Utilisation Des Techniques De Data-mining Pour La Détéction De Coincement De La Garniture De Forage

Résumé: Les societés specialiseés dans le domaine du forage travaillent toujours dans le but de minimiser les depenses accelerer la tache du projet en cour afin d'augmenter ses bene fices le maximum possible. Le coincement de la garniture de forage (stuck pipe) est le grond probléme qui peut rencontren ces societés. La capacité de prevoir les dangers les problemes d'extraie les donnés, les liens et faire l'etude necessaire afin d'abouttir aux solution adequates est le domaine intelligence artificielle.t Notre etude Preparation d'un exemple de prediction d' un coincement en utilisant les algorithmes de la fouille des données . The factories specialized in oil drilling field make to reduce the cost of digging and its acceleration. The stopped pipe during the digging operation its the biggest frequent problem wich the factories face make them financial loss and waste of time. The main aim of this factories' bosses is the reduce of (stock pipe) the drilling and finding out the information is a new field of the artificial intelligent fields used to solve this problem through the ability to control the information and find the relation between learning and machine learning directly through the actual information from the field. Generally, the trouble of the stopped pipe is fixed after the accident and that's through the use of the limited techniques. Here we try to predict the problem so that we can avoid the danger and it's financial loss. if we can detect this problem before happening we can provide the best solution to limit the consequence. the purpose of our studies is to make a predicted model that can predict the problem oft the stock pipe by the use of algerithme.

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