Downhole Tool Fault Identification via DTC Signature Classification
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Solution Overview
Problem
Current directional drilling technologies lack effective real-time fault diagnosis and decision support systems, relying heavily on driller expertise for interpreting machinery failures and deviations from pre-designated drilling paths, which can lead to inefficiencies and increased maintenance costs.
Innovation Solution
An intelligent and interactive real-time fault diagnosis and decision support system using diagnostic trouble codes (DTCs) and a determinative algorithm for real-time health assessment of rotary steerable systems, leveraging historical data to infer failure causes and probabilities, enabling quick debug and maintenance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If current failure notification technology is used, then personnel are notified of machinery failures, but the interpretation of failures depends heavily on driller's knowledge and manual evaluation of data
Solution Approach 1:
The system automatically classifies diagnostic trouble codes and identifies failure causes without requiring manual driller interpretation. The classification module autonomously processes DTCs, matches them with historical data, and generates failure probability assessments, enabling the system to serve itself rather than relying on human expertise for data interpretation.
Solution Approach 2:
The patent replaces the manual mechanical process of driller evaluation with an automated computational system. The classification module uses algorithmic processing to substitute human driller knowledge and manual data evaluation with automated code classification, historical data matching, and probabilistic failure cause identification.
2Ease of operation
If manual data evaluation by drillers is used, then real-time decisions can be made, but the process is time-consuming and reduces productivity
Solution Approach 1:
The system performs preliminary classification of diagnostic trouble codes and pre-calculates failure probabilities using historical data before actual failure occurs. By pre-processing and organizing DTC data with associated failure modes and probabilities, the system prepares decision-support information in advance, enabling faster real-time decisions without manual evaluation during critical moments.
Solution Approach 2:
The system continuously monitors DTCs, compares them with historical failure data, and provides real-time feedback on failure probabilities and causes. This closed-loop feedback mechanism automatically updates the classification and presents actionable insights to drillers, eliminating the need for manual data evaluation while maintaining real-time decision-making capability.
3Measurement precision
If extensive driller knowledge is required for failure interpretation, then accurate failure identification is possible, but training requirements and operational complexity increase
Solution Approach 1:
The classification module acts as an intermediary between raw diagnostic trouble codes and failure interpretation. It translates complex DTC data into classified failure categories with probability assessments, serving as a mediator that bridges the gap between machine-generated codes and actionable failure identification without requiring driller expertise in code interpretation.
Solution Approach 2:
The system creates a copied and structured version of historical failure data organized by DTC signatures and failure modes. By replicating and organizing historical failure patterns into a searchable classification database, the system provides pre-packaged failure identification templates that eliminate the need for drillers to possess extensive interpretive knowledge while maintaining accurate failure identification.
Data Source
AI summary
A method and system for identifying a fault in a downhole tool. The method may comprise storing a plurality of diagnostic trouble codes (DTCs) from one or more previous downhole operations in a database, mapping the plurality of DTCs in the database, performing a downhole operation wherein a DTC is generated, and performing a determinative algorithm that uses pattern recognition to identify a fault caused by a DTC signature based on the one or more of the plurality of DTCs.