Open AccessPrediction of Rate of Penetration for wells at Nam Con Son basin using Artificial Neural Networks models
Author(s) -
Vu Khanh Phat Ong,
Quang Khanh,
Thang Nguyen,
Hoang-Long Vo,
Ngoc Anh Thy Nguyen,
Ngoc Yen Linh Ly
Publication year - 2022
Publication title -
kalpa publications in engineering
Language(s) - English
Resource type - Conference proceedings
ISSN - 2515-1770
DOI - 10.29007/4sdt
Subject(s) - artificial neural network , rate of penetration , drilling , predictive modelling , computer science , penetration rate , petroleum engineering , structural basin , artificial intelligence , geology , machine learning , engineering , mechanical engineering , paleontology
The rate of penetration (ROP) is an important parameter that affects the success of a drilling operation. In this paper, the research approach is based on different artificial neural network (ANN) models to predict ROP for oil and gas wells in Nam Con Son basin. The first is the process of collecting and evaluating drilling parameters as input data of the model. Next is to find the network model capable of predicting ROP most accurately. After that, the study will evaluate the number of input parameters of the network model. The ROP prediction results obtained from different ANN models are also compared with traditional models such as the Bingham model, Bourgoyne & Young model. These results have shown the competitiveness of the ANN model and its high applicability to actual drilling operations.