Eine Plattform für die Wissenschaft: Bauingenieurwesen, Architektur und Urbanistik
Eine Plattform für die Wissenschaft: Bauingenieurwesen, Architektur und Urbanistik
Prediction of rate of penetration with data from adjacent well using artificial neural network
We present an assessment on the predictability of the rate of penetration using artificial neural networks when only drilling records from an adjacent are at hand. The study was carried out with data from two Enhanced Geothermal System wells in South Korea. We compare five data-arrangement cases for neural network training. Differences in the specific values of parameters from the adjacent and the predicted well, such as the weight on bit, and the rotary speed influenced the prediction errors. Among the five cases, the highest error (85.3%) occurred for the case of cumulative data up to the point of prediction. However, some data-arrangements decrease the error, e.g. when employing the whole data (46.8%), or when using small data sections near prediction (case 4: 42.4%, and case 5: 39.7%). We suggest the combination of these last tree approaches when prediction ROP with limited data.
Prediction of rate of penetration with data from adjacent well using artificial neural network
We present an assessment on the predictability of the rate of penetration using artificial neural networks when only drilling records from an adjacent are at hand. The study was carried out with data from two Enhanced Geothermal System wells in South Korea. We compare five data-arrangement cases for neural network training. Differences in the specific values of parameters from the adjacent and the predicted well, such as the weight on bit, and the rotary speed influenced the prediction errors. Among the five cases, the highest error (85.3%) occurred for the case of cumulative data up to the point of prediction. However, some data-arrangements decrease the error, e.g. when employing the whole data (46.8%), or when using small data sections near prediction (case 4: 42.4%, and case 5: 39.7%). We suggest the combination of these last tree approaches when prediction ROP with limited data.
Prediction of rate of penetration with data from adjacent well using artificial neural network
Lecture Notes in Civil Engineering
Duc Long, Phung (Herausgeber:in) / Dung, Nguyen Tien (Herausgeber:in) / Diaz, Melvin (Autor:in) / Kim, Kwang Yeom (Autor:in) / Lee, Jangguen (Autor:in) / Shin, Hyu-Seoung (Autor:in)
29.11.2019
6 pages
Aufsatz/Kapitel (Buch)
Elektronische Ressource
Englisch
Prediction of Pile Settlement Using Artificial Neural Networks Based on Cone Penetration Test Data
| British Library Conference Proceedings | 2010
TBM penetration rate prediction based on the long short-term memory neural network
| DOAJ | 2021
HPC Strength Prediction Using Artificial Neural Network
| British Library Online Contents | 1995