Shale Pore Structure Characterization Using Least Squares Fitting
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Solution Overview
Problem
Current methods for characterizing pore structures in shale reservoirs are inaccurate due to the deletion of overlapping data ranges in CO2, N2, and high-pressure mercury adsorption experiments, which affects the joint characterization results.
Innovation Solution
A data processing method using the least squares method to fit and process data in overlapping pore size ranges, specifically calculating average pore volumes and fitting them with exponential functions to obtain final pore volumes for more accurate characterization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If overlapping data ranges are simply deleted in joint characterization, then the processing is simplified, but the accuracy of joint characterization results deteriorates
Solution Approach 1:
The patent applies parameter changes by transforming the data processing approach from simple deletion to mathematical fitting. Specifically, it uses the least squares method to fit exponential functions to the overlapping data ranges, thereby changing the parameter representation from raw data to fitted curve parameters. This resolves the contradiction by maintaining processing simplicity while significantly improving characterization accuracy through mathematical modeling.
Solution Approach 2:
The patent replaces the mechanical/data-processing operation of simply deleting overlapping ranges with a mathematical substitution approach. Instead of removing data, it substitutes the fitting process where mathematical functions (exponential curves) are used to model and represent the overlapping data ranges, thereby improving accuracy without complicating the overall processing workflow.
2Ease of manufacture
If only non-overlapping data is used for characterization, then the data processing is straightforward, but the full potential of experimental data is not utilized
Solution Approach 1:
The patent merges the overlapping data ranges from different experiments (CO2, N2, and high-pressure mercury adsorption) into a unified characterization framework. By combining all available data and using mathematical fitting to integrate them, the method fully utilizes the experimental data potential while maintaining processing straightforwardness through a systematic approach.
Solution Approach 2:
The patent creates a universal data processing framework that handles multiple data types and overlapping ranges through a single mathematical model. The exponential function fitting serves multiple purposes: it processes overlapping data, characterizes different pore size ranges, and integrates results from different experiments, thereby maximizing data utilization without complicating the process.
Data Source
AI summary
A data processing method includes: collecting test data of a target rock sample in different gas adsorption experiments; the test data including pore sizes and pore volumes corresponding to the pore sizes and including at least two selected from the group consisting of the test data with pore sizes less than 3 nm in CO2 adsorption experiment, the test data with pore sizes in 1.5 nm to 250 nm in N2 adsorption experiment and the test data with pore sizes in 10 nm to 1000 μm in high-pressure mercury adsorption experiment; and fitting the test data in overlapping ranges of the pore sizes using a least square method, and obtaining target pore volumes corresponding to the pore sizes respectively. The accuracy of joint characterization of shale pore structures can be improved by using mathematical methods to process the data in overlapping ranges of pore sizes among different characterization methods.