hydraulic optimization predication using acritical neural networks (anns) in an iraq oil field

Independent factors in this study include rpm, torque, differential pressure, hook load, and mud characteristics, whereas the dependent parameters q, tfa, spm, and formation variables like depth. an accurate projection of pump pressure alerts the driller to potential issues like circulation issues, washouts, subterranean blowouts, and kicks, allowing for the safe implementation of corrective measures. presents an in-depth summary of drilling optimization methods. the matlab fitting tool was used to create an ann model for hydraulics prediction. this model successfully predicts pump pressure vs depth in similar formations. therefore, five input variables and a single output variable (pump pressure) were included in a three-layer feed-forward neural network that was trained using the levenberg-marquardt back-propagation technique. the ann technique s simulated results showed a high degree of agreement between the observed pump pressure and projected values for both the training and test data sets. based on the findings, the variables with the greatest influence on this model were: depth, effective diameter, and total flow area, followed by flow rate, differential pressure, mud characteristics, etc. however, this model should be utilized with care owing to the small sample size. since the information about inclination angle and dog leg severity was disregarded, this model may be used for vertical wells.