Petroleum Engineering Continuing Education
Our Certificates of Continuing Education program meets the needs of petroleum engineers and other professionals wanting to grow or update their understanding of specific areas within petroleum engineering. Our certificates are managed through the Texas A&M Engineering Experiment (TEES) EDGE program.
Deterministic Reserves Evaluation Part 1
This course will cover Petroleum Resources Management System (PRMS); U.S. Securities and Exchange Commission Regulations; Reserves Estimation Methods; Applications of Resources Estimation Procedures; Fluid Flow; Type Curve Analysis and Flow Regime Identification; Arps’ Decline Model; and Advanced Decline Curve Analysis.
Deterministic Reserves Evaluation Part 2
This course will cover Stretched Exponential Decline Model (SEDM); Long-Duration Linear Flow; Bilinear Flow; Duong Model; Type Well Construction Basics; and Rate Transient Analysis.
Machine Learning for Petroleum Engineers Using Python, Part 1: Python Basics
The purpose of this course is to prepare the participants to use Python programming to apply various machine learning algorithms to petroleum engineering problems. The course will include many practical exercises to help participants get hands-on experience building their own models with Python. A focus of the course will be on the application deep learning algorithms with Python. The participants will learn to use Tensorflow and PyTorch, two popular open-source Python libraries, for deep learning applications. The course will also present many recent examples for application of machine learning and deep learning algorithms in petroleum engineering. Part 1 of this course covers a crash course on Python, data import and visualization using Python, and machine learning workflow and types. We will discuss the application of Pandas, Numpy, matplotlib, seaborn, and plotly libraries with various examples. The python programs and datasets used in the programs will be provided.
Machine Learning for Petroleum Engineers Using Python, Part 2: Supervised and Unsupervised Machine Learning
The purpose of this course is to prepare the participants to use Python programming to apply various machine learning algorithms to petroleum engineering problems. The course will include many practical exercises to help participants get hands-on experience building their own models with Python. A focus of the course will be on the application deep learning algorithms with Python. The participants will learn to use Tensorflow and PyTorch, two popular open-source Python libraries, for deep learning applications. The course will also present many recent examples for application of machine learning and deep learning algorithms in petroleum engineering. Part 2 of this course covers application of following supervised and unsupervised machine learning techniques using Python: K-means clustering, hierarchical clustering, outlier detection, linear regression, logistic regression, k-nearest neighbor, support vector machine, decision tree, and random forest. There will be various examples for application of these techniques in Python. The Python programs and datasets used in the examples will be provided.
Machine Learning for Petroleum Engineers Using Python, Part 3: Deep Learning
The purpose of this course is to prepare the participants to use Python programming to apply various machine learning algorithms to petroleum engineering problems. The course will include many practical exercises to help participants get hands-on experience building their own models with Python. A focus of the course will be on the application deep learning algorithms with Python. The participants will learn to use Tensorflow and PyTorch, two popular open-source Python libraries, for deep learning applications. The course will also present many recent examples for application of machine learning and deep learning algorithms in petroleum engineering. Part 3 of this course covers the application of the following deep learning techniques using Python: artificial neural networks, convolutional neural networks, auto-encoders, self-organizing maps, and Boltzmann machines. There will be various examples for application of these techniques in Python. The Python programs and datasets used in the examples will be provided.
Petroleum Data Analytics and Machine Learning
This course will provide working knowledge about data analytics and machine learning suitable for engineers/scientists working in petroleum engineering and geoscience, including various subsurface engineering and characterization activities.
Rock Mechanics Related to Hydraulic Fracturing
This course addresses the basic principles of stress and strain, pore pressure and in situ stress estimation, rock failure description and analysis, linear elasticity, stress shadow analysis, fracture near-tip stress analysis, fracture propagation, and hydraulic fracturing. The emphasis will be on unconventional reservoirs.
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