Drilling Dynamics Interpretation Using Feature Zones and Neural Networks

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Solution Overview

Problem

Existing drilling operations face challenges in accurately assessing drilling risks and optimizing drilling parameters due to the complexity of interpreting drilling dynamics data, which is crucial for safe and efficient drilling in oil and gas wells.

Innovation Solution

A method and system utilizing a neural network to analyze a combination of simulated and collected drilling dynamics data, extracting feature zones, and adjusting drilling parameters based on these zones to optimize drilling operations.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Loss of information

If drilling dynamics data is collected using surface equipment and downhole equipment, then data related to drilling operations and formation characteristics can be obtained, but the complexity of interpreting the data increases

Engineering Contradiction:

Improvedrilling data collection completenessVSAvoiddata interpretation complexity

Core Design Contradiction:

Loss of informationVSDevice complexity

Solution Approach 1:

The patent replaces manual data interpretation methods with an automated machine learning system that processes drilling dynamics data. The system uses neural networks and algorithms to automatically analyze sensor data from drilling operations, substituting human analytical efforts with computational models that can handle the complexity of multi-source data integration and interpretation.

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

Solution Approach 2:

The drilling system performs self-diagnosis and self-optimization through automated analysis of its own operational data. The machine learning models enable the system to automatically identify drilling risks, assess formation characteristics, and optimize drilling parameters without external intervention, allowing the system to serve its own analytical needs.

Inventive Principle:

Principle #25Self-service

2Reliability

If traditional methods are used to assess drilling risk, then basic drilling operations can be monitored, but accurate identification of severe drilling dynamics modes is difficult

Engineering Contradiction:

Improvedrilling risk assessment accuracyVSAvoiddrilling dynamics mode identification precision

Core Design Contradiction:

ReliabilityVSMeasurement precision

Solution Approach 1:

The patent transforms the analysis from traditional single-dimensional monitoring to multi-dimensional feature space analysis. By extracting multiple features from drilling dynamics data and projecting them into a feature map with multiple dimensions, the system can identify severe drilling modes that are not detectable through conventional single-parameter monitoring approaches.

Inventive Principle:

Principle #17Another dimension (Dimensionality change)

Solution Approach 2:

The system performs preliminary analysis by extracting features and creating feature maps before making risk assessments. The machine learning models are pre-trained on historical data to recognize patterns associated with severe drilling dynamics modes, enabling early detection and prevention before actual hazards occur.

Inventive Principle:

Principle #10Preliminary action

3Productivity

If manual analysis of drilling dynamics data is performed, then basic drilling parameters can be monitored, but real-time optimization of drilling operations is not achieved

Engineering Contradiction:

Improvedrilling operation efficiencyVSAvoidresponse time for drilling parameter adjustment

Core Design Contradiction:

ProductivityVSLoss of time

Solution Approach 1:

The system implements continuous feedback loops where drilling dynamics data is continuously collected, analyzed, and used to automatically adjust drilling parameters. The machine learning models provide real-time feedback on optimal drilling conditions, enabling dynamic optimization of drilling operations based on current operational state and formation characteristics.

Inventive Principle:

Principle #23Feedback

Solution Approach 2:

The patent replaces manual monitoring and decision-making processes with automated real-time analysis systems. The machine learning algorithms continuously process incoming sensor data and automatically determine optimal drilling parameters, eliminating the time delays associated with manual analysis and enabling immediate response to changing drilling conditions.

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

Data Source

PatentUS12595727B2Automatic interpretation of drilling dynamics data

Publication Date: 2026.04.07 SCHLUMBERGER TECH CORP

AI summary

A system and method that include receiving drilling dynamics data simulated by a processor and drilling dynamics data collected by a sensor positioned in a drilling tool and extracting a feature map based on a combination of the drilling dynamics data simulated by the processor and the drilling dynamics data collected by the sensor positioned in the drilling tool. The system and method additionally include determining a feature zone from the feature map. The system and method further include selecting a drilling parameter for a drill string based on the feature zone.