Mohammad Mehrad

TL;DR: In this paper, the authors investigated casing collapse in wellbores from an established petroleum geomechanics perspective to develop and compare two hybrid neural-network models, multilayer perceptron's tuned, respectively, with a genetic algorithm (MLP-GA) and a particle swarm algorithm (MPA), which are configured to predict Poisson's ratio ( ϑ ) and maximum horizontal stress ( σ H ) from available well log input data.

TL;DR: Four-hybrid machine-learning-optimization algorithms evaluated all outperform the empirical relationships used for many decades in the oil industry to predict bubble point pressure (BPP) and oil formation volume factor (OFVF).

TL;DR: The small difference between the obtained levels of error in the training and testing stages with the LSSVM-COA model, as compared to the other models, revealed that the model can be used to predict the ROP at other wells across the field reliably and accurately provided the model be developed with larger sets of data across theField.

TL;DR: Comparison of the prediction performance of the HML models developed with those of the previous empirical equations and artificial intelligence models reveals that the novel MELM-PSO model presents superior prediction efficiency and higher computational accuracy.

TL;DR: Results show that hybrid intelligent models are highly capable of predicting lost circulation before drilling a certain formation, and show themselves to be superior to applying standalone machine-learning methodologies for predicting loss of circulation.