Artificial Lift Diagnostics via Modular Sensor Network
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Solution Overview
Problem
Artificial lift system failures in oil wells, such as electrical, mechanical, and operational failures, lead to costly and complex logistics for replacement, resulting in well downtime and production losses, necessitating advanced diagnostic technologies to minimize operational costs and maximize return on investment.
Innovation Solution
A diagnostics and control system (DCS) for artificial lift systems, comprising a sensor network, conditioning subsystem, and processing subsystem, which monitors and evaluates the health of the system by capturing sensor measurements, system performance data, and performing periodic automated testing to detect issues early and prevent failures.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If advanced diagnostic technologies are implemented, then early problem detection and reduced downtime are achieved, but system complexity and initial cost increase
Solution Approach 1:
The diagnostic system is divided into modular components: sensor network for data collection, conditioning subsystem for signal processing, processing subsystem for analysis, and permanent local wellsite monitor for coordination. This segmentation allows each module to be optimized independently while maintaining overall system reliability.
Solution Approach 2:
The system performs periodic automated testing and continuous monitoring to detect problems before they cause failures. By conducting preliminary diagnostics on motor overheating, hydraulic loading, voltage spikes, and cable insulation degradation, the system prevents catastrophic failures and reduces downtime.
2Measurement precision
If comprehensive sensor monitoring is deployed, then early failure detection is improved, but system cost and complexity increase
Solution Approach 1:
The sensor network is designed to monitor multiple parameters (temperature, pressure, voltage, current, cable insulation) using integrated sensors that can detect various failure modes. The permanent local wellsite monitor coordinates these diverse sensors and processes their data through a unified conditioning and processing subsystem, reducing overall system complexity.
Solution Approach 2:
The conditioning subsystem acts as an intermediary between the sensor network and processing subsystem, conditioning and preprocessing sensor signals before analysis. This intermediate processing layer simplifies the complexity by handling signal conditioning, filtering, and initial analysis, allowing the processing subsystem to focus on higher-level diagnostics.
3Reliability
If periodic automated testing is performed, then system health evaluation is improved, but operational time and resource usage increase
Solution Approach 1:
The system performs periodic automated testing at optimized intervals rather than continuous monitoring, reducing time loss while maintaining reliable health evaluation. The permanent local wellsite monitor schedules and coordinates these periodic tests, balancing the need for accurate health assessment with operational efficiency.
Solution Approach 2:
The diagnostic system operates autonomously, with the processing subsystem automatically analyzing sensor data and the permanent local wellsite monitor coordinating testing without requiring constant human intervention. This self-service capability reduces the time and resources needed for manual health evaluations while maintaining high reliability.
Data Source
AI summary
A diagnostics and control system (DCS) for an artificial lift system (ALS) in a well, comprising: a sensor network comprising a plurality of sensors for monitoring and obtaining measurements at a power source of the ALS and at a downhole pump of the ALS; a conditioning subsystem configured to measure ALS system performance data; a processing subsystem configured to receive communications from the conditioning subsystem and comprising a processor configured to process sensor data obtained by the sensor network; and a permanent local wellsite monitor that is controlled by the processing subsystem and is powered using a production controller of the ALS, wherein the permanent local wellsite monitor comprises a central surveillance center for transmitting commands and coordinating testing of the ALS among the sensor network, the conditioning subsystem, and the processing subsystem; wherein a condition of the ALS is evaluated by the permanent local wellsite monitor using the processed sensor data, testing results and system performance data to monitor a health of the ALS.