Drilling Process Improvement using Advanced Analytics and Machine Learning Algorithms

Disciplines: Data Science and Engineering Analytics | Drilling | Management

Course Description

Advanced analytics and ML algorithms are transforming subsurface decision making in the oil and gas industry. The democratization of analytical tools is seeing historical data being analyzed more routinely than was done earlier. This is helping accelerate decision making and hence provide ROI for digitization initiatives in the oil and gas industry.

This course presents a modern take on managing and transforming drilling operations – how the modern drilling approach can evolve to use more historical and more importantly real-time data to learn and predict well intervention. Several examples of models and technologies deployed are used to illustrate how you can implement and improve current drilling management practices. The course will examine the following -

  1. Basic understanding of analytics and ML tools that are available and their potential
  2. Challenges for Data acquisition, quality control, storage, retrieval and analyses during operations
  3. Charting a drilling process improvement system that includes advanced analytics & ML 
  4. Historical data and its impact on process improvement
  5. Real-time data and how it can improve well interventions
  6. Creating a road map and potential ways to manage challenges
  7. Implementation challenges that include identifying trouble and quick analyses, learning curve, technical limit and benchmarking
  8. Discussion of technology deployments to illustrate the concepts explored in the course

Learning Level


Course Length


Why Attend

By the end of the course, participants are expected to:

  • Gain basic understanding of the principle of advanced analytics and ML algorithms and how to frame problems
  • Understand the role of historical data and real-time data in helping chart drilling process improvements based on models deployed/implemented.
  • Frame new drilling process improvements to map business objectives and potential outcomes
  • Establish roadmap to implement data management and analytics techniques by bringing together key stakeholders (IT, drilling team, operations team)
  • Plan step by step implementation using performance metrics for continuous improvement

Who Attends

This course is suitable for anyone who would like to would like to improve drilling and related operations in their companies using AI/ML. Professionals typically responsible are in the following functional areas would be relevant:

  • Business Unit Heads
  • Data and Business Analysts
  • Data Mining/Data Managers/Data Science Analysts
  • Drilling and related operations
  • Information Technology

Special Requirements

Please bring your laptop--materials will be digital.


.08 CEU's will be given for this 1-day course

Cancellation Policy

All cancellations must be received no later than 14 days prior to the course start date. Cancellations made after the 14-day window will not be refunded. Refunds will not be given due to no show situations.

Training sessions attached to SPE conferences and workshops follow the cancellation policies stated on the event information page. Please check that page for specific cancellation information.

SPE reserves the right to cancel or re-schedule courses at will. Notification of changes will be made as quickly as possible; please keep this in mind when arranging travel, as SPE is not responsible for any fees charged for cancelling or changing travel arrangements.

We reserve the right to substitute course instructors as necessary.



Dr. Ashwin Venkatraman is the Founder and CEO of Resermine, a niche award-winning oil and gas technology company (MOST Promising at OTC 2018). He is the recipient of the inaugural SPE International Technical Award in Data Science and Engineering Analytics at SPE ATCE 2021 held in Dubai. The award recognized his contributions to bringing hybrid workflows that combine AI/ML with traditional approaches to accelerate subsurface decision making.
Resermine’s products have been used to optimize mature field injection operations and accelerate field development planning for fields in USA, Germany, Oman, UAE, Egypt, Mexico, India and Malaysia. Resermine is based in USA (HQ) with technology delivery centers in Kuala Lumpur (ARMC - Advanced Modeling Center), Dubai (UAE) and Muscat (Oman) to support projects in different regions.
Dr Venkatraman has published over 30 manuscripts and is on the advisory board of SPE’s Data Science and Engineering Analytics Committee. He previously worked with Shell for over 12 years at all their technology centers (India, Netherlands and Houston). Dr Venkatraman served as faculty in the Petroleum Engineering Department of University of Oklahoma (2019-2020) and held research appointments in Princeton University as well as at Institute of Computational Engineering & Sciences (ICES) at the University of Texas before founding the Resermine. Dr. Venkatraman holds BSc and MSc in Chemical Engineering from IIT Bombay (India) and earned his PhD from University of Texas at Austin in Petroleum Engineering.

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