Drilling Analytics using Python & Machine Learning Last Updated: 2 months ago Mentor: Edvantage Learning
Drilling Analytics using Python & Machine Learning
₹15000/ $215 ₹16500/ $240
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About the course:

Dive into the dynamic intersection of drilling operations and data analytics with our intensive "Drilling Analytics" course. This transformative journey empowers participants to harness the power of data for optimizing drilling efficiency, mitigating risks, and achieving safer and more productive outcomes. Through a comprehensive curriculum, participants will explore the integration of cutting-edge data analysis techniques and drilling methodologies across industries such as oil and gas, mining, construction, and geothermal energy.

Prerequisites:

This course is ideal for Petroleum Engineers, drilling engineers, data analysts, and professionals as well as students seeking to innovate drilling operations through data-driven approaches. No prior data analytics experience required.

Learning Objective:

  • Understand Drilling Fundamentals: Comprehend the essential concepts, terminologies, and processes involved in drilling operations across various industries.
  • Collect and Manage Drilling Data: Employ strategies to collect, organize, and manage drilling-related data, encompassing real-time sensor data, historical records, and geological information.
  • Apply Data Analysis Techniques: Utilize data analysis tools and methods to interpret drilling data, uncover trends, and identify actionable insights for optimization.
  • Visualize Data Effectively: Create informative visualizations that effectively communicate drilling data insights to facilitate decision-making.
  • Predict Drilling Outcomes: Implement predictive analytics to anticipate drilling challenges and forecast future outcomes, such as equipment failures and wellbore stability issues.
  • Implement Machine Learning: Apply machine learning algorithms for drilling parameter optimization, anomaly detection, and predictive maintenance.
  • Define Performance Metrics: Define and measure key performance indicators (KPIs) to evaluate drilling efficiency, downtime, rate of penetration, and safety protocols.
  • Mitigate Drilling Risks: Develop risk assessment strategies using data-driven insights and formulate effective risk mitigation plans.
  • Construct Decision Support Systems: Build real-time decision support systems integrating analytics to aid drilling engineers and operators in making optimal decisions.
  • Analyse Case Studies: Analyse real-world case studies to understand how drilling analytics have led to enhanced operational efficiency, cost reduction, and project success.
  • Navigate Ethical Considerations: Recognize ethical implications related to data privacy, security, and transparency when applying analytics in drilling contexts. 
  • Drive Responsible Analytics: Exercise responsible data usage and ethical decision-making principles while leveraging data analytics for drilling optimization.
  • Apply Learning to Industry Contexts: Demonstrate the ability to apply acquired knowledge and skills to address drilling challenges and opportunities in real-world scenarios.
  • Collaborate Effectively: Collaborate with peers to solve complex drilling analytics problems through teamwork and interdisciplinary perspectives.
  • Communicate Insights: Effectively communicate data-driven insights and recommendations to stakeholders, bridging the gap between technical analysis and practical application.

Topics to be covered:

1. Introduction to drilling data analytics

  • Real time drilling methodology
  • Drilling dataset types and formats
  • Analytical workflows
  • Value Proposition

2. Real Time Drilling Visualisations

  • 3D well trajectory visualisations
  • Drilling parameters heatmap
  • Radar chart for wellbore stability and drilling efficiency
  • Multipaneled time series plots
  • Drill string vibration spectrogram.

3. Mitigating Non-Productive Time (NPT) with Analytics

  • Cluster Analysis for NPT/strategy
  • Exploratory analytics for drilling performance qualifiers
  • Stuck pipe analysis

4. Drilling parameters Optimization

  • Drill bit optimization analytics
  • Rate of Penetration (ROP) optimization with ML
  • Weight on Bit (WOB) optimization with ML

5. Drilling Time Series Pattern Recognition

  • Predicting downhole motor failures
  • Identifying wellbore instability
  • Drill bit wear estimation
  • Borehole cleaning assessment
  • Detecting formation changes
  • Gas kicks and Influx detection

6. Drill string Dynamics and Analytics

  • Dynamic analysis of stick-slip motion
  • Torque and speed vs time
  • Wavelet decomposition and statistics for accelerometer signals
  • Time frequency analysis of Torsional, longitudinal and lateral vibration
  • Quantification of drill string integrity risks