Semester
Fall
Date of Graduation
2023
Document Type
Thesis
Degree Type
MS
College
Statler College of Engineering and Mineral Resources
Department
Petroleum and Natural Gas Engineering
Committee Chair
Mohamed El Sgher
Committee Member
Samuel Ameri
Committee Member
Kashy Aminian
Abstract
The distribution of the anisotropic minimum horizontal stress, both in horizontal and vertical directions, is necessary for effective hydraulic fracture treatment design in Marcellus Shale horizontal wells. Typically, the minimum horizontal stress can be estimated sonic logs. However, sonic log data is not commonly available for the horizontal Marcellus shale wells due to the complexity and cost. The objective of this research is to predict the anisotropic minimum horizontal stress by utilizing drilling parameters including depth, weight-on-bit (WOB), revolution per minute (RPM), standpipe pressure, torque, pump flow rate, and the rate of penetration (ROP). More specifically, artificial neural network (ANN) models will be developed to predict the anisotropic minimum horizontal stress for a horizontal Marcellus shale well from the drilling and well log data. Artificial neural networks are particularly useful to identify complex relationships to predict the properties of unconventional formations.
The available data from a Marcellus Shale horizontal well was collected and filtered to prepare data sets for ANN training, testing, and validation purposes. Two networks, for the vertical and lateral sections of the well, were developed. The preliminary results indicated that inclusion of lithology, gamma-ray, and bulk density well log data as inputs can improve the predictability of the networks. Finally, the networks were used to predict the anisotropic minimum horizontal stress in a different Marcellus shale horizontal well with the available sonic log data. To evaluate the applicability of the ANN models, the predicted stresses by the networks were compared against those estimated from the sonic logs. The predictions by both networks (vertical and horizontal) were found to be in close agreement with those estimated from the sonic logs. The results of this research can be utilized as a predictive tool to help fill in the need for an accurate estimation of static geomechanical properties including the minimum horizontal stress in Marcellus Shale horizontal wells and to improve the fracturing treatment design.
Recommended Citation
Alawadh, Moudhi, "An Artificial Neural Network Approach to Predicting Formation Stress in Multi-Stage Fractured Marcellus Shale Horizontal Wells Based on Drilling Operations Data" (2023). Graduate Theses, Dissertations, and Problem Reports. 12193.
https://researchrepository.wvu.edu/etd/12193