Machine Learning Downhole Formation Testing Data Smoothing
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Solution Overview
Problem
Current methods for downhole formation testing face challenges in accurately processing and interpreting time series data from boreholes, leading to inefficiencies in fluid and formation characteristic analysis, which hinders optimal reservoir management and production operations.
Innovation Solution
A method and system utilizing machine learning models to process downhole formation testing time series data, generating smoothed data, resampling it, and outputting fluid and formation characteristics over time, enabling improved data interpretation and decision-making.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional data processing methods are used for downhole formation testing time series data, then the processing approach is simple and straightforward, but the accuracy and reliability of fluid and formation characteristic analysis deteriorates
Solution Approach 1:
The patent replaces traditional mechanical/data processing methods with a machine learning model (neural network) to process downhole formation testing time series data. The machine learning model automatically learns patterns and relationships in the data, providing more accurate fluid and formation characteristic analysis without requiring manual programming of processing algorithms.
Solution Approach 2:
The patent introduces a machine learning model as an intermediary between raw time series data and final analysis results. This intermediary layer processes the complex non-linear relationships in the data, transforming raw measurements into accurate fluid and formation characteristics through learned patterns rather than direct traditional processing.
2Productivity
If manual or traditional automated methods are used for processing downhole formation testing data, then the system is easier to implement, but the efficiency and speed of data interpretation deteriorates
Solution Approach 1:
The patent replaces manual or traditional automated data interpretation methods with a machine learning model that automatically processes time series data. This substitution enables rapid, real-time analysis of downhole formation testing data, significantly improving interpretation efficiency and productivity while handling complex non-linear relationships that traditional methods struggle with.
3Reliability
If traditional processing methods are applied to time series data, then the implementation is straightforward, but the reliability and objectivity of data insights deteriorates
Solution Approach 1:
The patent replaces traditional processing methods with a machine learning model that provides more reliable and objective data insights. The model learns from training data and consistently applies learned patterns to new data, reducing subjectivity and improving reliability of fluid and formation characteristic analysis compared to manual or rule-based traditional methods.
Data Source
AI summary
A method can include receiving downhole formation testing time series data acquired at a location along a borehole in a subsurface region during a formation testing operation performed by a downhole tool; processing the downhole formation testing time series data using a machine learning model to generate smoothed time series data; resampling the smoothed time series data; generating fluid and formation characteristics with respect to time based on the smoothed time series data; and outputting the fluid and formation characteristics with respect to time.