Downhole Tool Behavior Prediction for Directional Drilling Control

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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.

Innovation Solution

A method utilizing machine learning models trained on drilling performance data to predict and optimize the behavior of downhole tools, incorporating real-time data and physics-based models to adjust control parameters in a closed-loop system for improved trajectory control.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Measurement precision

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

Engineering Contradiction:

Improvetrajectory control accuracyVSAvoidsystem complexity

Core Design Contradiction:

Measurement precisionVSDevice complexity

Solution Approach 1:

The patent replaces complex mechanical control systems with machine learning models that process drilling performance data to predict downhole tool behavior. The ML models substitute for traditional mechanical trajectory control mechanisms, enabling more accurate predictions through data-driven approaches rather than purely mechanical systems.

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

Solution Approach 2:

The patent introduces machine learning models as intermediary components between drilling performance data and trajectory control decisions. These models act as mediators that process raw drilling data, identify patterns, and generate predictive insights, thereby improving trajectory control accuracy without directly increasing mechanical system complexity.

Inventive Principle:

Principle #24Intermediary (Mediator)

2Productivity

If real-time data is incorporated into machine learning models for predictive control, then drilling efficiency is improved, but loss of information increases

Engineering Contradiction:

Improvedrilling efficiencyVSAvoiddata processing loss

Core Design Contradiction:

ProductivityVSLoss of information

Solution Approach 1:

The patent performs preliminary actions by pre-processing and storing drilling performance data in structured formats before it is needed for predictive control. Historical drilling data is prepared and organized in advance, allowing the machine learning models to access and process information efficiently during real-time operations without causing information loss.

Inventive Principle:

Principle #10Preliminary action

Solution Approach 2:

The patent implements feedback mechanisms where drilling performance data is continuously monitored, processed by machine learning models, and used to adjust control parameters in real-time. This closed-loop feedback system ensures that information from drilling operations is continuously utilized to optimize performance, preventing information loss by maintaining continuous data flow and processing.

Inventive Principle:

Principle #23Feedback

Data Source

PatentUS20250347212A1Drilling system

Publication Date: 2025.11.13 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.