Machine Learning in Oil and Gas Industry

Welcome and Introduction

• Host: Military Research Hub & Petro
• Speaker: Dr. Ramy (Petrochemicals Engineering Student)
• Session: Week 2 on Machine Learning
• Agenda: Recap of last week, core machine learning concepts, case studies, and onward challenge.

Core Machine Learning Concepts

General Workflow

1. Training Data: Information provided to the machine to learn.
2. Machine Learning Algorithm: Various types, each with unique methodologies and requirements.
3. Model Input Data: Different input requirements for different algorithms.
4. Training: Executing the algorithm to build a model.
5. Prediction/Inferences: Using the trained model to predict new data.
6. Accuracy & Evaluation: Assess model accuracy and refine as needed.

Types of Machine Learning

• Supervised Learning: Using labeled datasets (input + corresponding output).
  • Classification: Differentiating items into predefined categories (e.g., Spam vs. Non-Spam emails).
  • Regression: Predicting a numeric value (e.g., temperature forecasting, housing prices).
• Unsupervised Learning: Using unlabeled datasets to identify patterns or groupings.
  • Clustering: Grouping data based on similarities (e.g., customer segmentation).
  • Association: Identifying relationships between variables (e.g., buying patterns).
• Semi-Supervised Learning: Combines both labeled and unlabeled data.
• Reinforcement Learning: Learning via trial and error to achieve a specific objective (e.g., gaming AI).

Difference between Classification and Clustering

• Classification: Supervised learning with labeled data.
• Clustering: Unsupervised learning; finding inherent groupings in data without labels.

Key Examples

• Supervised Learning: Spam detection, image classification, weather forecasting, stock price prediction.
• Unsupervised Learning: Customer segmentation, anomaly detection.

Case Studies and Applications in Oil and Gas

AI in Regulations

• Capabilities: NLP for understanding regulations.
• Benefits: Predictive sentiment analysis, drafting regulations, efficiency improvements, fraud detection.

Pipeline Corrosion and Failure

• Review Papers: Summarize various research on machine learning applications in pipeline management.
  • Corrosion Rate Analysis: Artificial neural networks to predict corrosion patterns.
  • Failure Prediction: Hybrid machine learning techniques to predict pipeline failures.

Wellhead Choke in Gas Condensate Fields

• Study Methodology: Data collection, normalization, and error calculation.
• Machine Learning Usage: Different algorithms for real-time prediction and optimization.

Practical Hands-On with Onward Challenge

Overview of the Challenge

• Spot the Trend: 1D pressure data analysis.
• Objective: Predict if a well is flowing or shut in using bottom hole pressure data.
• Data Details: 1-minute interval bottom hole pressure data.
• Supervised Learning: Data includes labeled examples of flowing and shut-in wells.
• Considerations: Time series nature, diverse data conditions, avoiding overfitting.

Getting Started with Google Colab

• First Step: Open the provided notebook in Google Colab.
• Setup: Install necessary libraries and prepare the environment.
• Running the Notebook: Initial run to check for errors and prepare for data exploration.
• Data Exploration: Load and inspect the dataset to understand structure and content.

Next Steps

• Upcoming Session: Data processing, model training, and making predictions.
• Focus: Practical coding exercises to implement machine learning models.

Questions and Clarifications

• Reinforcement Learning: Exploration and score-based feedback without specific training data.
• AI vs. ML: AI as a general umbrella, ML as a subset focusing on data learning.

Closing Remarks

• Future Sessions: Continue with practical coding and challenge implementation.
• Q&A: Address questions from participants.

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Conclusion: We'll dive deeper into practical hands-on coding in the next session. Stay tuned!