Prediction of Rate of Penetration for wells at Nam Con Son basin using Artificial Neural Networks models

6 pages•Published: January 16, 2022

Vu Khanh Phat Ong, Quang Khanh Do, Thang Nguyen, Hoang Long Vo, Ngoc Anh Thy Nguyen and Ngoc Yen Linh Ly

Abstract

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.

Keyphrases: artificial neural network, oil and gas well, rate of penetration

In: Tich Thien Truong, Trung Nghia Tran, Thanh Nha Nguyen and Quoc Khai Le (editors). Proceedings of International Symposium on Applied Science 2021, vol 4, pages 116-121.

Links:	https://easychair.org/publications/paper/fMCz
	https://doi.org/10.29007/4sdt

BibTeX entry

@inproceedings{ISAS2021:Prediction_Rate_Penetration_wells,
  author    = {Vu Khanh Phat Ong and Quang Khanh Do and Thang Nguyen and Hoang Long Vo and Ngoc Anh Thy Nguyen and Ngoc Yen Linh Ly},
  title     = {Prediction of Rate of Penetration for wells at Nam Con Son basin using Artificial Neural Networks models},
  booktitle = {Proceedings of International Symposium on Applied Science 2021},
  editor    = {Tich Thien Truong and Trung Nghia Tran and Thanh Nha Nguyen and Quoc Khai Le},
  series    = {Kalpa Publications in Engineering},
  volume    = {4},
  publisher = {EasyChair},
  bibsource = {EasyChair, https://easychair.org},
  issn      = {2515-1770},
  url       = {/publications/paper/fMCz},
  doi       = {10.29007/4sdt},
  pages     = {116-121},
  year      = {2022}}

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