by Marcos Paulo Costa, Wavell Leite, Gabriel Leandro, Drilling Specialist Engineers,
and Josival Junior, Drilling Specialist Geologist at Vale S.A.
Introduction
Vale S.A. is globally recognized as a leader in iron ore production. As such, it recognizes that remaining competitive in the industry demands optimizing drilling operations in order to build accurate geological models that guide operations and reduce risks in iron ore mining over the coming years. In line with this goal, a project led by Marcos Paulo Costa, with team members Josival Junior, Wavell Leite, and Gabriel Leandro, aimed to structurally increase the operational performance of Vale’s Brazilian drilling operations.
Operational performance reflects what percentage of available hours are converted into productive drilling time. Improving this metric by optimizing physical utilization and mechanical availability directly impacts rig productivity, resource efficiency, and execution capacity, making it a key factor in increasing drilled meters without expanding the operational structure.
Low operational performance
At the beginning of the project, the average operational performance was around 20%. This was driven by high process variability, a significant amount of contractual idle hours, and limited conversion of installed capacity into actual production.
Recognizing the opportunity for improvement, the project followed the Lean Six Sigma methodology (a systematic methodology for removing operational waste and reducing process variation) structured around the data-driven DMAIC cycle (Define, Measure, Analyze, Improve, Control). The initiative was led by a team of experts in drilling engineering, data analytics, and operations management.
The focus was on identifying key operational losses, statistically analyzing variables that affect performance, and distinguishing between systemic and special causes. The project then validated these causes in the field and implemented targeted actions to increase productive hours and improve drilling efficiency.
Methodology
More than 1.2 million operational hours from different regions of Brazil were analyzed. These covered all drilling operations across various geological contexts, logistical conditions, and operational models. Data were extracted from corporate drilling management systems fed by operational records and daily drilling reports, and included details on worked hours, idle hours, downtime, productivity, field activities.
The dataset covered a 12-month period (April 2024 to March 2025), thus including seasonal variations related to weather conditions. This broad and diverse dataset ensured strong representation of real operating conditions and enabled consistent analysis that could be applied nationwide.
The analysis identified 190 potential causes using statistical tools such as Pareto analysis, sequential analysis, boxplots, control charts, normality tests, and performance analysis. Of these, 22 were prioritized and confirmed as root causes through field observation, with teams visiting all operational sites and shifts. Key causes included:
- Delays in starting shifts;
- Lack of supplies at drilling sites;
- Delays between site changes;
- Delays in site definition processes;
- Interference from mining operations (drilling in active pits);
- Inefficiencies in shift models;
- Excessive downtime for inspections.
Validation was carried out using applied statistical analyses to determine which variables truly impacted performance. Comparative tests and variability analyses were used to identify significant differences between operational conditions. Regression analysis quantified the effect of each variable on performance, enabling prioritization of the most impactful actions.
Key insight and data-driven action plan
Analytical results showed that increasing productive worked hours has an approximately 6.7 times greater impact on operational performance than reducing idle hours. This guided the project strategy toward converting operational availability into effective production.
A key insight was that the main impacts were consistent across regions, indicating a systemic issue and the need to review internal processes. Based on these findings, the improvement plan focused on implementing the following structural and operational changes:
- Reconfiguring shifts for 24-hour drilling operations;
- Weekly, monthly, and quarterly planning of drilling activities;
- Integrating drilling contractors, mine planning, and operations;
- Optimizing drilling sites sequencing;
- Revising team transportation routes;
- Strengthening safety practices;
- Redesigning supply requests processes.
The following two examples illustrate how data analysis enabled both simple and complex improvements.
Supply flow optimization
A simple yet high-impact action was optimizing the supply request flow at drilling sites. Data analysis showed that each rig was idle for an average of 13 hours per month due to lack of small but essential materials such as polymers and core lifters. Previously, this loss was not clearly visible because it occurred with no regularity. Once data was consolidated, it became evident that the cumulative impact exceeded one full shift per rig per month.
A straightforward solution was implemented using existing infrastructure: since all rigs have radio communication, a direct channel was established between the driller and the warehouse. This allowed immediate supply requests, with the warehouse staff responsible for organizing and dispatching materials via support drivers. This change reduced response time, eliminated intermediate steps, and directly reduced idle hours.
Shift model redesign
A more complex initiative involved redesigning the shift model to enable continuous 24-hour operations. Statistical analysis showed that the previous configuration limited the utilization of available hours due to inefficiencies in shift transitions and suboptimal work distribution. A new shift model was developed in compliance with Brazilian labor laws, which required coordination between teams and contractors. This change significantly increased effective time utilization and improved overall performance.
Results
The results demonstrate not only significant improvement in operational indicators but also a structural transformation in how drilling operations are managed.
A 36% increase in productive hours and a 13% gain in productivity per rig per day reflect improved efficiency in converting available hours into actual production. Increasing operational performance from around 20% to over 29%, along with reduced variability owing to better long-term planning, indicate greater operational stability and predictability—critical factors for planning and executing drilling campaigns.
Additionally, the project enabled more efficient asset utilization with the same production volume. This led to a 13% reduction in cost per drilled meter, reinforcing the economic impact of the improvements.
Maintaining these results involved updating operational procedures, training teams, and implementing Out of Control Action Plans that ensure structured and standardized responses to performance deviations: an approach informed by a commitment to sustainable operational efficiency.
Potential for replication
The applied methodology shows strong potential for replication in other drilling projects, especially in operations with a high incidence of unproductive time and low productive-hour conversion. Using a structured approach based on real operational data and applied statistical analysis makes it possible to adapt the model to different operational scenarios, regardless of regional or logistical variations.
However, replicating the methodology may present challenges, particularly related to the quality and standardization of available data, the maturity level of operational processes, and the degree of integration between planning, operations, and suppliers. In some cases, resistance to change and the need to adapt operational recording systems may also affect the speed of implementation.
Overall, the results indicate that applying a structured, data-driven approach enables not only significant productivity gains but also the development of a more efficient, predictable, and scalable operational model, with potential application across different drilling operations.
For more information: Get in touch with Marcos on LinkedIn, or visit vale.com
