Infrared Core Imaging for API Gravity Prediction
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current methods for determining American Petroleum Institute (API) gravity of hydrocarbons before drilling a conventional exploration well are limited, often requiring liquid well samples and lacking precision, which increases economic risk and uncertainty in drilling decisions.
Innovation Solution
A system utilizing infrared cameras capturing images of core samples across different wavelengths and machine learning techniques to predict API gravity values, allowing for non-invasive and precise assessment of hydrocarbon properties without extracting liquid samples, enabling determination of API gravity before drilling.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If liquid well samples are used to determine API gravity, then measurement can be performed, but the method lacks precision and increases economic risk
Solution Approach 1:
The patent replaces the mechanical/chemical sampling method with an optical detection system. Infrared cameras capture thermal radiation from core samples, and machine learning algorithms process the thermal images to predict API gravity values, eliminating the need for liquid sample extraction and manual measurement while improving precision and reliability
Solution Approach 2:
The patent introduces thermal imaging as an intermediary measurement method. Instead of directly measuring liquid hydrocarbon samples, the system captures thermal radiation patterns from core samples containing hydrocarbon stains, using these thermal patterns as intermediate data that machine learning models convert into accurate API gravity predictions
2Loss of information
If conventional exploration wells are drilled to obtain hydrocarbon information, then definitive data can be obtained, but the operation is expensive and high-risk
Solution Approach 1:
The patent performs preliminary assessment of hydrocarbon properties using infrared thermal imaging and machine learning before committing to expensive conventional drilling operations. This preliminary action provides sufficient information to make informed drilling decisions, reducing the risk of drilling unproductive wells and eliminating the need for complex exploration well programs in many cases
Solution Approach 2:
The patent creates a digital model or 'copy' of the hydrocarbon properties through machine learning predictions based on thermal imaging data. This digital representation of API gravity and other properties provides all the information needed for drilling decisions without requiring physical exploration wells, thereby simplifying the exploration process
3Ease of operation
If machine learning techniques are used to predict API gravity from infrared images, then non-invasive and precise assessment is enabled, but the system complexity increases
Solution Approach 1:
The patent implements a self-service system where the machine learning model automatically processes infrared images and generates API gravity predictions without requiring manual sample preparation, laboratory analysis, or expert interpretation. The system performs the entire assessment workflow autonomously, from image capture to prediction output, simplifying operation despite the underlying computational complexity
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
Enables accurate and non-invasive determination of API gravity, reducing the risk associated with drilling decisions and optimizing recovery methods for heavy oil reservoirs by providing essential information prior to conventional well exploration.
Implementation Method 1
a plurality of infrared cameras configured to capture infrared image data representing a plurality of infrared images of at least one core sample extracted from the borehole
Data Source
AI summary
Systems and methods for evaluating hydrocarbon properties. At least one of the systems includes: a drilling machine configured to drill a borehole; a plurality of infrared cameras configured to capture infrared image data representing a plurality of infrared images of at least one core sample extracted from the borehole; a computer-readable memory comprising computer-executable instructions; and at least one processor configured to execute the computer-executable instructions, in which when the at least one processor is executing the computer-executable instructions, the at least one processor is configured to carry out operations including: receiving the infrared image data captured by the plurality of infrared cameras; determining, based on the infrared image data, at least one hydrocarbon weight value of the at least one core sample.