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.
Energy and Sustainability
The course offers an overview of energy resources and use with emphasis on long-term sustainability; considers fossil, hydro, nuclear, and alternative energy sources, electricity and transportation, energy conversions, energy efficiency, energy security, and environmental impact. In our lectures we will discuss the following engineering concepts: a) Fundamental physical concepts of energy, power, energy density, and energy efficiency. b) Concept of continuous energy transformation. c) Concept of energy return on energy invested. d) Concept of levelized cost of energy, capital cost per unit of power for various energy generating technologies.
High Performance Drilling Engineering & Operational Practices | Spring 2025
The purpose of this course is to prepare the student to be able to achieve differentiating drilling performance in the most complex wells. The physics-based practices taught represent the state of the art in high-performance drilling. This includes the underlying physics of each major type of performance limiter, real time operational practices, engineering redesign practices, and effective workflows for achieving the required change in engineering and operational practices.
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.
Deep Learning for Petroleum Engineering Using Python: Part 1- Neural Networks Fundamentals
The purpose of this course is to prepare the students to use Python programming to apply various deep learning algorithms to petroleum engineering problems. Participants will gain hands-on experience in Python programming and popular deep learning libraries such as TensorFlow and Keras. The course will cover essential concepts of deep learning, including neural networks, convolutional neural networks, recurrent neural networks, generative models, and reinforcement learning. The course will also present many recent examples for application of deep learning algorithms in petroleum engineering. Part 1 of this course will cover Neural Networks Fundamentals (Introduction to artificial neural networks (ANNs), Activation functions and loss functions, Backpropagation algorithm, Implementing a simple neural network in Python and Deep Learning Libraries (Introduction to TensorFlow and Keras frameworks, Building, training, and evaluating neural networks using TensorFlow and Keras), Regularization techniques and Hyperparameter tuning and model evaluation techniques.
Deep Learning for Petroleum Engineering Using Python: Part 2- Convolutional Neural Networks and Transfer Learning
The purpose of this course is to prepare the students to use Python programming to apply various deep learning algorithms to petroleum engineering problems. Participants will gain hands-on experience in Python programming and popular deep learning libraries such as TensorFlow and Keras. The course will cover essential concepts of deep learning, including neural networks, convolutional neural networks, recurrent neural networks, generative models, and reinforcement learning. The course will also present many recent examples for application of deep learning algorithms in petroleum engineering. Advancing into more specialized domains, Part 2 of the course introduces Convolutional Neural Networks (CNNs), a class of deep neural networks highly effective in processing visual imagery. Students learn about the architecture of CNNs, including convolutional layers, pooling, and flattening, and how these components enable the model to automatically and adaptively learn spatial hierarchies of features from input images. An integral part of this section is dedicated to Transfer Learning, where students learn to leverage pre-trained models to solve tasks with limited data, significantly reducing training time and computational cost. This section combines theoretical knowledge with practical applications, providing hands-on experience in image recognition and classification tasks.
Deep Learning for Petroleum Engineering Using Python: Part 3- Recurrent Neural Network, Autoencoders, Generative Adversarial Networks, and Transformer Models
The purpose of this course is to prepare the students to use Python programming to apply various deep learning algorithms to petroleum engineering problems. Participants will gain hands-on experience in Python programming and popular deep learning libraries such as TensorFlow and Keras. The course will cover essential concepts of deep learning, including neural networks, convolutional neural networks, recurrent neural networks, generative models, and reinforcement learning. The course will also present many recent examples for application of deep learning algorithms in petroleum engineering. Advancing into more specialized domains, Part 2 of the course introduces Convolutional Neural Networks (CNNs), a class of deep neural networks highly effective in processing visual imagery. Students learn about the architecture of CNNs, including convolutional layers, pooling, and flattening, and how these components enable the model to automatically and adaptively learn spatial hierarchies of features from input images. An integral part of this section is dedicated to Transfer Learning, where students learn to leverage pre-trained models to solve tasks with limited data, significantly reducing training time and computational cost. This section combines theoretical knowledge with practical applications, providing hands-on experience in image recognition and classification tasks.
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.
Probabilistic Reserves Evaluation Part 1
This course will cover Petroleum Resources Management System (PRMS); U.S. Securities and Exchange Commission regulations; descriptive statistics; probability basics and expected value; decision trees; probability distributions; and probabilistic reserves estimates.
Probabilistic Reserves Evaluation Part 2
This course will cover Monte Carlo Simulation; Capen’s Alternative to Simulation; estimating proved undeveloped reserves using statistical techniques; Arps’ Decline Model; GOR trends; and well spacing.
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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