scholarly journals Real-Time Data Assimilation in Welding Operations Using Thermal Imaging and Accelerated High-Fidelity Digital Twinning

Mathematics ◽  
2021 ◽  
Vol 9 (18) ◽  
pp. 2263
Author(s):  
Pablo Pereira Pereira Álvarez ◽  
Pierre Kerfriden ◽  
David Ryckelynck ◽  
Vincent Robin

Welding operations may be subjected to different types of defects when the process is not properly controlled and most defect detection is done a posteriori. The mechanical variables that are at the origin of these imperfections are often not observable in situ. We propose an offline/online data assimilation approach that allows for joint parameter and state estimations based on local probabilistic surrogate models and thermal imaging in real-time. Offline, the surrogate models are built from a high-fidelity thermomechanical Finite Element parametric study of the weld. The online estimations are obtained by conditioning the local models by the observed temperature and known operational parameters, thus fusing high-fidelity simulation data and experimental measurements.

Author(s):  
Sridharan Chandrasekaran ◽  
G. Suresh Kumar

Rate of Penetration (ROP) is one of the important factors influencing the drilling efficiency. Since cost recovery is an important bottom line in the drilling industry, optimizing ROP is essential to minimize the drilling operational cost and capital cost. Traditional the empirical models are not adaptive to new lithology changes and hence the predictive accuracy is low and subjective. With advancement in big data technologies, real- time data storage cost is lowered, and the availability of real-time data is enhanced. In this study, it is shown that optimization methods together with data models has immense potential in predicting ROP based on real time measurements on the rig. A machine learning based data model is developed by utilizing the offset vertical wells’ real time operational parameters while drilling. Data pre-processing methods and feature engineering methods modify the raw data into a processed data so that the model learns effectively from the inputs. A multi – layer back propagation neural network is developed, cross-validated and compared with field measurements and empirical models.


2009 ◽  
Vol 26 (3) ◽  
pp. 556-569 ◽  
Author(s):  
Ananda Pascual ◽  
Christine Boone ◽  
Gilles Larnicol ◽  
Pierre-Yves Le Traon

Abstract The timeliness of satellite altimeter measurements has a significant effect on their value for operational oceanography. In this paper, an Observing System Experiment (OSE) approach is used to assess the quality of real-time altimeter products, a key issue for robust monitoring and forecasting of the ocean state. In addition, the effect of two improved geophysical corrections and the number of missions that are combined in the altimeter products are also analyzed. The improved tidal and atmospheric corrections have a significant effect in coastal areas (0–100 km from the shore), and a comparison with tide gauge observations shows a slightly better agreement with the gridded delayed-time sea level anomalies (SLAs) with two altimeters [Jason-1 and European Remote Sensing Satellite-2 (ERS-2)/Envisat] using the new geophysical corrections (mean square differences in percent of tide gauge variance of 35.3%) than those with four missions [Jason-1, ERS/Envisat, Ocean Topography Experiment (TOPEX)/Poseidoninterlaced, and Geosat Follow-On] but using the old corrections (36.7%). In the deep ocean, however, the correction improvements have little influence. The performance of fast delivery products versus delayed-time data is compared using independent in situ data (tide gauge and drifter data). It clearly highlights the degradation of real-time SLA maps versus the delayed-time SLA maps: four altimeters are needed in real time to get the similar quality performance as two altimeters in delayed time (sea level error misfit around 36%, and zonal and meridional velocity estimation errors of 27% and 33%, respectively). This study proves that the continuous improvement of geophysical corrections is very important, and that it is essential to stay above a minimum threshold of four available altimetric missions to capture the main space and time oceanic scales in fast delivery products.


2017 ◽  
Vol 326 ◽  
pp. 679-693 ◽  
Author(s):  
David González ◽  
Alberto Badías ◽  
Icíar Alfaro ◽  
Francisco Chinesta ◽  
Elías Cueto

2014 ◽  
Vol 519-520 ◽  
pp. 70-73 ◽  
Author(s):  
Jing Bai ◽  
Tie Cheng Pu

Aiming at storing and transmitting the real time data of energy management system in the industrial production, an online data compression technique is proposed. Firstly, the auto regression model of a group of sequence is established. Secondly, the next sampled data can be predicted by the model. If the estimated error is in the allowable range, we save the parameters of model and the beginning data. Otherwise, we save the data and repeat the method from the next sampled data. At Last, the method is applied in electricity energy data compression of a beer production. The application result verifies the effectiveness of the proposed method.


Author(s):  
Andreas Teigland ◽  
Sigbjørn Sangesland ◽  
Stein Inge Dale

Abstract Casing wear poses a significant safety hazard during drilling and production of hydrocarbons. Failure to maintain integrity due to inaccurate casing wear estimation can cause severe accidents or preclude prospective operations. Current industry practice is to estimate casing wear during the planning phase of the well and subsequently use assumed operational parameters with inherent uncertainties. This paper aims to study how to utilize real-time data to improve the industry standard methodology and evaluate the benefit of the modification. The research was conducted by applying the model on data from a well on the Norwegian continental shelf. There were two main objectives of the research. Firstly, the industry standard approach to casing wear estimation was expanded to include real-time data. Application of real-time data to the industry standard method for estimating casing wear caused a significant difference in results. The approach using real-time data resulted in an estimate of more casing wear compared to the standard approach. Secondly, an algorithm for continuous prediction of casing wear at the end of operation was developed. The predictive algorithm resulted in consistently more accurate estimates in relation to the final value throughout the operation. With variations in input parameters and consecutive casing wear of this magnitude, well integrity cannot be ensured during operation without application of real-time data. The failure to maintain well integrity demonstrates the necessity of the proposed approach.


2014 ◽  
Vol 70 ◽  
pp. 843-852 ◽  
Author(s):  
C.J. Hutton ◽  
Z. Kapelan ◽  
L. Vamvakeridou-Lyroudia ◽  
D. Savić

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