Use of neural networks in log's data processing: prediction and rebuilding of lithologic facies

Dominique Frayssinet , Sylvie Thiria (1) , Fouad Badran (2) , L. Briqueu

1 LODYC - Laboratoire d'océanographie dynamique et de climatologie
2 CEDRIC - Centre d'études et de recherche en informatique et communications

When a log is missing in a drilling hole, geologists hope to deduce it from others logs available in another part of the hole or in a neighbouring hole, in order to define the lithologic facies of the hole. This paper presents a neural network method to predict the missing log's measure from the other available log's measures. This method, based on Multi-Layer Perceptron (MLP) acts as a non linear regression method for the prediction task and as a probability density distribution approximation for the outlier rejection task. The result obtained when applied to actual log's data for prediction and rejection are presented in a separate section. The last section is dedicated to a non supervised neural method in order to reconstruct the lithologic facies of the concerned hole. This last experiment allows to validate and interpret the different results of the proposed methods.

Domaines: Informatique [cs]; Statistiques [math.ST]

Dominique Frayssinet, Sylvie Thiria, Fouad Badran, L. Briqueu. Use of neural networks in log's data processing: prediction and rebuilding of lithologic facies. Petrophysics meets Geophysics, Paris, 2000., Jan 2000, Paris, France. ⟨hal-01124635⟩