Control system for automating drilling operations
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Solution Overview
Problem
The lack of a standardized library for statistics-based or AI-based algorithmic models to manage and control well site operations, coupled with the inefficiency in processing large numbers of data variables, hinders the accurate automation of drilling operations.
Innovation Solution
A system comprising a library module and an analysis module, equipped with processors to classify, catalogue, and select variables, and a pattern recognition module to identify deviations using statistics-based algorithms, along with a predictive engine for generating predictive results to control drilling equipment.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a standardized library for statistics-based or AI-based algorithmic models is implemented, then the accuracy of drilling operations automation is improved, but the device complexity and development time increase
Solution Approach 1:
The patent applies preliminary action by pre-classifying and cataloging data variables into standardized categories and sub-categories before the actual drilling operations. The library module pre-processes and organizes variables such as rate of penetration, mechanical specific energy, and drilling parameters into a structured format, making them readily available for AI/ML algorithms during operations, thus improving accuracy without adding operational complexity
Solution Approach 2:
The patent segments the complex data variable processing task into distinct functional modules: a library module for variable classification and cataloging, an analysis module for selecting relevant variables, and a pattern recognition module for identifying deviations. This segmentation allows each module to handle specific aspects of data processing independently, improving overall system accuracy while managing complexity through modular architecture
2Productivity
If large numbers of data variables are processed to accurately manage and control well site operations, then the productivity and control quality are improved, but the loss of time and computational resources increase
Solution Approach 1:
The patent extracts only the most relevant data variables from the large set of available variables by using the analysis module to select variables based on their importance to specific drilling operations. Instead of processing all possible variables, the system extracts and focuses on key variables such as rate of penetration, mechanical specific energy, and drill string parameters, significantly reducing processing time while maintaining productivity
Solution Approach 2:
The library module performs preliminary classification and cataloging of all data variables into standardized categories and sub-categories before they are needed for analysis. This pre-organization of variables allows the analysis module to quickly retrieve and select relevant variables without having to process or filter through the entire dataset during operations, thus improving efficiency while minimizing time loss
3Reliability
If manual onsite management and control of well site operations is replaced with automated management, then safety and reduction of human error are improved, but the device complexity and initial cost increase
Solution Approach 1:
The patent implements feedback mechanisms where the pattern recognition module continuously monitors drilling operations by comparing actual performance against predicted performance from AI/ML models. When deviations are detected, the system automatically adjusts drilling parameters or alerts operators, creating a closed-loop control system that improves safety and reduces human error while managing complexity through automated decision-making protocols
Solution Approach 2:
The automated management system performs self-service by autonomously selecting relevant data variables, analyzing drilling performance, identifying deviations from standard operating procedures, and recommending or implementing corrective actions without requiring constant human intervention. The system serves itself by continuously learning from historical data and improving its predictive capabilities, thereby enhancing reliability while the modular architecture keeps complexity manageable
Data Source
AI summary
A method of generating, at an IIOT device mounted to equipment of a drilling system, a relay variable, a measurement variable, or a control variable and a message having an IP address. Classifying, by application services of a cloud service provider, the relay variable, the measurement variable, or the control variable; identifying a category or a category and sub-category from a plurality of categories and sub-categories based on variable; cataloguing the relay variable, the measurement variable, or the control variable based on the category or the category and the sub-category; selecting from a library of catalogued relay variables, measurement variables, and control variables, at least one selected from a group comprising a parameter and a value; and identifying a pattern using a statistics based algorithm, the statistics based algorithm using a standard operating procedure, the parameter and the value, the pattern indicating a deviation in the standard operating procedure.