DragonWave

Magdalena Szwed Doctoral School of Social Sciences, Maria Curie-Sklodowska University in Lublin, Poland https://orcid.org/0009-0005-5156-680X Oleg Gorbaniuk 1. Institute of Psychology, Maria Curie-Sklodowska University in Lublin, Poland; 2. Department of Psychology, Casimir Pulaski Radom University, Poland https://orcid.org/0000-0001-9830-8537 Julia Buglińska Fundacja Niepodzielni https://orcid.org/0009-0002-3278-8704 Weronika Boreczek Paderewski International Primary School in Lublin, Poland https://orcid.org/0009-0006-5489-3360 Mateusz Mazur iGaming Dragon https://orcid.org/0009-0003-1388-9558 DOI: https://doi.org/10.12923/2353-8627/2025-0006 Introduction: The Whole Trait Theory allows to conceptualise constructs as a short-term state and as a trait. Depression also can be understood in two ways as a short-term state and as a trait. The aim of the presented study was to verify the factor structure of depression scale at both the inter- and intra-individual levels and to examine the correlation between the density of depression state and depression as a trait. Material and methods: The study used the depression scale from the DASS-21, and conducted a diary study that lasted 14 days and was based on repeated measurements on a sample of 147 people (49.3% female, 50.7% male). The research was conducted as an online survey, with items displayed in random sequence for each person every day. Respondents reported daily on the intensity of their experience of depression. Results: A two-level confirmatory factor analysis proved that single-factor structures are the best way to explain the inter- and intra-individual variance of depression. The congeneric reliability for the measurement of depression as a state and as a density of states was high. The correlation between the average measurement of depression as a state and a single measurement of depression as a trait was very strong, thus providing empirical evidence of the Whole Trait Theory. Conclusions: The present study opens new possibilities for measuring depression in research and clinical practice using repeated measurements of depression as a state. The study of the dynamics of changes in depression states allows for more precise prediction of a person's tendency to experience depression. Keywords: depression, state, trait, DASS-21, multilevel approach

According to IMPACT Study, the annual global cost of corrosion is $2.5 trillion, equivalent to roughly 3.4% of the world's gross domestic product (GDP). Based on available studies and data, mining is an insignificant contributor to the cost of corrosion compared to other economic sectors such as infrastructure, oil and gas, and defense. However, mining operations can be highly corrosive and there have been documented catastrophic failures where corrosion has played a causal role. This raises the question: is corrosion cost properly measured and reported for mining industry? This paper presents case studies from different mining sites to show how corrosion cost is often hidden and not properly tracked, but significantly increases the operational expenditure (OPEX). It also discusses some available methodology and approach to overcome this issue. In addition, an example is included to illustrate how lifecycle corrosion cost data allows effective capital investment decision-making.

Reservoir characterization remains one of the most significant requirements in optimizing hydrocarbon recovery, yet traditional methods still struggle with data variability, complexity, and cross-well generalizability. This study presents a supervised machine learning (ML) approach to predict production profiles across three wells, focusing on dataset structure variabilities, specifically the use of the whole dataset compared to perforated-zone subsets, impact model performance, and predictive accuracy. In this study, 926 data points from two training wells (A and B) was used for training and validations, while 917 points from an additional well C was used for out-of-distribution testing. Advanced data processing techniques, including variance inflation factor (VIF) for mitigating multicollinearity and singular value decomposition (SVD) for identifying hidden correlations across the three wells, were used. Ten different ML models were trained via randomized search optimization, with the Light Gradient Boosting (LightGBM) model achieving the highest accuracy (MAE: 0.0679, R2: 0.88). Testing on Well C revealed a deteriorated performance (MAE: 1.1907, R2: 0.66) and poor generalizability, especially for variables out of range compared to the training set. This can be attributed to the inherent geological variability and differences. The predictions deteriorate even further when focusing on perforated zones from the three wells (MAE: 0.866, R2: 0.459), indicating that additional factors may require further investigation to enhance prediction accuracy in these specific reservoir intervals.

This study investigates the application of machine learning (ML) techniques for virtual flow metering (VFM) in oil wells. To develop a robust and accurate VFM model, Two Different Fields were tested for the new technique, one field offshore Egypt & the other field is onshore Iraq; This comprehensive dataset included detailed production data, well parameters, and operational information. Various ML algorithms, including Random Forest, Support Vector Regression, and Artificial Neural Networks, were rigorously tested and compared to identify the optimal model for VFM. The selected model was then calibrated against production test data to ensure accurate and reliable predictions. The calibrated ML model enables the daily verification of production allocation, providing real-time insights into the performance of individual wells. By continuously monitoring production rates, anomalies such as production declines, unexpected shutdowns, or changes in reservoir behavior can be promptly detected. These timely insights facilitate efficient decision-making, allowing for timely interventions and optimization of production strategies. Additionally, the ML-based VFM model supports accurate reservoir allocation by providing reliable estimates of individual well contributions, enabling informed decisions on production allocation and field development planning. After rigorous calibration, the model's performance was further validated through a blind test, where it was applied to a new set of wells without prior training. The results of the blind test confirmed the model's usability and its ability to provide accurate predictions. Moreover, the daily application of the model has demonstrated reasonable agreement with well test data, further solidifying its reliability and practical value. The findings of this study demonstrate the significant potential of ML-based VFM to enhance the efficiency and effectiveness of oil production operations in Egypt & Iraq Assets. By leveraging advanced ML techniques and a comprehensive dataset, this approach offers a reliable and cost-effective solution for improving production performance diagnostics, optimizing reservoir management, and ultimately maximizing oil recovery

The Gulf of Suez Petroleum Company (GUPCO), which has been overseeing mature offshore oil fields in Egypt since the late 1960s, seeks to harness advanced artificial intelligence (AI) technologies to optimize the management of its extensive field data. This initiative is centered on leveraging AI to maximize the value derived from GUPCO's substantial dataset, amassed through the drilling and operation of hundreds of producing and injecting wells, with particular emphasis on the company's two largest fields. Despite the inherent challenges and data gaps characteristic of brownfields, this dataset constitutes an invaluable yet underutilized asset, rich with potential insights. The primary objective of this project is the development of an AI-driven model designed to recommend optimal off-take strategies, identify opportunities for drilling and well interventions, and establish a scalable prototype applicable across GUPCO’s broader field portfolio. Furthermore, this initiative seeks to cultivate organizational expertise in AI applications for oilfield operations. The model is founded upon a purely data-driven methodology, relying exclusively on empirically measured field variables, thereby obviating the necessity for interpretative assumptions. This extensive dataset, spanning five decades, undergoes rigorous processing through advanced fuzzy pattern recognition techniques to extract key parameters. A highly integrated system of artificial neural networks (ANNs) is employed to train, calibrate, and validate the model. To ensure robust predictive capabilities, recent data is reserved for blind validation, enabling the AI model to discern and learn concealed field patterns. Once history-matched, the model is subsequently deployed across various scenarios to forecast production, conduct sensitivity analyses, and identify optimal locations for new drilling activities. The model continuously evolves as new data is assimilated, ensuring its adaptability and reliability over time. Although this initiative remains in its early stages at the time of writing This Paper, significant progress has already been realized. A meticulous process of data review and assurance has been prioritized to uphold model accuracy. Data for the most giant field of GUPCO have undergone extensive QC/QA. The model is expected successfully achieve history matching for wellhead pressure and production rates—including oil, gas, and water—across all wells via a fully automated process, yielding exceptionally high accuracy. Its forecasting capabilities will be rigorously validated through blind testing on spatial and temporal data previously unseen by the model. Extensive simulations—numbering in the hundreds—are expected to be conducted to account for reservoir uncertainties, and to produce reliable production forecasts and comprehensive sensitivity analyses for the projection period. Furthermore, the model is intended to identify several high-potential candidates for infill well drilling, underscoring its capacity to support critical operational decision-making. This study introduces a pioneering AI-based technological advancement to GUPCO, aiming to fundamentally transform the efficiency and depth of technical analyses that underpin field management strategies. This advancement will significantly enhance decision-making capabilities, enabling more rapid and informed responses to operational challenges. Additionally, this initiative represents a strategic investment in human capital, fostering expertise within GUPCO in the application of AI to oilfield operations. As a secondary benefit, the project entails a comprehensive review of GUPCO’s extensive data archive, rendering it a more structured and actionable resource to support future initiatives and assessments. Ultimately, this initiative positions GUPCO at the forefront of AI-driven oilfield management in Egypt, setting a precedent for technological innovation within the industry.