Predicting Rate of Penetration and optimization Weight on bit using Artificial Neural Networks
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Optimization drilling parameters, Rate of Penetration, Artificial Neural Network, Nam Rong – Doi Moi field
Abstract
Achieving the greatest Rate of Penetration (ROP) is the aim of each drilling engineer because it could save time, diminish cost and limit drilling problems. Nonetheless, ROP could be affected by many drilling parameters which lead to complication in its prediction. Subsequently, it is essential and critical to propose a new approach to predict ROP with high accuracy and optimize drilling parameters. In this review, another methodology utilizing Artificial Neural Network (ANN) has been proposed to estimate ROP from real – time drilling data of a few wells in Nam Rong - Doi Moi oil field, Vietnam with more than 900 datasets included significant parameters like rotary speed (RPM), the weight on bit (WOB), standpipe pressure (SPP), flow rate (FR), weight of mud (MW), torque (TQ). The number of neurons in the hidden layer were varied then the results of different ANN models were compared in order to obtain the optimal model. The final ANN model shows high exactness when contrasted with actual ROP, in this manner it tends to be suggested as a successful and reasonable approach to predict the ROP of different wells in Nam Rong – Doi Moi field. Also, based on the proposed ANN model, the optimal WOB was determine for the drilling interval from 1800 to 2300 m of oil wells in research region.
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