Image logs held important information of the subsurface sequences. They can provide not only information about bedding and fault/fracture spatial distribution and characteristics but they can also supply insight on the rock texture, textural organisation and porosity types and distribution. In order to reduce the subjectivity of the interpretation and cut the interpretation time we tested a new semi-automatic process for image log interpretation and extraction of the main characteristics of the image/formation. This approach uses image processing algorithms and artificial intelligence techniques to analyze and to classify borehole images. The final results of the process is a series of image facies that are identified along the image log and that can be calibrated using cores to sedimentary facies to assign them a geological meaning. In this study the image log from one well was processed using this method to identify different rock facies to be compared with those identified by the log interpreter. The results of this study are encouraging because up to 75% of automatic classes correctly correspond to those identified by the interpreter.

Can Image Logs Be Interpreted Using Artificial Intelligence Techniques? A Supervised Test over Two FMI Borehole Logs

FERRARETTI, Denis;TAGLIAVINI, Luca;GAMBERONI, Giacomo;LAMMA, Evelina
2008

Abstract

Image logs held important information of the subsurface sequences. They can provide not only information about bedding and fault/fracture spatial distribution and characteristics but they can also supply insight on the rock texture, textural organisation and porosity types and distribution. In order to reduce the subjectivity of the interpretation and cut the interpretation time we tested a new semi-automatic process for image log interpretation and extraction of the main characteristics of the image/formation. This approach uses image processing algorithms and artificial intelligence techniques to analyze and to classify borehole images. The final results of the process is a series of image facies that are identified along the image log and that can be calibrated using cores to sedimentary facies to assign them a geological meaning. In this study the image log from one well was processed using this method to identify different rock facies to be compared with those identified by the log interpreter. The results of this study are encouraging because up to 75% of automatic classes correctly correspond to those identified by the interpreter.
Intelligenza Artificiale; Immagini; Geologia; Idrocarburi
File in questo prodotto:
Non ci sono file associati a questo prodotto.

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11392/527404
 Attenzione

Attenzione! I dati visualizzati non sono stati sottoposti a validazione da parte dell'ateneo

Citazioni
  • ???jsp.display-item.citation.pmc??? ND
  • Scopus ND
  • ???jsp.display-item.citation.isi??? ND
social impact