Wear-Factor Prediction Based on Data-Driven Inversion Technique for Casing Wear Estimation

2020 ◽  
Author(s):  
Manish K. Mittal ◽  
Robello Samuel ◽  
Aldofo Gonzales
Keyword(s):  
Physical Model ◽  
Factor Model ◽  
Data Driven ◽  
Wear Volume ◽  
Model Parameters ◽  
Wear Factor ◽  
Side Force ◽  
Inversion Technique ◽  
Casing Wear ◽  
Volume Output

Abstract Wear factor is an important parameter for estimating casing wear, yet the industry lacks a sufficient data-driven wear-factor prediction model based on previous data. Inversion technique is a data-driven method for evaluating model parameters for a setting wherein the input and output values for the physical model/equation are known. For this case, the physical equation to calculate wear volume has wear factor, side force, RPM, tool-joint diameter, and time for a particular operation (i.e., rotating on bottom, rotating off bottom, sliding, back reaming, etc.) as inputs. Except for wear factor, these values are either available or can be calculated using another physical model (wear-volume output is available from the drilling log). Wear factor is considered the model parameter and is estimated using the inversion technique method. The preceding analysis was performed using soft-string and stiff-string models for side-force calculations and by considering linear and nonlinear wear-factor models. An iterative approach was necessary for the nonlinear wear-factor model because of its complexity. Log data provide the remaining thickness of the casing, which was converted into wear volume using standard geometric calculations. A paper [1] was presented in OMC 2019 discussing a method for bridging the gap. A study was conducted in this paper for a real well based on the new method, and successful results were discussed. The current paper extends that study to another real well casing wear prediction with this novel approach. Some methods discussed are already included in the mentioned paper.

Dynamic Data-Driven Spatiotemporal System Behavior Prediction With Simulations and Sensor Measurement Data

2018 ◽  
Author(s):  
Xiangxue Zhao ◽  
Shapour Azarm ◽  
Balakumar Balachandran
Keyword(s):  
Dynamical System ◽  
Real Time ◽  
Measurement Data ◽  
Data Driven ◽  
Sensor Data ◽  
Model Parameters ◽  
High Fidelity ◽  
System Behavior ◽  
Sensor Measurement

Online prediction of dynamical system behavior based on a combination of simulation data and sensor measurement data has numerous applications. Examples include predicting safe flight configurations, forecasting storms and wildfire spread, estimating railway track and pipeline health conditions. In such applications, high-fidelity simulations may be used to accurately predict a system’s dynamical behavior offline (“non-real time”). However, due to the computational expense, these simulations have limited usage for online (“real-time”) prediction of a system’s behavior. To remedy this, one possible approach is to allocate a significant portion of the computational effort to obtain data through offline simulations. The obtained offline data can then be combined with online sensor measurements for online estimation of the system’s behavior with comparable accuracy as the off-line, high-fidelity simulation. The main contribution of this paper is in the construction of a fast data-driven spatiotemporal prediction framework that can be used to estimate general parametric dynamical system behavior. This is achieved through three steps. First, high-order singular value decomposition is applied to map high-dimensional offline simulation datasets into a subspace. Second, Gaussian processes are constructed to approximate model parameters in the subspace. Finally, reduced-order particle filtering is used to assimilate sparsely located sensor data to further improve the prediction. The effectiveness of the proposed approach is demonstrated through a case study. In this case study, aeroelastic response data obtained for an aircraft through simulations is integrated with measurement data obtained from a few sparsely located sensors. Through this case study, the authors show that along with dynamic enhancement of the state estimates, one can also realize a reduction in uncertainty of the estimates.

scholarly journals An integrated framework for building trustworthy data-driven epidemiological models: Application to the COVID-19 outbreak in New York City

2021 ◽  
Author(s):  
Sheng Zhang ◽  
Joan Ponce ◽  
Zhen Zhang ◽  
Guang Lin ◽  
George Karniadakis
Keyword(s):  
New York ◽  
New York City ◽  
York City ◽  
Robustness Analysis ◽  
Data Driven ◽  
Model Parameters ◽  
Calibration Model ◽  
Epidemiological Models ◽  
Analysis Model ◽  
Identifiability Analysis

AbstractEpidemiological models can provide the dynamic evolution of a pandemic but they are based on many assumptions and parameters that have to be adjusted over the time when the pandemic lasts. However, often the available data are not sufficient to identify the model parameters and hence infer the unobserved dynamics. Here, we develop a general framework for building a trustworthy data-driven epidemiological model, consisting of a workflow that integrates data acquisition and event timeline, model development, identifiability analysis, sensitivity analysis, model calibration, model robustness analysis, and forecasting with uncertainties in different scenarios. In particular, we apply this framework to propose a modified susceptible–exposed–infectious–recovered (SEIR) model, including new compartments and model vaccination in order to forecast the transmission dynamics of COVID-19 in New York City (NYC). We find that we can uniquely estimate the model parameters and accurately predict the daily new infection cases, hospitalizations, and deaths, in agreement with the available data from NYC’s government’s website. In addition, we employ the calibrated data-driven model to study the effects of vaccination and timing of reopening indoor dining in NYC.

scholarly journals NetPyNE, a tool for data-driven multiscale modeling of brain circuits

eLife ◽  
2019 ◽  
Vol 8 ◽  
Author(s):  
Salvador Dura-Bernal ◽  
Benjamin A Suter ◽  
Padraig Gleeson ◽  
Matteo Cantarelli ◽  
Adrian Quintana ◽  
...  
Keyword(s):  
Multiple Scales ◽  
Network Models ◽  
Brain Regions ◽  
Data Driven ◽  
Model Parameters ◽  
Biophysical Modeling ◽  
Neuron Network ◽  
Graphical Interfaces ◽  
Information Theoretic Measures ◽  
High Level

Biophysical modeling of neuronal networks helps to integrate and interpret rapidly growing and disparate experimental datasets at multiple scales. The NetPyNE tool (www.netpyne.org) provides both programmatic and graphical interfaces to develop data-driven multiscale network models in NEURON. NetPyNE clearly separates model parameters from implementation code. Users provide specifications at a high level via a standardized declarative language, for example connectivity rules, to create millions of cell-to-cell connections. NetPyNE then enables users to generate the NEURON network, run efficiently parallelized simulations, optimize and explore network parameters through automated batch runs, and use built-in functions for visualization and analysis – connectivity matrices, voltage traces, spike raster plots, local field potentials, and information theoretic measures. NetPyNE also facilitates model sharing by exporting and importing standardized formats (NeuroML and SONATA). NetPyNE is already being used to teach computational neuroscience students and by modelers to investigate brain regions and phenomena.

scholarly journals Quantify Photovoltaic Module Degradation using the Loss Factor Model Parameters

2017 ◽  
Author(s):  
C. Birk Jones ◽  
Bruce H. King ◽  
Joshua S. Stein ◽  
Justin S. Fada ◽  
Alan J. Curran ◽  
...  
Keyword(s):  
Loss Factor ◽  
Factor Model ◽  
Photovoltaic Module ◽  
Model Parameters

scholarly journals A data-driven approach to measuring epidemiological susceptibility risk around the world

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Alessandro Bitetto ◽  
Paola Cerchiello ◽  
Charilaos Mertzanis
Keyword(s):  
Factor Model ◽  
Principal Component ◽  
Data Driven ◽  
Dynamic Factor ◽  
Country Level ◽  
Epidemiological Risk ◽  
Data Driven Approach ◽  
Managerial Implications ◽  
Total Variability ◽  
Pca Method

AbstractEpidemic outbreaks are extreme events that become more frequent and severe, associated with large social and real costs. It is therefore important to assess whether countries are prepared to manage epidemiological risks. We use a fully data-driven approach to measure epidemiological susceptibility risk at the country level using time-varying information. We apply both principal component analysis (PCA) and dynamic factor model (DFM) to deal with the presence of strong cross-section dependence in the data. We conduct extensive in-sample model evaluations of 168 countries covering 17 indicators for the 2010–2019 period. The results show that the robust PCA method accounts for about 90% of total variability, whilst the DFM accounts for about 76% of the total variability. Our index could therefore provide the basis for developing risk assessments of epidemiological risk contagion. It could be also used by organizations to assess likely real consequences of epidemics with useful managerial implications.

scholarly journals Aircraft Engines Remaining Useful Life Prediction with an Improved Online Sequential Extreme Learning Machine

2020 ◽  
Vol 10 (3) ◽  
pp. 1062 ◽  
Author(s):  
Tarek Berghout ◽  
Leïla-Hayet Mouss ◽  
Ouahab Kadri ◽  
Lotfi Saïdi ◽  
Mohamed Benbouzid
Keyword(s):  
Extreme Learning Machine ◽  
Life Prediction ◽  
Remaining Useful Life ◽  
Data Driven ◽  
Selection Strategy ◽  
Mapping Technique ◽  
Model Parameters ◽  
Aircraft Engines ◽  
Useful Life ◽  
Learning Machine

The efficient data investigation for fast and accurate remaining useful life prediction of aircraft engines can be considered as a very important task for maintenance operations. In this context, the key issue is how an appropriate investigation can be conducted for the extraction of important information from data-driven sequences in high dimensional space in order to guarantee a reliable conclusion. In this paper, a new data-driven learning scheme based on an online sequential extreme learning machine algorithm is proposed for remaining useful life prediction. Firstly, a new feature mapping technique based on stacked autoencoders is proposed to enhance features representations through an accurate reconstruction. In addition, to attempt into addressing dynamic programming based on environmental feedback, a new dynamic forgetting function based on the temporal difference of recursive learning is introduced to enhance dynamic tracking ability of newly coming data. Moreover, a new updated selection strategy was developed in order to discard the unwanted data sequences and to ensure the convergence of the training model parameters to their appropriate values. The proposed approach is validated on the C-MAPSS dataset where experimental results confirm that it yields satisfactory accuracy and efficiency of the prediction model compared to other existing methods.

Using Integration of Thermal-Balance Based and Data-Driven Models to Determine Single Duct Variable Air Volume System Cooling Baseline

2013 ◽  
Author(s):  
Gyujin Shim ◽  
Li Song ◽  
Gang Wang
Keyword(s):  
Physical Model ◽  
Least Square Method ◽  
Energy Performance ◽  
Least Square ◽  
Physical Models ◽  
Data Driven ◽  
Lumped Model ◽  
Variable Air Volume ◽  
Data Driven Approach ◽  
Cooling Coil

In order to use real-time energy measurements to identify system operation faults and inefficiencies, a cooling coil energy baseline is studied in an air-handling unit (AHU) through an integration of physical models and a data driven approach in this paper. A physical model for an AHU cooling coil energy consumption is first built to understand equipment mechanism and to determine the variables impacting cooling coil energy performance, and then the physical model is simplified into a lumped model by reducing the number of independent variables needed. Regression coefficients in the lumped model are determined statistically through searching optimal fit using the least square method with short periods of measured data. Experimental results on an operational AHU (8 ton) are presented to validate the effectiveness of this approach with statistical analysis. As a result of this experiment, the proposed cooling energy baselines at the cooling coil have ±20% errors at 99.7% confidence. Six-day data for obtaining baseline is preferred since it shows similar results as 12-day.

scholarly journals A Multi-Stage Algorithm for Acoustic Physical Model Parameters Estimation

2019 ◽  
Vol 27 (8) ◽  
pp. 1229-1240 ◽  
Author(s):  
Leonardo Gabrielli ◽  
Stefano Tomassetti ◽  
Stefano Squartini ◽  
Carlo Zinato ◽  
Stefano Guaiana
Keyword(s):  
Physical Model ◽  
Parameters Estimation ◽  
Model Parameters ◽  
Multi Stage
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