Introduction: The Subsurface Challenge
Oil and gas drilling is a highly technical and costly process. Drilling can be done several miles into the ground, where drilling equipment faces high temperatures, high pressure, varying rock formations, and complex wellbore environments. Rig day rates in deepwater drilling operations can amount to close to $500,000. In an example in the Gulf of Mexico in 2024, Transocean entered a contract for $485,000 per day, and in some deepwater drillship contracts, costs are higher than $500,000 per day.
As a result of costly rig time, improved drilling performance and reduced non-productive time (NPT) are critical objectives. Conventionally, drilling decisions depend largely on the expertise of drillers and drilling engineers based on measurements made from surface and downhole systems. New digital technology can augment the current practice.
Using artificial intelligence (AI), machine learning (ML), automation, and increased telemetry speed helps drilling teams feel more confident and reassured that operations are safer and more controlled by providing timely insights and alerts.
Real-Time Telemetry and Edge-Based Drilling Systems
Drilling systems in use today gather data not only from the equipment at the surface but also from the downhole logging tools. Both MWD and LWD tools can collect information on formation parameters, well deviation and azimuth, pressure, temperature, vibrations, torque, and other drilling dynamics. Such data enables drilling personnel to assess both the formation and the bottomhole assembly efficiency.
The traditional method of mud-pulse telemetry is still commonly used; however, the speed of data transmission can be an issue due to limited information transfer between the bottomhole and the surface. Wired drill pipe technology creates another communication channel with higher speed. Commercial systems developed by such companies as NOV and Halliburton enable near real-time data transmission on drilling and formation parameters.
There is great variation in the amount of bandwidth that is provided by different technologies and configurations, and hence the claim about today’s wired drill pipe having a capacity for transmitting drilling data at gigabit speeds is not backed by any empirical evidence. For instance, NOV talks about an IntelliServ transfer rate of up to 57,600 bits per second, while a relatively new field test by Halliburton indicates 200 kbps.
High-speed communication systems combined with AI-driven automation inspire decision-makers by demonstrating the industry’s move toward more innovative and efficient drilling operations.
Using Machine Learning to Optimize Rate of Penetration
The Rate of Penetration (ROP) refers to how fast the drill bit cuts into the formation and is a key parameter in assessing drilling performance. Factors that affect the ROP include Weight on Bit (WOB), rotary speed, fluid flow rate, properties of the bit, strength of the formation, and drilling inefficiencies.
An increase in ROP cannot be attained solely through an increase in the amount of WOB or the rotational speed of the machine. The mechanical loads will increase vibration, torque, cutting tooth wear, and the chances of breakdown. Optimization thus entails finding a balance between the efficiency of drilling and the drilling stability.
Drilling performance can be assessed through machine learning algorithms, which are able to reveal correlations between these factors. Mechanical Specific Energy (MSE), which is the mechanical energy required for removing a certain volume of rock, is one such metric. An increase in MSE can mean inefficiency in transferring energy into the formation and can serve as a signal of inefficiencies. Recently, research has merged MSE-based physical relations and neural networks to enhance ROP predictions while preserving physical limitations in the model.
Based on studies and practical applications of ML for optimization, it is possible to achieve significant improvement in production rates. Still, outcomes may depend on specific rock formations, drill bits and other equipment used, and many other factors. In one study, an increase of ROP by 31% on average and a decrease of MSE by 49% were observed during the validation stage. An example of field application in the Permian Basin shows ROP improvements in the range from 19% to 33% compared to the control footage.
Outcomes above should be viewed as examples only, not as benchmarks of AI optimization performance.
Predicting and Reducing Non-Productive Time
NPT involves intervals during which it is not possible to conduct drilling operations according to the plan due to equipment, wellbore instability, drilling dysfunctions, and other technical issues. Typical problems include stick-slip vibration, high torque and drag, lost circulation, wellbore instability, and stuck pipe.
AI monitoring technology processes sensor data to predict equipment issues and wellbore problems, helping reduce non-productive time.
Thus, for instance, one could monitor torque and vibration trends in order to determine developing stick-slip vibrations and suggest changes to rotary speed. In order to detect the development of the danger of a stuck pipe, one could use particular machine learning algorithms to examine trends in torque, drag, pressure, and other drilling data. The same could be done in order to detect potential problems with wellbore instability and equipment operation. However, such technologies would not eliminate problems, but would make it possible to collect additional information for engineers.
The example of stick-slip clearly demonstrates the potential value of the approach. Stick-slip is an example where friction can bring about a temporary halt of the bit rotation while the rest of the drilling assembly is rotating, accumulating energy which will then be released. Measurements of torque, vibration, RPM, etc. may be used to determine the conditions leading to this dysfunction.
Automated Directional Drilling and Geosteering
For horizontal drilling, precise management of the well path is especially important where the reservoir layer is thin. Drilling within the reservoir may expose the reservoir more effectively and minimize the possibility of drilling outside the target zone.
The contemporary geosteering process uses LWD data along with the geological model for assessing the position of the well relative to the limits of the formation. AI and machine learning technologies may be useful in interpreting LWD data and adjusting the path of the drilling well. ML has already been used for optimizing well trajectories, while commercially available technologies incorporate AI-based geosteering with automatic directional drilling.
RSS technology would now be capable of making such directional maneuvers while drilling. In autonomous systems, the algorithms could make decisions or take appropriate actions based on downhole data within certain limits. As an example, SLB has described autonomous systems for directional drilling that rely on continuous communication between surface and downhole systems for automation of trajectory control processes.
It is also essential to mention that the aforementioned statement that AI is able to keep more than 98% of the horizontal lateral in the optimal pay zone cannot be regarded as a universal industry achievement. It all depends on the reservoir thickness, geological uncertainties, tool capabilities, well design, and the automation system in question. The performance in each particular field would differ significantly.
Conclusion: Moving Toward More Autonomous Drilling
However, while AI becomes a crucial part of the digital drilling system, the role of AI should be considered as an extension of engineering and automation capabilities rather than a replacement for drilling capabilities.
The real-time transmission of data results in faster receipt of information about downhole situations. Machine learning algorithms could help with predicting ROP, detecting drilling dysfunctions and optimizing parameters. The automated direction control and AI-based geosteering may help to achieve better trajectory control by considering changes in the underground formation.
Another trend that the industry is developing is the use of closed-loop systems, where software continuously analyzes drilling situations and makes adjustments to certain parameters according to certain boundaries. Field experience in 2024 shows that this approach is being applied in parts of the well construction process. Autonomous control accounted for 99% of the 2.6 km well length in one project by SLB and Equinor, while five well programs showeda 60% increase in ROP.
The trend in the longer term is towards increasing the integration of the downhole sensors, telemetry, artificial intelligence models, surface automation, and human oversight. Far from taking engineers out of the process altogether, these technologies would allow engineers to concentrate on the activities of planning, interpretation, risk management, and decision-making that require a wider perspective on the operations involved. The result will be a drilling environment where more decisions will be data-driven, but where human judgment is key.
Conclusion
AI drilling optimization is transforming how the energy industry operates, making drilling faster, safer, and more sustainable. From real-time data analysis to smarter decision-making, AI is helping companies get more out of every well while protecting people and the planet. If you want to learn how these advances can impact your investments or how Optimum Energy Partners leverages these technologies for better results, contact us to learn more.
