Reliability Testing of Real Time Medium Voltage Drive Simulation by Benchmarking with Field Data

2017 ◽  
Author(s):  
J. Bapiraju ◽  
Ashish Lukka ◽  
B. Shanthibhushan ◽  
S. Shriram
Keyword(s):  
Real Time ◽  
Field Data ◽  
Reliability Testing ◽  
Medium Voltage

Application of Real-Time Field Data to Optimize Drilling Hydraulics Using Neural Network Approach

2015 ◽  
Vol 137 (6) ◽  
Author(s):  
Yanfang Wang ◽  
Saeed Salehi
Keyword(s):  
Neural Network ◽  
Real Time ◽  
Field Data ◽  
Ann Model ◽  
Time Data ◽  
Neural Network Approach ◽  
Lost Circulation ◽  
Drilling Performance ◽  
Artificial Neural Network Ann ◽  
Pump Pressure

Real-time drilling optimization improves drilling performance by providing early warnings in operation Mud hydraulics is a key aspect of drilling that can be optimized by access to real-time data. Different from the investigated references, reliable prediction of pump pressure provides an early warning of circulation problems, washout, lost circulation, underground blowout, and kicks. This will help the driller to make necessary corrections to mitigate potential problems. In this study, an artificial neural network (ANN) model to predict hydraulics was implemented through the fitting tool of matlab. Following the determination of the optimum model, the sensitivity analysis of input parameters on the created model was investigated by using forward regression method. Next, the remaining data from the selected well samples was applied for simulation to verify the quality of the developed model. The novelty is this paper is validation of computer models with actual field data collected from an operator in LA. The simulation result was promising as compared with collected field data. This model can accurately predict pump pressure versus depth in analogous formations. The result of this work shows the potential of the approach developed in this work based on NN models for predicting real-time drilling hydraulics.

Proxy Model for Real-Time Estimation of Cool Down Time of Subsea Production System to Prevent Hydrates Formation

2021 ◽  
Author(s):  
Joseph Rizzo Cascio ◽  
Antonio Da Silva ◽  
Martino Ghetti ◽  
Martino Corti ◽  
Marco Montini
Keyword(s):  
Real Time ◽  
Production System ◽  
Field Data ◽  
Oil And Gas ◽  
Time Estimation ◽  
Integrated Approach ◽  
Proxy Model ◽  
Real Time Estimation ◽  
Subsea Production System ◽  
Formation Of Hydrates

Abstract Objectives/Scope The benefits of real-time estimation of the cool down time of Subsea Production System (SPS) to prevent formation of hydrates are shown on a real oil and gas facility. The innovative tool developed is based on an integrated approach, which embeds a proxy model of SPS and hydrate curves, exploiting real-time field data from the Eni Digital Oil Field (eDOF, an OSIsoft PI based application developed and managed by Eni) to continuously estimate the cool down time before hydrates are formed during the shutdown. Methods, Procedures, Process The Asset value optimization and the Asset integrity of hydrocarbon production systems are complex and multi-disciplinary tasks in the oil and gas industry, due to the high number of variables and their synergy. An accurate physical model of SPS is built and, then, used to develop a proxy model, which integrates hydrate curves at different MeOH concentration, being able to estimate in real time the cool down time of SPS during the shutdown exploiting data from subsea transmitters made available by eDOF in order to prevent formation of hydrates. The tool is also integrated with a user-friendly interface, making all relevant information readily available to the operators on field. Results, Observations, Conclusions The integrated approach provides a continues estimation of cool down time based on real time field data (eDOF) in order to prevent formation of hydrates and activate preservation actions. An accurate physical model of SPS is built on a real business case using Olga software and cool down curves simulated considering different operating shutdown scenarios. Hydrate curves of the considered production fluid are also simulated at different MeOH concentration using PVTsim NOVA software. Off-line simulated curves are then implemented as numerical tables combined with eDOF data by an Eni developed fast executing proxy model to produce estimated cool down time before hydrates are formed. A graphic representation of SPS behavior and its cool down time estimation during shutdown are displayed and ready to use by the operators on field in support of the operations, saving cost and time. Novel/Additive Information The benefits of real time estimation of the cool down time of SPS to prevent hydrates formation are shown in terms of saving of time and cost during the shutdown operations on a real case application. This integrated approach allows to rely on a continue, automatic and acceptably accurate estimate of the available time before hydrates are formed in SPS, including the possibility to be further developed for cases where subsea transmitters are not available or extended to other flow assurance issues.

Use of geographic information systems and remote sensing as automated tool for variable rate irrigation prescriptions

2019 ◽  
Author(s):  
◽  
Anh Thi Tuan Nguyen
Keyword(s):  
Remote Sensing ◽  
Real Time ◽  
Regression Models ◽  
Field Data ◽  
Irrigation Management ◽  
Irrigation Scheduling ◽  
Crop Growth ◽  
Variable Rate ◽  
Water Application ◽  
Center Pivot

[ACCESS RESTRICTED TO THE UNIVERSITY OF MISSOURI AT REQUEST OF AUTHOR.] Economic as well as water shortage pressure on agricultural use of water has placed added emphasis on efficient irrigation management. Center pivot technology has made great improvement with variable rate irrigation (VRI) technology to vary water application spatially and temporally to maximize the economic and environmental return. Proper management of VRI systems depends on correctly matching the pivot application to specific field temporal and areal conditions. There is need for a tool to accurately and inexpensively define dynamic management zones, to sense within-field variability in real time, and control variable rate water application so that producers are more willing to adopt and utilize the advantages of VRI systems. This study included tests of the center pivot system uniformity performance in 2014 at Delta Research Center in Portageville, MO. The goal of this research was to develop MOPivot software with an algorithm to determine unique management areas under center pivot systems based on system design and limitations. The MOPivot tool automates prescriptions for VRI center pivot based on non-uniform water needs while avoiding potential runoff and deep percolation. The software was validated for use in real-time irrigation management in 2018 for VRI control system of a Valley 8000 center pivot planted to corn. The water balance model was used to manage irrigation scheduling. Field data, together with soil moisture sensor measurement of soil water content, were used to develop the regression model of remote sensing-based crop coefficient (Kc). Remote sensing vegetation index in conjunction with GDD and crop growth stages in regression models showed high correlation with Kc. Validation of those regression models was done using Centralia, MO, field data in 2016. The MOPivot successfully created prescriptions to match system capacity of the management zone based on system limitations for center pivot management. Along with GIS data sources, MOPivot effectively provides readily available graphical prescription maps, which can be edited and directly uploaded to a center pivot control panel. The modeled Kc compared well with FAO Kc. By combining GDD and crop growth in the models, these models would account for local weather conditions and stage of crop during growing season as time index in estimating Kc. These models with Fraction of growth (FrG) and cumulative growing degree days (cGDD) had a higher coefficient of efficiency, higher Nash-Sutcliffe coefficient of efficiency and higher Willmott index of agreement. Future work should include improvement in the MOPivot software with different crops and aerial remote sensing imagery to generate dynamic prescriptions during the season to support irrigation scheduling for real-time monitoring of field conditions.

scholarly journals Validation of a robust neural real-time voltage estimator for active distribution grids on field data

2018 ◽  
Vol 154 ◽  
pp. 182-192 ◽  
Author(s):  
Michael Pertl ◽  
Philip J. Douglass ◽  
Kai Heussen ◽  
Koen Kok
Keyword(s):  
Real Time ◽  
Field Data ◽  
Distribution Grids

scholarly journals Predicting Traffic Dynamics with Driver Response Model for Proactive Variable Speed Limit Control Algorithm

2018 ◽  
Vol 2018 ◽  
pp. 1-10
Author(s):  
Huixuan Ye ◽  
Lili Tu ◽  
Jie Fang
Keyword(s):  
Real Time ◽  
Traffic Flow ◽  
Control Algorithm ◽  
Field Data ◽  
Speed Limit ◽  
Response Model ◽  
Variable Speed ◽  
Variable Speed Limit ◽  
Driver Response ◽  
Traffic State

Variable Speed Limit (VSL) control contributes to potential crash risk reduction by suggesting a suitable dynamic speed limit to achieve more stable and uniform traffic flow. In recent studies, researchers adopted macroscopic traffic flow models and perform prediction-based optimal VSL control. The response of drivers to the advised VSL is one of the most critical parameters in VSL-controlled speed dynamics modeling, which significantly affects the accuracy of traffic state prediction as well as the control reliability and performance. Nevertheless, the variations of driver responses were not explicitly modeled. Thus, in this research, the authors proposed a dynamic driver response model to formulate how the drivers respond to the advised VSL during various traffic conditions. The model was established and calibrated using field data to quantitatively analyze the dynamics of drivers’ desired speed regarding the advised VSL and current traffic state variables. A proactive VSL control algorithm incorporating the established driver response model was designed and implemented in field-data-based simulation study. The design proactive control algorithm modifies VSL in real-time according to the traffic state prediction results, aiming to reduce potential crash risks over the experiment site. By taking into account the real-time driver response variations, the VSL-controlled traffic state dynamics was more accurately predicted. The experimental results illustrated that the proposed control algorithm effectively reduces the crash probabilities in the traffic network.

Real-Time Prediction Method for Performance Degradation Trend Based on Reliability Experimental Data

2013 ◽  
Vol 321-324 ◽  
pp. 757-761 ◽  
Author(s):  
Chen Liang Song ◽  
Zhen Liu ◽  
Bin Long ◽  
Cheng Lin Yang
Keyword(s):  
Experimental Data ◽  
Real Time ◽  
Field Data ◽  
Prediction Method ◽  
Field Method ◽  
Performance Degradation ◽  
New Method ◽  
Time Prediction ◽  
Prediction Result ◽  
The Relationship

According to the real-time prediction for performance degradation trend, the commonly used method is just based on field data. But this methods prediction result will not be so much ideal when the fitting of degradation trend of field data is not good. To solve the problem, the paper introduces a new method which is not only based on field method but also based on reliability experimental data coming from the history experiment. We use the relationship between the field data and reliability experimental data to get the result of the two kinds of data respectively and then get the weights according to the two prediction results. Finally, the final real-time prediction result for performance degradation tendency can obtain by allocating the weights to the two prediction results.

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