Drilling Optimization using Data Management & AI

This training is designed for:
•    Drilling engineers and supervisors.
•    Data scientists and digital engineers working in upstream oil & gas.
•    Petroleum engineers involved in drilling and production optimization.
•    Rig managers, toolpushers, and operations supervisors.
•    HSE and performance improvement engineers.
•    Professionals responsible for digital transformation in drilling operations.

• Basic knowledge of drilling operations and engineering principles.
• Familiarity with data analysis or digital tools in oil & gas (recommended but not mandatory).
• Suitable for drilling engineers, data analysts, and operations professionals aiming to integrate AI-driven solutions in drilling.

Fundamentals of Drilling Optimization
•    Introduction to drilling performance challenges in modern wells.
•    Key optimization parameters: ROP, WOB, torque, drag, vibrations, fluid hydraulics.
•    Economic impact of NPT and flat-time reduction.
•    Traditional vs. data-driven approaches to optimization.
•    Overview of drilling data sources: surface sensors, downhole MWD/LWD, logging, rig reports.
•    Case study: The role of drilling optimization in reducing well delivery costs.
Data Management for Drilling Operations
•    Data lifecycle in drilling: acquisition, storage, processing, and utilization.
•    Real-time data transmission and WITSML standards.
•    Data quality challenges: noise, missing values, inconsistencies.
•    Data cleansing and preprocessing for AI/ML applications.
•    Building structured databases for drilling analytics.
•    Hands-on workshop: Cleaning and visualizing drilling data sets.
Artificial Intelligence & Machine Learning in Drilling
•    Introduction to AI concepts relevant to drilling: supervised/unsupervised learning, neural networks, reinforcement learning.
•    Applications of AI in drilling optimization:
o    Predicting ROP.
o    Wellbore stability forecasting.
o    Early detection of stuck pipe/loss circulation.
o    Bit wear prediction.
•    Advantages and limitations of AI models.
•    Workshop: Training a simple ML model to predict ROP from drilling parameters.
Real-Time Optimization & Digital Twins
•    Real-time drilling monitoring and performance dashboards.
•    Predictive maintenance using AI models (pumps, mud motors, BHA).
•    Digital twin applications in drilling operations.
•    Integration of AI with managed pressure drilling (MPD) systems.
•    Visualization and decision support systems for drilling teams.
•    Case study: Digital twin reducing NPT in offshore drilling.
Implementation & Future Trends in AI for Drilling
•    Building a drilling optimization workflow with AI integration.
•    Change management: adopting digital technologies at rig sites.
•    Cybersecurity and data governance in drilling data.
•    Human-AI collaboration: keeping decision-making transparent and safe.
•    Emerging technologies: edge computing, autonomous rigs, advanced robotics.
•    Final group exercise: Designing a data-driven drilling optimization strategy for a new field development.
•    Course wrap-up and action plan for participants.