SPE Online Education
Fully Automated Collision Avoidance Analysis and Wellbore Quality Monitoring in Real-Time
Recorded On: 07/28/2021
One of the major obstacles that makes the collision avoidance analysis very cumbersome is to enter the location and survey of the offset wells. This study presents a novel approach to leverage real-time survey and a centralized database to conduct collision avoidance analysis and monitor wellbore quality in (near) real-time in a fully automated fashion.
As a first step, a quality check is conducted to process the real-time survey. Then, the actual wellbore path is compared with the planned path. Several metrics such as cumulative dogleg severity, tortuosity, cumulative tortuosity, unwanted lateral, vertical, and total curvature, lateral and vertical tortuosity index are implemented (according to latest industry standards) to quantify the wellbore quality. To conduct the automated collision avoidance analysis, a centralized database with more than 5 million wells and half a million surveys is used.
The Discrete Boundary Model is deployed to compute the center to center distance between the reference well and offset wells. The offset wellbore is divided into several nodes (according to the required accuracy, 10 ft. is used as a default) and the distance between the current survey stations of the reference well to all the nodes is computed to generate the “ladder plot”. Since a centralized database is used in this analysis, there is no need for the user to enter the offset well locations or surveys, which makes it very desirable for real-time applications. For wells with missing surveys, a synthetic wellbore path is generated, and the user is notified about the missing survey. When the real-time survey for each well is updated, collision avoidance analysis and wellbore quality calculations are updated accordingly. A user-friendly web-based visualization has been developed to share the results with the end user. In addition to the real-time applications, the developed tool can also be leveraged for future planning.
This webinar is categorized under the Drilling technical discipline
All content contained within this webinar is copyrighted by Dr. Ali Karimi and its use and/or reproduction outside the portal requires express permission from Dr. Ali Karimi.
Dr. Ali Karimi
Senior Analytics Engineer, Occidental Petroleum
Dr. Ali Karimi is a senior analytics engineer at Occidental Petroleum. In his current role, he is responsible for developing physics-based and data-driven models/algorithms to identify bottlenecks and improve efficiency in the fields of drilling, completion, and production. During his 2.5 years with Occidental, he has developed several tools for real-time drilling operations (Invisible Lost Time identification, Torque & Drag, Hydraulics, Collision Avoidance, etc.), rig path optimization, skin reduction, gas lift diagnostics, and production forecast (using artificial intelligence). Prior to Occidental, he was a technical team lead for SpeedWise® Drilling Solutions at Quantum reservoir impact where he directed the development of a drilling analytics tool to identify the drilling bottlenecks, mitigate the non-Productive time, and propose practical solutions to improve drilling efficiency. Before QRI, he was a research scientist at The University of Texas at Austin and led/assisted with research efforts in several areas such as multi-phase gas influx modeling, drilling fluids automation, thermal fluid design, managed pressure drilling, and wellbore strengthening.
Dr. Karimi earned a PhD degree in Petroleum Engineering from The University of Tulsa and also master’s and bachelor’s degrees in Petroleum Engineering from Curtin University and Petroleum University of Technology. He has (co-)authored more than 35 technical papers and holds 5 patents.
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