Output details
15 - General Engineering
London South Bank University
Article title
Artificial neural network forward modelling and inversion of electrokinetic logging data
Type
D - Journal article
Title of journal
Geophysical Prospecting
Article number
-
Volume number
59
Issue number
4
First page of article
721
ISSN of journal
00168025
Year of publication
2011
URL
-
Number of additional authors
5
Additional information
This is a novel, fast approach in determining subsurface formation properties such as porosity and hydraulic permeability using artificial neural networks with electrokinetic data. The methodology, due to both its rapidity and accuracy, has major practical implications for the oil industry and can be used both as a new tool at new sites of explorations as well as a confirmation tool at existing sites.
Verification: Dr. Negah Ardjmandpour, GE Oil and Gas, London, UK (Negah.Ardjmandpour@ge.com)
Interdisciplinary
-
Cross-referral requested
-
Research group
5 - Environmental Engineering
Proposed double-weighted
No
Double-weighted statement
-
Reserve for a double-weighted output
No
Non-English
No
English abstract
-
