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Keywords: neural network
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Proceedings Papers

Paper presented at the SPE/IADC Middle East Drilling Technology Conference and Exhibition, May 23–25, 2023
Paper Number: SPE-214559-MS
.... This proposed system involves the use of a BCI with Electroencephalography (EEG) mounted within a traditional hardhat. The electrical signals generated from the thoughts of the wearer are used to train a Convolutional Neural Network (CNN) and modeled to recognize the wearer's imagined words, primarily open...
Proceedings Papers

Paper presented at the SPE/IADC Middle East Drilling Technology Conference and Exhibition, May 23–25, 2023
Paper Number: SPE-214527-MS
... on surface measurements which have the major influence on the bit torque (downhole torque) values while drilling. Artificial intelligence technology and its related applications such as; artificial neural network (ANN), support vector machine (SVM) and adaptive neuro fuzzy interference system (ANFIS...
Proceedings Papers

Paper presented at the SPE/IADC Middle East Drilling Technology Conference and Exhibition, May 23–25, 2023
Paper Number: SPE-214565-MS
... the online logging of logging in the field. sedimentary rock geologist upstream oil & gas neural network artificial intelligence identification asia government rock type machine learning classification recognition cuttings recognition resnet-50 automated identification quantity...
Proceedings Papers

Paper presented at the SPE/IADC Middle East Drilling Technology Conference and Exhibition, May 23–25, 2023
Paper Number: SPE-214603-MS
.... Specifically, a bi-directional Long short-term Memory-based Variational Autoencoder (biLSTM-VAE) projects raw drilling data into a latent space in which the real-time bit-wear can be estimated through classification of the incoming real time data in the latent space. The deep neural network was trained...
Proceedings Papers

Paper presented at the SPE/IADC Middle East Drilling Technology Conference and Exhibition, May 23–25, 2023
Paper Number: SPE-214599-MS
... identification model based on YOLOv5 algorithm is designed and established by introducing feature aggregation, loss function and attention mechanism, and the algorithm model is trained and tested by using neural network method. In addition, based on the risk identification of drilling operation, the approach...
Proceedings Papers

Paper presented at the SPE/IADC Middle East Drilling Technology Conference and Exhibition, May 25–27, 2021
Paper Number: SPE-202159-MS
...), Artifical Neural Networks (ANN), K-Nearest Neighbor (KNN), Random Forests (RF). The model parameters that were found closely related and useful for ROP optimization were drilling parameters (RPM, WOB, etc…), ROP of offset wells, bit/hole depth, block height, hook load, differential pressure, standpipe...
Proceedings Papers

Paper presented at the SPE/IADC Middle East Drilling Technology Conference and Exhibition, May 25–27, 2021
Paper Number: SPE-202202-MS
... quantitative evidences for drilling parameters optimization, drilling tools selection and well time estimation. machine learning rop prediction pdc bit parameter optimization neural network prediction performance rop model pipe unit upstream oil & gas accuracy flow rate prediction rop...
Proceedings Papers

Paper presented at the SPE/IADC Middle East Drilling Technology Conference and Exhibition, May 25–27, 2021
Paper Number: SPE-202191-MS
...-slip which is essential in achieving an autonomous drilling system. : artificial intelligence accuracy drilling operation neural network presented optimization input parameter rop exhibition proceedings derived controllable variable drilling data machine learning upstream oil...
Proceedings Papers

Paper presented at the SPE/IADC Middle East Drilling Technology Conference and Exhibition, May 25–27, 2021
Paper Number: SPE-202184-MS
... by using drilling fluid properties, that are routinely measured every quarter an hour such as marsh funnel viscosity, density, and solid percentages, and to predict the lithology by applying artificial neural networks to logging data ( Al-AbdulJabbar et al. 2020 , AlKinani et al. 2019 ). Going back to ROP...
Proceedings Papers

Paper presented at the SPE/IADC Middle East Drilling Technology Conference and Exhibition, January 29–31, 2018
Paper Number: SPE-189390-MS
... in the petroleum industry especially in predicting the well performance. Alajmi et al., (2015) predicted the choke performance using Artificial Neural Network (ANN). Alarifi et al., (2015) estimated the productivity index for oil horizontal wells using ANN, functional network and fuzzy logic. Elkatatny et al...
Proceedings Papers

Paper presented at the SPE/IADC Middle East Drilling Technology Conference & Exhibition, October 7–9, 2013
Paper Number: SPE-166698-MS
.... The fine solids content can increase and cause difficulties controlling rheological properties. Linear swell meter (LSM) testing is a well-known laboratory procedure used to characterize shale swelling in a WBMs. A mathematical modeling tool known as the artificial neural network (ANN) was used to model...
Proceedings Papers

Paper presented at the SPE/IADC Middle East Drilling Technology Conference & Exhibition, October 7–9, 2013
Paper Number: SPE-166793-MS
... Artificial neural networks have the ability to recognize complex patterns quickly with a high degree of accuracy without any assumptions about the nature and distribution of the data. McCulloch and Pitts proposed the first systematic neural networks in 1943. Major researches have been done...

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