Edge Field Operations Control for Real-Time Wellsite Optimization
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Solution Overview
Problem
Existing field operations in mature oil and gas fields face challenges such as operational inefficiencies, equipment failures, and high carbon footprints due to disconnection and silos in data processing and control systems, leading to increased costs and reduced production efficiency.
Innovation Solution
Implementing a framework that utilizes edge computing and cloud platforms for real-time data processing and control, integrating machine learning and artificial intelligence to optimize field operations, enabling intelligent and autonomous monitoring and control of equipment, and reducing manual intervention.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If traditional centralized data processing systems are used, then system complexity is reduced, but data processing time and loss of information increase
Solution Approach 1:
The patent segments the centralized data processing system into distributed edge computing nodes deployed at multiple well sites. Each edge device processes data locally, reducing information loss by enabling real-time analysis while the modular architecture manages complexity through standardized interfaces and protocols.
Solution Approach 2:
The patent adds a spatial dimension to data processing by distributing compute resources across multiple geographic locations (well sites) rather than concentrating them in a single center. This dimensional shift enables parallel processing and reduces latency while maintaining system manageability through hierarchical coordination.
2Productivity
If manual monitoring and control methods are used, then system complexity is reduced, but productivity and response time deteriorate
Solution Approach 1:
The patent implements self-service automation where the system autonomously monitors field equipment, detects anomalies, and executes control actions without manual intervention. Machine learning models automatically optimize operational parameters and predict equipment failures, significantly improving productivity while the modular design keeps automation management tractable.
Solution Approach 2:
The patent establishes closed-loop feedback systems where edge devices continuously monitor operational data, compare it against optimal parameters determined by machine learning models, and automatically adjust field equipment. This real-time feedback automation enhances productivity while maintaining manageable complexity through standardized control protocols.
3Speed
If real-time data processing is implemented, then response speed improves, but energy consumption and device complexity increase
Solution Approach 1:
The patent applies partial processing at the edge and selective transmission to the cloud, processing only critical data locally in real-time while batching non-urgent data for later transmission. This approach achieves necessary response speeds for safety-critical operations while minimizing overall energy consumption through intelligent data prioritization.
4Reliability
If distributed edge computing is deployed, then data processing speed and reliability improve, but device complexity and initial costs increase
Solution Approach 1:
The patent employs universal edge computing devices that can be deployed across multiple well sites with identical hardware and software configurations. These multi-functional devices handle data collection, processing, storage, and communication, simplifying deployment while achieving high reliability through redundancy and standardized interfaces.
Data Source
AI summary
A method can include receiving data from field equipment at a number of well sites via a number of local edge devices; processing the data to determine optimal field operation parameters for field operations at the number of well sites; and controlling the field operations using the determined optimal field operation parameters.