Machine Learning (ML) Methods In Petroleum Industry Seminar

This AL/ML Focused Seminar Presented by Sr. Petroleum Engineering Data Consultant

What you'll learn
Machine Learning Overview
Descriptive Statistics
Regression
Classification
Clustering
Time Series forecasting

Requirements
Interest in Oil and Gas Drilling Engineering
Passion to Learn Artificial intelligence (AI) and machine learning (ML)

Description
This seminar, presented by Sr. Petroleum Engineering Data Consultant, which covers a broad overview of machine learning concepts and their application within the oil and gas sector. It starts with the definition of machine learning (ML), emphasizing its ability to learn from data without explicit programming. The presentation highlights the wide range of ML applications, from image and speech recognition to fraud detection and financial forecasting, with following agenda: Introduction to Machine LearningDescriptive StatisticsRegressionClassificationClusteringTime Series forecastingThe core of the presentation focuses on key ML techniques: supervised learning, unsupervised learning, and reinforcement learning. Supervised learning is detailed with methods like linear regression, logistic regression, support vector machines (SVM), decision trees, and ensemble techniques. Unsupervised learning is highlighted with K-Means clustering, hierarchical clustering, and dimensionality reduction. Feature engineering and selection are discussed as critical steps in the ML workflow, involving the creation of new features from existing data and the identification of the most relevant features for model building.Descriptive statistics are presented as essential for understanding data, using P-values and correlation coefficients to determine significance and relationships between variables. The presentation outlines data types as qualitative (attributes) and quantitative (categorical). A significant portion is dedicated to regression analysis, including linear, multiple linear, and non-linear regression models. Specific applications in the petroleum industry are highlighted, which are including seismic interpretation, reservoir characterization, PVT modelling, etc.Finally, the presentation covers time series forecasting using statistical, machine learning, and deep learning methods. Statistical methods such as Moving Average are talked about with more advanced Machine Learning Methods such as Random Forest, ending with Deep Learning techniques like Recurrent Neural Networks.

Geologist, Petroleum Engineers, Oil and Gas Employees,Petrophysicist, Geoscientist, Cased Hole Logs Analysts and Interpreters,Geology and Petroleum Engineering College and University Students,Python ,Artificial intelligence (AI) and Machine learning (ML) Enthusiast,Workover and Drilling Professionals