About the client
The client is a global leader in oilfield services, delivering advanced technologies and integrated solutions to support upstream energy operations. Operating in over 70 countries, the company specializes in drilling, evaluation, completion, and production services that optimize reservoir performance and enhance efficiency. Its offerings include cementing, stimulation, well intervention, and digital platforms for real-time insights.
Need for Change
To keep pace with the demands of modern drilling operations, it became essential to move beyond traditional, manual monitoring and delayed decision-making. The transition toward edge-based, real-time data analytics was driven by the need to empower rig crews with immediate, actionable insights. This shift would enable optimization of drilling parameters, proactive response to operational risks, and streamline workflows. The organization wanted to leverage advanced data analysis and visualization directly at the rig site to foster a culture of data-driven decision-making, reduce reliance on centralized cloud resources, and accelerate the transformation of raw data into operational value.
Challenges
The client faces several operational challenges in managing rig sites and drilling activities. These challenges stemmed from limitations in connectivity, real-time monitoring, equipment reliability, and the need for timely, data-driven decision-making, all of which impacted safety, efficiency, and overall performance. A range of stakeholders including field operators, drilling engineers and operations managers were impacted by these challenges.
Overcoming Remote Monitoring Limitations at Rig Sites
Restricted internet connectivity and bandwidth at rig sites hindered timely access to operational data.
Mitigating Real-Time Monitoring Risks in Drilling Operations
The client struggled to detect and respond quickly to critical drilling incidents, such as well kicks, interference, or collisions due to limited real-time monitoring at rig sites. This created safety risks and operational hazards for on-site teams.
Addressing Equipment Reliability and Human Error
Equipment failures and human errors sometimes led to operational delays and increased costs, ultimately reducing overall project efficiency and profitability.
Sub-Optimal Operational Efficiency due to Manual Monitoring
Non-optimized drilling processes and reliance on manual monitoring led to increased non-productive time (NPT) and delayed decision-making.
Slow Responses due to Reliance on Manual Expertise in Drilling Operations
Depending on individual expertise to interpret real-time rig data slowed response times and made consistent decision-making challenging.
Key objectives
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Establish centralized governance and monitoring for the entire agent lifecycle and cost tracking
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Enable dynamic orchestration of agents across real-time and static data sources
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Integrate seamlessly with internal systems and support open-source frameworks
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Implement robust AI testing and evaluation mechanisms
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Reduce time-to-market through reusable components and streamlined deployment
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Ensure secure access and global compliance through role-based access control (RBAC)
LTIMindtree’s Solution
To address the operational challenges faced at rig sites, an edge-based, real-time drilling data analysis solution was implemented to optimize oil and gas drilling operations. This approach leveraged advanced engineering models, visualization, and monitoring tools to transform raw drilling data into actionable insights, driving efficiency and informed decision-making.
Key features of the solution included:
Real-Time Data Analysis
Applied business logic to real-time drilling data streams, transforming raw data into actionable operational insights for immediate decision-making.
Operational Insights
Utilized detailed analytics to clarify drilling efficiency improvements and cost reductions through real-time well monitoring.
Data-Driven Analytical Models
Leveraged advanced dashboards like torque-drag, anti-collision monitoring, bit wear prediction, and kick/loss detection to identify drilling irregularities and trigger timely warnings.
Visualization Tools
Employed interactive charts and graphs to enhance the visualization of drilling data, supporting better operational understanding and faster decision-making.
Microservices-based Architecture
Adopting a microservices-based architecture and ensuring compatibility with various communication protocols enabled support for diverse rig environments and operational needs.
Multiple Test Iterations
Conducting multiple test iterations with varied datasets significantly enhanced the reliability and effectiveness of the software solution.
The solution comprised several specialized modules, each targeting critical aspects of well engineering. A few of the important modules were:
Torque and Drag (T&D)
This module enabled real-time friction factor calibration, generated broomstick plots, and applied Bayesian optimization for subsurface friction factors. It supported advanced analysis of hook load, torque and drag trends. It enabled continuous monitoring and adjustment of wellbore conditions for safer and more cost-effective operations.
Anti-Collision Monitoring
This module provided real-time 3D visualization and analysis to prevent wellbore collisions during directional drilling. It integrated survey data and planned trajectories, enabling early detection of potential collision risks with offset wells for safer drilling operations.
Bit Wear Prediction
Real-time monitoring and forecasting of bit wear, integrating mechanical specific energy, gamma ray, and formation data to optimize bit life and reduce unnecessary trips. It helped operators make informed decisions about bit replacement and avoid unnecessary trips. By analyzing drilling parameters and formation data, it reduced costs and improved drilling efficiency.
Kick/Loss Prediction
Analyzed real-time drilling and tripping data to detect early signs of well control risks such as kicks and fluid losses. It provided automated risk indices and alerts, enabling proactive intervention to enhance safety and prevent costly incidents.
Some of the other key aspects of the solution included:
Alert System
It enabled users to configure automated notifications for critical drilling parameters, operational risks, and custom conditions across all modules. It supported real-time alerts creation and customization, helping teams respond quickly to potential issues and maintain safe, efficient operations.
Unit Conversion
Flexible unit system across all modules for global usability.
Architecture Diagram
Figure 1: System Architecture Diagram
Tech Stack
| Category | Key Technology/Services |
| Programming Language | Python, NodeJS |
| Framework | .NET, AngularJS |
| Containerization | Docker |
| Orchestration | Kubernetes |
| Message Broker | RabbitMQ |
| CI/CD Tool | Jenkins |
| API Testing Tool | Postman |
| Database | MongoDB |
Benefits
The implementation of an edge-based, rig-installable application for drilling optimization and hazard prevention has delivered measurable benefits for the client. These benefits include operational efficiency, safety improvements, cost savings, and accelerated data-driven decision-making for well construction projects.
Optimized Drilling Parameters
Faster Turnaround Times
Proactive Approach to Issue Management
Lowered Operational Costs
Swifter Data Analysis in Minutes
Ultra-low Latency
Significant Bandwidth Savings
Reduced Downtime
Improved Efficiency
Conclusion
The deployment of LTIMindtree’s edge-based, real-time drilling data analytics solution has empowered the customer to overcome operational challenges at rig sites, driving significant improvements in efficiency, safety, and cost savings. By transforming raw drilling data into actionable insights and enabling rapid, data-driven decision-making, the solution, powered by predictive analytics for drilling, has set a new benchmark for drilling optimization and hazard prevention.
Internal Quote
“The successful deployment of LTIMindtree’s edge-based, real-time drilling analytics solution is a testament to our unwavering commitment to innovation and operational excellence. By harnessing advanced analytics at the rig site, we have empowered our clients to achieve safer, faster, and more cost-effective drilling operations. This solution not only delivers measurable improvements in efficiency and safety but also sets a new benchmark for data-driven decision-making in the energy sector. We are proud to partner with our clients on their digital transformation journey and look forward to driving even greater value together.”
— Associate Vice President, LTIMindtree
Ready to unlock the full potential of your drilling operations?
Connect with us at eugene.comms@ltimindtree.com to explore how edge analytics can transform your rig site.
