Since AI entered the drilling industry, it has often been mistakenly perceived as simply “replacing field engineers with a single automated model.” In reality, in high-risk, highly constrained drilling environments, the most valuable AI applications function more like decision-support systems: they rapidly aggregate well history, formation data, logging-while-drilling measurements, drill-bit condition, and on-site operating conditions to generate well-defined selection recommendations, optimized parameter ranges, and risk alerts—rather than making stand-alone decisions that disregard engineering constraints.
Figure 1: Data Pipeline and Closed-Loop Positioning of AI in Drilling
Figure 2: Schematic of the hybrid model stack for AI drilling
Figure 3: Parameter Optimization and Risk Early-Warning Scenarios Driven by AI
I. The foundation of AI-driven drilling is not models, but rather a viable data pipeline.
For AI to play a role in drilling decision-making, the data must first be seamlessly integrated. This data pipeline typically encompasses geological and well-history data, BHA and drill-bit configurations, parameters such as weight on bit, rotary speed, and pump rate, torque and mud pressure, measurement-while-drilling data, trajectory changes, wear and failure records, and field-event logs. If this data lacks a unified time axis, standardized operating-condition labels, and consistent naming conventions, the insights generated by model training will often appear plausible on paper but prove impossible to apply in the field.
For drill-bit manufacturers, the most critical data are not necessarily the most complex; rather, they are the insights that best reflect tool performance, such as well-section type, cutting-structure version, failure location, wear morphology, parameter windows, and single-run results. By standardizing these data into a structured format, AI can first help companies establish more reliable tool-selection recommendations and problem-identification frameworks.
II. High-Value AI Applications in Drilling Are Primarily Concentrated in Six Key Stages
First, provide recommendations for drill bit and BHA selection by leveraging historical well performance and matching with similar operating conditions to narrow the scope of trial-and-error. Second, optimize drilling parameters by delivering more conservative and reliable recommendations for weight-on-bit, rotary speed, and pump rate based on real-time operating conditions. Third, implement anomaly early warning to promptly detect issues such as stick-slip, drill string vibration, mud cake buildup, and trajectory deviation. Fourth, perform performance prediction to estimate single-trip tool life, maximum penetration rate, and optimal tripping timing. Fifth, conduct post-operation analysis to automatically map failure experiences back to design or operational process issues. Sixth, facilitate knowledge accumulation by codifying fragmented expertise held by various drilling crews and engineers into reusable rules and guidelines.
Among these six capability categories, anomaly detection and parameter-window recommendations are the easiest to implement first, as they deliver direct on-site value, feature short feedback loops, and have clearer data boundaries. By contrast, “fully automated drilling decision-making” typically requires more mature data and stricter safety constraints, and should generally be treated as a long-term goal rather than an initial marketing priority.
III. AI solutions that can be practically implemented in engineering projects are typically hybrid models combining “mechanism + rules + data.”
Relying solely on black-box models makes it difficult to gain sufficient trust in the drilling field, because on-site decision-making requires clear explanations of “why the parameters are tuned this way, what the boundary conditions are, and what risks would arise from incorrect adjustments.” Therefore, a truly deployable AI system typically consists of three components: first, mechanistic models that provide physical constraints and reasonable operating ranges; second, rule-based knowledge bases that encapsulate empirical expertise; and third, machine-learning models that identify patterns, forecast trends, and match similar well sections.
The advantage of this hybrid architecture is that it both preserves engineering common sense and safety thresholds while leveraging data for rapid iteration. This is particularly important in drilling-bit-related applications, as bit failure is typically influenced by a combination of structural, hydraulic, formation, and operational factors, making it difficult to fully capture with a single algorithm. By integrating mechanistic models, domain rules, and data, the approach better aligns with real-world field conditions.
IV. The key to online optimization lies not in “computing quickly,” but in “controlling the feedback loop.”
For AI to evolve from an analytical tool into an online optimization system, four key questions must be clearly addressed: who provides the recommendations, who approves and implements them, how often reviews are conducted, and under what circumstances human intervention is required. Without answers to these four questions, even the most sophisticated models will remain confined to the reporting level. Particularly in high-risk well sections, the system must not only advise the field on “how to adjust” but also explain “why the adjustment is necessary, what the associated risks are, and when to halt operations for review.”
Therefore, state-of-the-art online optimization systems typically adopt a semi-automatic approach: the model runs continuously and provides real-time recommendations, while execution remains constrained by mechanistic limits and human authorization. This architecture leverages the advantages of data-driven decision-making without relinquishing on-site control. It is also the fundamental reason why AI-powered drilling systems are currently more readily accepted.
V. For AI to truly deliver value, it must be integrated into the post-mortem and R&D processes.
Many enterprises see only modest results after deploying AI systems, and the root cause is not poor algorithms—it’s that the outcomes fail to feed back into downstream processes. If a model merely provides a one-time recommendation without feeding insights back into design, manufacturing, testing, and customer service to update rules and procedures, the system’s value will quickly erode. Conversely, when AI-generated insights can be used to refine wear databases, adjust cutting parameters, and develop templates for typical application scenarios, the system becomes increasingly accurate over time.
For drill-bit manufacturers, this closed-loop process—moving seamlessly from field operations back to R&D—is particularly critical. Every alert for abnormal vibration, every proactive decision to pull out the drill string ahead of schedule, and every identification of wear patterns should serve as input for the next round of product upgrades, rather than being relegated to one-off project summaries.
VI. Recommendations for Xingtong: First develop a drill-bit database and a post-mortem analysis engine, then discuss building a large-scale AI platform.
To establish a credible presence in the AI-plus-drilling space, Xingtong is advised to first build three foundational pillars around its most valuable data assets: first, develop a unified data dictionary covering drill-bit models, cutting-plate geometries, cutting-tooth configurations, applicable well sections, and historical performance; second, create a comprehensive atlas of wear morphologies and failure modes, linking field photographs with operational-condition labels; and third, establish parameter windows and a single-run-results database, enabling the comparison and reuse of successful practices across different well sections.
On this foundation, we will progressively develop features such as product selection recommendations, parameter optimization suggestions, and anomaly alerts. At the same time, the official website should prominently highlight our “data closed-loop capabilities” and “parameter optimization capabilities,” rather than making vague claims like “we’re doing AI.” What technology-driven customers truly care about is whether an organization can leverage data to seamlessly integrate design, operations, and post-event reviews, ultimately reducing trial-and-error costs.
References
- Baker Hughes: Publicly available information on Kantori software and i-Trak automated directional drilling.
- Halliburton: Publicly available information on intelligent drilling and drill-bit data integration.
- Liu Qingyou: Research materials related to future intelligent drilling systems, among others.
- Publicly available materials related to the 2025 China Oil and Gas Artificial Intelligence Technology Conference.