We’ll look at the realities of a pure machine learning approach through the lens of applied field cases relevant to formation evaluation, reservoir engineering, and production forecasting.
Entry Level and Intermediate
Prerequisites (Knowledge/Experience/Education required)
Basic Computer Programming, Numerical Methods, Statistics, Familiarity with concepts like regression, interpolation, and curve fitting.
The instructor will use Windows OS during the course. Participants will execute python and tensorflow modules/codes to understand various Machine Learning concepts. All software used for the course is open source, so participants should bring computers where they can install the open-source software. Participants need at least 4GB of storage and 4GB RAM on their computer.
- Exploratory Data Analysis
- Supervised Learning - Regression and Classification
- Unsupervised Transformations
- Unsupervised Learning - Clustering
- Feature Extraction and Feature Ranking
- Anomaly Detection
- Cross Validation and Model Scores
- Parameter Selection
- Advanced Regression, Classification, and Clustering
- Neural Network
- TensorFlow and Deep Neural Networks
- Machine Learning for Time Series Data
- Hands on Petroleum Case Study 1: Applications of Shallow Learning Methods
- Hands on Petroleum Case Study 2: Applications of Deep Neural Network
- Hands on Petroleum Case Study 3: Applications of Data Mining
- Interesting Use Cases of Machine Learning in Upstream Oil and Gas
- Participants can perform exploratory data analysis on large datasets containing numerical and categorical data.
- Participants can perform exploratory data analysis on time-series data and unsupervised transformations.
- Participants will be proficient with using Decision Tree Classifiers, kNN classifier, Random forest tree classifier, and K-Means Clustering on various datasets.
- Participants can construct training, testing, cross validation, feature elimination, feature ranking, parameter selection, and anomaly detection tasks.
- Participants can implement advanced clustering, regression, and classification techniques, such as DBSCAN, Hierarchical Clustering, neural networks, ElasticNet, and Support Vector Machines.
- Participants can construct deep neural networks for time-series analysis.