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PRACTICE IMPACT STATEMENTThe current study investigated to what extent personalised information on interrelated risk-relevant behavioural, psychological, and contextual features obtained using experience sampling method (ESM) can be deployed to inform forensic case formulations of adult men with a history of sexual offences. Five adult men in outpatient forensic treatment for committing sexual offences monitored personal risk-relevant features using ESM and discussed the resulting feedback report with their therapist. Data were collected using the Twente engagement with Ehealth technologies Scale, an online questionnaire, a semi-structured interview and log data. Participants reported increased awareness of personal patterns of risk-relevant features and their possible association with the risk of sexual reoffending. The participants did not perceive the ESM procedure as burdensome or intrusive. Obtaining personalised information on risk-relevant features by ESM was feasible and achievable for adult men with a history of sexual offences. Insights derived from ESM measurements could enhance traditional forensic case formulation.Combining experience sampling method (ESM) and traditional forensic case formulation can help adult men with a history of sexual offences to better understand patterns in their risk-relevant characteristics. A more thorough understanding of such patterns may improve treatment plans focused on desistance of sexual reoffending. [ABSTRACT FROM AUTHOR]

This paper is aimed at introducing a method for the division of development units of thick carbonate reservoirs. This method consists of eight steps, ranked according to impact weight, each of which is independent but intrinsically linked. When there is a conflict between studies in different steps, the results of the previous step take precedence. (1) Pressure is the most important and reliable data. When the wells with an abnormal pressure gradient in the same interval account for more than 60%, further division of the reservoir is appropriate. (2) Baffles are the second most important and reliable basis. When there are continuous baffles or poor reservoirs that encounter more than 80% wells, it is appropriate to further divide reservoirs. (3) Without the two mentioned above, but with an unconformity surface or discontinuity surface between two sequences, it is appropriate to treat these two sequences as two development units. (4) Without the three mentioned above, if the permeability above and below the sequence boundary differs by 10 or more times, reservoirs above and below the sequence boundary are appropriate as distinct development units. (5) When the thickness, continuity, distribution pattern, and microstructure of two adjacent sequences are significantly different, it is appropriate to treat these two sequences as two development units. (6) If the development units are based on sequences, check for homogeneity within the stratigraphy. If depositional facies or physical property discontinuities are present, the sequence would be reconsidered for subdivision. (7) The reserves in each development unit should account for more than 20% of that of the whole reservoir. (8) When the division of the development units is complete, it needs to be examined to ensure that there is a consistent structure, fluid property, and free water level. If inconsistencies exist, then subdivision is considered. The method was successfully applied to A oilfield, Central Arabian Basin, in combination with the cores, cast thin sections, regular or special core analysis, wireline logging data from 450 wells, formation pressure from 63 wells, and more than 100 wells with a production logging test. The Mishrif reservoir was divided into four development units, in which different well patterns and well types were deployed. The improved development effect proves that the division of development units for thick bioclastic limestone reservoir is reasonable. [ABSTRACT FROM AUTHOR]

Machine learning (ML) methods are among the most promising technologies with wide-ranging research opportunities, particularly in the field of education, where they can be used to enhance student learning outcomes. This study explores the potential of machine learning algorithms to build and train models using log data from the "3D Modeling" e-course on the Moodle platform at TTK University of Applied Sciences, Tallinn, Estonia. By predicting trends, identifying patterns, and optimizing resource allocation, machine learning can improve the efficiency of e-learning and provide students with tailored recommendations for acquiring relevant knowledge and skills. The results of the study show that machine learning algorithms can be used to process the available e-course log data, using the clickstream of e-course resources and for their automated processing. The results suggest potential applications in personalized course recommendations, prediction and dropout prevention strategies, resulting in a more effective and personalized educational experience. Future research will focus on improving models of available registration data, exploring and using advanced machine learning techniques to improve the accuracy and usefulness of predictions, and providing faster recommendations to help students navigate their studies more effectively. [ABSTRACT FROM AUTHOR]

In groundwater modeling studies, accurate spatial and intensity identification of water sources and sinks is of critical importance. Precise construction data about wells (water sinks) are particularly difficult to obtain. The collection of well log data is expensive and laborious, and government records of historic well log data are often imprecise and incomplete with respect to the precise location or pumping rate. In many groundwater modeling studies, such as groundwater quality assessments, a precise representation of the horizontal and vertical distribution of well screens is required to accurately estimate contaminant breakthrough curves. The number of wells under consideration may be very large, for example, in the assessment of nonpoint source pollution. In this paper, we propose an imputation framework that allows for proper reconstruction of missing well data. Our approach exploits available information and tolerates data gaps and imprecisions. We demonstrate the value of this method for a subregion of the Central Valley aquifer (California, USA). We show that our framework imputes missing values that preserve statistical properties of available data and that remain consistent with the known spatial distribution of well screens and pumping rates in the three‐dimensional aquifer system. [ABSTRACT FROM AUTHOR]

The information systems supporting business processes of organizations generate and collect a large number of records in event logs that are exploitable in process mining tasks (discovery, conformance and enhancement). Under a Big Data scenario, Process Mining as a Service (PMaaS) can be attractive for organizations to outsource the storage of event logs and the processing resources for process mining tasks to the cloud in the presence of large event logs. However, the Cloud Service Provider (CSP) may be honest but curious, thus posing security and privacy risks when event log data are sensitive or subject to data privacy laws and regulations. In this work, a cryptography-based method is presented that preserves the privacy of event log data outsourced to an untrusted CSP, which executes the process discovery task, the most common task in process mining. The method conveniently encrypts the event log on the data owner's side to enable the CSP to apply access control over the discovered models (encrypted) through proxy re-encryption. The proposed method is implemented as a software tool and validated and evaluated in terms of performance, scalability, and data utility using real medical (sensitive) data logs under recommended security levels. The results demonstrate the feasibility of the proposed approach to support Process Discovery as a Service (PDaaS), which enables privacy preservation and access control. [ABSTRACT FROM AUTHOR]

Reflectivity inversion is a key step in reservoir prediction. Conventional sparse‐spike deconvolution assumes that the reflectivity (reflection coefficient series) is sparse and solves for the reflection coefficients by an L1‐norm inversion process. Spectral inversion is an alternative to sparse‐spike deconvolution, which is based on the odd–even decomposition algorithm and can accurately identify thin layers and reduce the wavelet tuning effect without using constraints from logging data, from horizon interpretations or from an initial model of the reflectivity. In seismic processing, an error exists in wavelet extraction because of complex geological structures, resulting in the low accuracy of deconvolution and inversion. Blind deconvolution is an effective method for solving the problem mentioned above, which comprises seismic wavelet and reflectivity sequence, assuming that the wavelets that affect some subsets of the seismic data are approximately the same. Therefore, we combined blind deconvolution with spectral inversion to propose blind spectral inversion. Given an initial wavelet, we can calculate the reflectivity based on spectral inversion and update the wavelet for the next iteration. During the update processing, we add the smoothness of the wavelet amplitude spectrum as a regularization term, thus reducing the wavelet oscillation in the time domain, increasing the similarity between inverted and initial wavelets, and improving the stability of the solution. The blind spectral inversion method inherits the wavelet robustness of blind deconvolution and high resolution of spectral inversion, which is suitable for reflectivity inversion. Applications to synthetic and field seismic datasets demonstrate that the blind spectral inversion method can accurately calculate the reflectivity even when there is an error in wavelet extraction. [ABSTRACT FROM AUTHOR]

High-performance computing (HPC) systems are becoming increasingly important as contemporary exascale applications with demand extensive computational and data processing capability. To optimize these systems, efficient scheduling of HPC applications is important. In particular, because I/O is a shared resource among applications and is becoming more important due to the emergence of big data, it is possible to improve performance by considering the architecture of HPC systems and scheduling jobs based on I/O resource requirements. In this paper, we propose a scheduling scheme that prioritizes HPC applications based on their I/O requirements. To accomplish this, our scheme analyzes the IOPS of scheduled applications by examining their execution history. Then, it schedules the applications at pre-configured intervals based on their expected IOPS to maximize the available IOPS across the entire system. Compared to the existing first-come first-served (FCFS) algorithm, experimental results using real-world HPC log data show that our scheme reduces total execution time by 305 h and decreases costs by USD 53 when scheduling 10,000 jobs utilizing public cloud resources. [ABSTRACT FROM AUTHOR]

Detection of the low resistivity-low contrast (LRLC) reservoirs is among the main challenges in the oil industry. In this concern, the LRLC pay zones of the Upper Messinian Abu Madi clastic reservoirs in the onshore Nile Delta Gas fields became a main challenge for significant exploration. This type of reservoirs, including low resistivity-low contrast zones and thin-bedded intervals, are often overlooked using the conventional petrophysical evaluation techniques, especially in the wildcat exploratory wells or highly agitated shoreline depositional environments like the Nile Delta of Egypt. These hidden low contrast reservoirs are generally challenging due to the presence of many shale intercalations/laminations and/or due to increasing the shale volume represented in the form of dispersed distribution, and the dominance of conductive clay minerals. Therefore, in this study, the expected high resistivity values of the gas-bearing reservoir intervals of the Abu Madi Formation in the onshore Begonia gas Field, as a typical case study of the LRLC reservoirs, are masked due to the relatively high shale conductivity, particularly when the thickness of these intervals is less than the vertical resolution of the utilized conventional resistivity log. To verify the LRLC phenomena of the Begonia gas Field, the obtained data was compared to the South Abu El Naga gas Field as a normal case study with a relatively high resistivity gas-bearing pay zone. To overcome the impact of the conductive clay mineral content and identify these hidden low resistivity reservoir intervals, it is necessary to integrate the conventional logging data (gamma-ray, shallow and deep resistivity, density, and neutron) with the acoustic log data including shear and compressional sonic data. In this way, a useful relationship can be established enabling the detection of these hidden LRLC reservoir intervals. This integration is based on the principle that shear waves are not influenced by the fluids types, whereas the compressional sonic waves are influenced by the reservoir fluids. However, to effectively investigate these concealed LRLC reservoir intervals, which can boost production and increase the potential reserves, it is essential to have a low water cut value. The present study represents introduces an efficient workflow, which can be extended to other similar LRLC pay zones in the Nile Delta and northeast Africa. It is also extendible to the LRLC reservoirs in similar deltaic systems having conductive minerals-bearing reservoirs or thin beds. [ABSTRACT FROM AUTHOR]

Lacustrine mixed carbonate/siliciclastic sediment is an important type of oil and gas reservoir with significant potential. Although previous studies have investigated the sedimentary characteristics of the mixed depositional system in numerous oil and gas-bearing basins worldwide, a detailed sedimentary architecture model is still lacking for guiding reservoir characterization at the hydrocarbon reservoir scale. In this paper, a typical lacustrine mixed carbonate/siliciclastic sedimentary system, preserved in the lower member of the ShangGanchaigou Formation, Neogene, in the western Qaidam Basin, Western China, was deeply investigated based on well logging data from 640 wells, 438 m of cores from 3 core wells, and outcrop studies. The results demonstrate that 1) seven types of architecture elements, namely, distributary channel, channel mouth bar, distal bar, sheet-like sand, shallow water mud, algal mound, and marl flat characterized by different lithofacies associations, were recognized based on core and well logging data. 2) The lacustrine mixed carbonate/siliciclastic depositional system can be divided into three facies belts. Along the lakeward direction, the proximal facies belt is dominated by delta front deposits and characterized by gradually downstream bifurcating distributary channels and associated lateral amalgamated delta lobes. The middle facies belt is characterized by isolated and small-scale delta lobes and inter-lobe deposits, including sheet-like sand, small-scale algal mounds, and marl flats, and the distal facies belt is a combination of large-scale algal mounds and marl flats. 3) Within a depression, short-term base-level cycles controlled the facies belt transition, and the proximal, middle, and distal facies belts formed under relatively low, middle, and high base-level conditions, respectively. 4) The scale and connectivity of reservoirs gradually decreased from the proximal to the distal belt. [ABSTRACT FROM AUTHOR]

AbstractIn this study, we explore the application of process mining techniques on assessment log data to explore problem-solving strategies in Algebra. By analyzing sequences of student activities, we demonstrate the significant potential of process mining in identifying problem-solving strategies that lead to successful and unsuccessful outcomes. Our findings reveal that students who successfully solve the problem tend to follow one of three structured strategies, displaying a systematic process in filling the boxes of a Pascal’s triangle. Conversely, those who falter often start with a correct strategy but deviate by inserting incorrect values, especially in central boxes. Further analysis provides insights into the strategies and potential misconceptions among students from various disability groups. Notably, autistic students exhibit unique patterns, such as initiating the solution from the triangle’s right side, contrary to the common left-to-right strategy, and consistently applying this approach even when errors occur. [ABSTRACT FROM AUTHOR]

ABSTRACT Fractures influence the mechanical strength of coal roof and floor, constraining the design of hydraulic fracturing for coalbed methane production. Currently, the predominant approach involves the integration of petrophysical logging with machine learning for fracture prediction. Nevertheless, challenges exist regarding the model's accuracy. In this study, we present a novel approach to predict fracture density. Our method optimises a back‐propagation (BP) neural network and utilises principal component analysis for feature extraction. We employ logging parameters (density, compensated neutron and acoustic time difference) obtained from Shouyang Block well SY‐1 and fracture density data from electrical imaging logging to construct the FVDC model's dataset. The BP neural network model is optimised using the Sparrow Search algorithm and Tent Chaotic Mapping. The results demonstrate a substantial enhancement over the BP neural network model, with reductions of 80.102% in mean absolute error, 94.182% in mean square error, 75.879% in root mean square error and 79.764% in mean absolute percentage error. When considering accuracy, the optimised model (97.098%) surpasses the support vector regression model (96.478%), the random forest model (94.404%) and the BP neural network model (85.657%). Scalability testing for the optimised model was conducted using data from well SY‐2, yielding a remarkable prediction accuracy of 96.775%. This performance exceeds that of the BP neural network (with an accuracy of 85.102%), as well as the random forest and support vector regression models (with accuracies of 91.234% and 90.384%, respectively). These results underscore the potential of well logging and machine learning in FVDC prediction. [ABSTRACT FROM AUTHOR]

Assessing self-regulated learning (SRL)--the interplay between monitoring and control behavior--remains challenging, particularly in young learners. The unobtrusive assessment with log data to investigate SRL offers a promising method to deepen the understanding of the SRL process of young students. Despite the significant potential of log data to enhance the measurement of SRL, recent research encounters new challenges of operationalization, transparency, generalizability, validity, and reproducibility. This study introduces an innovative instrument, the digital train track task (TTT), for assessing SRL with log data in young learners, focusing on monitoring and controlling behavior. Log data of 85 primary school students (second to fifth grades, aged 7-13 years) performing one simple and one complex TTT were analyzed. As a novel method, finite state machines (FSM) were applied to extract SRL-related actions and states from the log data. To evaluate and explore the potential of the digital TTT, monitoring, and control behavior during simple and complex tasks were compared, employing frequency-based statistical analysis and transition graphs. Additionally, the log data were multimethodically linked with think-aloud data. The results revealed differences in monitoring and control behavior during the simple and the complex tasks regarding frequency, duration, and transitions between the SRL-related states. Extracted SRL-related states from log data and corresponding think-aloud data showed significant correlations. Adding to the growing body of log data research, this study offers an innovative task to validly assess the metacognitive self-regulation processes of young learners during problem-solving. The transparent, theory-based operationalization of SRL in this study, taking into account recent demands for SRL log data research, allows better reproducibility and transfer and adds to the generalizability of findings from SRL log data research. [ABSTRACT FROM AUTHOR]

With the deepening of oilfield development, logging data proliferate, and their complexity makes manual stratigraphic division both difficult and time-consuming. Aimed at the current network model widely used to solve the problem of stratigraphic delineation, which has problems such as not considering the multi-scale features of logging curves and insufficient accuracy, the YOLOv8x target detection algorithm in deep learning is utilized to detect the target strata, which has the ability to characterize the multi-scale features and can improve the efficiency and accuracy of the division. In order to better localize and identify targets, this paper proposes a new stratigraphic automatic division method, YOLOv8x-CAMDP, which introduces a CA (Coordinate Attention) mechanism module into the original YOLOv8x model to improve the model's ability to identify stratigraphic interval boundaries. In addition, the CIOU loss function in the original YOLOv8x network model was replaced using the MDPIOU loss function to effectively improve the accuracy and efficiency of bounding box regression. Based on the logging data from the Xing 10 area pure oil zone, a thorough comparison of the YOLOv8x-CAMDP and YOLOv8x models' training results is presented. The YOLOv8x-CAMDP model achieves a mean Average Precision (mAP) value of 98.7%, outperforming the YOLOv8x model by one percentage point. Moreover, the YOLOv8x-CAMDP model demonstrates greater precision in boundary division for each stratigraphic interval. The application of the YOLOv8x-CAMDP model to project implementation achieved significant results in stratigraphic division, reduced workload, and optimized manual division. These results not only confirm the practical value of the YOLOv8x-CAMDP model but also demonstrate the prospect and potential of its wide application. [ABSTRACT FROM AUTHOR]

AbstractThis study aims to identify engagement profiles in the citizen humanities and assess whether these profiles match those found in prior studies on crowd-based projects in the natural sciences. To this purpose, we use the log data from the citizen humanities project ‘Historical Database Suriname and the Caribbean’, in which volunteers transcribe the civil records of Suriname, and analyze the differences between engagement in data entry and forum activity. We identify seven engagement profiles, six of which are similar to the profiles found in crowd projects in the natural sciences. However, their pattern of occurrence differs with a more equal distribution of effort. Additionally, we discuss implications for project design and recommend choices that match project goals and foster engagement diversity. [ABSTRACT FROM AUTHOR]

Remote reflection waves, essential for acquiring high-resolution images of geological structures beyond boreholes, often suffer contamination from strong direct mode waves propagating along the borehole. Consequently, the extraction of weak reflected waves becomes pivotal for optimizing migration image quality. This paper introduces a novel approach to extracting reflected waves by sequentially operating in the spatial frequency and curvelet domains. Using variation mode decomposition (VMD), single-channel spatial domain signals within the common offset gather are iteratively decomposed into highwavenumber and low-wavenumber intrinsic mode functions (IMFs). The low-wavenumber IMF is then subtracted from the overall waveform to attenuate direct mode waves. Subsequently, the curvelet transform is employed to segregate upgoing and downgoing reflected waves within the filtered curvelet domain. As a result, direct mode waves are substantially suppressed, while the integrity of reflected waves is fully preserved. The efficacy of this approach is validated through processing synthetic and field data, underscoring its potential as a robust extraction technique. [ABSTRACT FROM AUTHOR]

The Pinghu slope belt in the Xihu sag of the East China Sea Shelf Basin (ECSSB) is a crucial hydrocarbon production area in eastern China. However, due to the complex geological conditions, publications have lacked comprehensive research on the spatial-temporal coupling relationships of primary factors that impact hydrocarbon accumulation in the Pinghu slope belt. Furthermore, the hydrocarbon distribution patterns and the controlling factors across different study areas within the same slope belt are not yet fully understood. This study extensively utilized three-dimensional seismic data, well logging data, geochemical analysis, fluorescence analysis, and oil testing and production data to address these issues. Following a "stratification and differentiation" approach, the study identified seven distinct hydrocarbon migration and accumulation units (HMAU) in the Pinghu slope area based on the structural morphology characteristics, hydrocarbon source-reservoir-cap rock patterns, hydrocarbon migration pathways, and hydrocarbon supply range. Detailed analysis was conducted to examine the hydrocarbon distribution patterns and controlling factors within each migration and accumulation unit across different structural units, including high, medium, and low structural components. All data sources support a "southern-northern sub-area division, eastern-western sub-belt division, and variations in hydrocarbon accumulation" pattern in the Pinghu slope belt. The degree of hydrocarbon accumulation is controlled by the factors of structural morphology, hydrocarbon generation potential of source rocks, the spatial position of source slopes, fault sealing capacity, and sand body distribution. Furthermore, different coupling patterns of faults and sand bodies play a pivotal role in governing hydrocarbon enrichment systems across various migration and accumulation units. These observations indicate that three hydrocarbon accumulation patterns have been established within the slope belt, including near-source to far-source gentle slope with multiple hydrocarbon kitchens in the XP1-XP4 zones, near-source to middle-source gentle slope with dual-hydrocarbon kitchens in the XP5 zone, and near-source steep slope with a single hydrocarbon kitchen in the XP6-XP7 zones. These findings contribute to enhancing the theoretical system of hydrocarbon accumulation in the slope belt. [ABSTRACT FROM AUTHOR]

Geophysical logging series are valuable geological data that record the physical and chemical information of borehole walls and in-situ formations, and are widely used by geologists for interpreting geological problems due to their continuity, high resolution, and ease of access. Recently, machine learning methods are gradually bringing data science and geoscience closer together, and Intelligent Recognition using Logging Data (IRLD) is increasingly becoming an important interpretation task. However, due to the specificity of geological information, relatively low data quality makes the direct application of machine learning models to IRLD often not optimal. And to the best of our knowledge, IRLDs are not highly generalizable and technical surveys are still lacking. Therefore, this paper presents a comprehensive review of IRLD. Specifically, after systematically reviewing geophysical well logging and machine learning techniques, the main applications and general processes for the cross-discipline task of IRLD are summarized. More importantly, the key challenges of IRLD in the four stages of data acquisition, feature engineering, model building, and practical application are discussed in this review. The potential risks of these challenges are visualized by using real logging data from a study area in the South China Sea and the example of a lithology identification task. For these challenges, we give the current state-of-the-art methods and feasible strategies in conjunction with published research. This comprehensive review is expected to provide insights for practitioners to construct more robust models and achieve more effective application results in IRLD. [ABSTRACT FROM AUTHOR]

Fluid classification is a fundamental task in the field of geological sciences to achieve effective reservoir characterization and hydrocarbon exploration. Traditional fluid classification methods are often limited by long processing times and an inability to capture complex relationships within the data. To address this issue, this paper proposes a novel deep learning approach—the Deep Graph Attention Multi-channel Transfer Learning Network (DGMT), aimed at improving the efficiency and accuracy of fluid classification from logging data. This model comprises three key components: a graph attention layer, a multi-channel feature extractor, and a transfer learning module. The graph attention layer is designed to handle spatial dependencies between different logging channels, enhancing classification accuracy by focusing on critical features. The multi-channel feature extractor integrates information from various data sources, ensuring comprehensive utilization of the rich information in logging data. The transfer learning module allows the model to transfer knowledge from pre-trained models of similar tasks, accelerating the training process and significantly improving the model's generalization ability and robustness. This feature enables the DGMT model to adapt to different geological environments and logging conditions, showing superior performance over traditional methods. To validate the effectiveness of the DGMT model, we conducted experiments on actual logging datasets containing multiple oil wells. The experimental results indicate that, compared to common machine learning algorithms and other deep learning methods, the DGMT model significantly improves in accuracy and other classification performance metrics. [ABSTRACT FROM AUTHOR]

Predicting facies and petrophysical properties along and between wells is challenging in carbonate reservoir modeling. In the Nullipore carbonate reservoir, Ras Fanar field, depositional and long-term diagenetic processes result in a high degree of heterogeneity and complex distribution of facies, which in turn affect the reservoir quality. This provides a significant obstacle to building accurate geological models. This study integrates thin sections, routine core analyses, and well logging data to overcome such difficulties and model the Nullipore carbonate facies and permeability. The detailed petrographic analysis revealed the existence of seven microfacies in the reservoir, which are summed up into three facies associations (FAs), each of which represents a specific reservoir rock type (RRT): (1) supratidal FA, (2) intertidal FA, and (3) shallow subtidal FA. The three FAs were correlated with the gamma-ray logs to create facies logs for the studied wells, which were further populated via the Truncated Gaussian Simulation method. Cross-validation was used to evaluate the model's accuracy. The analysis of the available core data infers that the three RRTs are prospective and have a wide permeability distribution. However, RRT3 constitutes the best reservoir quality. The sedimentological analysis revealed that the long-term diagenetic events, involving the dolomitization of limestone and the dissolution of allochems have a major role in improving the pore connectivity and permeability of the reservoir. Fracture characterization discloses that fractures play a significant role in fluid storage and migration. Three Machine Learning (ML) models, including Adaptive boosting (AdaBoost), Gradient Boosting (GB), and Extreme Gradient Boosting (XGB), were developed to integrate the RRTs, porosity, and permeability to improve permeability prediction. Statistical analysis revealed that the XGB model outperforms other models and exhibits the highest prediction performance. The present study provides further insights into the characterization and modeling of facies and permeability of complex carbonate reservoirs. It can be applied in similar geological settings to better interpretation of depositional and diagenetic controls on reservoir quality assessment and aid in the field development plan. [ABSTRACT FROM AUTHOR]

Background There are limited objective studies regarding the effectiveness of strategies to alleviate the documentation burden on resident physicians. Objective To develop and implement diagnosis-specific templates for the plan of care section of inpatient admission notes, aiming to reduce documentation time. Methods Twelve templates for the plan of care section of admission notes were written by the study authors, reviewed by attending physicians, and shared with the residents through the electronic health record (EHR) on September 23, 2022. EHR audit log data were collected to examine admission note writing times, supplemented by resident feedback on acceptability via an anonymous survey. Feasibility measures included time investment, experience with the EHR, and resident training. Results Between July 1, 2021 and June 30, 2023, 62 pediatric residents contributed 9840 admission notes. The templates were used in 557 admission notes. The mean total time spent on an admission note decreased from 97.9 minutes pre-intervention to 71.0 minutes post-intervention with the use of a template; an adjusted reduction of 23% (95% CI 16%-30%; P<.001). The mean attending time spent editing an admission note was unchanged. The survey results underscored wide acceptability of the templates among the residents. Feasibility data showed that the project required minimal time investment from the health care informatics team and minimal resident training. Conclusions Using templates in the care plan section of admission notes reduces the time residents spend writing admission notes. [ABSTRACT FROM AUTHOR]

Discrepancies between laboratory vehicle performance and real-world traffic conditions have been reported in numerous studies. In response, emission and fuel regulatory frameworks started incorporating real-world traffic evaluations and vehicle monitoring using portable emissions measurement systems (PEMS) and on-board diagnostic (OBD) data. However, in regions with technical and economic constraints, such as Latin America, the use of PEMS is often limited, highlighting the need for low-cost methodologies to assess vehicle performance. OBD interfaces provide extensive vehicle and engine operational data in this context, offering a valuable alternative for analyzing vehicle performance in real-world conditions. This study proposes a straightforward methodology for assessing vehicle fuel efficiency and carbon dioxide (CO2) emissions under real-world traffic conditions using OBD data. An experimental campaign was conducted with three gasoline-powered passenger vehicles representative of the Ecuadorian fleet, operating as urban taxis in Ibarra, Ecuador. This methodology employs an OBD interface paired with a mobile phone data logging application to capture vehicle kinematics, engine parameters, and fuel consumption. These data were used to develop engine maps and assess vehicle performance using the vehicle-specific power (VSP) approach based on the energy required for vehicle propulsion. Additionally, VSP analysis combined with OBD data facilitated the development of an energy-emission model to characterize fuel consumption and CO2 emissions for the tested vehicles. The results demonstrate that OBD systems effectively monitor vehicle performance in real-world conditions, offering crucial insights for improving urban transportation sustainability. Consequently, OBD data serve as a critical resource for research supporting decarbonization efforts in Latin America. [ABSTRACT FROM AUTHOR]

In order to efficiently and correctly identify abnormal behaviors in Linux logs, this paper proposes a Linux log anomaly detection method based on the improved isolated forest algorithm. The method introduces an attention mechanism on the basis of the isolated forest algorithm, which can dynamically adjust the attention features and sample points when processing log data, and dynamically adjust the degree of attention according to the degree of abnormality of the samples. Experimental results show that the method achieves high efficiency in the Linux log anomaly detection task compared with traditional methods, and can effectively discover potential security threats and abnormal behaviors. [ABSTRACT FROM AUTHOR]

Natural fractures are effective storage spaces and the main seepage channels of tight reservoirs, and the spacing and role of structural fractures of different scales in tight reservoirs are also different. However, the opening or height of underground natural fractures is often difficult to determine directly using seismic data or imaging logging, which restricts the characterization, modeling, and analysis of the formation mechanisms of reservoir fractures. This paper uses the morphology of water absorption profiles and conventional logging data to calculate the ratio of the isotope intensity of a single well to natural gamma values from logging and characterize the types of water absorption profiles. The size (opening and height) of the fractures is closely related to the height of the water absorption profile and the fluctuation in the water absorption intensity. The calculated rock mechanics parameters along a single well, combined with the internal friction angle of the rock and the fracture strike, are used to determine the magnitude and direction of the paleostress of the Y290-Y414 Block in the southeastern Ordos Basin during the Himalayan period. By establishing a single-well geomechanical model to simulate the three-dimensional distribution of paleostress and paleostrain during the formation of fractures, a database model for constructing the scale of fractures is established. Through correlation analysis, a model for predicting the strength and thickness of the water absorption profile is established. Finally, the three-dimensional distribution pattern of the scale of structural fractures is predicted, and the reliability of the prediction results is verified using dynamic development data. The research results have certain reference significance for explaining the mechanism of fracture formation, characterizing the scale of underground fractures, and modeling reservoir fractures. Highlights: A model for predicting the height and opening of structural fractures is established. The scale of fractures is closely related to the mechanical structure of the rock layers. The presence of large-scale fractures is the main cause of water channeling and flooding. [ABSTRACT FROM AUTHOR]

Well logging can be classified under the general category of big data, as the datasets are intricate for conventional data processing application software to handle. This study aims at working on three entire wells drilled up to 1751–2690.4 m in upper Assam basin for lithofacies identification with each well comprising of huge datasets. Conventional methods for lithofacies classification are a challenging task for this enormous amount of data. It can be subject to biases, substantially laborious and time consuming. To deal with this problem, machine learning (ML) classification algorithms have come into picture, which systematically speed up accurate prediction of lithofacies. Five such algorithms (Support vector, Random forest, Multi-layer Perceptron, K Nearest Neighbors and Decision tree) are chosen and then evaluate each of the model's performance exhibiting varying hyperparameters and input features. We established a thorough approach that entails all classifiers operating concurrently within the same ML framework. The best algorithm was selected following the comparison of each model's Jaccard index and F1-score based on optimized hyperparameters. In addition, introduction of water saturation, a derived petrophysical property affects the model's performance. This feature selection process brags the importance of inclusion of useful features to improve the accuracy of any ML model's prediction capability. We observed that after introduction of water saturation profile, Jaccard index and F1-score of the best classification model, Random Forest has increased by on average 10.74% and 7.82%, respectively. Finally, we correlated identified lithofacies, including the reservoir facies from well to well in the study area. [ABSTRACT FROM AUTHOR]

As the development of most domestic and international oilfields progresses, many fields have entered a mature phase characterized by high water cut and high recovery, with water cut levels often exceeding 90%. Carbon/oxygen ratio logging has proven to be an indispensable tool for distinguishing oil layers from water layers in complex environments, especially where salinity is low, unknown, or highly variable. This logging method has become one of the most effective techniques for determining residual oil saturation in cased wells, providing critical insights into the oil–water interface. In this study, we evaluate two key interpretation models for carbon/oxygen ratio logging: the fan chart method and the ratio chart method. We optimize the interpretation parameters in the ratio chart model using an improved genetic algorithm, which significantly enhances interpretation precision. The optimized parameters enable a more seamless integration of logging results with reservoir and conventional logging data, reducing the influence of lithological variations and physical property differences on the measurements. This research establishes a robust theoretical foundation for enhancing the interpretation accuracy of carbon/oxygen ratio logging, which is crucial for effectively identifying water-flooded layers. These advancements provide vital technical support for monitoring oil–water dynamics, optimizing reservoir management, and improving production efficiency in oilfield development. [ABSTRACT FROM AUTHOR]

Plug-in hybrid electric vehicles (PHEVs) are designed to enable the electrification of a large portion of the distance vehicles travel while utilizing relatively small batteries via taking advantage of the fact that long-distance travel days tend to be infrequent for many vehicle owners. PHEVs also relieve range anxiety through seamless switching to hybrid driving—an efficient mode of fuel-powered operation—whenever the battery reaches a low state of charge. Stemming from the perception that PHEVs are a well-rounded solution to reducing greenhouse gas (GHG) emissions, various metrics exist to infer the effectiveness of GHG reduction, with utility factor (UF) being prominent among such metrics. Recently, articles in the literature have called into question whether the theoretical values of UF agree with the real-world performance of PHEVs, while also suggesting that infrequent charging was the likely cause for observed deviations. However, it is understood that other reasons could also be responsible for UF mismatch. This work proposes an approach that combines theoretical modeling of UF under progressively relaxed assumptions (including the statistical distribution of daily traveled distance, charging behavior, and attainable electric range), along with vehicle data logs, to quantitatively infer the contributions of various real-world factors towards the observed mismatch between theoretical and real-world UF. A demonstration of the proposed approach using data from three real-world vehicles shows that all contributing factors could be significant. Although the presented results (via the small sample of vehicles) are not representative of the population, the proposed approach can be scaled to larger datasets. [ABSTRACT FROM AUTHOR]

Emerging applications such as blockchain, autonomous vehicles, healthcare, federated learning, self-consistent large language models (LLMs), and multi-agent LLMs increasingly rely on the reliable acquisition and provision of data from external sources. Multi-component networks, which supply data to the applications, are defined as data provision networks (DPNs) and prioritize accuracy and reliability over delivery efficiency. However, the effectiveness of the security mechanisms of DPNs, such as self-correction, is limited without a fine-grained log of node activities. This paper presents DPTracer: a novel logging system designed for DPNs that uses tamper-evident logging to address the challenges of maintaining a reliable log in an untrusted environment of DPNs. By integrating logging and validation into the data provisioning process, DPTracer ensures comprehensive logs and continuous auditing. Our system uses Process Tree as a data structure to store log records and generate proofs. This structure permits validating node activities and reconstructing historical data provision processes, which are crucial for self-correction and verifying data sufficiency before results are finalized. We evaluate the overheads introduced by DPTracer regarding computation, memory, storage, and communication. The results demonstrate that DPTracer incurs reasonable overheads, making it practical for real-world applications. Despite these overheads, DPTracer enhances security by protecting DPNs from post-process and in-process tampering. [ABSTRACT FROM AUTHOR]

This paper utilizes prevalent deep learning techniques, such as Convolutional Neural Networks (CNNs) and Residual Neural Networks (ResNets), along with the well-established machine learning technique, Random Forest, to efficiently distinguish between common lithologies including coal, sandstone, limestone, and others. This approach is highly significant for resource extraction—such as coal, oil, natural gas, and groundwater—by streamlining the process and minimizing the need for the time-consuming manual interpretation of geophysical logging data. The natural gamma ray, density, and resistivity log data were collected from 22 wells in the mountainous region of Southern Sichuan, China, yielding approximately 70,000 samples for developing lithofacies prediction models. All the models achieved around 80% accuracy in classifying carbonaceous lithologies and up to 88% accuracy in predicting other lithologies. The trained models were applied to the logging data in the validation dataset, and the outputs were validated against geological core data, showing overall consistency, although variations in the classification results were observed across different wells. These findings suggest that deep learning techniques have the potential to develop a general model for effectively handling lithology classification with well logging data. [ABSTRACT FROM AUTHOR]

In this study, the Xgboost method is employed for TOC estimation in mixed carbonate and siliciclastic shale from the Hashan area, Junggar Basin. The results show that this approach is effective for TOC estimation in this area although the model performance is not very excellent with a correlation coefficient of 0.54 between measured TOC and predicted TOC values, likely due to a small samples dataset. Therefore, the PCA method is applied to debase dimension of well log data from five dimensional to two-dimensional data, which enhances the correlation coefficient between the predicted and measured TOC from 0.54 to 0.68. Based on the model, the isopleth maps of TOC distributions in Fengcheng Formation were redrawn showing two shale oil exploration targets, which likely correspond to two depositional centers of this strata. All the same, the model in this work provides reliable data for shale oil evaluation in the study area and a good example under similar geological setting. [ABSTRACT FROM AUTHOR]

AbstractThis study presents a comprehensive characterization of the Pliocene reservoir formation in the Temsah gas field, located 65 km NNW of Port Said in the northeastern Nile Delta Basin. The research integrates seismic data with well log analysis to enhance reservoir characterization. The Temsah gas field is distinguished by its intricate geological structure, featuring numerous normal faults oriented along northeast-southwest and northwest-southeast trends. The field’s stratigraphy is predominantly composed of sandstone and shale facies, which are considered highly prospective for hydrocarbon accumulation. A full geological field analysis was conducted to identify key prospects for enhancing productivity. To accurately delineate seismic features, data from four wells and twenty-nine 2D seismic lines were interpreted. Depth maps of the reservoir layers were constructed, highlighting critical transitions between sandstone and shale facies, which are pivotal for future exploration efforts. A detailed 3D static model was developed to illustrate the impact of lithological composition on reservoir characteristics, with a specific focus on the Upper Sand of Kafr El-Sheikh Formation. Petrophysical properties, including effective porosity (13–23%), shale content (7–31%), and water saturation (30–45%), were meticulously evaluated to inform the creation of a robust 3D property model. This model provides a detailed spatial distribution of rock facies and petrophysical parameters within the Temsah gas field. The integration of these studies identified two promising areas, with estimated original gas in place (OGIP) ranging from 2.86 to 4.04 stock cubic feet (SCF), indicating significant potential for field development and enhancement. [ABSTRACT FROM AUTHOR]

This paper describes a measuring unit for synchronously collecting the air dose rate and measurement position for efficient dosimetry surveying and data logging. The developed prototype comprises a three-dimensional light detection and ranging-based mapping part and dosimetry part, which are integrated into a single measuring unit through an embedded computer that installs a ROS (robot operating system) framework. The unit can function as a standalone system with embedded batteries. Since it is portable, on-line data gathering in the workspace can be realized, thereby maintaining consistency between the air dose rate and measurement position. In this paper, we describe the functional requirements for the measuring unit, the prototype system configuration, and the experimental results obtained in the mockup environment and nuclear facility to discuss its performance. [ABSTRACT FROM AUTHOR]

By measuring the variation of the P- and S-wave velocities of tight sandstone samples under water saturation, it was confirmed that with the decrease in water saturation, the P-wave velocity first decreased and then increased. The variation in velocity was influenced by the sandstone's porosity. The commonly used Gassmann equation based on fluid substitution theory was studied. Comparing the calculated results with the measured data, it was found that the Gassmann equation agreed well with the measured data at high water saturation, but it could not explain the bending phenomenon of P-wave velocity at low saturation. This indicated that these equations could not accurately describe the relationship between fluid content and rock acoustic velocity. The reasons for this phenomenon were discussed through Taylor's expansion. The coefficients of the fitting formula were calculated and verified by fitting the measured acoustic velocity changes of the cores. The relationship between P-wave velocity and saturation was discussed, which provides experimental support for calculating saturation using seismic and acoustic logging data. [ABSTRACT FROM AUTHOR]

Seismic inversion holds significant potential for providing crucial lithostratigraphic information in hydrocarbon reservoir characterisation and in identifying new traps. However, one of the major challenges in achieving reliable reservoir models in Algeria stems from the inherent uncertainties associated with seismic inversion algorithms and the non-linear relationship between petrophysical measurements. Due to their usefulness, several Artificial Neural Network algorithms have been developed and employed for seismic inversion and reservoir characterisation in the last few years. Nevertheless, only few researchers have addressed this issue in terms of optimisation of Multilayer Feed-Forward Neural Network (MLFN) architecture. In this case study, the use of an MLFN to address these challenges is proposed. The primary contribution of this research lies in the optimisation of the MLFN architecture based on trial and error procedures. The goal is to ensure that the computational demands are manageable within the constraints of available computing resources and that the process is time-efficient for geo-modellers. This practical approach is particularly valuable when applied at the reservoir scale. MLFN supervised training is conducted using logging data, where measured log curves serve as inputs, and core porosity, obtained from laboratory analysis, serves as target output. Moreover, coloured inversion is employed to generate a 3D seismic acoustic impedance cube, which, in turn, serves as input for a model-based inversion method designed to calculate porosity volume using the trained network. Furthermore, the usefulness of the resulting density cube is demonstrated through the correlation with density logs and core density values at wells 1, 2, and 3. Thence, the obtained correlation ranges validate the reliability of the obtained porosity volume in enhancing the characterisation of the targeted reservoir within the Algerian Saharan field. [ABSTRACT FROM AUTHOR]

The construction of an accurate and high‐resolution reservoir parameter model is crucial for reservoir characterization. However, due to the band‐limited characteristics of seismic data, the inversion results heavily rely on the accuracy of the initial model. Most existing techniques for constructing an initial model interpolate well logging data within the stratigraphic framework, neglecting the effect of the stratigraphic sequence, which compromises the reliability of the initial model. The stratigraphic sequence is essential for dividing stratigraphic evolution stages and defining a geological relationship between reservoirs within the stratigraphic framework. Therefore, an initial model construction method constrained by stratigraphic sequence representation is proposed for pre‐stack seismic inversion. The process begins with establishing the stratigraphic framework using horizon and fault data. Subsequently, the collaborative sparse representation algorithm is used to learn a joint dictionary that captures the relationship of structural features between seismic data and stratigraphic sequence from the well logging data. In the process of seismic data representation, the stratigraphic sequence is accurately represented in three‐dimensional space by sharing sparse coefficients in the joint dictionary. Finally, the elastic parameter model is constructed by integrating the stratigraphic framework, stratigraphic sequence and well logging data, providing a reliable initial model for pre‐stack seismic inversion. The main innovation of the proposed method is the three‐dimensional representation of the stratigraphic sequence. A synthetic example demonstrates that the proposed method produces a more accurate initial model than conventional interpolation methods. Additionally, when applied to field data, it yields satisfactory results even without complete S‐wave velocity well logging data. [ABSTRACT FROM AUTHOR]

Due to COVID19 pandemic, most of educational institutions and schools changed the traditional way of teaching to online teaching and learning using well-known Learning Management Systems (LMS) such as Moodle, Canvas, Blackboard, etc. Accordingly, LMS started to generate a large data related to students’ characteristics and achievements and other course-related information. This makes it difficult to teachers to monitor students’ behaviour and performance. Therefore, a need to support teachers with a tool alerting student who might be in risk based on their recorded activities and achievements in adopted LMS in the school. This paper focuses on the benefits of using recorded data in LMS platforms, specifically Moodle, to predict students' performance by analysing their behavioural data and engagement activities using data mining techniques. As part of the overall process, this study encountered the task of extracting and selecting relevant data features for predicting performance, along with designing the framework and choosing appropriate machine learning techniques. The collected data underwent pre-processing operations to remove random partitions, empty values, duplicates, and code the data. Different machine learning techniques, including k-NN, TREE, Ensembled Tree, SVM, and MLPNNs were applied to the processed data. The results showed that the MLPNNs technique outperformed other classification techniques, achieving a classification accuracy of 93%, while SVM and k-NN achieved 90% and 87% respectively. This indicates the possibility for future research to investigate incorporating other neural network methods for categorizing students using data from LMS. [ABSTRACT FROM AUTHOR]

Understanding the distribution of shear wave velocity (VS) in hydrocarbon reservoirs is a crucial concern in reservoir geophysics. This geophysical parameter is utilized for reservoir characterization, calculating elastic properties, assessing fractures, and evaluating reservoir quality. Unfortunately, not all wells have available VS data due to the expensive nature of its measurements. Hence, it is crucial to calculate this parameter using other relevant features. Therefore, over the past few decades, numerous techniques have been introduced to calculate the VS data using petrophysical logs in wells with limited information. Unfortunately, the majority of these methods have a drawback they only offer insight into the location of the wells and do not provide any details regarding the distribution of VS in the space between the wells. In this article, we employed three-dimensional post-stack seismic attributes and well-logging data integration to predict the distribution of VS in the Asmari formation in an Iranian oil field. To accomplish this objective, the model-based seismic inversion algorithm was utilized to convert the seismic section into the acoustic impedance (AI) section. Then, AI and seismic data were utilized in the cross-validation method to determine the relevant attributes for predicting the spatial distribution of VS throughout the entire reservoir area, using an artificial neural network. The proposed method was shown to provide 94% correlation and 109 m/s error between the actual and estimated VS. Also, the calculated VS section has a high correlation with the actual logs at the location of the wells. [ABSTRACT FROM AUTHOR]

Acknowledging the significant impact of temperature deviations on vaccine efficacy, this study examines the prevalence of exposure to non-recommended temperatures, particularly focusing on freeze-sensitive vaccines. This study aims to identify critical points of temperature breaches during transportation from storage to last-mile delivery. In this cross-sectional study, the temperature integrity of vaccines has been examined during transportation across three states in India (Karnataka, Maharashtra, and Rajasthan), utilizing data loggers to track and record temperatures in real time. By analyzing instances of exposure to temperatures beyond the recommended 2–8 °C range, the study aims to identify the critical junctures within the vaccine supply chain susceptible to temperature excursions. The methodology entailed a systematic data logger analysis at each stage of transportation up to the last mile. In this study, descriptive statistics with the analysis of variance test has been applied to investigate the significance of variations in the duration of temperature breaches. The novelty of this study lies in its comprehensive examination of vaccine temperature integrity across critical transportation phases in India, utilizing real-time data-logging technology. Based on the results, the study provides recommendations using the ABCD strategic framework, encompassing awareness, best practices, continuous monitoring, and data management and documentation. This framework will be instrumental in addressing the identified challenges and significantly improving the temperature monitoring flexibility. The findings will inform targeted interventions, optimizing the cold chain to enhance the preservation of vaccine potency and identifying flexible solutions for maintaining the required temperatures from storage to transportation. [ABSTRACT FROM AUTHOR]

Identifying and categorizing drilling-induced fractures is pivotal for understanding the mechanisms underlying wellbore instability, drilling fluid loss, and assessing reservoirs using imaging logging data. This study employs a linear elastic stress model around the wellbore, coupled with a tensile failure criterion, to establish a predictive framework for the orientation of drilling-induced fractures. It investigates how engineering parameters like wellbore trajectory and bottomhole pressure influence the distribution of principal stresses around the wellbore, as well as the angle and orientation of drilling-induced fractures relative to the wellbore axis, across various faulting scenarios. The results indicate that drilling-induced fractures exhibit structured arrangements and consistent patterns, often appearing at approximately 180° symmetric intervals and descending in similar orientations. This provides a theoretical basis for their systematic identification and classification. Under different stress conditions, these fractures can manifest as feather-like shapes, "J"-shaped, or transitional states between feather-like and "J"-shaped orientations, as well as "V"-shaped or "M"-shaped orientations. Accurate detection and classification of these fractures are essential for interpreting effective fractures, conducting thorough reservoir evaluations, and predicting appropriate drilling fluid densities to mitigate the wellbore failure risk. Moreover, this knowledge aids in effectively determining the magnitude and direction of in situ stress inversion. [ABSTRACT FROM AUTHOR]

A log data analysis plays an important role in the uranium mining process. Automating this analysis using machine learning methods improves the results and reduces the influence of the human factor. In particular, the identification of reservoir oxidation zones (ROZs) using machine learning allows a more accurate determination of ore reserves, and correct lithological classification allows the optimization of the mining process. However, training and tuning machine learning models requires labeled datasets, which are hardly available for uranium deposits. In addition, in problems of interpreting logging data using machine learning, data preprocessing is of great importance, in other words, a transformation of the original dataset that allows improving the classification or prediction result. This paper describes a uranium well log (UWL) dataset generated with the employment of floating data windows and designed to solve the problems of identifying ROZ and lithological classification (LC) on sandstone-type uranium deposits. Comparative results of the ways of solving these problems using classical machine learning methods and ensembles of machine learning algorithms are presented. It has been shown that an increase in the size of the floating data window can improve the quality of ROZ classification by 7–9% and LC by 6–12%. As a result, the best-quality indicators for solving these problems were obtained, f1_score_macro = 0.744 (ROZ) and accuracy = 0.694 (LC), using the light gradient boosting machine and extreme gradient boosting, respectively. [ABSTRACT FROM AUTHOR]

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Copyright of Natural Gas Geoscience is the property of Natural Gas Geoscience and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

A proper investigation of geomechanical properties of reservoir sediments allows accurate prediction of both magnitude and direction of different stress regimes. These are crucial for hydrocarbon production and development particularly in deformed and structurally controlled petroliferous basins. The rift basins in the north Western Desert (NWD) provide analogues for these structurally controlled prolific regions, where the syn-rift Jurassic sedimentary successions host prolific reservoir targets. However, a detailed assessment of the geomechanical behavior of the Jurassic reservoirs has never been investigated. Thus, the present study utilizes wireline log data to determine the geomechanical properties of the syn-rift Jurassic facies of the Khatatba Formation in Shushan Basin in the NWD. Mechanical earth model was constructed and formation microimager logs (FMI) were interpreted for to investigate the geomechanical behavior and wellbore stability of the studied deformed facies. The stress direction was determined from FMI by identifying drilling-induced fractures, breakout zones and formation fractures. Resistive, conductive and partially conductive fractures are oriented in NE–SW, ESE–WSW, NW–SE, NE–SW and SW–NE directions coinciding with the Jurassic syn-rift structures. The ENE–WSW direction of breakout and minimum horizontal stress (SHmin) are the same as that of the primary fault in the study region contrasting with the NNW–SSE direction of maximum horizontal stress (SHmax) and induced fractures. The present study highlights the significance of integrating wireline logging results in interpreting the critical and non-critical stress orientations which are necessary for optimal production plans in structurally controlled prolific basins around the globe. [ABSTRACT FROM AUTHOR]

Lithium in coal, as a new type of associated mineral resource, has considerable potential for exploration. Exploration of high-lithium coal seams is essential for developing and using the associated lithium resources. To explore the distribution of lithium resources in the early stages of development in coal seams, the relationship between coal seam logging data and lithium content was analyzed by taking Guojiadi Coal Mine (China) as example. By analyzing the correlation between the different logging curves and the lithium content in coal and combining the K-means algorithm to identify the logging characteristics of different lithium-containing coal seams, we finally obtained the logging identification characteristics of high-lithium coal seams. The results reveal differences in the logging curves of coal seams with different lithium contents. The natural gamma and lateral resistivity of high-lithium coal seams are approximately 80 API and 100 Ω.M, respectively. Our study shows that the early identification of high-lithium coal seams can be evaluated from a logging perspective. We propose a preliminary identification method of high-lithium coal seam based on logging curve parameters by clustering analysis of borehole logging data to achieve accurate prediction. [ABSTRACT FROM AUTHOR]

Log messages from enterprise-level software systems contain crucial runtime details. Engineers can convert log messages into structured data through log parsing, laying the foundation for downstream tasks such as log anomaly detection. Existing log parsing schemes usually underperform in production environments for several reasons: first, they often ignore the semantics of log messages; second, they are often not adapted to different systems, and their performance varies greatly; and finally, they are difficult to adapt to the complexity and variety of log formats in the real environment. In response to the limitations of current approaches, we introduce IPLog (Intelligent Parse Log), a parsing method designed to address these issues. IPLog samples a limited set of log samples based on the distribution of templates in the system's historical logs, and allows the model to make full use of the small number of log samples to recognize common patterns of keywords and parameters through few-shot learning, and thus can be easily adapted to different systems. In addition, IPLog can further improve the grouping accuracy of log templates through a novel manual feedback merge query strategy based on the longest common prefix, thus enhancing the model's adaptability to handle complex log formats in production environments. We conducted experiments on four newly released public log datasets, and the experimental results show that IPLog can achieve an average grouping accuracy (GA) of 0.987 and parsing accuracy (PA) of 0.914 on the four public datasets, which are the best among the mainstream parsing schemes. These results demonstrate that IPLog is effective for log parsing tasks. [ABSTRACT FROM AUTHOR]

The degree and amount of sand production in hydrate reservoirs is related to the selection of stable production processes, but there is currently a lack of quantitative sand production prediction research using real logging data and formation samples from hydrate reservoirs. To reveal the dynamic change characteristics of in-situ reservoirs during hydrate decomposition, and explore quantitative prediction methods for guiding production practice, it is conducted a series of numerical simulations and quantitative prediction experiments. The numerical simulations are carried out using different sand-out prediction methods by using hydrate logging data during drilling, while quantitative prediction experiments of water production and sand-out are carried out based on in-situ reservoir samples. Our experiments indicate that hydrate mining is facing a serious risk of sand-out. The particle transport in the reservoir changes from "large-channel seepage" to "umbrella seepage" and then to "uniform fine flow" as the replacement flow rate decreases. A quantitative prediction model for water and sand production is also established. As a result, our study can provide support for the advancement of technology for long-term stable production and sand control of hydrates, laying the groundwork for developing a stable production plan for natural gas hydrates in offshore areas and determining the optimal depressurisation method. [ABSTRACT FROM AUTHOR]