Semester
Spring
Date of Graduation
2022
Document Type
Thesis
Degree Type
MS
College
Statler College of Engineering and Mineral Resources
Department
Petroleum and Natural Gas Engineering
Committee Chair
Shahab D. Mohaghegh
Committee Co-Chair
Samuel Ameri
Committee Member
Samuel Ameri
Committee Member
Mehrdad Zamirian
Abstract
This paper captures the ability of AI neural network technology to analyze petrophysical datasets for pattern recognition and accurate prediction of the pay zone of a vertical well from the Santa Fe field in Kansas.
During this project, data from 10 completed wells in the Santa Fe field were gathered, resulting in a dataset with 25,580 records, ten predictors (logs data), and a single binary output (Yes or No) to identify the availability of Hydrocarbon over a half feet depth segment in the well. Several models composed of different predictors combinations were also tested to determine how impactful some logs were compared to others for the prediction process.
With 32 tested models using a base set of 5 logs (X, Y GR, DEPT, and CALI) and different combinations of 5 other logs ( RT90, RHOB, NPHI, PE, DT). All models containing RT90, NP, or DT led to a better prediction matching the pay zone established based on a petrophysical analysis and completion data from the well.
Results from this project could be used as another support to help and justify decision-making for a Petro physicist regarding work in the field with less experience.
Recommended Citation
Guedon, Darren D., "Hydrocarbon Pay zone Prediction using AI Neural Network Modeling." (2022). Graduate Theses, Dissertations, and Problem Reports. 11297.
https://researchrepository.wvu.edu/etd/11297