Next Meeting

Q222 – Drilling technology and lessons learned to improve performance and reduce non-productive time

Meeting Introduction

The learning curve in drilling operations is often marked by key events in the course of a campaign or well sequence. They often lead to lessons learned that have a direct impact on non-productive time occurrence and overall operating time reduction. These lessons learned can extend to other companies, fields, geographies or applications, and flow into our industry’s common practices and operational knowledge. Although there are some outstanding achievements and lessons on record-breaking wells (reach, depth, hostile environment) equally valuable and re-usable knowledge can be gathered from less publicized operations. Sharing and discussing such case histories with peers is one of the main objectives of the DEA(e).

Hence for this quarter’s meeting we would like to bring lessons and experience to the forefront that had an impact on non-productive time reduction or learning curve acceleration.
The perception exists that this is a reactive approach to dealing with adversity, but there is also a focus in the planning phase on optimization including avoidance of problems, particularly on those that can fall between the cracks, and require a multi-disciplinary approach, such as Stuck Pipe, Loss Circulation, and well placement. Reducing the likelihood of unexpected events in the planning phase remains complex relative to learning on the job where everything comes together and boundaries are set.

Technology can play an important role in the learning and optimization cycle in various ways: application of new technology to avoid non-productive time, lessons learned on the application of new technology itself, or even the data analytics techniques that generate or make use of lessons learned. Can technology provide a path to significantly reduce non-productive time?

We are looking for contributions in relation to this topic and suggested items:

• Case histories with quantifiable performance optimization (multi-well cases, campaigns) and lessons learned.
• Lessons learned on Real-Time Center contribution on the well cycle time improvement (given recent remote working challenges).
• Non-productive time avoidance in the planning phase addressing the human impact (competency, remote working, control measures, planning tools innovation, simplification, QA/QC checks)
• Application of Artificial Intelligence methods on drilling analytics, success cases and forecasting.
• Organizational solutions and cooperation models that have contributed to improved efficiency and/or reduced NPT
• Drilling dysfunctions (bit/tool failures, low penetration rate, drillstring failure): success cases in prediction operating limits or potential failure in planning and execution phases.