Secondary Porosity Identification Using Correlated Subsurface Data

Resolve Bottlenecks,
Find Innovative SolutionsGenerate Solutions

Solution Overview

Problem

The variability in size, shape, and origins of secondary porosities in subsurface formations complicates their identification and quantification, impacting volumetric estimates and field development in the oil and gas industry.

Innovation Solution

A method and system that correlate multiple data sets from core samples, borehole images, caliper enlargement, bad-hole flags, and loss circulation data to identify and quantify secondary porosities, using image processing and calibration techniques to generate accurate volumetric estimates.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Measurement precision

If multiple data sets are correlated to identify secondary porosities, then measurement precision is improved, but device complexity increases

Engineering Contradiction:

Improveidentification accuracy of secondary porositiesVSAvoidcomplexity of data correlation system

Core Design Contradiction:

Measurement precisionVSDevice complexity

Solution Approach 1:

The system segments the complex identification process into distinct functional modules: a quality control module that evaluates image quality using multiple criteria (focus, noise, artifacts), a data correlation module that integrates multiple data sets, and a secondary porosity identification module that applies classification algorithms. This segmentation allows each module to specialize in one aspect, improving overall precision while managing complexity through modular design.

Inventive Principle:

Principle #1Segmentation

Solution Approach 2:

The patent introduces intermediate processing steps between raw data and final identification results. Quality control metrics serve as intermediaries to filter and validate input images before they enter the correlation process. Additionally, the system uses intermediate classification stages that progressively refine the identification of secondary porosities, acting as mediators between complex multi-dimensional data and simplified interpretive results.

Inventive Principle:

Principle #24Intermediary (Mediator)

2Measurement precision

If quality control measures are applied to images, then measurement precision is improved, but loss of time increases

Engineering Contradiction:

Improvequality of subsurface imagesVSAvoidtime for quality control processing

Core Design Contradiction:

Measurement precisionVSLoss of time

Solution Approach 1:

The system performs preliminary quality control assessments automatically as images are acquired or imported, evaluating multiple quality metrics (focus, noise levels, artifacts) before the images enter the main analysis workflow. This preliminary action ensures that only quality-controlled images proceed to secondary porosity identification, maintaining measurement precision while minimizing time loss by catching quality issues early in the process.

Inventive Principle:

Principle #10Preliminary action

Solution Approach 2:

The patent replaces manual image quality assessment with automated computational algorithms that evaluate quality metrics programmatically. This substitution of mechanical/manual inspection with automated image processing and analysis algorithms significantly reduces the time required for quality control while maintaining or improving precision through consistent, objective evaluation criteria.

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

Data Source

PatentUS12517029B2Systems and methods for identifying secondary porosity

Publication Date: 2026.01.06 SAUDI ARABIAN OIL CO

AI summary

In some examples, a method for identifying secondary porosities can include correlating multiple sets of data of a subsurface formation, identifying one or more secondary porosities based on the correlated data, and determining a volumetric estimation of the subsurface formation based on the one or more secondary porosities. The multiple sets of data are selected from one or more of core sample images, borehole images, caliper enlargement data, bad-hole flag data, and loss circulation data.