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2025 International Petroleum Technology conference, IPTC 2025

作者： Mustafa, Azreen Ravuri, Meher Vierkandt, Steven Razi, Siti Abdullah Ismail, Wan Akmal Wan Heah, Lianne Rodriguez, Dryonis Pertuso Sabharwal, Varad Chauhan, Prithvi Singh Sankaran, Sathish Hess Corporation United States Xecta Digital Labs United States

ISBN: (纸本)9781959025436

Optimizing hydrocarbon field production is a multifaceted and demanding process that hinges on precise reservoir understanding and effective data management. Traditional well and reservoir monitoring methods are manual and interpretive and struggle to scale across numerous wells with large data volumes, hindering timely decision-making. Can hybrid models, combining physics and data-driven approaches, be utilized for automated real-time performance monitoring, early detection of anomalies, identification of actionable opportunities, and continuous system optimization? A digital solution integrating various reservoir and production workflows was deployed to enhance asset operational efficiency. Event detection algorithms for identifying well shut-ins and stable flow periods catalog planned and unplanned occurrences during regular field operations. Shut-in analyses are conducted automatically to derive key well parameters (e.g., productivity index, average reservoir pressure) and reservoir diagnostics using a hybrid model (a fusion of physics-based and data-driven methods). Any performance deviations, such as a decline in productivity, liquid loading, choke erosion, or water/gas breakthrough, are flagged as anomalies, facilitating exception-based surveillance. Well, steady-state detection algorithms update inflows in response to changes in operational points. The integrated production system, including wells and pipeline networks, is continuously calibrated and leveraged for production optimization and short-term business forecasting by assessing multiple scenarios. This paper outlines the application of the integrated hybrid modeling system to shallow water assets. The system enabled rapid evaluation and identification of several valuable opportunities for fields with significant reservoir uncertainties. Event detection algorithms improved model calibration data density for shut-in analysis by over 200%. An unsupervised learning method for predicting choke erosion was

关键词： Efficiency

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27th International conference on Distributed Computer and Communication Networks, DCCN 2024

27th International Conference on Distributed Computer and Co...

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27th International conference on Distributed Computer and Communication Networks, DCCN 2024

ISBN: (纸本)9783031808524

The proceedings contain 36 papers. The special focus in this conference is on Distributed Computer and Communication Networks. The topics include: Radio Resources Management Model of 5G Network with Two NSIs and Priority Service;energy-Efficient Framework for Task Caching and Computation Offloading in Multi-tier Vehicular Edge-Cloud Systems;autoregressive and Arima Pro-integrated Moving Average Models for Network Traffic Forecasting;stability Conditions of Two-Class Preemptive Priority Retrial System with Constant Retrial Rate;on Physical Proximity Serverless Presentations;measurement-Based Received Signal Time-Series Generation for 6G Terahertz Cellular Systems;optimizing Energy Efficiency via Small Cell-controlled Power Management for Seamless data Connectivity;analysing Performance Metrics of an All-Optical Network in Fault Conditions and Traffic Surges;analysis of Polling Queueing System with Two Buffers and Varying Service Rate;simulation of M/G/1//N System with Collisions, Unreliable Primary and a Backup Server;state-Dependent Admission control in Heterogeneous Queueing-Inventory System with Constant Retrial Rate;stochastic analysis of a Multi-server Production Inventory System with N-Policy;simulation-Based Optimization for Resource Allocation Problem in Finite-Source Queue with Heterogeneous Repair Facility;tandem Retrial Queueing System with Markovian Arrival process and Common Orbit;Polling Model for analysis of Round-Trip Time in the IAB Network;reliability analysis of a k-out-of-n Single Server System Extending Service to External Customers Under N-Policy and Server Vacations;reliability analysis of a Double Hot Standby System Using Marked Markov processes;modeling Distributions of Node Characteristics in Directed Graphs Evolving by Preferential Attachment;convolution Algorithm for Evaluation of Probabilistic Characteristics of Resource Loss Systems with Signals;controlled Markov Queueing Systems Under Uncertainty with Deep RL Algorithm;probability Chara

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10th Joint conference on Serious Games, JCSG 2024

10th Joint Conference on Serious Games, JCSG 2024

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10th Joint conference on Serious Games, JCSG 2024

ISBN: (纸本)9783031741371

The proceedings contain 41 papers. The special focus in this conference is on Serious Games. The topics include: A Taxonomy for Enhancing Metacognitive Adaptivity and Personalization in Serious Games Using Multimodal Trace data;an Architecture for Repeatable, Large-Scale Educational Game data analysis: Building on Open Game data;identifying When and Why Students Choose to Quit Jobs in a Science Exploration Game;integrating data from Multiple Sources in Evaluation Studies of Educational Games: An Application of Cross-Classified Item Response Theory modeling;identifying Player Strategies Through Segmentation: An Interactive process Visualization Approach;examining Student Responses to Game Layers in Cultural Geography: A Study About Game Spatiality in a Role-Playing Game Design;game-Based Learning Analytics: Insights from an Integrated Design process;crossing Valley: Development of a Serious Game to Measure Cognitive Flexibility in a Problem-Solving Context;serious Practices for Interactive Waste Sorting Mini-game;Kongruent - A Shader Language and Compiler for Efficient and Approachable GPU-Programming;"Masters Against Food Waste" Providing Children with Strategies to Avoid Food Waste;understanding Player Experience in Museum-Based Learning Games: A Mixed-Methods analysis;tracing Emerging Complexity of Scientific Reasoning Actions During Game-Based Learning;sky Dash: Evaluating the Effects of a Serious Low-Threshold Mobile Game on Learning Efficacy and User Experience in a Repetitive Learning Task;collaborative Knowledge Development: An Exploration of Knowledge Space Theory in Multiplayer Learning Games;playful Locative Interaction in Museums and Exhibitions with Immersive Augmented Reality;assessing the Impact of Haptic Feedback on Stress and Performance in Virtual Reality-Based Police Training;against Isolation in the Museum: Playful Co-presence with Immersive Augmented Reality;exploring Emotional Design Features for Virtual Reality Games;transforming Museum Experie

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Exploring the Assimilation of All-Sky FY-4A GIIRS Radiances and Its Forecasts for Binary Typhoons

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IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING 2025年 18卷 5949-5959页

作者： Xie, Qian Li, Deqin Yang, Yi Zhao, Yonghong Li, Hong Zhu, Shengjie Pan, Xiao Lanzhou Univ Coll Atmospher Sci Res Ctr Weather Forecasting & Climate Predict Lanz Key Lab Climate Resource Dev & Disaster Prevent Ga Lanzhou 730000 Peoples R China China Meteorol Adm Inst Arid Meteorol Lanzhou 730020 Peoples R China China Meteorol Adm Inst Atmospher Environm Opening Key Lab Northeast Cold Vortex Res Shenyang 110166 Peoples R China Shenyang Agr Univ Agron Coll Shenyang 110866 Peoples R China

Cloud interference significantly affects infrared (IR) satellite observations, posing substantial challenges in data assimilation. The geostationary interferometric infrared sounder (GIIRS) onboard Fengyun-4A (FY-4A), the first IR hyperspectral instrument carried on a geostationary satellite, has been extensively evaluated through direct clear-sky assimilation. However, its utility in cloud-affected areas has yet to be thoroughly evaluated. This study investigates the impact of all-sky assimilating long-wave temperature channels from GIIRS on forecasts of binary typhoons Maysak and Haishen (2020) using the Weather Research Forecast model. Quality control procedures, observation error settings, and variational bias corrections are incorporated into the three-dimensional variational data assimilation system for both clear-sky and all-sky scenarios. These approaches mitigate negative observation-minus-background statistics, producing a more symmetric distribution, along with a better brightness temperature simulation under all-sky conditions. Furthermore, assimilating all-sky GIIRS observations enhances the depiction of detailed typhoon structures, such as the upper level warmer distribution in Maysak, accompanied by larger analysis variations. This process also improves subsequent typhoon track and landfall precipitation forecasts. This research highlights the significance of employing high-temporal IR data in cloudy regions for the all-sky assimilation of FY-4A GIIRS data.

关键词： Clouds Weather forecasting Tropical cyclones Predictive models Atmospheric modeling data models Precipitation Analytical models Satellites Forecasting Binary typhoons geostationary interferometric infrared sounder (GIIRS) satellite all-sky assimilation three-dimensional variational (3D-Var) data assimilation weather research forecast (WRF)

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Sensitivity analysis for considering the process dynamics during the calibration of process force models 20

Sensitivity analysis for considering the process dynamics du...

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20th CIRP conference on modeling of Machining Operations in Mons, CIRP CMMO 2025

作者： Wenzel, Melina Welling, Daniel Biermann, Dirk Wiederkehr, Petra Virtual Machining TU Dortmund University Otto-Hahn-Str. 12 Dortmund44227 Germany Institute of Machining Technology TU Dortmund University Baroper Str. 303 Dortmund44227 Germany Lamarr Institute for Machine Learning and Artificial Intelligence TU Dortmund University Joseph-von-Fraunhofer-Str. 25 Dortmund44227 Germany

When analyzing milling processes, various characteristics such as process forces and tool deflections can be investigated using process simulations. The analysis of cutting forces is subject to the dynamic effects of the tool and workpiece, whereby the force measurement technology used has a further influence due to its own modal properties and its specific force transfer behavior. Particularly during finishing with spherical milling tools, the variation of radius and helix angle along the cutting edge leads to variations of the engagement situation and effective cutting speed. These different geometric properties and orientations of the tools infuence the process forces and dynamics and their interactions. Due to various influences on force measurements and dynamic superimposition, a higher model quality can be achieved through a sophisticated calibration methodology. In this paper, a methodology is presented in which the effects of tool dynamics are included in the calibration of force model coefficients to improve the accuracy of the resulting model. To this end, the inclusion of non-stationary tooth engagements during run-in and run-outs are considered, where process dynamics are more prevalent. For this analysis the cutting edge of spherical tools was divided into sections using confocal microscopy, which were replicated as analogous tools using cylindrical end mills. Peripheral milling tests with different process scenarios were conducted with these analogy tools, which had different geometric properties, in order to generate an experimental database. Based on this data, a sensitivity analysis was carried out for process force calibration taking process dynamics into account. © 2025 The Author(s).

关键词： Milling (machining)

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Design of University Innovation and Entrepreneurship System Based on Cloud Model data Mining Algorithm

Design of University Innovation and Entrepreneurship System ...

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2024 International conference on Mechatronics and Intelligent control, ICMIC 2024

作者： Ma, Yingying Shandong Institute of Commerce and Technology Shandong Jinan250103 China

ISBN: (数字)9781510686847

ISBN: (纸本)9781510686830

This article delves into the design and implementation of innovation and entrepreneurship systems based on cloud model data mining algorithms in university environments. Firstly, through in-depth communication with stakeholders such as university management, teachers, and students, a comprehensive analysis and planning of the system's requirements were conducted. Next, the article provides a detailed description of the system architecture design, including key components such as data collection and management, data preprocessing, cloud model data mining, user interface, reporting and visualization, security and privacy protection, as well as user feedback and support. During the system development process, emphasis was placed on the design principles and construction of cloud model data mining algorithms, as well as the optimization measures taken to improve system efficiency and accuracy. The system implementation process follows a series of strategies, including preliminary planning, system development, data preparation and migration, system integration, user training, as well as system deployment and continuous maintenance. Each stage is carefully designed and executed to ensure the effectiveness and reliability of the system. After implementation, a comprehensive performance evaluation of the system was conducted, including accuracy, efficiency, and user satisfaction. The results showed that the system performed well in multiple aspects, but also pointed out some areas that needed improvement. Finally, the article summarizes the main advantages and limitations of the system, and puts forward suggestions for future work directions, including further optimizing user experience, algorithm performance, and expanding application scope. This study provides an efficient and reliable data support and decision-making assistance tool for innovation and entrepreneurship activities in universities, which is of great significance for promoting the development of the innovati

关键词： Decision making

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Numerical Diffusion and its Impact on System-Identification for an Industrial Heating process 11

Numerical Diffusion and its Impact on System-Identification ...

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11th Vienna International conference on Mathematical Modelling, MATHMOD 2025

作者： Weiss, Ruven Diehl, Moritz Reuter, Johannes Instutute of System Dynamics HTWG Konstanz Germany Department of Microsystems Engineering Universität Freiburg Freiburg Germany

This paper deals with system-identification for a distributed parameter heating process where a solid substrate is moving through a spatially extended heating zone and heated up by applying hot air to its surface. The temperature distribution inside the substrate is modeled in a spatial plane, where heat conduction is considered in the direction, perpendicular to the direction of movement. In contrast to previous work, where scalar model parameters (*** thermal parameters of the substrate) have been identified, here, the quantities for the heat transfer (heat transfer coefficient and air temperature) are identified as functions yielding a significantly improved fit to the measurement data. This improved system-identification is performed for two early-lumping modeling approaches, which differ in the way the advection term in the governing Partial Differential Equation is discretized: one uses Eulerian coordinates, where the computational grid is stationary, whereas the second employs Lagrangian coordinates where the grid is moving with the substrate. The differences of the two approaches are discussed with the main focus on numerical diffusion. Especially its impact on the system-identification is investigated: Although the fit to the measurement is comparably good in both cases, very different solutions are obtained for the identified functions which, we argue, is due to the optimizer counteracting the smoothing effect of numerical diffusion. Copyright © 2025 The Authors.

关键词： Partial differential equations

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4th Energy *** conference, EI.A 2024

4th Energy *** Conference, EI.A 2024

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4th Energy Informatics.Academy conference, EI.A 2024

ISBN: (纸本)9783031747373

The proceedings contain 48 papers. The special focus in this conference is on Energy ***. The topics include: A Cost-Effective Edge Computing Gateway for Smart Buildings;leveraging Internet of Things Network Metadata for Cost-Effective Automatic Smart Building Visualization;process-to-Market: A Web-Based Evaluation Tool for Electricity Market Participation;ioT Based Smart Air Ventilation and Energy Management System;leveraging Open data for Energy Source Selection in Bi-valent Industrial processes;legal Overview of Latest Developments in the Energy Sector Regarding data Protection and Cybersecurity;Energy data Collection Protocol: A Case Study on the ADRENALIN Project;dataPro – A Standardized data Understanding and processing Procedure: A Case Study of an Eco-Driving Project;detection of Municipal Heat Plan Documents Using Semantic Recognition Methods;challenges in Transitioning from Co-simulation to Practical Application: A Case Study on Economic Emission Dispatch in a Greenhouse Compartment;multi-agent Based Simulation for Investigating Centralized Charging Strategies and Their Impact on Electric Vehicle Home Charging Ecosystem;leveraging Digital Twins for Sustainable District Heating: A Study on Waste Heat from Power-to-X Plants;hardware-in-The-Loop-Based Validation of Distribution System control Applications with Grid Operators, Customer and Market Participants;geospatial Semantic Enriched Digital Twin with Logical Reasoning Rules for Managing control Loops;data-Driven Digital Twin for Foundry Production process: Facilitating Best Practice Operations Investigation and Impact analysis;automation Level Taxonomy for Time Series Forecasting Services: Guideline for Real-World Smart Grid Applications;enhanced Consumer Segmentation Through Load Profile analysis Using Autoencoder and K-Shape Clustering;occupants Experiencing Energy Poverty: Where are They in Energy datasets and Time Use Surveys?;extracting Daily Aggregate Load Profiles from Monthly Consu

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4th Energy *** conference, EI.A 2024

4th Energy *** Conference, EI.A 2024

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4th Energy Informatics.Academy conference, EI.A 2024

ISBN: (纸本)9783031747403

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A Comprehensive analysis of process Energy Consumption on Multi-socket Systems with GPUs 11th

A Comprehensive Analysis of Process Energy Consumption on Mu...

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11th Latin American conference on High Performance Computing

作者： Leon-Vega, Luis G. Tosato, Niccolo Cozzini, Stefano Univ Trieste Trieste Italy AREA Sci Pk Trieste Italy

ISBN: (纸本)9783031800832;9783031800849

Robustly estimating energy consumption in High-Performance Computing (HPC) is essential for assessing the energy footprint of modern workloads, particularly in fields such as Artificial Intelligence (AI) research, development, and deployment. The extensive use of supercomputers for AI training has heightened concerns about energy consumption and carbon emissions. Existing energy estimation tools often assume exclusive use of computing nodes, a premise that becomes problematic with the advent of supercomputers integrating microservices, as seen in initiatives like Acceleration as a Service (XaaS) and cloud computing. This work investigates the impact of executed instructions on overall power consumption, providing insights into the comprehensive behaviour of HPC systems. We introduce two novel mathematical models to estimate a process's energy consumption based on the total node energy, process usage, and a normalised vector of the probability distribution of instruction types for CPU and GPU processes. Our approach enables energy accounting for specific processes without the need for isolation. Our models demonstrate high accuracy, predicting CPU power consumption with a mere 1.9% error. For GPU predictions, the models achieve a central relative error of 9.7%, showing a clear tendency to fit the test data accurately. These results pave the way for new tools to measure and account for energy consumption in shared supercomputing environments.

关键词： Energy measurement Supercomputers Microprocessors Multicore processing Load modeling High performance computing Cluster computing

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