Course Description:

This intensive course is designed to equip participants with the knowledge and skills necessary to leverage machine learning techniques for enhanced production and reservoir forecasting analytics in the oil and gas industry.

Through a combination of theoretical lectures, hands-on exercises, and real-world case studies, participants will gain a comprehensive understanding of reservoir analysis and forecasting, along with practical experience in implementing machine learning algorithms to address complex challenges in the field.

Prerequisites:

This course is suitable for both freshers and professionals in the oil and gas industry.

Prerequisites include a basic understanding of Petroleum engineering concepts and familiarity with programming fundamentals in Python.

Benefits of Learning:

Upon completion of this course, participants will:

➢ Comprehensive Understanding: Gain insight into reservoir analysis and forecasting's critical role in the oil and gas sector, fostering informed decision-making.

➢ Machine Learning Competence: Develop fundamental machine learning skills, enabling you to apply advanced analytics techniques effectively.

➢ Data Preparation Proficiency: Acquire skills in data collection, preprocessing, and feature engineering tailored for reservoir data, ensuring high-quality input for analysis.

➢ Predictive Modeling Expertise: Learn to select, train, and evaluate machine learning models for accurate reservoir forecasting, facilitating proactive decision-making.

➢ Hands-on Implementation: Gain practical experience in implementing machine learning algorithms using Python libraries, enhancing your coding and analytical capabilities.

Production Forecasting and Analytics, and ML Applications for PCP, SRP, ESP

1.    Introduction

2.    Production Systems Fundamentals

3.    Data Collection and Preprocessing

4.    Feature Engineering

5.    Machine Learning Models for Production Forecasting

6.    Model Evaluation and Performance Metrics

7.    Time Series Forecasting Techniques

8.    ML Applications for PCP, SRP, and ESP

Reservoir Analytics

1.    Introduction to Reservoir Analytics

2.    Fundamentals of Reservoir Engineering

3.    Reservoir Data Collection and Preprocessing

4.    Feature Engineering for Reservoir Data

5.    Machine Learning Models for Reservoir Analysis

6.    Model Evaluation and Performance Metrics

7.    Advanced Topics in Reservoir Analytics

8.    Real-world Applications and Case Studies

Q. What study materials will be provided during the training?

Participants will receive comprehensive study materials, including presentations, handouts, and recommended readings, to supplement their learning experience.

Q. Will certificates be awarded upon completion of the training?

Yes, participants who successfully complete the program requirements will receive a certificate of completion, acknowledging their participation and achievement.

Q. Is prior experience in the oil & gas industry required to join the training?

No prior experience is required. The program is designed to accommodate participants with varying levels of knowledge and experience.