Revolutionizing Operational Efficiency with Edge Computing and AI

Table of Contents

1. Introduction
2. Challenges of using AI and IoT for operational efficiency in satellite field Wells
3. Platform solution for monitoring and classifying Dyna cards
4. Results: Reduced anomaly downtime for connected Wells
5. Digitizing satellite fields: Data flow architecture and infrastructure
6. Data visualization layer: Real-time Dyna card interpretation
7. Visualizing field status and Sucker Rod Pump parameters
8. Machine learning algorithm for predictive failure diagnosis
9. Machine learning pipeline for continuous model improvement
10. Results: Downtime reduction and smart alarms
11. Sucker Rod pump optimization based on the platform
12. Conclusion

AI and IoT for Operational Efficiency in Satellite Field Wells

Satellite field Wells Present unique challenges when it comes to monitoring and optimizing operational efficiency. These remote locations often lack the necessary infrastructure for collecting and analyzing data, making it difficult to prevent well downtime and production loss. However, with the advancement of artificial intelligence (AI) and the Internet of Things (IoT), there is now a solution to these challenges.

Challenges of Using AI and IoT for Operational Efficiency in Satellite Field Wells

The manual data collection on satellite field Wells leads to insufficient and discrete data for post-failure analysis. This limited data also makes it challenging to investigate pre-failure events. Real-time monitoring is necessary to prevent well downtime and production loss. To overcome these challenges, a new data flow architecture is needed to digitize these fields economically and enable the sending of data to the cloud for AI and machine learning applications.

Platform Solution for Monitoring and Classifying Dyna Cards

To address these challenges, a platform solution has been developed that runs a machine learning algorithm at the edge to classify Dyna cards and enable smart alerts. Dyna cards are critical in analyzing the health of a downhole pump in a sucker Rod pump system. By visualizing the Dyna cards in real time, operators can interpret the pump health and identify anomalies. The platform also allows for visualizing field status and various Sucker Rod pump parameters, providing insights for optimizing pump performance.

Results: Reduced Anomaly Downtime for Connected Wells

The implementation of this platform has resulted in significant results, including reduced anomaly downtime of up to 70% for connected Wells. By generating smart alarms before a catastrophic failure occurs, the maintenance team can take preemptive actions to normalize the pump conditions and prevent costly downtime. The platform's machine learning algorithm accurately predicts the type of failure that may occur, enabling proactive maintenance and improving overall operational efficiency.

Digitizing Satellite Fields: Data Flow Architecture and Infrastructure

To digitize satellite fields, an economical infrastructure is essential. This includes the deployment of an Edge IoT Gateway that allows for data collection from controllers in the field. The collected data is then sent to the cloud, where it powers the platform's data visualization layer and machine learning algorithms. This architecture enables real-time monitoring and predictive analysis, even in remote and digitized fields without existing infrastructure.

Data Visualization Layer: Real-time Dyna Card Interpretation

The data visualization layer of the platform provides real-time visualization of Dyna cards, allowing operators to interpret the pump health and track its evolution over time. Time-lapse visualizations of Dyna cards plotted over one another provide valuable insights into pump performance and potential anomalies. Operators can easily identify abnormal pump operating conditions and make informed decisions to prevent failures and optimize production.

Visualizing Field Status and Sucker Rod Pump Parameters

In addition to Dyna card visualization, the platform also allows for visualizing the field status and various Sucker Rod pump parameters. Operators can check the pumping status of individual Wells, including over-pumping, under-pumping, or optimal pumping conditions. They can also monitor important parameters such as motor current, strokes per minute, dynacard area, and pump village. This comprehensive visualization enables operators to assess pump health and make necessary adjustments for optimal performance.

Machine Learning Algorithm for Predictive Failure Diagnosis

To address the challenge of predicting failures before they happen, the platform incorporates a machine learning algorithm. This algorithm utilizes an imbalanced class problem with unique Dyna card shapes representing different failure types. Subject matter experts tag and classify Dyna cards, and the neural network-based algorithm predicts failure types with high accuracy. These predictions trigger smart alarms that proactively notify the maintenance team of potential anomalies, allowing for Timely intervention and reduced downtime.

Machine Learning Pipeline for Continuous Model Improvement

The platform's machine learning pipeline consists of six stages, focusing on continuous model improvement. The pipeline includes orchestrated experimentation, continuous integration, continuous delivery, automated triggering, model delivery, and performance monitoring. This pipeline ensures that the predictive models stay up-to-date and accurate. Automated triggers for retraining the model are based on factors such as F1 score degradation and data drift, maintaining the integrity and efficacy of the models.

Results: Downtime Reduction and Smart Alarms

The implementation of the platform has resulted in significant benefits for satellite field Wells. Downtime has been reduced by up to 70% due to proactive maintenance and the ability to address potential failures before they occur. Smart alarms enable the maintenance team to take preemptive actions to normalize pump conditions and prevent catastrophic failures. These results Translate into substantial cost savings and improved operational efficiency for satellite field Wells.

Sucker Rod Pump Optimization Based on the Platform

In addition to downtime reduction, the platform also enables optimization of Sucker Rod pumps. By visualizing low pump village based on Dyna card area, operators can ramp down a well to prevent failures and optimize pump performance. Similarly, high gas interference and friction can be addressed through strategies such as hot water circulation, reducing friction and optimizing pump health. These optimization techniques further contribute to increased production efficiency and reduced maintenance costs.

Conclusion

The combination of AI and IoT has revolutionized operational efficiency in satellite field Wells. By digitizing these fields and employing real-time monitoring and machine learning algorithms, operators can prevent failures, reduce downtime, and optimize production. The platform solution discussed in this article showcases the transformative power of AI and IoT in the oil and gas industry. With continuous model improvement and proactive maintenance, satellite field Wells can achieve higher efficiency, cost savings, and improved overall productivity.

Highlights

• Implementation of AI and IoT for operational efficiency in satellite field Wells
• Challenges of data collection and analysis in remote locations
• Platform solution for monitoring and classifying Dyna cards
• Real-time visualization and interpretation of pump health
• Reducing downtime and optimizing Sucker Rod pump performance
• Continuous model improvement through machine learning pipeline
• Results: significant reduction in anomaly downtime and cost savings
• Smart alarms for preemptive maintenance actions
• Improving pump health and production efficiency
• Enhanced predictive capabilities and proactive maintenance

FAQ:

Q: What are the challenges of collecting and analyzing data in remote satellite field Wells? A: The lack of infrastructure and the need for manual data collection pose challenges in collecting sufficient and real-time data for analysis.

Q: How does the platform solution monitor and classify Dyna cards? A: The platform uses a machine learning algorithm to classify Dyna cards and enable real-time visualization and interpretation of pump health.

Q: What are the results achieved with the implementation of AI and IoT in satellite field Wells? A: The implementation has led to significant reduction in anomaly downtime, cost savings, and improved overall operational efficiency.

Q: How does the platform optimize Sucker Rod pumps? A: The platform allows for visualizing and optimizing Sucker Rod pump parameters, leading to improved pump health and production efficiency.

Q: How is the machine learning model continuously improved? A: The machine learning pipeline incorporates continuous model improvement through retraining triggered by factors like F1 score degradation and data drift.

Resources:

• Schlumberger: www.slb.com

Note: The headings and subheadings are not bolded here as Markdown language cannot be utilized in this text-based format.