An accurate approach for drilling rate prediction using a new soft computing approach is introduced in this paper. Drilling rate prediction is an important issue due to its crucial role in minimizing drilling cost. However, a large number of unforeseen factors and events influence the drilling rate and make it a complex and stochastic process and consequently difficult to predict. Many different techniques have been introduced for this task. Among those, Bourgoyne and Young model and its extensions have been widely used in drilling rate prediction during last decades. However, they did not provide satisfactory accuracy. In this research, a new soft computing approach is proposed over this problem and predicts the drilling rate with acceptable accuracy. Our practical data sets are nine wells of an Iranian gas field called “Khangiran”. Simulation results show that the proposed intelligent approach is superior to the conventional methods in drilling rate prediction accuracy.
Key words: Rate of penetration, simulated annealing Fuzzy logic, drilling rate prediction, K-mean clustering, soft computing.
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