Drilling Webinars

In the presented approach to deploying tripping automation, a team consisting of operator, drilling contractor, and drilling equipment OEM collaborated to successfully deploy and improve the well construction process.

This presentation examines in detail the mathematical basis for well test interpretation, starting with the concept of an ideal reservoir model and the partial differential equations used to describe single-phase and multi-phase flow in porous media. 

The University of Texas at Austin has been working on advanced sensor technologies over the last 6 years in a bid to improve the safety and efficiency of drilling operations, and to reduce uncertainty with regards to data and situational awareness. 

A ROP optimization methodology is presented, using offset drilled wells data. Data is used to train a neural network model for different rock properties which is presented in the vertical section of the reservoir. 

The promise of drilling systems automation is to increase well construction efficiency, delivering quality wells in a safe, reliable and predictable manner.

A ROP optimization methodology is presented, using offset drilled wells data. Data is used to train a neural network model for different rock properties which is presented in the vertical section of the reservoir. 

Wellbore tortuosity is a critical element in drilling complex horizontal wells and deep verticals. The current three-dimensional borehole trajectory model, based on the minimum curvature method [MCM], tends to mathematically smoothen the wellpath.