Key Takeaways
- Edge computing and IIoT platforms enhance production operations across various basins while minimizing manual intervention.
- Real-time data analysis leads to significant production increases and reduced equipment failures.
- The approach is applicable across different artificial lift methods and geographies, proving its versatility and efficacy.
The Challenge of Traditional Methods
Operators managing extensive inventories of artificial lift systems face significant issues with equipment anomalies and production optimization, particularly in geographically dispersed locations. Traditional methods like manual checks, centralized polling, and reactive maintenance often result in production losses and equipment failures, impacting efficiency and safety.
In specific regions, unique challenges persist. For instance, in Ecuador’s mature brownfield area, an operator oversees over 60 wells from a remote jungle location, exposing personnel to hazards while experiencing frequent ESP failures. In the Permian Basin of Texas, operators grapple with gas-lift optimization in unconventional wells, while the Bakken’s SRP wells face excessive shutdowns due to lack of real-time diagnostics. The Haynesville Basin struggles with suboptimal gas well operations and frequent manual interventions.
Innovative Solutions Through Edge Computing
Deployments across four basins leverage ruggedized edge computing to process high-frequency sensor data and manage operations autonomously. This architecture enables near-instantaneous analytics and controls, sending only essential data to the cloud, which reduces transmission volumes significantly.
In the Amazon, the Automated Well Operator (AWO) application integrates multiple workflows to support remote operations, optimizing chemical injection and well testing. Conversely, the Permian Basin’s gas-lift optimization operates autonomously without requiring labor-intensive models. In the Bakken, integrated machine learning enhances operations through real-time diagnostics, while the Haynesville Basin uses an autonomous application for liquid unloading, improving choke actuation without manual input.
Field Results and Impact
Each basin demonstrated impressive results. In the Amazon Basin, automation led to a 6% production increase and a significant reduction in ESP failures, enhancing operator efficiency by 80% and decreasing carbon emissions. The Permian Basin’s automation yielded 5-25% production gains, proving its value over manual operations. The Bakken recorded a 15% increase in inferred production and reduced pump cycling. The Haynesville Basin achieved gas production increases of 70-139%, showcasing the adaptability and reliability of edge-based solutions.
Broad Applicability and Future Potential
The success across varied environments confirms that the edge IIoT architecture is suitable for different artificial lift methods and operational challenges. Combining physics-based models with data-driven analytics allows for unprecedented optimization workflows, enhancing scalability and minimizing hardware requirements. This innovative approach holds promise for ongoing advancements in oilfield management and production efficiency.
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