Real-time Analytics
An operator drilling deeper and more complex horizontal wells in Colombia needed to reduce trajectory uncertainty, drilling risk, and well-quality issues caused by complex geology, subsurface seismic faults, collision avoidance constraints, casing-setting depth limits, torque and drag, and other well design constraints.
Reported impact
The operator improved drilling consistency and trajectory accuracy across three horizontal wells, increasing rate of penetration, improving casing and liner running speed, reducing tortuosity, and lowering time and effort needed to keep wells on the planned path.
Halliburton implemented an autonomous drilling workflow for three horizontal wells in Colombia that combined LOGIX™ drilling performance with LOGIX™ automation and remote operations and the iCruise® intelligent rotary steerable system. Downhole intelligent sensors embedded in the iCruise RSS supplied operational data from the wellbore, which was integrated into continuous predictive models. LOGIX drilling performance used machine learning together with a digital twin and a physics-based bottomhole assembly model to move the operator from descriptive optimization to predictive analytics for real-time trajectory design. LOGIX automation and remote operations connected these models with the remote drilling center, multiple PSLs, and the directional engineer so recommendations could be applied during well construction. The source does not name a separate storage product, but it describes continuous model access to live drilling data to predict future conditions, reduce uncertainty, and keep execution aligned with planned DDI and KPI targets.
Downhole intelligent sensors in the iCruise® intelligent rotary steerable system capture live drilling data, which feeds LOGIX™ drilling performance models that combine machine learning, a digital twin, and a physics-based BHA model. Predictions and trajectory recommendations are then delivered through LOGIX™ automation and remote operations to the remote drilling center and directional engineer for execution during well construction.
"Casing and liner run speed additionally improved by 15 to 45%"
"Deploy the iCruise® intelligent rotary steerable system and optimization services"
"Implement LOGIX® automation and remote operations to combine physics-based models with machine learning"
"Intelligent sensors were integrated into the iCruise intelligent rotary steerable system (RSS) and LOGIX drilling performance to establish the foundational infrastructure for machine learning models."
"The operator selected to transition from a conventional optimization approach, which relies on descriptive analytics models, to a predictive analytics framework where machine learning algorithms play a pivotal role."
"The team achieved consistent performance improvement and drilling trajectory accuracy, which resulted in a remarkable 33% increase to the rate of penetration (ROP)"
"These learning platforms analyze extensive downhole data integrated with a digital twin and a physics-based model of the BHA."
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Scoring methodology
Customer Reference Value measures usefulness as a customer reference. Evidence Confidence measures the quality and traceability of available evidence, not independent verification.
Traceability
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