BACK-PROPAGATION AND SELF-ORGANIZING NETWORKS TO FRACTURE ANALYSIS USING THE IMAGE LOGS FMI IN ORDOVICIAN-DEVONIAN RESERVOIRS, ILLIZI BASIN, ALGERIA - INSU - Institut national des sciences de l'Univers Accéder directement au contenu
Communication Dans Un Congrès Année : 2018

BACK-PROPAGATION AND SELF-ORGANIZING NETWORKS TO FRACTURE ANALYSIS USING THE IMAGE LOGS FMI IN ORDOVICIAN-DEVONIAN RESERVOIRS, ILLIZI BASIN, ALGERIA

Résumé

The technique of imagery used in well-logs is an effective tool for the systems of in situ recording to obtain a high-resolution image for a better interpretation of the recorded data.This technology, widely used in Petroleum industry, provides invaluable information on stratifications, fractures, faults, textures, and secondary porosity.The present study deals with the Formation Micro Imager (FMI) data recorded on Ordovician and Devonian Formations of the Illizi Basin within both Takw-1 and Take-1 wells. The aim is to highlight their stratigraphic and structural characteristics. In most Saharan reservoirs, the measurement of fractures represents the most important factor, especially in the field of production. In fact, the increasing need for qualitative and quantitative analyses of the imagery to describe, in undesirable way, fractures, faults and folds may affect the interpretation and thus the structure of the reservoirs. A study carried out on two wells of the Illizi oilfield made it possible to obtain detailed parameters of all sedimentary and structural events identified on electrical and sonic images using FMI. The automatic interpretation of the seismages, revealed important information on the following characteristics: structures, stratifications, fractures, faults, textures as well as secondary porosities. They play a significant role in the description of the reservoir. A neural network (NN) study carried out on fractures using back-propagation (BP) and autoorganized (SO) techniques, starting from the whole FMI data allowed to obtain a classification of fractures and prediction of their inclination values.The results obtained showed that BP-NN can be used to predict fracture models in other wells of the study area in order to get a broader model of dynamic fracture.
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Dates et versions

insu-01713453 , version 1 (20-02-2018)

Identifiants

  • HAL Id : insu-01713453 , version 1

Citer

Rafik Baouche, Tahar Aifa, Kamel Baddari, R. Chaouchi. BACK-PROPAGATION AND SELF-ORGANIZING NETWORKS TO FRACTURE ANALYSIS USING THE IMAGE LOGS FMI IN ORDOVICIAN-DEVONIAN RESERVOIRS, ILLIZI BASIN, ALGERIA. 7 éme COLLOQUE MAGHREBIN DE GEOPHYSIQUE APPLIQUEE CMGA 7, Feb 2018, Alger, Algeria. pp.47. ⟨insu-01713453⟩
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