Machine Learning Model for Geological Prediction
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Solution Overview
Problem
Current machine-learning techniques for wellbore operations and natural resource exploration often fail to accurately predict geological phenomena due to limitations in integrating historical geological data, leading to inaccurate location identification and topographical predictions.
Innovation Solution
The integration of historical geological data into machine-learning models using relative-time pre-processing techniques, which involve transforming data to reflect the temporal relationships between geological events and phenomena, allowing for more accurate predictions of natural resource locations and topographical changes through time.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If historical geological data is integrated into machine-learning models, then prediction accuracy of geological phenomena is improved, but data processing complexity increases
Solution Approach 1:
The patent applies preliminary action by pre-processing historical geological data before training the machine-learning model. This includes transforming the data into appropriate formats and structures in advance, which simplifies the subsequent model training process while maintaining high prediction accuracy. The pre-processing step handles the complexity upfront rather than during inference.
Solution Approach 2:
The patent uses an intermediary approach by introducing a pre-processing layer between the raw historical geological data and the machine-learning model. This intermediary layer transforms and standardizes the data, making it more suitable for model input while reducing the direct complexity burden on the model itself.
2Loss of information
If relative-time pre-processing techniques are used, then temporal relationship analysis is improved, but computational requirements increase
Solution Approach 1:
The patent extracts temporal relationships from historical geological data as a separate feature dimension. By isolating and specifically processing the temporal aspect of the data using relative-time pre-processing, the system can focus computational resources on this critical dimension without unnecessarily processing all data aspects with equal intensity.
Solution Approach 2:
The patent applies parameter changes by transforming temporal parameters into relative-time representations. This transformation restructures the time-related data into a format that preserves temporal relationships while being more computationally efficient for the machine-learning model to process, balancing information retention with computational cost.
Data Source
AI summary
A system can be used to incorporate historical geological data into machine learning techniques. The system can receive historical geological data. The system can pre-process the historical geological data by applying a selected, relative-time pre-processing technique to the historical geological data with respect to time-attributed geological phenomena. The system can train a machine-learning model using the pre-processed historical geological data. The system can apply the trained machine-learning model to generate predictions of geological phenomena. The system can provide a user interface to provide a visualization of the predictions of geological phenomena.