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AiroAV Antivirus Divulge: JPT Adaptive Drilling Application Uses AI To Enhance On-Bot…

JPT Adaptive Drilling Application Uses AI To Enhance On-Bot...

AiroAV Antivirus Divulge: JPT Adaptive Drilling Application Uses AI To Enhance On-Bot…

The drilling optimizer employs artificial intelligence to help mitigate drilling dysfunction and improve performance.

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Ever since the first commercial well was spudded, operators have looked for ways to drill wells faster without sacrificing safety or incurring huge costs. While saving time and money through efficient drilling is not a new concept, the more recent adoption of drilling optimization and automation services has certainly become one of the biggest drivers to achieving those goals.

As the current downturn has shown limited signs of recovery, it has continued to evolve in ways never imagined, and the effects are taking their toll in every facet of the oil and gas industry. While rigs and drilling equipment can be set aside to ride out the storm, what about the drilling teams who are working on the rigs and in remote operation centers? As these teams are being removed from the field, the expectation is that many of them won’t return for a myriad of reasons. So, what happens when that experience is lost?

The exodus of seasoned crews, otherwise known as the “great crew change,” has been discussed for several years, but recent conditions could expedite the process. Considering the recent shutdown of rigs and the loss of personnel, the question remains whether we will see a noticeable gap in knowledge and experience once crews return to the drilling rigs in full force.

The lack of individual skills can be offset over time with hands-on experience, but a drilling crew needs to operate at the highest level possible, preferably with few to no gaps in experience. To assist the drilling process, NOV’s M/D Totco division recently launched its KAIZEN intelligent drilling optimization application, which performs as an adaptive autodriller. The system features continuous learning capabilities, enabling it to provide proactive drilling dysfunction mitigation while maximizing rate of penetration (ROP) and optimizing mechanical specific energy. It also reduces human dependence in the drilling process, lowering the risk of slow or incorrect ­responses to drilling dysfunction. In turn, the system assesses wellbore conditions and drilling performance, then automatically applies appropriate parameters to mitigate those dysfunctions.

Fig. 1—The KAIZEN system was assessed over a three-well span against a benchmark. On the first well, the customer held rev/min static. On the second and third wells, the system was given a window in which to perform optimization functions. An ROP improvement of 61% and 39%, respectively, was observed in Wells 2 and 3 vs. the benchmark. The customer allowed the largest parameter window for optimization in Well 2, and therefore the highest performance gains were achieved in that well.

Intelligent Drilling Optimizer

When faced with distinct interbedded formations, drillers often encounter drilling dysfunction due to varying formations, and optimal setpoints are required to identify and proactively mitigate dysfunction.

While drillers are inundated with large amounts of data, the system takes the human dependence away and employs artificial intelligence (AI) to continuously optimize the drilling process. Utilizing an array of machine-learning algorithms and a digital twin that is updated each second, the AI system builds a store of knowledge that the drilling application leverages to make more accurate and timely decisions. This automated parameter application approach enables the system to remove distractions from the driller so their focus can be on critical items such as keeping the crew safe and the well under control, while the system instantly responds to changing conditions and provides optimal weight on bit (WOB) and revolutions per minute (rev/min) setpoints.

The AI and machine-learning feature stores thousands of hours of processed drilling data. This capability allows the system to recommend surface parameters that deliver the best expected performance as well as select the correct dataset to mitigate changes detected in drilling dynamic behaviors.

Additionally, the system’s digital twin uses an advanced, physics-based model to analyze several key dysfunctions in real time by way of distributed stress modeling, torque and drag modeling, and critical rev/min calculations. The result is a system that is capable of providing guidance to the parameter search engine, identifying which parameters to avoid. Drillstring buckling, downhole vibration (torsional, axial, and lateral), and mud motor stalls can all be diagnosed, and thus, mitigated in real time.

The system’s optimization functions can be run in either advisory or control mode. The advisory mode sends recommended rev/min and WOB setpoints for the driller to implement, while control mode sends those same setpoints directly to the autodriller.

Advisory mode is typically run if a rig’s control system is not fully compatible with the control mode. When a rig has a control system that is compatible with the KAIZEN command requests, control mode is activated, and the system consistently achieves a higher level of performance by continuously and automatically adjusting the setpoints.

The KAIZEN system has been successfully integrated into multiple ­control ­systems, including NOV’s NOVOS reflexive drilling system. The application is currently compatible in closed-loop mode with approximately 50% of the active North American rig fleet.

The drilling system’s installation comprises a minor update to the rig control layer to accept the system’s commands, along with the physical installation of a KAIZEN system box. The rig…


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