Drilling Stuck Pipe Prediction in Algerian Oil Fields: Time Series Forecasting Approach
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The Companies specialized in oil drilling field make to reduce the cost of digging and optimize the operation time. The Drill string stuck (stuck pipe) is the biggest frequent problem during drilling operation which imposes rises to the drilling cost of services.
The main goal of the drillers is the minimization of the Drilling Time and Reduce the Drilling Cost with avoiding the Drilling Problems (e.g. Stuck pipe). They are trying to use new methods based on artificial intelligence to solve these problems. Generally, the stuck pipe is discovered too late after the accident. The aim of this study is trying to predict this problem before happening which is possible to provide the best solution to limit the consequences and avoid the danger and its financial loss.
To solve this problem, it is more convenient to propose a new architecture based on time series forecasting for analyzing the huge Algerian oils fields drilling datasets and implement it in the Hadoop Ecosystem.
KeywordsStuck pipe Hadoop Big data Time series forecasting Artificial intelligence
This work is supported by Laboratory LINATI (Kasdi Merbah University, Ouargla, Algeria), Laboratory LINFI (Mohamed Khider University, Biskra, Algeria) and SONATRACH company represented by ENAFOR, ENTP and ENSP.
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