Wave Dragon (Denmark)

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

Abstract 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.

Abstract Due to influence of strike-slip, thrust and other tectonic processes as well as mud volcano eruption, the structural characteristics of South Caspian Basin are very complex, the strike-slip faults are developed with small fault thrown and steep sections, which is difficult to identify, resulting in unimplemented structures and unclear reservoir characteristics, it seriously restricts the development of oil fields. Based on OBN seismic data and advanced artificial intelligence small fault identification method, this paper proposes a set of multi-disciplinary structural compartment interpretation method. It is found that the strike-slip fault of Block CH in the South Caspian Basin is consistent with Riedel's simple shear model, and six strike-slip fault modes are mainly developed. The seismic interpretation of strike-slip faults can be completed by combining AI with curvature attributes, and the interpretation accuracy of strike-slip faults can be significantly improved by combining the results of multi-disciplinary fault cut identification. Two structure belts are developed in Block CH, controlled by two branches of the Apsheron strike-slip, and under different stress effects, the shallow faults of the oilfield are en echelon distributed on plane, while the deep formation faults are horsetail shaped. Flower-like structure is the main rich area of hydrocarbon, and the reservoir is mainly controlled by the lateral shield of faults, each fault block is an independent compartment. The oil-water relationship of ZH oilfield has been successfully cleared by the above method, which provides a reliable geological basis for the fine development of the oilfield, it also can be provided technical support for compartments identification of similar structure-lithology composite reservoirs.

Abstract Data collection and analysis is vital for evaluating the performance of any equipment or system. Though the methodology of collection, transmission, storage and retrieval varies in different scenarios, the basic components of the set-up remain the same. The Data Acquisition and Monitoring System evolved from the need for remote monitoring of facilities that did not have an independent reliable infrastructure to implement a full-fledged SCADA system. Back when IT infrastructure was prioritized for business communications within the limited bandwidth allocated, collection and transmission of process data through the available network infrastructure was a challenge. More so when there are concerns regarding the confidentiality of data being collected and transmitted. Individual facilities were provided with local instrumentation and controls for reliable operations. With the growing production rates, the focus generally goes for improving the processing facilities rather than reading, reporting or recording. Instead of an independent dedicated network for Supervisory Control and Data Acquisition system that mandate a huge infrastructural cost and dedicated skilled personnel, a pilot project was initiated with a minimum budget to test data transfer through existing communication network. Field transmitters were installed on Wellheads, Risers and Manifolds and Remote Terminal Units were provided to collect data from two platforms and to facilitate data transfer to the servers through existing microwave communication network with minimum field instrumentation. Considering bandwidth limitations and priority for business communications, servers in redundant configuration were placed strategically to ensure continuous data gathering. Pilot project was successful in providing reliable data and trends with minimum disruptions. Augmentation of DAMS network was envisioned to implement Digital Oil Field for monitoring multiple assets in real time. Implementation of Digital Oil Field continued with the addition of new field instrumentation and RTUs followed by server upgrades and integration of PLC based platform control systems to add four major producing platforms to the Data Acquisition and Monitoring System through existing microwave communication network. Upon successful commissioning, it was possible to gather data from various wellheads, process equipment and other independent meters for remote real time monitoring and analysis through user friendly HMI screens and customized dashboards to manage multiple assets effectively, improving performance evaluation, safety and reliability of operations.

Abstract The Cheleken fields, located in southern Caspian Sea, are made of a succession of stacked sand reservoirs over a 6km time window, with significant areal and vertical variations. A seismic inversion was carried out with the aim of improving the understanding and delineation of these complex reservoirs. 3D OBN seismic data, covering the area of interest, was recently acquired. A tailored processing and velocity model building workflow was designed to produce a good quality seismic data volume and a reliable velocity model which were used as inputs for a subsequent reservoir characterization study. Deterministic and stochastic inversions followed by facies and porosity estimation based on statistical methods were applied to predict reservoir occurrence and quality. Due to the large time window of the stacked sand reservoirs, the inversion required separate wavelets and facies/porosity model calibration per interval. An integrated process, including seismic data, well logs, seismic velocity fields as well as the interpreted horizons, was adopted to better characterize the targets and succeeded in predicting reliable rock properties away from well boreholes. The inverted elastic attributes were converted into sand/shale facies using a Bayesian classification approach. Despite the limited seismic resolution and a significant overlap in the elastic domain between facies, the method showed good predictability at the well locations and provided a better understanding of the spatial sand distribution. In addition, a new multi-attribute statistical transform approach applied for reservoir property estimation helped predicting the porosity to further optimize future well planning. One of the benefits of the employed statistical methods is the ability to gauge uncertainties associated to the estimates in the form of facies probability and porosity standard deviation. In particular, the facies probability proved valuable for detecting thin sands and mapping their spatial extension. The main challenge of the Cheleken fields is the presence of thin stacked sands that are difficult to interpret and to map on the seismic data. The statistical methods applied to interpret the inversion results allowed a better mapping of the thin sands across the volume. A new approach for reservoir property estimation based on a multi-attribute statistical transform was successfully tested to provide additional reservoir quality estimation.

Abstract Safa Field is a newly established onshore oilfield located in the southern central region of the Gulf of Suez, Egypt. It features a complex Miocene stratigraphic reservoir known as the "Hawara Sandstone Formation," which is capped by thick layers of shale and carbonate that act as a cap rock. After 22 years of production, the field has begun to show signs of water-cutting. Significant depletion has occurred within the sandstone layers due to continuous production. As a result, the reservoir pressure has dropped from an initial 4,200 psi at 9,600 feet to approximately 2,200 psi. Initially, using high mud weight was not problematic when drilling the reservoir. However, with the significant drop in pressure, it has become impossible to drill through all formations as before. The shale formations require high mud weight to prevent collapse, while the sandstone layers need lower mud weight to avoid mud losses. This paper emphasizes the role of high-resolution 3D geomechanical modeling in optimizing well costs for the Safa Oil Field. By integrating geological, geophysical, petrophysical, reservoir pressure, and drilling data, the study introduced a new casing set and optimized mud weight design tailored to the current stress conditions following depletion. The revised mud weight calculations positively influenced the casing design for two new wells. Additionally, an extensive uniaxial compressive strength analysis was conducted on the reservoir, resulting in the development of a new unconfined compressive strength equation. This equation, derived from an empirical relationship with total porosity, helped to reduce uncertainties in calculations for the reservoir section.

Derelict crab pots pose a major risk to marine biodiversity and the Dungeness crab (Metacarcinus magister) fishery in Washington State. These traps continue to fish after loss, resulting in a reduction of crab population which threatens local economies and ecosystem health. The current diver-based methods for recovering these lost, discarded, or otherwise abandoned crab pots are expensive and inaccessible for many small coastal communities. This project proposes the use of Remotely Operated Vehicles (ROVs) and geospatial analysis techniques as an alternative low-cost recovery methodology enabling community-driven derelict gear location and retrieval. A comprehensive methodology is developed inclusive of site selection, surveying, ROV-based crab pot recovery, and disposal. This methodology was tested in the coastal waters of Jefferson County, Washington State, where thirty crab pots were located and twenty approved to be removed. Additionally, the study outlines a model for community-based research that involves stakeholders, students, and invested residents in this marine resource management. It is demonstrated that the ROV-based method is an effective low-cost and community-driven way to recover derelict crab pots on a localized scale.

Abstract Excess water production is becoming a major problem in mature oil fields as the oil reservoir is subject to long water flooding process. As the oil field matures, producing more oil out of maturing reservoirs entails more water production. It is essential to have a robust water management process in place. Gulf of Suez Petroleum Company (GUPCO) is a pioneer in Egypt and middle east when it comes to applying a very advanced water flood projects for secondary recovery, Starting from Morgan field, Ramadan, October and July field by injecting into different reservoir Lower Rudies, Upper Rudies and Cretaceous, these successful examples have been done and still running to recover great amount of oil. Some of company reservoirs as Nubia Reservoirs produce under strong water aquifer drive mechanism other reservoirs like Morgan field where the majority of production comes from Kareem and Belayim reservoirs under depletion drive mechanism that's why water flooding was crucial to maintain reservoir energy and increase recoverability thus increasing total water production as well The presence of water in the production stream can have several effects specially on GUPCO offshore production facilities, and managing water production is a critical aspect of offshore operations as it may affect several aspects of production oil cycle like; Increasing Operating Costs, Corrosion and Scaling, Wellbore Issues and Environmental and Disposal Challenges. Produced water management is now under great concern from the company management that's why a multidisciplinary team consisting of Reservoir Engineers, Production Engineers and field engineers aiming to work on reducing water production by 30% as well as saving 30% from injection gas lift gas. The team worked in an integrated and innovated approach with a detailed workflow with 3 phases of implementation, the output was an initiative plan, which includes actions in the high-water rate producers like direct water shut-offs for the water source zone, surveillance for future actions and Actions in the offset injectors that includes isolating perforation intervals or surveillance in addition to new water injection conformance.