Artificial Lift Control With Real-Time Predictive Adjustment
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Solution Overview
Problem
Current control systems for artificial lift units at wellsites are limited in their ability to monitor and adjust operations in real-time, leading to inefficiencies, downtime, and production losses due to delayed communication and manual intervention, especially when conditions change.
Innovation Solution
A control system with distributed processing equipment and automated machine learning to interface with installed controllers and sensing equipment, allowing real-time monitoring, analysis of trends, prediction of conditions, and automated control adjustments to optimize artificial lift unit operations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of time
If manual monitoring and control methods are used with desktop software programs, then initial configuration can be achieved, but real-time optimization and response to changing conditions cannot be provided
Solution Approach 1:
The control system performs self-service by automatically monitoring operating parameters, analyzing trends, predicting conditions, and adjusting artificial lift unit configurations without requiring manual intervention. The system serves itself by implementing automated machine learning models that continuously optimize production parameters based on real-time data from sensors and distributed processing equipment.
Solution Approach 2:
The system implements continuous feedback loops where operating parameters are monitored in real-time, analyzed against predictive models, and used to automatically adjust control settings. The feedback mechanism compares actual performance with predicted optimal performance and automatically implements corrections, eliminating the time delays associated with manual feedback cycles.
2Productivity
If real-time monitoring and automated control are implemented, then optimization and proactive management are enabled, but system complexity increases
Solution Approach 1:
The control system is segmented into distributed processing units deployed across multiple locations (wellsites, regional centers, cloud platforms). Each segment handles specific functions such as data collection, local analysis, or model training, allowing the complex system to be divided into manageable, independent modules that can operate autonomously or cooperatively.
Solution Approach 2:
The control system implements multi-functional processing equipment that can perform diverse tasks including real-time data acquisition, historical data analysis, predictive modeling, automated control, and communication coordination. This universal approach reduces overall system complexity by using standardized platforms that handle multiple functions rather than dedicated specialized hardware for each function.
3Measurement precision
If distributed processing equipment is deployed across multiple locations, then real-time data collection and analysis are improved, but communication infrastructure requirements increase
Solution Approach 1:
The communication system transitions from traditional two-dimensional point-to-point connections to a multi-dimensional network architecture that includes wired and wireless interfaces, cloud-based communication channels, and hierarchical data transmission paths. This dimensional expansion allows distributed processing equipment at various locations to communicate efficiently through multiple simultaneous pathways.
Solution Approach 2:
Communication brokers are deployed as intermediary components that facilitate data exchange between distributed processing equipment, controllers, and external systems. These brokers manage communication protocols, data formatting, and transmission scheduling, reducing the complexity of direct peer-to-peer communication between all system components.
4Reliability
If automated machine learning models are used for prediction, then proactive issue identification is enabled, but computational requirements and energy consumption increase
Solution Approach 1:
The machine learning models implement dynamic computational strategies that adjust their processing intensity based on operational conditions. During normal operation, models use lighter computational approaches, while automatically increasing processing power when anomalies are detected or when predictive accuracy requirements increase, thereby optimizing energy consumption relative to reliability needs.
Solution Approach 2:
Computational tasks are segmented and distributed across multiple processing locations rather than centralized in one high-power facility. Local distributed processing equipment performs initial data processing and filtering, while more computationally intensive model training and complex predictions are performed at regional or cloud-based centers, reducing the energy burden on any single location.
Data Source
AI summary
A system and method controls a plurality of artificial lift units at a plurality of wellsites. Processing equipment installs at a plurality of the wellsites. Operating parameters of each of the artificial lift units are obtained with sensing equipment at the wellsites and are communicated in real-time from the wellsites to the installed processing equipment at the plurality of the wellsites. A modelling function of the processing equipment analyzes a trend of the operating parameters of the artificial lift units, and automated machine learning of the processing equipment predicts a condition of at least one of the artificial lift units based on the analyzed trend. The processing equipment determines at least one automated control for the determined condition of the at least one artificial lift unit and counters the determined condition by implementing the at least one automated control at the at least one artificial lift unit.