Drill Bit Acoustic Sensing for Real-Time Rock Identification
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Solution Overview
Problem
Hydrocarbon exploration and drilling face challenges in accurately identifying rock types and properties during drilling operations, leading to inefficiencies and potential hazards.
Innovation Solution
The use of a machine learning model within a control loop for controlling a drill, where sensor data from acoustic and pressure sensors attached to the drill bit is processed to inform drill controls, allowing for real-time adjustments of drilling parameters.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional drilling methods are used without real-time rock identification, then drilling operations can proceed without complex equipment, but accuracy of rock type identification deteriorates leading to inefficiencies and hazards
Solution Approach 1:
The patent replaces traditional mechanical rock identification methods with acoustic sensing and machine learning algorithms. Acoustic sensors capture drill bit vibrations and acoustic emissions, which are then processed by ML models to identify rock types, eliminating the need for complex mechanical sampling and laboratory analysis equipment.
Solution Approach 2:
The drilling system performs self-diagnosis and self-adjustment by using acoustic sensors to continuously monitor rock properties and automatically adjusting drilling parameters through ML-driven control. This eliminates the need for external geological experts to manually analyze rock samples during drilling operations.
2Productivity
If drilling parameters are adjusted in real-time based on rock identification, then drilling efficiency improves, but system complexity and computational requirements worsen
Solution Approach 1:
The patent implements a closed-loop feedback system where acoustic sensors continuously monitor drilling conditions, ML models analyze the data in real-time to identify rock types, and the system automatically adjusts drilling parameters based on the identification results. This continuous feedback loop enables real-time optimization of drilling efficiency.
Solution Approach 2:
The system dynamically changes drilling parameters such as rotational speed, feed rate, and weight on bit based on real-time rock type identification. The ML model selects optimal parameter combinations from pre-defined sets or generates new parameter values, allowing the drilling system to adapt to different rock formations and maintain high efficiency.
3Measurement precision
If acoustic sensors and machine learning models are deployed on the drill, then real-time rock identification accuracy improves, but energy consumption and device complexity worsen
Solution Approach 1:
The patent processes only the most relevant acoustic signal features extracted from the sensor data, rather than analyzing the complete raw signal spectrum. The ML model focuses on identifying key frequency components and patterns that are most indicative of rock types, reducing computational load and energy consumption while maintaining high identification accuracy.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
This approach enhances drilling efficiency by improving the accuracy of rock type identification and optimizing drilling parameters, potentially reducing drilling time and minimizing the risk of drilling hazards.
Implementation Method 1
Acoustic signals generated during drilling operations are processed using a machine learning model to identify rock types and properties
Implementation Method 2
pressure data values associated with the acoustic data values
Data Source
AI summary
Disclosed are methods, systems, and computer-readable medium to perform operations including: obtaining sensor data from at least one sensor attached to a surface of a drill bit; providing the sensor data from the at least one sensor to one or more machine learning models, wherein the one or more machine learning models are trained using a library of (i) sensor signatures and (ii) geological formations to output one or more drilling parameters; and adjusting one or more parameters of an operation performed by a drill controlling the drill bit based on the one or more drilling parameters output by one or more machine learning models.