Shale Oil Yield Prediction Model Using TOC and Ro
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Solution Overview
Problem
Current methods for predicting oil and gas yields in in-situ oil shale exploitation are inaccurate and inefficient, as they rely on single-factor models and simulation experiments that fail to accurately reflect formation conditions, leading to high errors and costs.
Innovation Solution
A method and apparatus that utilize thermal simulation data to establish models for predicting oil and gas yields based on total organic carbon (TOC), vitrinite reflectance (Ro), and hydrogen index (HI) values, allowing for the calculation of residual oil and gas generation amounts, retention amounts, and yields without the need for extensive simulation experiments.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If simulation experiments are performed to determine oil and gas yields, then measurement precision is improved, but loss of time and productivity deteriorate
Solution Approach 1:
The patent pre-establishes prediction models through simulation experiments before actual field application. The models are trained using laboratory simulation data that captures the relationship between kerogen properties (TOC, Ro, H/C ratio) and hydrocarbon generation characteristics. Once established, these models can rapidly predict oil and gas yields without requiring repeated time-consuming simulation experiments for each new shale formation.
Solution Approach 2:
The patent creates a virtual model that replicates the complex thermal simulation experiment process. Instead of physically performing simulation experiments on every new shale sample, the system uses the pre-established model to copy and replicate the prediction results that would otherwise require extensive laboratory experimentation, thereby dramatically reducing time while maintaining prediction accuracy.
2Measurement precision
If multiple simulation experiments are conducted to account for different kerogen types, then measurement precision is improved, but device complexity and loss of time worsen
Solution Approach 1:
The patent develops a universal prediction model that can handle multiple kerogen types (Type I, II, III) and different shale formations through a single integrated system. The model uses key parameters (TOC, Ro, H/C ratio) that are applicable across different kerogen types, eliminating the need for separate prediction models for each kerogen type while maintaining high prediction accuracy for all types.
Solution Approach 2:
The patent incorporates local quality by using the H/C ratio parameter to differentiate and account for variations in kerogen type within the unified model. Rather than creating separate models for different kerogen types, the model adjusts its predictions based on the local H/C ratio characteristics of the specific shale formation being evaluated, allowing accurate predictions tailored to each local condition within a single framework.
3Ease of operation
If traditional prediction methods using H/C ratio are used, then ease of operation is maintained, but measurement precision deteriorates due to contamination and measurement errors
Solution Approach 1:
The patent introduces vitrinite reflectance (Ro) as an intermediary parameter that mediates between the H/C ratio measurement and the final hydrocarbon yield prediction. The Ro parameter serves as a more reliable indicator of thermal maturity that is less susceptible to contamination errors. By using Ro alongside H/C ratio in the prediction model, the system compensates for measurement errors in H/C ratio while maintaining the relative simplicity of the approach.
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 accurate and efficient prediction of oil and gas yields, improving the accuracy and efficiency of in-situ oil shale exploitation by using pre-established models based on thermal simulation data, reducing the necessity for repeated simulation experiments and accounting for different kerogen types.
Implementation Method 1
the unconverted organic matter in the shale with low to medium maturity is converted into oil and gas by using an in-situ heating method
Implementation Method 2
a thermal simulation experiment on a plurality of different shale samples
Data Source
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AI summary
Provided is a method and apparatus for predicting oil and gas yields in in-situ oil shale exploitation, the method includes: acquiring an original TOC value, a Ro value and an original HI value of a shale to be measured; and obtaining oil and gas yields in in-situ exploitation of the shale based on the original TOC value, Ro value, original HI value thereof and pre-established models for predicting oil and gas yields in in-situ oil shale exploitation, the models are pre-established based on oil and gas yield data obtained by performing a thermal simulation experiment on a plurality of different shale samples, and the original TOC value, Ro value and original HI value thereof. The above technical solution achieves a quantitative prediction of oil and gas yields in in-situ oil shale exploitation, and improves the accuracy and efficiency of prediction of oil and gas yields in in-situ oil shale exploitation.