Smarter Drilling at Scale: Closed-Loop Automation in Action

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THE ENERGY EDIT

This summary is based on a paper presented at the 2025 SPE/IADC Drilling Conference and Exhibition by Corva AI LLC and Nabors Industries. The paper explores how a machine learning-powered, closed-loop drilling system was deployed across multiple unconventional basins in the United States, delivering measurable gains in rate of penetration (ROP) and consistency in well delivery.

 

As digital transformation reshapes the energy sector, drilling automation has emerged as a major area of innovation. This case study presents one of the most extensive real-world deployments of closed-loop drilling automation to date, spanning over 30 rigs and 500 wells across multiple U.S. shale basins. It demonstrates how machine learning–powered control systems can consistently optimise the rate of penetration (ROP) and improve drilling performance across a wide range of geological conditions.

The automation framework in this study integrates multiple modules that work together to adjust drilling parameters in real time. A core component is the ROP Optimization Advisory, an application that leverages historical offset data and real-time surface parameters to recommend weight-on-bit (WOB) and rotary speed (RPM) setpoints. Once approved by the driller, these setpoints are automatically sent to a downhole automation platform, which fine-tunes parameters while drilling to maintain optimal performance.

The system operates in a closed-loop fashion, meaning it can self-correct as conditions change. Data from previous wells is used to create predictive models for new wells in similar formations, allowing the system to anticipate challenges and adapt quickly. On the rig, the downhole automation engine interfaces directly with the rig’s control system to execute commands within safety and mechanical limits. Meanwhile, real-time dashboards provide the driller with transparency and control.

The study’s scope is notable for its diversity. It includes wells drilled across six different U.S. shale plays with varying lithologies, rig types, and bit designs. Despite these variations, the automated system achieved repeatable improvements in ROP and reduced drilling dysfunctions, such as stick-slip and excessive torque. For example, in the Permian Basin, operators saw a 20 percent average increase in ROP compared to offset wells drilled without automation.

Importantly, the automation approach was not a rigid system imposed on the driller. The interface was designed to maintain a “human-in-the-loop” dynamic, where the driller could review, adjust, or override recommendations at any time. This flexibility helped build confidence and led to faster adoption across different crews.

The paper also emphasizes lessons learned about operationalizing AI at scale. One key insight is the importance of clean, consistent data from rig sensors and control systems. Another is the value of standardizing workflows and interfaces so that automation modules can be easily deployed across rigs with different equipment and control architectures. The team also invested in extensive field training and change management to align field personnel with the new workflow.

Looking ahead, the authors propose expanding the automation framework to include more modules, such as automated bit guidance and downhole dysfunction detection. They also see potential in integrating subsurface data, such as gamma ray and vibration logs, to further enhance decision making. Ultimately, the goal is to evolve toward fully automated drilling systems that can optimize entire well sections from spud to TD with minimal human intervention.

This case study highlights a key transition in the oil and gas industry: moving from isolated machine learning pilots to enterprise-scale, real-time automation systems. It shows that with the right architecture, data foundation, and human collaboration, AI-enabled drilling optimization can be more than a concept. It can be a competitive advantage.

 

Further Reading:
Access the full article on SPE.org

 

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