COURSE OVERVIEW:

Introduction to Machine Learning for Geoscience Interpretation is designed to empower geoscientists with the data-driven tools necessary to manage the "Big Data" challenges of modern exploration and production. As the volume and complexity of seismic, well-log, and satellite data continue to grow, traditional manual interpretation methods are increasingly supplemented by automated algorithms. This course introduces the fundamental concepts of Artificial Intelligence (AI) and Machine Learning (ML), explaining how these technologies can be applied to recognize patterns, classify facies, and predict reservoir properties.

The coverage includes the primary branches of machine learning: supervised, unsupervised, and reinforcement learning. Participants will explore practical workflows for data cleaning and feature engineering—the critical steps that ensure algorithms receive high-quality input. The curriculum details specific geoscience applications, such as automated horizon picking, well-log correlation, and the identification of seismic geobodies. By understanding the "black box" of machine learning, attendees will learn how to evaluate model performance and avoid common pitfalls like overfitting and data bias.

Furthermore, the course addresses the integration of machine learning into existing geoscience software and workflows. Participants will be introduced to the Python ecosystem and popular libraries such as Scikit-Learn and TensorFlow, which are widely used for developing custom geoscience solutions. The training emphasizes the importance of the "Human-in-the-loop," where AI acts as an assistant to enhance, rather than replace, human expertise. By the end of the course, participants will have a clear roadmap for implementing machine learning to accelerate interpretation and improve subsurface predictions.

COURSE OBJECTIVES:

After completion of this course, the participants will be able to:

• Define the differences between Artificial Intelligence, Machine Learning, and Deep Learning.
• Explain the concepts of Supervised and Unsupervised Learning in a geoscience context.
• Prepare and clean messy geoscience datasets for machine learning applications.
• Perform feature engineering to extract meaningful signals from raw data.
• Utilize Clustering algorithms for automated lithology and facies classification.
• Apply Regression models to predict petrophysical properties from well logs.
• Understand the role of Neural Networks in seismic pattern recognition.
• Evaluate machine learning model accuracy using confusion matrices and cross-validation.
• Identify the risks of Overfitting and Underfitting in subsurface models.
• Navigate the Python environment for basic data analysis and visualization.
• Utilize Dimensionality Reduction (PCA/t-SNE) to simplify complex datasets.
• Implement automated workflows for well-to-well correlation.
• Describe the potential of Deep Learning for automated seismic interpretation.
• Develop a strategy for integrating AI into a multidisciplinary asset team.

TARGET AUDIENCE:

Geologists, Geophysicists, and Data Analysts who wish to leverage data science and automation to improve subsurface interpretation efficiency and accuracy.

TRAINING COURSE METHODOLOGY:

A highly interactive combination of lectures, discussion sessions, and case studies will be employed to maximise the transfer of information, knowledge, and experience. The course will be intensive, practical, and highly interactive. The sessions will start by raising the most relevant questions and motivating everybody to find the right answers. The attendants will also be encouraged to raise more of their questions and to share in developing the right answers using their analysis and experience. There will also be some indoor experiential activities to enhance the learning experience. Course material will be provided in PowerPoint, with necessary animations, learning videos, and general discussions.

The course participants shall be evaluated before, during, and at the end of the course.

COURSE CERTIFICATE:

National Consultant Centre for Training LLC (NCC) will issue an Attendance Certificate to all participants completing a minimum of 80% of the total attendance time requirement.