PI Real-Time Tag Automation for Well and Equipment QA/QC
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Solution Overview
Problem
The manual configuration of plant information (PI) real-time data tags in the oil and gas industry is time-consuming, prone to errors, and results in inconsistencies, affecting data quality and hindering real-time monitoring and maintenance of wells and equipment.
Innovation Solution
An intelligent automation system that uses machine learning algorithms to recommend PI real-time data tags based on well and equipment information, automates their configuration, and periodically checks for compliance with predetermined standards, integrating with existing communication channels for continuous data transmission and quality assurance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual configuration of PI real-time data tags is used, then flexibility and human judgment are maintained, but the process is time-consuming and prone to errors
Solution Approach 1:
The system performs preliminary actions by pre-defining data tag templates, structures, and validation rules before actual configuration is needed. When a new well or equipment is installed, the automated system retrieves and applies these pre-configured templates, eliminating the need for manual configuration while ensuring consistency and accuracy through pre-established standards.
Solution Approach 2:
The system enables self-service by allowing the automated configuration process to independently retrieve well/equipment information, generate appropriate data tags, validate configurations, and update systems without human intervention. The periodic checking mechanism also operates autonomously to ensure ongoing compliance, reducing reliance on manual oversight while maintaining high data quality.
2Manufacturing precision
If manual configuration of PI real-time data tags is used, then human control is maintained, but errors and inconsistencies increase
Solution Approach 1:
The system implements feedback through periodic checking mechanisms that automatically verify configured data tags against pre-defined standards and logic. This continuous validation provides feedback on configuration accuracy, identifying and flagging any deviations or inconsistencies. The feedback loop ensures high manufacturing precision by catching errors before they propagate, while maintaining operational simplicity through automated monitoring rather than manual verification.
Solution Approach 2:
Pre-defined standards, validation rules, and configuration templates are established beforehand to guide the automated configuration process. These preliminary actions ensure that all data tags are configured with consistent structure, naming conventions, and validation logic, eliminating human variability and improving configuration accuracy while keeping the operational process simple through rule-based automation.
3Productivity
If automated configuration is implemented, then efficiency and consistency are improved, but system complexity increases
Solution Approach 1:
The automated configuration system is designed with multi-functionality to handle various well and equipment types using a unified approach. It can retrieve information from multiple sources (well logs, equipment specifications, operational data), generate different types of data tags (process variables, alarm settings, historical trends), and interface with multiple systems (SCADA, DCS, databases) through standardized protocols. This universality improves productivity across diverse applications while managing system complexity through a single, flexible platform rather than multiple specialized systems.
Solution Approach 2:
The system employs intermediary components such as standardized data interfaces, template libraries, and validation layers that mediate between raw well/equipment information and the final configured data tags. These intermediaries simplify the automation process by providing structured transformation rules and validation logic, reducing the complexity of direct manual configuration while maintaining high productivity through automated processing.
4Reliability
If periodic checking is implemented, then data quality is maintained, but additional processing time is required
Solution Approach 1:
The system implements periodic action by scheduling automatic checking of configured data tags at defined intervals rather than continuously monitoring every parameter. This periodic validation maintains data accuracy by regularly verifying configurations against standards while avoiding the excessive processing time of continuous monitoring. The periodic checks are triggered by events such as configuration changes, system restarts, or scheduled maintenance windows, optimizing the balance between reliability and processing duration.
Data Source
AI summary
A computer-implemented method for plant information (PI) real-time data tag automatic configuration and quality assurance/quality control (QA/QC), includes detecting installation of a new well or piece of equipment. A plant information (PI) real-time data tag configuration workflow is triggered. A notification of a set of one or more potential PI real-time data tags to configure is received, where the set of one or more potential PI real-time data tags are associated with the new well or piece of equipment. As selected PI real-time data tags, a selection is received from the set of one or more potential PI real-time data tags to configure. The selected PI real-time data tags are automatically configured and the PI real-time data tags are periodically checked against pre-determined standards or logic.