ISSN: 2157-7617

Journal of Earth Science & Climatic Change
Open Access

Our Group organises 3000+ Global Conferenceseries Events every year across USA, Europe & Asia with support from 1000 more scientific Societies and Publishes 700+ Open Access Journals which contains over 50000 eminent personalities, reputed scientists as editorial board members.

Open Access Journals gaining more Readers and Citations
700 Journals and 15,000,000 Readers Each Journal is getting 25,000+ Readers

This Readership is 10 times more when compared to other Subscription Journals (Source: Google Analytics)
  • Research Article   
  • J Earth Sci Clim Change 2015, Vol 6(3): 265
  • DOI: 10.4172/2157-7617.1000265

Artificial Intelligence for Lithology Identification through Real-Time Drilling Data

Alireza Moazzeni* and Mohammad Ali Haffar
Department of Engineering Science, Omidiyeh Branch, Islamic Azad University, Omidiyeh, Iran
*Corresponding Author : Alireza Moazzeni, Department of Engineering Science, Omidiyeh Branch, Islamic Azad University, Omidiyeh, Iran, Tel: +982147911, Email: Moazzeni@iauo.ac.ir

Received Date: Feb 22, 2015 / Accepted Date: Feb 26, 2015 / Published Date: Mar 06, 2015

Abstract

In order to reduce drilling problems such as loss of circulation and kick, and to increase drilling rate, bit optimization and shale swelling prohibition, it is important to predict formation type and lithology in a well before drilling or at least during drilling. Although there are some methods for finding out the lithology such as log interpretation, there is no method for determining lithology before or during drilling by a great degree of accuracy. Determination of formation type and lithology is very complicated and no analytical method is presented for this problem so far. In this situation, it seems that artificial intelligence could be really helpful. Neural networks can establish complicated non-linear mapping between inputs and outputs. In this paper, formation type and lithology of the formation will be predicted using real-time drilling data with an acceptable accuracy, while drilling that formation using artificial neural network. 47500 sets of data from 12 wells in South Pars gas field (in south of Iran) were selected and, after data mining and quality control, were imported to artificial neural networks. Results show that neural networks can determine type of formation and lithology with near 90% accuracy.

Keywords: Formation type; Lithology; Virtual intelligence; Artificial neural network

Citation: Moazzeni A, Haffar MA (2015) Artificial Intelligence for Lithology Identification through Real-Time Drilling Data. J Earth Sci Clim Change 6: 265. Doi: 10.4172/2157-7617.1000265

Copyright: ©2015 Moazzeni A, et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Top