SLB, an energy technology company, has launched its AI-driven Neuro autonomous geosteering technology to improve the efficiency of drilling operations.
This innovation leverages cloud and edge AI to dynamically navigate through complex subsurface conditions, enabling the drilling of more efficient and productive wells with a reduced carbon footprint.
Traditionally, geologists manually interpret subsurface data to determine the well target and plan, a process that is time-consuming and prone to human error.
Neuro autonomous geosteering represents an advancement in drilling operations. The new technology automates these tasks, allowing for real-time, accurate adjustments without human intervention.
The technology is an extension of SLB's existing Neuro autonomous directional drilling solutions. It incorporates high-fidelity downhole measurements, ensuring precise well placement in the most productive parts of the reservoir, maximising output and efficiency.
In a recent deployment in Ecuador for Shaya Ecuador, SLB's Neuro autonomous geosteering solution drilled a 2,392ft lateral section of an onshore well.
The system executed 25 autonomous geosteering trajectory changes, with each decision cycle taking mere seconds.
As a result, the well has emerged as one of the top producers in the nation, demonstrating the technology's potential to revolutionise the oil and gas industry's approach to drilling.
SLB Well Construction president Jesus Lamas said: “Neuro autonomous geosteering is a remarkable industry-first achievement that is for drillers what the autonomous vehicle is for drivers.
“Using advanced cloud and edge AI capabilities, the system automatically selects the best route for drilling the well based on high-fidelity downhole measurements, bringing the well trajectory in line with the real-world conditions of the reservoir.
“By drilling more consistent and higher-producing wells, our customers can optimise their field development plan while reducing operational emissions from drilling over the lifetime of the asset.”
