Analysis Of Log Data Research Articles

Supporting equitable access to learning via SMS in Kenya: Impact on engagement and learning outcomes

AbstractThe use of SMS messaging for education has grown in recent years, with particular attention recently during the Covid‐19 pandemic. Mobile phones often have high levels of ownership in low‐income contexts compared to computers, and lower connectivity requirements, which arguably make this a more equitable medium than data‐heavy online instruction, for example. However, given that gender can be a factor to influence mobile device access and use, it is also important to consider educational applications through a gender lens, to avoid further exacerbating digital divides. In this paper, we present an analysis of server log and evaluation data in relation to a literacy‐focused initiative for primary‐aged learners carried out in Kenya as part of the Tusome programme and through the SMS‐based M‐Shule education platform, which does not require an Internet connection or smartphone to run. The extent of engagement with the platform varies according to gender and location within the country. The data also demonstrate a positive impact on learning outcomes regardless of learners' gender and location. Furthermore, the learning gains are shown to be relatively cost‐effective in comparison with educational technology interventions in similar contexts. The findings show that this low‐connectivity adaptive model has a positive impact on learning outcomes. It is a scalable approach to support a range of learners in Kenya, providing more support to learners who need it, and leading to increased foundational learning outcomes overall. As such, the findings will also be of highly relevant to other low‐connectivity contexts. Practitioner notesWhat is already known about this topic Mobile phones can be used as a means to support learning, through mobile learning and SMS, particularly in low‐connectivity contexts, although there is a lack of rigorous evidence of impact upon learning outcomes. Mobile phone device ownership tends to be higher than computer or wired Internet connections in many low‐income contexts. Software applications which adapt to the learners' level have shown good potential for gender‐equitable learning outcomes in low‐income contexts; however, these often require an Internet connection in addition to computers or tablets to be run on. What this paper adds There is a lack of contextually relevant evidence of the impact of SMS‐based mobile learning applications in low‐resource and low‐connectivity contexts upon learning outcomes. Through analysis of data generated via an experimental design, this study provides evidence that literacy materials delivered through an SMS‐based educational platform—M‐Shule—can have a positive impact upon learning outcomes. Furthermore, gains are equitable in terms of learners' gender, and location, within Kenya. Implications for practice and/or policy Mobile phones can be an effective way of reaching learners to provide additional educational support as part of existing education programmes in low‐connectivity environments. Learning gains using M‐Shule are evidenced as significant and relatively cost‐effective. Existing high‐quality learning materials developed in other media can be effectively adapted to SMS to reach learners particularly who are out‐of‐school or during periods of educational disruption.

Analysis of Student Activities Based on Log Files in E-Learning Using Clustering Algorithm

This study was conducted to analyse log data generated by e-learning platforms such as Moodle or similar platforms. The main objective of this research is to identify patterns and insights that can help improve the student learning experience and the efficiency of platform management. This study identifies the most effective clustering algorithms for grouping students based on their behaviour and achievements in the e-learning environment, namely K-means and K-Medoids. The methods used to determine the optimal number of clusters are the Silhouette Coefficient method and the Elbow method, utilizing both methods to determine the best clustering algorithm results. Based on the analysis using K-means and K-Medoids clustering methods on the log data of the programming algorithm course, the total number of action logs over a one-month period is 95,461, with an average of 892 action logs per participant. The distribution of action logs based on class (A, B, and C) shows variation in the number and average of action logs per class. Types of activities such as assignments, forums, video views, and course views have different average frequencies, with video views being the most frequently visited activity. Pearson correlations between activity types show strong relationships between activities, with the highest correlation between visits and course views. The optimal number of clusters based on the Elbow and K-Medoids methods is three clusters. Cluster 3 in K-Means has the best performance with the smallest DBI value (0.424) and the smallest centroid distance (21,168.534).

Investigating Older Adults' Use of a Socially Assistive Robot via Time Series Clustering and User Profiling: Descriptive Analysis Study.

The aging population and the shortage of geriatric care workers are major global concerns. Socially assistive robots (SARs) have the potential to address these issues, but developing SARs for various types of users is still in its infancy. This study aims to examine the characteristics and use patterns of SARs. This study analyzed log data from 64 older adults who used a SAR called Hyodol for 60 days to understand use patterns and their relationship with user characteristics. Data on user interactions, robot-assisted content use, demographics, physical and mental health, and lifestyle were collected. Time series clustering was used to group users based on use patterns, followed by profiling analysis to relate these patterns to user characteristics. Overall, 4 time series clusters were created based on use patterns: helpers, friends, short-term users, and long-term users. Time series and profiling analyses revealed distinct patterns for each group. We found that older adults use SARs differently based on factors beyond demographics and health. This study demonstrates a data-driven approach to understanding user needs, and the findings can help tailor SAR interventions for specific user groups. This study extends our understanding of the factors associated with the long-term use of SARs for geriatric care and makes methodological contributions.

Sequence analysis and process mining perspectives to goal setting: What distinguishes business students with high and low self-efficacy beliefs?

This study investigates the relationship between students' self-efficacy beliefs, goal-setting, and learning tactics in an online business course. Using sequence analysis and process mining techniques, we analyzed log data from 209 students to identify distinct learning tactics and their association with self-efficacy beliefs, inferred from initial goal-setting and final grades. Four learning tactics were identified, with significant differences observed between students with high and low self-efficacy beliefs. High self-efficacy students demonstrated more varied and adaptive tactics, including greater use of quizzes and project-focused activities. In contrast, low self-efficacy students and those who didn't set goals showed less engagement and fewer monitoring activities. The project-focused tactic had the strongest correlation with final grades and goal achievement. Goal-setting at the course's start was linked to more effective learning behaviors and better outcomes. The study reveals how students' online learning behavior changes based on initial goal-setting and subsequent reflection. These findings contribute to research on self-regulated learning in online environments and offer practical implications for designing online courses and learning analytics interventions. Understanding these differences enables the development of targeted interventions to improve learning tactics and self-efficacy beliefs among students, ultimately enhancing their ability to achieve learning goals in online educational settings.

Classification of Logging Data Using Machine Learning Algorithms

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.

How to Tell Whether Patients Engage and Use a Patient Portal - An Analysis of Five Functions.

The road to a more sustainable healthcare system includes creating a digital interface to the healthcare system that patients can use to engage in their health problems while outside the consultation room. The aim of the study was to evaluate trends in the use of functions in a patient portal and assess which functions were most likely to contribute to sustainable healthcare. Open, uncontrolled retrospective analysis of citizens' use of the patient portal. Extraction and statistical analysis of log data. Log analysis revealed that patients engaged with the patient portal to make appointments, fill out and submit questionnaires, send messages to their care provider, inspect their laboratory results, and view notes about themselves. The functions that displayed a significantly increasing trend were the number of appointments made, the number of messages sent, and the number of checked test results. While portal engagement can reduce patient dependency on healthcare services, external factors also influence this outcome. Further research is needed to investigate which functions support healthcare sustainability and enhance patient empowerment, possibly through other study designs.

Revolutionizing SIEM Security: An Innovative Correlation Engine Design for Multi-Layered Attack Detection.

Advances in connectivity, communication, computation, and algorithms are driving a revolution that will bring economic and social benefits through smart technologies of the Industry 4.0 era. At the same time, attackers are targeting this expanded cyberspace to exploit it. Therefore, many cyberattacks are reported each year at an increasing rate. Traditional security devices such as firewalls, intrusion detection systems (IDSs), intrusion prevention systems (IPSs), anti-viruses, and the like, often cannot detect sophisticated cyberattacks. The security information and event management (SIEM) system has proven to be a very effective security tool for detecting and mitigating such cyberattacks. A SIEM system provides a holistic view of the security status of a corporate network by analyzing log data from various network devices. The correlation engine is the most important module of the SIEM system. In this study, we propose the optimized correlator (OC), a novel correlation engine that replaces the traditional regex matching sub-module with a novel high-performance multiple regex matching library called "Hyperscan" for parallel log data scanning to improve the performance of the SIEM system. Log files of 102 MB, 256 MB, 512 MB, and 1024 MB, generated from log data received from various devices in the network, are input into the OC and simple event correlator (SEC) for applying correlation rules. The results indicate that OC is 21 times faster than SEC in real-time response and 2.5 times more efficient in execution time. Furthermore, OC can detect multi-layered attacks successfully.

Identification of Multi-Parameter Fluid in Igneous Rock Reservoir Logging—A Case Study of PL9-1 in Bohai Oilfield

Since the “13th Five-Year Plan”, the exploration of large-scale structural oil and gas reservoirs in the Bohai oilfield has become more complex, and the exploration of igneous oil and gas reservoirs has become the focus of current attention. At present, igneous rock reservoir fluid identification methods are mainly based on the evaluation method of logging single parameter construction, which is primarily a qualitative identification due to lithology, physical property, and engineering factors. Accurate acquisition of interference logging data, and multi-parameter coupling and recording coupling methods are few, lacking systematic and comprehensive evaluation and analysis of logging data. Since conventional logging data in the study area have difficulty accurately and quickly identifying reservoir fluid properties, a systematic analysis was conducted of three factors: lithology, physical properties, and engineering, as well as a variety of logging parameters (gas measurement, three-dimensional quantitative fluorescence, geochemical, FLAIR, etc.) that can reflect fluid properties were integrated. Based on parameter sensitivity analysis, the quantitative characterization index FI of multi-parameter coupling fluid identification was established using the data from testing, sampling, and laboratory testing to develop the identification standard. The sensitivity analysis and optimization of characteristic parameters were carried out by integrating the data reflecting fluid properties such as gas surveys, geochemical data, and related logging data. Combined with gas logging-derived parameters and improved engineering parameters (the value of alkanes released by rock cracking per unit volume Cadjust, C1 abnormal multiple values, three-dimensional quantitative fluorescence correlation factor N), the fluid properties were identified, evaluation factors were constructed based on factor analysis, and fluid identification interactive charts were established. By analyzing test wells in the PL9-1 well area, the results of comparison test data are more reliable. Compared with conventional methods, this method reduces the dependence of a single parameter by synthesizing multiple parameters and reduces the influence of lithology, physical properties, and engineering parameters on fluid identification. It is more reasonable and practical. It can accurately and quickly identify the fluid properties of igneous rock reservoirs in the study area. It has a guiding significance for improving the accurate evaluation of logging data and increasing exploration benefits.

Systematic Evaluation of Deep Learning Models for Log-based Failure Prediction

With the increasing complexity and scope of software systems, their dependability is crucial. The analysis of log data recorded during system execution can enable engineers to automatically predict failures at run time. Several Machine Learning (ML) techniques, including traditional ML and Deep Learning (DL), have been proposed to automate such tasks. However, current empirical studies are limited in terms of covering all main DL types—Recurrent Neural Network (RNN), Convolutional Neural Network (CNN), and transformer—as well as examining them on a wide range of diverse datasets. In this paper, we aim to address these issues by systematically investigating the combination of log data embedding strategies and DL types for failure prediction. To that end, we propose a modular architecture to accommodate various configurations of embedding strategies and DL-based encoders. To further investigate how dataset characteristics such as dataset size and failure percentage affect model accuracy, we synthesised 360 datasets, with varying characteristics, for three distinct system behavioural models, based on a systematic and automated generation approach. Using the F1 score metric, our results show that the best overall performing configuration is a CNN-based encoder with Logkey2vec. Additionally, we provide specific dataset conditions, namely a dataset size >350\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$>350$$\\end{document} or a failure percentage >7.5%\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$>7.5\\%$$\\end{document}, under which this configuration demonstrates high accuracy for failure prediction.

Analisis Data Well Logging Untuk Mengetahui Lapisan Akuifer Sumur Bor Di Kabupaten Hulu Sungai Selatan, Kalimantan Selatan

This research aims to analyze well logging data to determine the aquifer layer of drilled wells in Hulu Sungai Selatan Regency, South Kalimantan. This information is very important for sustainably developing groundwater resources in the region. This research uses well logging data collected from various sources, including resistivity, gamma rays, and spontaneous potential logs. These data are then analyzed using various techniques such as correlation analysis, log interpretation charts, and software to identify aquifer zones based on their characteristic log responses. The analysis succeeded in identifying potential aquifer layers in the wells studied. Aquifers are characterized by resistivity, gamma rays, and lithology. Based on the results of the analysis using WellCad data, four types of rock were obtained, namely: weathered rock (top soil), sand, clay, and sandy clay. Demonstrates the effectiveness of well logging data analysis in identifying and characterizing aquifer layers. Based on these results, it is known that the distribution of confined aquifers and unconfined aquifers has water-carrying properties. So overall it can be concluded that Hulu Sungai Selatan Regency has quite large deep groundwater potential because there are many layers of sand.KEYWORDS: Well logging; aquifer; groundwater; Hulu Sungai Selatan; South Kalimantan.

Source Rock Prediction Using Well Log and Seismic Data: A Study of the Albian Stage in the Côte d’Ivoire Basin

The Côte d’Ivoire Basin is one of the most important oil production areas in Africa; and it is also the focus of global oil and gas exploration, but the degree of oil exploration and development in this basin are still in the early stages. Based on a comprehensive analysis of drilling, logging, and geochemical data in the Côte d’Ivoire Basin, this study comprehensively evaluates the Cretaceous lacustrine source rocks in the study area, establishes a total organic carbon (TOC) quantitative prediction model by using the total organic carbon method, determines the relationship between seismic reflections and the geochemical characteristics of the source rocks, and tracks the main source rocks horizontally. The results show that the TOC of the Lower Cretaceous Albian stage source rocks is 2.63 wt.%, the kerogen type II is dominant, and the vitrinite reflectance (RO) ranges from 0.3% to 0.9%. The source rocks are mainly in the low-mature to mature stage, and they are generally of good and premium quality. The distribution range of high-quality source rocks with TOC values of 4.00~5.00% is predicted using the total organic carbon method, and useing the seismic method, we determine that high-quality source rocks with larger thickness values and better continuity are mainly developed in the eastern depression center of the basin.

Variation of vertical stress in the McArthur and Beetaloo basins

The McArthur and Beetaloo basins have been the focus of unconventional hydrocarbon exploration, specifically targeting shale gas and tight gas resources. Vertical stress (SV), which is one of the three principal stresses, is a critical parameter in geomechanical analysis of sedimentary basins and has implications in fracture gradient calculation, pore pressure prediction, assessing the present-day stress regime, and hydraulic fracturing design. In addition, variations in vertical stresses can yield additional insights into the tectonic history of an area. In this study, we examine the variability of vertical stress magnitude and vertical stress gradients by employing a systematic analysis of density log and check-shot velocity data obtained from 31 wells located within the McArthur and Beetaloo basins. Our analysis revealed that vertical stress gradients range from 23 to 27 MPa/km (equivalent to ~1.0–1.2 psi/ft), with an average gradient of 25 MPa/km ± 1.0 (1.1 psi/ft). This variability in SV gradients underscores the inadequacy of the commonly assumed 1.0 psi/ft approximation for geomechanical analyses within these basins. The origins of such diverse SV gradients can be attributed to several factors; however, the likely cause of the SV gradient within this region is the presence, thickness, and depth of the shallow high-density volcanic rocks and a complex history of uplift and erosion.

Development and influencing factors of middle‐deep tight sandstone reservoir ‘sweet spots’ in fault basin—Taking the Eocene Shahejie Formation in Suning area of Raoyang Sag in Bohai Bay basin as an example

This study takes the tight sandstone of the lower sub‐member of the first member of the Eocene Shahejie Formation (Es1x) in the Suning area, Raoyang Sag, Bohai Bay Basin. Furthermore, it determines the rock type of the middle‐deep tight sandstone through lithologic profiling, core observation and log data analysis. The genetic control factors of tight sandstone in the study area are analysed using conventional thin section, pore‐casted thin section and scanning electron microscope, and the characteristics and genetic control factors of tight sandstone reservoirs are summarized. The research results show that the tight sandstones in this area are primarily feldspathic sandstone and feldspathic lithic sandstone, with high quartz contents, around 61.50%. The average porosity of the reservoir in the study area is 4.69%, and the average permeability is 2.92 mD, which are characteristics of low‐porosity and low‐permeability reservoirs. Through research, it has been found that the distribution of sedimentary facies and diagenesis in the study area control the development and distribution of ‘sweet spots’. Both compaction and cementation reduce the physical properties of the reservoir, while dissolution can increase porosity, thereby increasing the physical properties of the reservoir; dissolution resulted in an increase of approximately 3.57%–3.69% in the porosity of the tight reservoir in the study area, with an average porosity increase of 3.63%, and is the main controlling factor for the development of ‘sweet spots’ in the reservoir. The results provide an important basis for guiding the exploration and development of tight sandstone oil and gas in the lower sub‐member of the first member of the Eocene Shahejie Formation (Es1x)in the Suning area.

Sensitivity analysis of similarity learning models for well-intervals based on logging data

The problem of the interpretability of neural network predictions is crucial for industry, especially in areas where the cost of a mistake is incredibly high, to which oil&gas belongs. In this sphere, similarity models play an important role. One of the pre-required input data for constructing a geological model of hydrocarbon field are interwell correlation results through well logging data analysis. Recent publications address the problem of similarity assessment between distinct wells, which is the manual, time-consuming, and expert-based process. To enhance the interpretability of the developed deep learning models, we propose a visualization tool consisting of two methods. Our first method is an adaptation of a saliency map, which has already shown its visualization and interpretability quality. However, as the possibility of dealing with black-box models exists and is high enough, we propose another method based on the masking of the original well-interval. The case study from the Norway basin and our tool’s visualization quality evaluation demonstrate the effectiveness of the developed tool.

Analysis of the truck haulage system in an underground mine using long-term log data

ABSTRACT This paper addresses the collection, analysis, and optimization of log data related to the truck haulage system in a mine. After selecting an underground mine in South Korea as the research area, the Mine Production Management System (MPMS) using Bluetooth beacons and tablet PCs was utilized to collect log data related to the haulage system over 18 months. Through the analysis of the collected log data, strategies to enhance the productivity and efficiency of the haulage system were derived. Weekly analysis of log data files was performed, identifying periods with abnormal truck cycle times and conducting an analysis of the underlying causes. It was revealed that the truck cycle time is influenced by the haulage system, equipment operation, and the log data collection system. To shorten truck cycle times and improve the productivity and efficiency of transportation systems, continuous collection and regular analysis of log data and dynamic system and equipment operation based on this are important. Analyzing log data collected over an extended period can help predict future trends in the haulage system and formulate measures and strategies in response. Therefore, it can serve as a valuable decision-making tool for establishing short-term, medium-term, and long-term production and operation plans.

Sedimentary architecture of thin-layer beach bar sand bodies in the G oilfield, Niger

The G oilfield in the southeastern Termit Basin of Niger is characterized by thin-bed beach bar deposits exhibiting strong reservoir heterogeneity and suboptimal production efficiency, necessitating internal structural dissection of the beach bar sand bodies. Employing a well-seismic integration approach, we systematically dissect the architecture of these sand bodies layer by layer to determine their spatial distribution. A classification scheme for beach bar architecture is proposed, with core and log data analysis identifying essential architectural elements and their logging responses. Seismic amplitudes, thin bed delineation, frequency decomposition inversion attributes, and attribute fusion technology delineate the architectural boundaries. Integrating five indicators from four-level architectural recognition at wellbores—shallow lake mudstone appearance, bar margin/beach microfacies occurrence, logging curve morphology differences, beach bar thickness variations, and elevation differences between adjacent bars—enables detailed dissection of the beach bar architecture, corroborated by connectivity analysis. In the study area, beach bar distribution primarily develops in two modes: vertical stacking (accumulation of multiple main bars from different episodes) and isolated (stable mudstone interlayers between main bar sand bodies appearing relatively isolated). This research provides a basis for dissecting beach bar architecture reservoirs under sparse well conditions.

Characterization of reservoir quality in tight sandstones from the Benxi Formation, eastern Ordos Basin, China

The Benxi Formation in the eastern Ordos Basin harbors abundant natural gas resources and shows promising exploration and development potential. However, the reservoir characteristics are complex, and the primary controlling factors are unclear, presenting significant challenges for reservoir characterization. In response to these challenges, we conducted a systematic study on the characteristics of tight sandstone reservoirs in the Benxi Formation by integrating thin section analysis, scanning electron microscopy, high-pressure mercury injection, and conventional petrophysical analysis alongside well log data analysis. By applying empirical calculation formulas of a porosity evolution quantitative model, we elucidated the primary controlling factors of reservoir heterogeneity. Our research identified that the reservoirs in the eastern Ordos Basin, Benxi Formation, are predominantly composed of quartz sandstone and lithic quartz sandstone, with pore-filling cementation as the dominant cement type and the main storage spaces being intergranular pores and dissolved pores. The homogenization temperatures of fluid inclusions in authigenic quartz range from 92.8 to 185.7°C, indicating that the target layer is in the mesodiagenesis phase B. The main reasons for the differences in reservoir quality in the Benxi Formation are attributed to both sedimentation and diagenesis. In terms of sedimentation, two distinct sedimentary microfacies control the distribution of reservoir quality differences based on variations in quartz content and soluble components. Regarding diagenesis, the Benxi Formation underwent compaction, cementation, and dissolution, with compaction being the fundamental cause of widespread reservoir compaction.

Characterization of the deformation of the Jurassic-Cretaceous deposits of the Toulouk sector (Tim Mersoï basin, North Niger), relation with the uranium mineralizations

The Toulouk sector, located in the Tim Mersoï basin, is a particular area due to the presence of a Horst structure within the vast expanses of the Irhazer mudstones. This structure has shown sub-surface uranium mineralization. This raises the problem of understanding the deformation of this sector as well as the mechanisms that favoured the establishment of these mineralizations. Thus, the approach implemented consisted in: · Filed work, analysis of satellite imagery data, cartographic and logging data, · A correlation of geological and logging data, This approach allowed the establishment of a local lithostratigraphic framework and the visualization of the uranium distribution and copper mineralization in this sector. It appears from this work that the Toulouk sector would be subjected to an extensive synsedimentary phase that would have favored a structuring in horst and half-graben. The latter would have favored the development of several tectonic structures. These served as drains for the mineralizing fluids.

Exploring the relationship between LMS interactions and academic performance: A Learning Cycle approach

Research on the relationship between the digital traces of students in Learning Management Systems (LMS) and their academic performance has traditionally been an area of interest in the field of learning analytics. Aiming at achieving high interpretability and generalizability, this study reviews past research, defines a new categorization scheme for interactions in LMS and investigates the relationships between clickstream data of students’ activity and course performance, measured as final grade. The results of the multiple regression analysis of Moodle log data collected from three courses of diverse nature using various classifications of interactions suggest that the new categorization, the CILC (Classification of Interactions based on the Learning Cycle), improves the explanatory power when compared to previous classifications. The analysis suggests that the predictive ability of the models could depend on the delivery mode, with predictions improving as the delivery mode transitions from face-to-face learning to online learning. This finding highlights the need for context-specific considerations about the learning process. Compared to previous research, the analysis also reveals nuances that suggest that the relationships may depend on the instructional design. Finally, the findings also seem to support the notion that increased data quantity and quality may improve predictive models. In summary, the study contributes with valuable insights into the interplay between LMS interactions, course delivery mode, instructional design, and academic performance, and advocates for a balanced exploration of white-box and black-box modeling approaches in learning analytics research.

Integrating Well Logs and Core Data for Better Reservoir Characterization of Mamuniyat Formation, Murzuq Basin, Libya

Increasing the number of dry holes in the last two decades to more than twenty wells in Murzuq Basin necessitates a comprehensive reservoir evaluation study to resolve this issue. The current work combines well logging and core data analysis to precisely estimate petrophysical parameters and evaluate the Mamuniyat reservoir. The available data includes well logs from eleven wells and core data from one of the studied wells (I-14 Well). Utilizing well logs and core data, the Mamuniyat Formation is found to be made up of sandstone with a low shale content between 5 and 10%. It has effective porosity values that vary between moderate (13%) and good (16%), with oil saturation ranging from 62 to 91%. Using core data analysis, it was found that the grain density is 2.65 g/cc. A good match between the core-derived and log-derived porosity values is observed. It could be concluded that the Mamuniyat Formation is a sandstone reservoir with low shale volume, moderate effective porosity, and high oil saturation. The reason for the large number of dry holes in Murzuq Basin could be attributed to other factors other than reservoir quality.