Electrofacies as a Tool for the Prediction of True Resistivity Using Advanced Statistical Methods—Case Study

Stanisław Baudzis, Joanna Karłowska-Pik and Edyta Puskarczyk
Additional contact information
Stanisław Baudzis: Geofizyka Toruń S.A., 87-100 Toruń, Poland
Joanna Karłowska-Pik: Faculty of Mathematics and Computer Science, Nicolaus Copernicus University in Toruń, 87-100 Toruń, Poland
Edyta Puskarczyk: Faculty of Geology Geophysics and Environmental Protection, AGH University of Science and Technology, 30-059 Kraków, Poland

Energies, 2021, vol. 14, issue 19, 1-18

Abstract: Statistical analysis methods have been widely used in all industries. In well logs analyses, they have been used from the very beginning to predict petrophysical parameters such as permeability and porosity or to generate synthetic curves such as density or sonic logs. Initially, logs were generated as simple functions of other measurements. Then, as a result of the popularisation of algorithms such as the k-nearest neighbours (k-NN) or artificial neural networks (ANN), logs were created based on other logs. In this study, various industry and general scientific programmes were used for statistical data analysis, treating the well logs data as individual data sets, obtaining very convergent results. The methods developed for processing well logs data, such as Multi-Resolution Graph-Based Clustering (MRGBC), as well as algorithms commonly used in statistical analysis such as Kohonen self-organising maps (SOM), k-NN, and ANN were applied. The use of the aforementioned statis-tical methods allows for the electrofacies determination and prediction of an Rt log based on the other recorded well logs. Correct determination of Rt in resistivity measurements made with the Dual Laterolog tool in the conditions of the Groningen effect is often problematic. The applied calculation methods allow for the correct estimation of Rt in the tested well.

Keywords: well-logging; true resistivity; formation evaluation; Groningen effect; electrofacies; clustering analysis; artificial neural networks (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/1996-1073/14/19/6228/pdf (application/pdf)
https://www.mdpi.com/1996-1073/14/19/6228/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jeners:v:14:y:2021:i:19:p:6228-:d:646974

Access Statistics for this article

Energies is currently edited by Ms. Agatha Cao

More articles in Energies from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().