scholarly journals A Data-Driven Simulation Model for China Haze Monitor and Governance

2016 ◽  
Vol 04 (02) ◽  
pp. 374-381
Author(s):  
Xiaoyan Lu ◽  
Hong Chen ◽  
Miao Wang ◽  
Zhengying Cai
Keyword(s):  
Simulation Model ◽  
Data Driven

INSIM-FPT-3D: A Data-Driven Model for History Matching, Water-Breakthrough Prediction and Well-Connectivity Characterization in Three-Dimensional Reservoirs

2021 ◽  
Author(s):  
Hui Zhao ◽  
Wei Liu ◽  
Xiang Rao ◽  
Guanglong Sheng ◽  
Huazhou Andy Li ◽  
...  
Keyword(s):  
Simulation Model ◽  
History Matching ◽  
3D Model ◽  
Reference Model ◽  
Three Dimensional ◽  
Data Driven ◽  
Series Data ◽  
Reference Models ◽  
Water Breakthrough

Abstract The data-driven interwell simulation model (INSIM) has been recognized as an effective tool for history matching and interwell-connectivity characterization of waterflooding reservoirs. INSIM-FT-3D (FT: front tracking) was recently developed to upgrade the applicationdimension of INSIM series data-driven models from two-dimensional (2D) to three-dimensional (3D). However, INSIM-FT-3D cannot accurately infer the dynamic change of well-connectivity and predict well's bottom-hole pressure (BHP). The main purpose of this study intends to expand the capability of INSIM-FT-3D to empower for the assimilation of BHPs, the reliable prediction of water breakthrough and the characterization of dynamic interwell-connectivities. The default setting of well index (WI) in INSIM-FT-3D based on Peaceman's equation does not yield accurate BHP estimates. We derive a WI that can honor the BHPs of a reference model composed of a set of 1D connections. When history matching BHPs of a 3D reservoir, we show that the derived WI is a better initial guess than that obtained from Peaceman's equation. We also develop a flow-path-tracking (FPT) algorithm to calculate the dynamic interwell properties (allocation factors and pore volumes (PVs)). Besides, we discuss the relationship between the INSIM-family methods and the traditional grid-based methods, which indicates that the INSIM-family methods can calculate the transmissibility of the connection between coarse-scale cells in a more accurate manner. As an improvement of INSIM-FT-3D, the newly proposed data-driven model is denoted as INSIM-FPT-3D. To verify the correctness of the derived WI, we present a 1D problem and a T-shaped synthetic reservoir simulation model as the reference models. BHPs and oil production rates are obtained as the observed data by running these two reference models with total injection/production-rate controls. An INSIM-FPT-3D model is created by specifying the transmissibilities and PVs that are the same as those in the reference model. By applying the derived WIs in INSIM-FPT-3D, the resulting BHPs and oil rates obtained agree well with the reference model without further model calibration. Applying INSIM-FPT-3D to a synthetic multi-layered reservoir shows that we obtain a reasonable match of both BHPs and oil rates with INSIM-FPT-3D. Compared with the FrontSim model, the INSIM-FPT-3D model after history matching is shown to match the dynamic PVs from FrontSim reasonably well and can correctly predict the timing of water breakthrough. By allowing for the assimilation of BHP data, we enable INSIM-FPT-3D to history match a green field with limited production history and forecast the timing of water breakthrough. The improved INSIM-FPT-3D leads to more accurate characterization of the interwell connectivities.

Designing And Optimizing Production In A High Variety / Low Volume Environment Through Data-Driven Simulation

2021 ◽  
Author(s):  
Virginia Fani ◽  
Bianca Bindi ◽  
Romeo Bandinelli
Keyword(s):  
Simulation Model ◽  
Production Systems ◽  
Discrete Event ◽  
Simulation Models ◽  
Product Variety ◽  
Data Driven ◽  
Business Decisions ◽  
Footwear Industry ◽  
Data Driven Approach ◽  
High Level

HVLV environments are characterized by high product variety and small lot production, pushing companies to recursively design and optimize their production systems in a very short time to reach high-level performance. To increase their competitiveness, companies belonging to these industries, often SMEs working as third parties, ask for decision-making tools to support them in a quick and reactive reconfiguration of their production lines. Traditional discrete event simulation models, widely studied in the literature to solve production-related issues, do not allow real-time support to business decisions in dynamic contexts, due to the time-consuming activities needed to re-align parameters to changing environments. Data-driven approach overcomes these limitations, giving the possibility to easily update input and quickly rebuild the model itself without any changes in the modeling code. The proposed data-driven simulation model has also been interfaced with a commonly-used BI tool to support companies in the iterative comparison of different scenarios to define the optimal resource allocation for the requested production plan. The simulation model has been implemented into a SME operating in the footwear industry, showing how this approach can be used by companies to increase their performance even without a specific knowledge in building and validating simulation models.

Development of a Connected Corridor Real-Time Data-Driven Traffic Digital Twin Simulation Model

2021 ◽  
Vol 147 (12) ◽  
Author(s):  
Abhilasha J. Saroj ◽  
Somdut Roy ◽  
Angshuman Guin ◽  
Michael Hunter
Keyword(s):  
Simulation Model ◽  
Real Time ◽  
Data Driven ◽  
Time Data ◽  
Digital Twin ◽  
Real Time Data

scholarly journals Data-Driven Simulation Model of Operating Rooms in Hospital

2019 ◽  
Vol 39 ◽  
pp. 371-380
Author(s):  
Mandvi Malik ◽  
Dušan Šormaz
Keyword(s):  
Simulation Model ◽  
Operating Rooms ◽  
Data Driven

scholarly journals Updated projections for COVID-19 omicron wave in Florida

2022 ◽  
Author(s):  
Thomas J. Hladish ◽  
Alexander N. Pillai ◽  
Ira M. Longini
Keyword(s):  
Simulation Model ◽  
Discrete Time ◽  
Explicit Representation ◽  
Data Driven ◽  
Agent Based Model ◽  
Agent Based ◽  
Delta Wave ◽  
Detailed Simulation

In this report, we use a detailed simulation model to assess and project the COVID-19 epidemic in Florida. The model is a data-driven, stochastic, discrete-time, agent based model with an explicit representation of people and places. Using the model, we find that the omicron variant wave in Florida is likely to cause many more infections than occurred during the delta variant wave. Due to testing limitations and often mild symptoms, however, we anticipate that omicron infections will be underreported compared to delta. We project that reported cases of COVID-19 will continue to grow significantly and peak in early January 2022, and that the number of reported COVID-19 deaths due to omicron may be 1/3 of the total caused by the delta wave.

A Data Driven Modeling and Simulation Methodology for Automotive Assembly Plant

2011 ◽  
Vol 346 ◽  
pp. 228-235 ◽  
Author(s):  
Nan Wang ◽  
Shi Qi Li ◽  
Jun Feng Wang
Keyword(s):  
Data Structure ◽  
Modeling And Simulation ◽  
Simulation Model ◽  
Production System ◽  
Assembly Line ◽  
Data Driven ◽  
Simulation Methodology ◽  
Automotive Assembly ◽  
Data Driven Modeling ◽  
And Control

Simulation offers a quick, controllable and tunable approach for prototyping complex assembly processes in manufacturing environments. In order to improve the responsiveness of production system, this paper proposes a new approach based on data driven modeling and simulation methodology, it can automatically generate the simulation model of production system and realize rapid modification. The generated model also has high efficiency, readability and reusability. The approach is developed for an automotive assembly line. Through the modeling approach of IDEF1X, a data structure of simulation model was constructed, a model simulator was implemented in Arena by VBA program, and then a simulation model for target production system was built automatically through the attribute values in the data structure. The management and control process of assembly line is divided into different levels by hierarchical modeling approach, modeling element and control logic of simulation are separated. The case study of the automotive assembly line demonstrates the validity of data driven modeling and simulation methodology.

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