[Editor's Note: This article is part two of a three-part series. Part one, Before the First Well: AI-Driven Field Development Planning, is available here. Part two, Engineering the Path: AI-Driven Well Design and Planning, is available here.]
Execution is the phase where the well plan is translated into action and encounters the realities of the subsurface. Every decision made during field development and well design planning rested on an interpretation of a subsurface that could not be observed directly. Drilling progressively uncovers subsurface formations through sequential measurements. This process has a high data velocity and is more data intensive than any individual can fully absorb. Surface and downhole sensors stream weight on bit, torque, standpipe pressure, flow, and vibration continuously, alongside logging-while-drilling measurements that update the geological picture as the well gets deeper. Two capabilities matter most at this stage: detecting problems early enough to prevent them and closing the loop so that the plan adapts to what the bit encounters. AI now contributes to both.
Reading the Real-Time Stream
Drilling data is high-frequency and noisy. Distinguishing a meaningful trend from ordinary fluctuation has traditionally depended on the experience of the driller and the mudlogger. Machine-learning (ML) models trained on historical drilling records learn the normal signature of an operation and detect when live data begins drifting toward patterns that have preceded issues, for instance a stuck-pipe precursor, the early indication of losses or an influx, or a developing tool dysfunction. Raising an alarm after deviating from a given range value is a very simple deterministic approach that many systems have been using for decades. ML algorithms can do more than just raise an alarm. They identify which parameter has deviated, how far it lies from the expected range, and how similar situations have evolved in the past. Incorporating physics, mass balance for flow anomalies, and friction models for torque and drag, reduces false alarms to a level at which the alerts are trusted and therefore acted upon.
Reporting is another key source of operational intelligence throughout the drilling process. AI systems can assemble the daily drilling report from rig-state detection, activity logs, fluid reports, generate the associated performance summaries, and quantify variance against the plan. Engineers validate and interpret automated reports using their operational expertise, while the resulting structured data preserves organizational knowledge for future planning and performance improvement. The same framework supports predictive maintenance, where ML models leverage historical failure patterns to forecast issues in critical drilling equipment, enabling planned maintenance interventions and reducing nonproductive time.
From Monitoring to Control
Detection and reporting are valuable, but both are fundamentally reactive. They describe what is happening or what has already happened. The more demanding objective is to close the loop, to decide what action to take when conditions change. Geosteering is a prime example, requiring continuous adaptation to subsurface uncertainty. The goal is to keep the wellbore within a specific and often undulating reservoir target whose exact position is never known with certainty. The engineer holds a probabilistic understanding of where the boundary lies, and each new logging-while-drilling measurement should update that understanding and inform the next steering decision.
What a Geosteering Agent Actually Does
This is a sequential decision problem under uncertainty; the same structure encountered in field development and well path planning. An AI agent can continuously integrate new measurements, maintain an evolving understanding of the position relative to geology around and ahead of the bit, and recommend the steering adjustments most likely to keep the well within the target zone. Rather than selecting from a fixed set of predefined responses, the agent applies a learned policy for steering through uncertainty, developed in the same way the development agent learned to sequence wells and the planning agent learned to design trajectories. The same principle extends to drilling-parameter optimization, where an agent holds operating parameters within the envelopes associated with high-performing offset wells.
The stakes are highest at the execution stage, where decisions can directly affect well placement and performance within minutes. A steering policy that relies on an inaccurate state estimate can drive the wellbore out of the target with little opportunity to recover. For this reason, real-time AI recommendations that affect the well must operate within a management-of-change framework, with a qualified engineer able to review and override each decision and a complete record of the actions taken. The appropriate model is not an autonomous system but an agent that proposes the next action and an engineer who retains the authority to accept or reject it.
The Thread Across the Life Cycle
Viewed together, the three stages of well construction tell a single, consistent story. Leveraging ML/AI workflows improves perception. They support activities such as seismic interpretation, volumetric assessment, prospect screening, well design evaluation, and drilling monitoring, as well as field development planning, well trajectory design, and geosteering. Nearly every application of AI across the well construction life cycle falls into one of these two categories, and recognizing which is essential to applying these tools with confidence rather than credulity.
For the engineer, that distinction defines where lasting value lies. AI tools will continue to improve and become more accessible, and much of the routine interpretation and reporting will be automated. What remains scarce is the judgment required to frame the problem correctly, to recognize when a model's assumptions no longer hold, and to decide whether a recommendation is physically and operationally sound. This will always be the role of the engineer.
As the first article in this series argued, AI does not replace the petroleum engineer; it elevates the role of engineering judgment. Across concept selection, well design, and real-time execution, that conclusion holds. The future of well construction is not autonomous drilling but collaborative intelligence, in which AI supplies speed, scale, and the capacity to reason across uncertainty, while petroleum engineers supply the domain expertise and accountability that turn recommendations into safely constructed wells.
The opportunity now is to translate this collaboration into measurable operational value. As AI moves from planning to execution, we invite readers to consider two questions.
How can AI help reduce field development planning from months to days and create measurable value ($) across the well construction life cycle?
Where can human expertise and AI work together most effectively to reduce nonproductive time, improve execution efficiency, lower operating costs, and create measurable value ($)?
Ashish Fatnani, SPE, is an energy technology leader with over 15 years of international experience spanning petroleum and drilling engineering, digital transformation, AI, commercial strategy, and business development. He is an active contributor to SPE, serving in leadership and volunteer roles while mentoring young professionals. He has authored several technical publications and holds patents related to predictive analytics, drilling-data automation, and digital workflows. He holds an MS in petroleum engineering from the University of Alaska Fairbanks, a BE in petroleum engineering from the University of Pune, and an MBA from the Indian Institute of Management Bangalore.
Shashwat Verma, SPE, is a data scientist at Halliburton based in London where he develops and deploys advanced data science and AI solutions for the global energy industry. His expertise spans machine learning, quantitative analytics, optimization, and predictive modeling, with a focus on solving complex challenges across the oil and gas value chain. By combining domain knowledge with modern AI techniques, he helps improve operational efficiency, optimize decision-making, and unlock greater value from engineering data. He is an advocate for data-driven innovation and enjoys sharing insights on the evolving role of AI in transforming the future of energy through smarter, more efficient operations.
Yusuf Ajibola Falola, SPE, is a senior technologist at Halliburton based in Houston where he develops advanced digital solutions that leverage AI, machine learning, and data analytics to address complex challenges in the energy industry. His expertise spans production optimization, predictive analytics, digital twins, intelligent field operations, and industrial AI, helping operators improve efficiency, enhance decision-making, and maximize asset performance. He is passionate about applying emerging technologies to solve real-world engineering problems and bridging the gap between data science and production engineering. He is also an active advocate for innovation and enjoys sharing his knowledge on the growing role of AI and advanced analytics in shaping the future of energy.
Vivek Kesireddy, SPE, is a senior data scientist at Halliburton Landmark, where he is part of the computational sciences and engineering for energy team. His work focuses on developing advanced AI and machine learning solutions for the energy industry, with particular expertise in drilling automation, reinforcement learning, deep learning, and intelligent decision support systems. His interests include autonomous drilling systems, optimization algorithms, and the application of next-generation AI technologies to enhance operational efficiency, safety, and sustainability across the energy sector. He holds a PhD in petroleum engineering from Texas A&M University and an MS from Missouri University of Science and Technology.
Aman Srivastava, SPE, is a product owner at Halliburton Landmark with more than 17 years of experience in the energy industry, specializing in well construction, drilling engineering, digital technologies, and AI. He is an inventor, published author, and recipient of two SPE regional awards. He is deeply involved with SPE, serving as chair of the webinars and virtual meetings program for the SPE Research and Development Technical Section, an editorial review board member of the Journal of Petroleum Technology, and a frequent speaker on digital transformation, AI, and the future of energy engineering. He holds a master's degree in petroleum engineering from the University of Oklahoma and a bachelor’s degree in mechanical engineering from the National Institute of Technology, Surat.
Geetha Gopakumar Nair, SPE, is a computational physicist and data science leader with more than 2 decades of international research. She is the global lead for the Data Science Center of Excellence at Halliburton and leads the development and application of AI and machine learning solutions that address complex challenges in the energy industry while driving collaboration with global operators and technology teams. She previously held research faculty positions at the University of Tokyo and Tokyo Metropolitan University. She has authored numerous peer-reviewed publications and is passionate about advancing data-driven innovation through research, education, and mentorship. A frequent speaker at technical conferences and workshops, Nair is recognized for her ability to bridge research with practical industry applications, inspiring the next generation of scientists, engineers, and AI practitioners.