Spectral Well Pad Detection Using Machine Learning
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Solution Overview
Problem
Conventional computer-based detection systems for well pads rely on outdated and inaccurate RGB imagery, leading to high margins of error in identifying well pads and assessing environmental risks such as flooding, landslides, and wildfires, as they are not trained on current or accurate data.
Innovation Solution
The system employs machine learning techniques using spectral image data to detect well pads, gas emissions, and environmental conditions, generating real-time training data to improve the accuracy of well pad identification and risk assessment, incorporating features like infrared signatures and geospatial-temporal data to analyze terrain and environmental events.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional RGB imagery is used for well pad detection, then the system is simple to operate, but the detection precision and reliability are low due to outdated and inaccurate data
Solution Approach 1:
The system transitions from using conventional RGB imagery to multi-spectral imagery, changing the data parameters from standard color channels to multiple spectral bands including near-infrared, shortwave infrared, and thermal infrared. This parameter change enables more precise detection of well pads and environmental conditions by capturing additional information beyond visible light
Solution Approach 2:
The system combines multiple data sources and spectral bands into a composite analysis framework. By integrating near-infrared, shortwave infrared, and thermal infrared data, the system creates a composite view that improves detection precision while managing complexity through unified processing algorithms
2Reliability
If machine learning models are trained on outdated data, then the training process is simpler, but the reliability of well pad identification and risk assessment deteriorates
Solution Approach 1:
The system performs preliminary actions by continuously collecting and storing spectral data from multiple sources before analysis is needed. Historical spectral data is accumulated and prepared in advance, allowing machine learning models to be trained on current, accurate data when assessments are conducted, thereby improving reliability without significant time loss
Solution Approach 2:
The system maintains continuous data collection from satellite and aerial spectral imagery, ensuring that training data is always current. This continuous action eliminates gaps in data freshness, allowing the system to reliably identify well pads and assess environmental risks based on the most recent spectral information available
3Measurement precision
If spectral data from multiple sources is integrated, then the accuracy of environmental risk assessment is improved, but the device complexity and data processing requirements increase
Solution Approach 1:
The system segments the complex spectral data from multiple sources into distinct processing streams for near-infrared, shortwave infrared, and thermal infrared data. Each spectral band is processed separately through specialized algorithms before being integrated into the final risk assessment, reducing overall complexity by breaking down the integrated processing task
Solution Approach 2:
The system introduces intermediary processing layers that standardize and normalize spectral data from different satellite and aerial sources before integration. These intermediary steps include calibration, atmospheric correction, and feature extraction, which simplify the integration process and reduce complexity while maintaining high risk assessment accuracy
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
This approach enables precise detection of well pads and environmental hazards, providing timely warnings and compliance with regulations by reducing errors and utilizing current data for training machine learning models.
Implementation Method 1
spectral data describing a gas emission from the first location
Data Source
AI summary
Aspects of the invention include includes detecting, using a first machine learning model, a first well pad at a first location based at least in part on a first set of data comprising spectral data describing a gas emission from the first location. Detecting an environmental event within a threshold distance of the well pad. Determining a probability of damage to the first well pad from the environmental event.