Date of Graduation
Statler College of Engineering and Mineral Resources
Petroleum and Natural Gas Engineering
H, Ilkin Bilgesu
Artificial neural networks have been applied to different petroleum engineering disciplines. This is contributed to the powerful prediction capability in complex relationships with enough data available. The objective of this study is to develop a new methodology to predict the vertical and horizontal stresses using artificial neural networks for Marcellus shale well laterally drilled in Monongalia County, WV.;This approach coupled the drilling surface measurements with the recorded well logging data. Drilling parameters included depth, WOB, RPM, standpipe pressure, torque, pump flow rate and rate of penetration. Well logging data included gamma ray and bulk density. The model output was the minimum horizontal stress and vertical stress. The well trajectory was divided into two main parts, the vertical and lateral section since the change in the drilling direction along with changing structural geology and sedimentation impacted the resultant stresses.;Several neural networks were designed with a different number of feedforward backpropagation architectures. The collected data was filtered and normalized before neural networks were trained using part of data. A percentage of the data was used to validate the trained model. Finally, a blind data set aside was used to test the model prediction accuracy and to estimate error percentages. Preliminary results show that adding logging data such as gamma ray and bulk density improves the model accuracy. Also, increasing the number of hidden layers and neurons improved the efficiency. However, higher the number of neurons and hidden layers higher was the computational cost due to increased model convergence time.;The correlation coefficients of the predicted and observed values ranged between 0.76 and 0.99. This approach is beneficial regarding hydraulic fracturing design and fracture orientation prediction in unconventional shales.
Abusurra, Mousa S. Mohamed, "Using Artificial Neural Networks to Predict Formation Stresses for Marcellus Shale with Data from Drilling Operations" (2017). Graduate Theses, Dissertations, and Problem Reports. 5023.