complete gradation closely aligns with the actual values (R-squared > 0.99), opening a new avenue for evaluating pavement quality.

CT-guided Coaxial Lung Biopsy: Number of Cores and Association with Complications.

Background Percutaneous CT-guided lung core-needle biopsy is a frequently performed and generally safe procedure. However, with advances in the management of lung cancer, there is a need for a greater amount of tissue for tumor genomic profiling and characterization. Purpose To determine whether the number of core samples obtained with percutaneous CT-guided lung biopsy is associated with postprocedural complications. Materials and Methods This retrospective study included consecutive patients who underwent percutaneous CT-guided coaxial lung core-needle biopsy for suspected primary lung cancer between November 2012 and August 2023 at an academic tertiary referral hospital. Patient data from medical records were collected, including demographics, lesion size and distance from pleura, and number of obtained biopsy samples. Postprocedural complications of pneumothorax, chest tube placement, perilesional hemorrhage, and hemoptysis were recorded. Multivariable logistic regression models were used to assess whether the number of cores was a predictive factor for lung biopsy complications. Results A total of 827 patients (mean age, 70.9 years ± 9.6 [SD]; 474 [57.3%] female patients) were included. The median lesion size was 22 mm (IQR, 15-34 mm), with 517 of 827 (62.5%) patients diagnosed with lung adenocarcinoma. Pneumothorax was noted in 171 of 827 (20.7%) patients, with a chest tube placed in 32 of 827 (3.9%), perilesional hemorrhage in 353 of 827 (42.7%), and hemoptysis in 20 of 827 (2.4%) patients. The median number of samples obtained was four (range, one to 12). Multivariable analysis showed no evidence of an association between the number of core samples obtained and any complications: pneumothorax (coefficient, -0.02; P = .81), chest tube (coefficient, 0.18; P = .26), perilesional hemorrhage (coefficient, -0.03; P = .63), or hemoptysis (coefficient, -0.10; P = .60). Conclusion In patients suspected of having lung cancer who underwent percutaneous CT-guided coaxial lung core biopsy, there was no evidence of an association between the number of core biopsy samples obtained and any postprocedural complications. © RSNA, 2024 See also the editorial by Zuckerman in this issue.