Drilling System With Machine Learning for Downhole Tool Prediction

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Solution Overview

Problem

Existing drilling technologies face challenges in accurately predicting and optimizing the behavior of downhole tools during directional drilling, leading to inefficiencies and suboptimal trajectory control in resource fields.

Innovation Solution

A method utilizing machine learning models trained with drilling performance data to predict the behavior of downhole tools, incorporating real-time data and physics-based models to enhance steering performance and optimize drilling parameters in a closed-loop system.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Manufacturing precision

If machine learning models are trained with drilling performance data to predict downhole tool behavior, then drilling trajectory control accuracy is improved, but device complexity increases

Engineering Contradiction:

Improvedrilling trajectory control accuracyVSAvoidsystem complexity

Core Design Contradiction:

Manufacturing precisionVSDevice complexity

Solution Approach 1:

The system implements a closed-loop feedback mechanism where drilling performance data is continuously collected from downhole tools, fed into machine learning models for prediction, and used to adjust drilling parameters in real-time. This feedback loop enables continuous improvement of trajectory control accuracy while automating the decision-making process.

Inventive Principle:

Principle #23Feedback

Solution Approach 2:

The patent replaces traditional mechanical control systems with machine learning-based predictive models. Instead of relying solely on conventional drilling control mechanisms, the system uses AI algorithms trained on historical performance data to predict tool behavior and optimize drilling parameters, thereby achieving higher precision without proportionally increasing mechanical complexity.

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

2Productivity

If real-time drilling parameter adjustments are made using machine learning predictions, then drilling efficiency is improved, but loss of time in data processing increases

Engineering Contradiction:

Improvedrilling efficiencyVSAvoiddata processing time

Core Design Contradiction:

ProductivityVSLoss of time

Solution Approach 1:

The machine learning models are pre-trained using historical drilling performance data before actual drilling operations begin. This preliminary training allows the models to make rapid predictions during real-time drilling without requiring extensive computational processing at the moment of decision, thus minimizing data processing time while maintaining high drilling efficiency.

Inventive Principle:

Principle #10Preliminary action

Solution Approach 2:

The system automatically uses its own collected drilling performance data to refine and update the machine learning models continuously. This self-service mechanism allows the system to improve its predictive accuracy over time without requiring external intervention or additional manual data processing, thereby maintaining high productivity with minimal time loss.

Inventive Principle:

Principle #25Self-service

Data Source

PatentUS12404762B2Drilling system

Publication Date: 2025.09.02 SCHLUMBERGER TECH CORP

AI summary

A method can include acquiring drilling performance data for a downhole tool; modeling drilling performance of the downhole tool to generate results; training a machine learning model using the drilling performance data and the results to generate a trained machine learning model; and predicting behavior of the downhole tool using the trained machine learning model.