Prediction of penetration Rate and cost with Artificial Neural Network for Alhafaya Oil Field
Prediction of penetration rate (ROP) is important process in optimization of drilling due to its crucial role in lowering drilling operation costs. This process has complex nature due to too many interrelated factors that affected the rate of penetration, which make difficult predicting process. This paper shows a new technique of rate of penetration prediction by using artificial neural network technique. A three layers model composed of two hidden layers and output layer has built by using drilling parameters data extracted from mud logging and wire line log for Alhalfaya oil field. These drilling parameters includes mechanical (WOB, RPM), hydraulic (HIS), and travel transit time (DT). Five data set represented five formations gathered from five drilled wells were involved in modeling process.Approximatlly,85 % of these data were used for training the ANN models, and 15% to assess their accuracy and direction of stability. The results of the simulation showed good matching between the raw data and the predicted values of ROP by Artificial Neural Network (ANN) model. In addition, a good fitness was obtained in the estimation of drilling cost from ANN method when compared to the raw data.
For papers published in IJCPE, authors and their institutions all have the same rights to reuse articles published in the journal in accordance with the journal by IJCPE is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License. This permits users to copy, redistribute, remix, transmit and adapt the work provided the original work and source is appropriately cited. Please check the license for full license terms and attribution requirements.