Machine Learning Metadata Classification for Wellbore Job Design Automation
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Solution Overview
Problem
The oil and gas well construction process faces challenges in optimizing material and equipment selection due to unplanned changes in wellbore conditions, leading to inefficiencies in job design revisions and data retrieval from past job observations.
Innovation Solution
A method utilizing a managing application with machine learning to classify and apply metadata to job observations, enabling efficient retrieval and application of relevant data for future job designs, reducing the need for revisions and improving data reporting.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If manual methods are used to transfer and apply changes from stage reports to future well construction planning, then flexibility in handling unplanned occurrences is maintained, but time consumption and inefficiency increase
Solution Approach 1:
The patent replaces manual mechanical processes with an automated computer-based system that uses machine learning algorithms to retrieve, analyze, and apply job observations automatically. The system substitutes human operators with automated software that can process data much faster and more efficiently.
Solution Approach 2:
The patent introduces a managing application as an intermediary between stage reports and future job designs. This application acts as a mediator that automatically retrieves relevant job observations, analyzes them, and applies appropriate changes to future well construction planning without requiring manual intervention.
2Reliability
If comprehensive job observations are collected and stored for future reference, then better decision-making is enabled, but data management complexity and resource requirements increase
Solution Approach 1:
The patent extracts only the most relevant features and metadata from comprehensive job observations using machine learning algorithms. Instead of managing and processing all raw data, the system identifies and retrieves only the critical information needed for future job designs, reducing data management complexity while maintaining reliability.
Solution Approach 2:
The patent transforms comprehensive job observation data into structured metadata with specific parameters and tags. This parameterization allows the system to efficiently store, search, and retrieve relevant information without managing the full complexity of raw observational data.
Data Source
AI summary
A method of classifying job observations with metadata tags for retrieval from a database during the designing of a wellbore treatment. A machine learning process applies a set of metadata tags to the observation description and observation object based on a training set of job observations. The machine learning process validates the metadata tags based on a classification grade determined by the ranking of the job observation within a search result. A managing application can modify a job design comprising an inventory of wellbore treatment materials and pumping equipment based on job observations with metadata tags that match the metadata tags of the job design.