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SLB unveils AI-powered tech to enhance drilling efficiency and reduce emissions

SLB has introduced Neuro, an AI-driven autonomous geosteering system that optimizes well drilling by responding to complex subsurfaces, enhancing efficiency, and reducing carbon emissions. Photo courtesy of SLB

Houston energy technology company SLB introduced a new autonomous geosteering system called Neuro, which can reduce the carbon footprint of the drilling operations. Neuro can respond to complex subsurfaces to drill more efficiently with higher-performing wells.

Neuro, which is an AI-based platform,expands the technological foundation of SLB’s Neuro autonomous directional drilling, which drills wells to a specific target. Now, the Neuro autonomous geosteering incorporates high-fidelity downhole measurements that ensure certainty of well placement in the best part of the reservoir.

“Neuro autonomous geosteering is a remarkable industry-first achievement that is for drillers what the autonomous vehicle is for drivers,” Jesus Lamas, president of Well Construction at SLB, says in a news release. “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.”

SLB deployed Neuro autonomous geosteering that drilled a 2,392-foot lateral section of an onshore well for Shaya Ecuador S.A. SLB's autonomous system completed 25 autonomous geosteering trajectory changes in a matter of seconds according to SLB. By remaining in the most productive layer of the reservoir, the well has become one of the best producers in Ecuador, according to SLB.

“By drilling more consistent and higher-producing wells, our customers can optimize their field development plan while reducing operational emissions from drilling over the lifetime of the asset,” Lamas adds.

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A View From HETI

Rice University researchers have published new findings that shed new light on processes like photosynthesis and solar energy conversion. Photo by Jorge Vidal/Rice University.

Rice University scientists have used a programmable quantum simulator to mimic how energy moves through a vibrating molecule.

The research, which was published in Nature Communications last month, lets the researchers watch and control the flow of energy in real time and sheds light on processes like photosynthesis and solar energy conversion, according to a news release from the university.

The team, led by Rice assistant professor of physics and astronomy Guido Pagano, modeled a two-site molecule with one part supplying energy (the donor) and the other receiving it (the acceptor).

Unlike in previous experiments, the Rice researchers were able to smoothly tune the system to model multiple types of vibrations and manipulate the energy states in a controlled setting. This allowed the team to explore different types of energy transfer within the same platform.

“By adjusting the interactions between the donor and acceptor, coupling to two types of vibrations and the character of those vibrations, we could see how each factor influenced the flow of energy,” Pagano said in the release.

The research showed that more vibrations sped up energy transfer and opened new paths for energy to move, sometimes making transfer more efficient even with energy loss. Additionally, when vibrations differed, efficient transfer happened over a wider range of donor–acceptor energy differences.

“The results show that vibrations and their environment are not simply background noise but can actively steer energy flow in unexpected ways,” Pagano added.

The team believes the findings could help with the design of organic solar cells, molecular wires and other devices that depend on efficient energy or charge transfer. They could also have an environmental impact by improving energy harvesting to reduce energy losses in electronics.

“These are the kinds of phenomena that physical chemists have theorized exist but could not easily isolate experimentally, especially in a programmable manner, until now,” Visal So, a Rice doctoral student and first author of the study, added in the release.

The study was supported by The Welch Foundation,the Office of Naval Research, the National Science Foundation CAREER Award, the Army Research Office and the Department of Energy.

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