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检索条件"任意字段=Conference on Simulation-Driven Modeling and Optimization"

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Well Management Logic optimization: simulation Case Study on Kashagan Field

Well Management Logic Optimization: Simulation Case Study on...

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2024 SPE Caspian Technical conference and Exhibition, CTC 2024

作者： Nogerbek, Nurbol Pradja, Kurnia Galiyeva, Gulnara Hayat, Sharjeel North Caspian Operating Company N.V. Atyrau Kazakhstan

ISBN: (纸本)9781959025634

This paper discusses the optimization of well management strategies for production forecasting using dynamic reservoir modeling in a giant oil field that employs gas injection. Over the years of production, a significant evolution in the field knowledge has occurred, driven by the integration of additional data and information into the reservoir model. Continuous model updates have become essential, ensuring that the reservoir simulations reflect the latest field conditions. In parallel, these updates are accompanied by enhancements to the field and well management logic within the simulation. The well management logic is continuously refined based on the deepening understanding of the reservoir, with the ultimate goal of maximizing field recovery. Depending on the field phase, well management logic evolves to address specific needs. In the predevelopment stage, the logic primarily guided rate balancing based on well potential. As production commenced, surveillance data provided more detailed information, triggering adjustments in the management logic. The challenges encountered during production were integrated into the 3D dynamic model to improve forecasting accuracy. Furthermore, this information led to updates in the well and field management logic, ensuring more informed guidance for production and operational strategies, ultimately aimed at maximizing recovery. This paper shares the lessons learned and implementation of well management strategies throughout the eight years of production, offering valuable insights for optimizing recovery in similar large-scale, gas-injected oil fields. Copyright 2024, Society of Petroleum Engineers.

关键词： Production control

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Deep Learning-driven Molecular Dynamics for the Prediction of 3d Transition Metal Multi-principal Alloys Structure 30th

Deep Learning-Driven Molecular Dynamics for the Prediction o...

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30th International conference on Computational and Experimental Engineering and Sciences

作者： Yu, Hanzhang Yao, Zhixuan Han, Tianyi Guan, Zhenjie Qu, Nan Zhu, Jingchuan Harbin Inst Technol Sch Mat Sci & Engn 92 Xidazhi St Harbin 150001 Peoples R China

ISBN: (纸本)9783031774911;9783031774898;9783031774881

Multi-principal element alloys (MPEAs) have attracted much attention in recent years due to the optimization of performance by adjusting the contents and ratios of different elements. Since the phase structure of MPEAs that determine the macroscopic properties are directly affected by the solidification process, the construction of an accurate solidification model is the basis and key to the prediction and optimization of alloy properties. Therefore, this work establishes a microscopic model that can enable to predict the solidification structure of 3d transition metal MPEAs exactly. We have calculated the interatomic interactions between 3d transition MPEAs using first principle calculation as a dataset for deep potential molecular dynamics to construct a new force field which provide an accurate potential function for the molecular dynamics to achieve an accurate prediction of the atomic configurations of 3d transition metal MPEAs after solidification. The results show that laminar dislocations were formed near the Co-rich and Fe-rich body-centered cubic phases along the densely arranged surfaces. The FeCoNi laminar dislocation energy is lower than that of pure Ni and NiFe, which is more likely to form laminar dislocations. It suggests that the compromise between accuracy and cost can be effectively solved by our strategy established by solidification structure microscopic modeling, which provides an effective method for phase formation prediction of MPEAs, and contributes to the design and further development of MPEAs.

关键词： Multi-principal element alloys Deep learning potential Molecular dynamics Structure evolution simulation

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Advancements in Production optimization through an Innovative Hybrid Data-Physics Architecture

Advancements in Production Optimization through an Innovativ...

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2024 SPE Annual Technical conference and Exhibition, ATCE 2024

作者： Matoorian, Raya Malaieri, Mohammadreza Shor, Roman Aguilera, Roberto Department of Chemical and Petroleum Engineering University of Calgary AB Canada Computer Modeling Group CalgaryAB Canada Department of Chemical and Petroleum Engineering Texas A&M University TX United States

ISBN: (纸本)9781959025375

This research addresses the challenges in using machine learning (ML) to assess and optimize production in unconventional wells, where computational costs (dependence on the accuracy of physical models) and complexity of wellbore design create significant challenges for decision-making and field development. A novel Hybrid Data-Physics (HDP) architecture is proposed that integrates data-driven models with physics equations embedded in a deep neural network (DNN). This approach optimizes both network and physical parameters simultaneously to predict short and long-term production rates, thereby generating more realistic operational scenarios. Using a comprehensive dataset from nearly 1300 Duvernay wells within the Western Canadian Sedimentary Basin (WCSB), the proposed HDP model significantly improves production performance estimations by refining physical parameters through iterative neural network processes. The model performs exceptionally well even with limited data, particularly in unconventional wells where geological complexities pose challenges for traditional simulation methods. By incorporating simpler equations such as decline curves, the HDP model bypasses the need for complex physics, capturing hidden complexities and operational trends. This study highlights the HDP model's transformative potential in production optimization, merging data analytics with physics-based modeling to enhance operational insights and decision-making. This pioneering approach reduces uncertainty and adapts to dynamic conditions, offering a robust, efficient tool for unconventional reservoir management. Additionally, the HDP model's ability to integrate various data types and adapt to evolving conditions underscores its versatility and practical applicability in real-world scenarios. Through this integration, the model not only enhances predictive accuracy but also provides a scalable solution that can be tailored to diverse reservoir conditions and operational needs. Copyright

关键词： Deep neural networks

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8th International conference on Modelling and Development of Intelligent Systems, MDIS 2022

8th International Conference on Modelling and Development of...

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8th International conference on Modelling and Development of Intelligent Systems, MDIS 2022

ISBN: (纸本)9783031270338

The proceedings contain 21 papers. The special focus in this conference is on Modelling and Development of Intelligent Systems. The topics include: Morphology of Convolutional Neural Network with Diagonalized Pooling;challenges and Opportunities in Deep Learning driven Fashion Design and Textiles Patterns Development;feature Selection and Extreme Learning Machine Tuning by Hybrid Sand Cat optimization Algorithm for Diabetes Classification;Enriching SQL-driven Data Exploration with Different Machine Learning Models;analytical Solution of the Simplest Entropiece Inversion Problem;latent Semantic Structure in Malicious Programs;innovative Lattice Sequences Based on Component by Component Construction Method for Multidimensional Sensitivity Analysis;on an optimization of the Lattice Sequence for the Multidimensional Integrals Connected with Bayesian Statistics;numerical optimization Identification of a Keller-Segel Model for Thermoregulation in Honey Bee Colonies in Winter;gaze Tracking: A Survey of Devices, Libraries and Applications;gradient optimization in Reconstruction of the Diffusion Coefficient in a Time Fractional Integro-Differential Equation of Pollution in Porous Media;Flash Flood simulation Between Slănic and Vărbilău Rivers in Vărbilău Village, Prahova County, Romania, Using Hydraulic modeling and GIS Techniques;group Decision-Making Involving Competence of Experts in Relation to Evaluation Criteria: Case Study for e-Commerce Platform Selection;Transparency and Traceability for AI-Based Defect Detection in PCB Production;tasks Management Using Modern Devices;a Method for Target Localization by Multistatic Radars;Intrusion Detection by XGBoost Model Tuned by Improved Social Network Search Algorithm;bridging the Resource Gap in Cross-Lingual Embedding Space;classification of Microstructure Images of Metals Using Transfer Learning.

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Augmented Reality-driven Reservoir Management Via Generative Ai: Transforming Pore-Scale Fluid Flow simulation

Augmented Reality-Driven Reservoir Management Via Generative...

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2024 Abu Dhabi International Petroleum Exhibition and conference, ADIPEC 2024

作者： Ansari, U. Qureshi, H.A. Soomro, N.A. Memon, A.R. Mehran University of Engineering & Technology Sindh Jamshoro Pakistan Mari Petroleum Company Limited Islamabad Pakistan Dawood University of Engineering & Technology Sindh Karachi Pakistan Wettherford Islamabad Pakistan

ISBN: (纸本)9781959025498

The current revolution of generative artificial intelligence is transforming global dynamics which is also essential to petroleum engineers for effectively completing technical *** the main aim of this study is to investigate the application of generative AI techniques for improving the efficiency of petroleum reservoir *** outcomes of this study will help in developing and implementing generative AI algorithms tailored for reservoir management tasks, including reservoir modeling, production optimization, and decision *** this study generative AI technique is employed to integrate with augmented reality (AR) to enhance reservoir *** methodology involves developing a generative AI model to simulate pore-scale fluid flow, validated against experimental *** is utilized to visualize and interact with the simulation results in a real-time, immersive *** integration process includes data preprocessing, model training, and AR *** metrics such as accuracy, computational efficiency, and user interaction quality are evaluated to assess the effectiveness of the proposed approach in transforming traditional reservoir management *** developed generative AI model demonstrated high accuracy in simulating pore-scale fluid flow, closely matching experimental data with a correlation coefficient of *** AR interface provided an intuitive visualization, significantly improving user comprehension and decision-making *** efficiency was enhanced by 40% compared to traditional methods, enabling real-time simulations and ***, it was observed that Users found the AR-driven approach more engaging and easier to understand, with a reported 30% increase in correct decision-making in reservoir management *** integration of generative AI with AR allowed for dynamic adjustments and immediate feedback, which was particularly beneficial in complex scenarios requiring

关键词： Augmented reality

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Dynamic Power optimization and Tracking Control for Permanent Magnet Synchronous Generator-Based Wind Energy Harvesting

Dynamic Power Optimization and Tracking Control for Permanen...

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2023 International conference on Sustainable Communication Networks and Application, ICSCNA 2023

作者： Prasanna, Chinthagumpala Chenchireddy, Kalagotla Gayathri, Nimmala Jayanth, Pathkula Kumar, Penchikala Kranthi Kumar, Vangala Praveen Teegala Krishna Reddy Engineering College Dept of Eee Hyderbad India

ISBN: (纸本)9798350313987

The invention and implementation of Maximum Power Point Tracking control algorithms for a Permanent Magnet Synchronous Generator-based Wind Energy Conversion System are the main objectives of this work. By precisely tracking the greatest power point under a range of wind conditions, the goal is to increase the effectiveness and energy capture of the PMSG-driven wind turbine. modeling, simulation, and experimental validation will be used to investigate and improve various MPPT algorithms and control tactics. This work improves the development of renewable energy systems, specifically in the context of wind power generation, with possible applications in the generation of clean and sustainable power. © 2023 IEEE.

关键词： Clean Power Control Efficiency Modelling MPPT PMSG Renewable Energy Storage Wind Energy

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Ship Equipment modeling Method Based on Neural Networks and Particle Swarm optimization

Ship Equipment Modeling Method Based on Neural Networks and ...

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Communication Technology and Information Technology (ICCTIT), International conference on

作者： Yanshan Shao LNG Equipment Division Lianyungang JARI Automation CO. LTD Lianyungang China

ISBN: (数字)9798331528973

ISBN: (纸本)9798331528980

To address the challenges in ship equipment control simulation and the complexity of building equipment mechanism models, a data-driven equipment modeling approach is proposed. This method aims to enhance model prediction accuracy and generalization capability through the construction of neural network models and optimization via Particle Swarm optimization (PSO). First, a data-driven neural network model is developed using real ship operational data. Then, the connection weights of the neural network are optimized using PSO to reduce prediction errors and improve model performance. Experimental results demonstrate that the proposed method significantly improves the prediction accuracy of critical ship equipment, reduces the Mean Absolute Error (MAE), and increases the Coefficient of Determination ($\mathrm{R}^{\mathbfoptimization}$). The proposed neural network and PSO-based modeling approach offers a novel perspective and effective tools for the simulation and optimization of ship equipment.

关键词： Training Accuracy Neural networks Predictive models Data models Compressors Liquefied natural gas Marine vehicles Particle swarm optimization optimization

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Trajectory Following Controller Design with Physicsbased Data-driven Vehicle Dynamics Modelling

Trajectory Following Controller Design with Physicsbased Dat...

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Mechatronics, Control and Robotics (ICMCR), International conference on

作者： Yong Lu He Tian Diange Yang Kun Jiang Ziwang Lu Runfeng Li School of Vehicle and Mobility Tsinghua University Beijing China DIAS Automotive Electronic Systems Co. Ltd Beijing China

ISBN: (数字)9798331544546

ISBN: (纸本)9798331544553

Accurate modeling of vehicle dynamics plays a crucial role in accelerating algorithm development and reducing associated costs. This study presents a trajectory-following controller that integrates physics-based and data-driven approaches for vehicle dynamics modeling. A hybrid model, combining a two-degree-of-freedom (2DOF) physical model with a data-driven long short-term memory (LSTM) framework, is developed to capture vehicle dynamics. Using this hybrid model, the feedforward steering angle is determined by solving an optimization problem. The dataset is specifically designed to align with the operational design domain of advanced driver assistance systems (ADAS). simulation results indicate that the hybrid model achieves higher accuracy than traditional physics-based models and demonstrates superior generalization compared to purely data-driven models. Furthermore, the findings validate the enhanced performance of the proposed control algorithm.

关键词： Accuracy Trajectory tracking Computational modeling Heuristic algorithms simulation Trajectory Vehicle dynamics Feedforward systems Long short term memory optimization

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Design, optimization and Analysis of a Novel Compliant Guiding Mechanism for Piezo-driven Vibration Microinjection 8

Design, Optimization and Analysis of a Novel Compliant Guidi...

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8th IEEE International conference on Advanced Robotics and Mechatronics, ICARM 2023

作者： Li, Yitong Ye, Tingting Ling, Jie Xiao, Xiaohui Feng, Zhao School of Power and Mechanical Engineering Wuhan University Wuhan430072 China The Hong Kong Polytechnic University Department of Industrial and Systems Engineering Hong Kong SAR999077 Hong Kong College of Mechanical and Electrical Engineering Nanjing University of Aeronautics and Astronautics Nanjing210016 China

ISBN: (纸本)9798350300178

Cell microinjection, as a significant tool of biomedical research, calls for advanced equipment with high efficiency, high success rate, high reliability, and repeatability. With the intent of providing a useful manipulator for robotic cell microinjection, this paper presents a novel complaint guiding mechanism for piezo-driven vibration cell microinjection. Mechanism design, kinematic modeling, parametric optimization, and simulation of the designed guiding mechanism are introduced in detail. The compliant guiding mechanism has a symmetrical and parallel connection structure with lumped compliance at its double-notch right-circle flexure hinges. Aiming at avoiding the resonance of the mechanism at actuation frequency, the response surface algorithm is utilized to optimize the dominant structural parameters of the designed guiding mechanism based on kinematic modeling with the pseudo-rigid-body method. The finite element simulation results show that the optimized compliant mechanism has the advantages of high off-axis stiffness and ideal working performance at the actuation frequency of 15 kHz, where the output amplitude of in-plane and out-of-plane lateral vibrations are diminished to 40% and 25% of the input lateral vibration amplitude respectively. © 2023 IEEE.

关键词： Vibration analysis

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Characterization and modeling of Cure-Induced Thermo Mechanical Properties and Chemical Shrinkage of Epoxy Resins 25

Characterization and Modeling of Cure-Induced Thermo Mechani...

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25th International conference on Thermal, Mechanical and Multi-Physics simulation and Experiments in Microelectronics and Microsystems, EuroSimE 2024

作者： Kasinikota, Venu Prakash Muehleisen, Wolfgang Grinschgl, Markus Steiner, Alexander Hasil, Siegfried Lang, Margit Christa Leoben8700 Austria Institute of Mechanics Montanuniversität Leoben Leoben8700 Austria Villach9524 Austria GIPRO GmbH Peggau8120 Austria

ISBN: (纸本)9798350393637

Epoxy resins are widely used in molding electrical insulators and electronic components due to their superior dielectric properties and mechanical strength. However, due to poor process control along with the complex geometry of their components, parts can entail air pockets, cracks, or deformations driven by residual stresses. To address these challenges, this research uses the Computational Fluid Dynamics (CFD) software Autodesk Moldflow Synergy 2023 to visualize epoxy resin flow behavior and predict the curing behavior as it flows through the mold using respective models. When simulating the mold-filling behavior, comprehensive thermo-mechanical data are required for both uncured and cured states of cycloaliphatic epoxy resins. Therefore, it was crucial to thoroughly characterize the material to obtain precise material parameters for using them in simulations, as these parameters play a vital role in the accuracy of the simulations. Python-based formulations of the Prout-Tompkins cure kinetic and Cross-Castro-Macosko viscosity models are used to comprehensively analyze the flow front and curing behavior during an Automatic Pressure Gelation (APG) process. This research provides valuable insights into the behavior of epoxy resin when molded in complex scenarios. It enables the design of optimized molds while allowing for fast and efficient evaluation and optimization of process parameters, ultimately reducing high rejection rates as safety critical parts, like insulators, need to be flawless. © 2024 IEEE.

关键词： cure kinetics process simulation reactive-viscosity modeling Thermoset material characterization

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