Wellbore Transition Detection Using Machine Learning for Accurate Reports

Solution Overview

Problem

Existing methods for correcting time signature discrepancies in wellbore operation reports are inefficient, costly, and prone to human error, leading to inaccurate analyses and resource mismanagement in oil and gas operations.

Innovation Solution

A transition detection system using a machine learning model to analyze wellbore measurement data, generating accurate transition types and times by processing statistical attributes, and comparing them to operation reports to identify and correct errors.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Measurement precision

If manual review and correction of time signatures is performed, then accuracy of operation reports can be improved, but time consumption and labor cost increase significantly

Engineering Contradiction:

Improveaccuracy of time signaturesVSAvoidtime consumption

Core Design Contradiction:

Measurement precisionVSLoss of time

Solution Approach 1:

The system enables self-service by having the machine learning model automatically detect transitions and correct time signatures without requiring manual review. The model processes wellbore measurement data autonomously to identify activity transitions and generate corrected operation reports, eliminating the need for personnel to manually verify and adjust time signatures.

Inventive Principle:

Principle #25Self-service

Solution Approach 2:

The patent replaces the mechanical manual review process with an automated machine learning system. The ML model algorithms process measurement data, detect transitions, and correct time signatures automatically, substituting human labor with computational mechanisms that operate continuously without fatigue or error.

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

2Measurement precision

If manual correction of time signatures is performed, then accuracy can be improved, but labor cost and complexity increase

Engineering Contradiction:

Improveaccuracy of time signaturesVSAvoidprocess complexity

Core Design Contradiction:

Measurement precisionVSDevice complexity

Solution Approach 1:

The system enables self-service by having the machine learning model automatically detect transitions and correct time signatures without requiring manual review. The model processes wellbore measurement data autonomously to identify activity transitions and generate corrected operation reports, eliminating the need for personnel to manually verify and adjust time signatures.

Inventive Principle:

Principle #25Self-service

Solution Approach 2:

The patent replaces the mechanical manual review process with an automated machine learning system. The ML model algorithms process measurement data, detect transitions, and correct time signatures automatically, substituting human labor with computational mechanisms that operate continuously without fatigue or error.

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

3Measurement precision

If manual review of operation reports is performed, then accuracy of time signatures can be corrected, but productivity decreases due to labor intensive process

Engineering Contradiction:

Improveaccuracy of time signaturesVSAvoidoperational efficiency

Core Design Contradiction:

Measurement precisionVSProductivity

Solution Approach 1:

The machine learning model operates continuously to process wellbore measurement data, detect activity transitions, and generate corrected operation reports without interruption. This continuous automated processing eliminates downtime associated with manual review and ensures that time signature corrections are performed constantly as new data becomes available, maximizing operational efficiency.

Inventive Principle:

Principle #20Continuity of useful action

Solution Approach 2:

The patent replaces the mechanical manual review process with an automated machine learning system. The ML model algorithms process measurement data, detect transitions, and correct time signatures automatically, substituting human labor with computational mechanisms that operate continuously without fatigue or error.

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

Data Source

PatentUS20250264634A1Determining downhole operation transitions from wellbore measurement data using machine learning

Publication Date: 2025.08.21 SCHLUMBERGER TECH CORP

AI summary

This application relates to a downhole system that uses a transition detection system to determine transitions between downhole activities or operations for a wellbore based on wellbore measurement data. In various implementations, the transition detection system uses a transition identification machine learning model to generate downhole transition types between downhole operations from wellbore measurement data. Additionally, the transition detection system identifies errors and inaccuracies with activity transitions reported in a downhole operation report based on comparing the downhole operation report to the determined transition times generated by the transition identification machine learning model.