CO2-Crude Oil MMP Prediction With Reservoir Well Spacing
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Solution Overview
Problem
Existing empirical formula methods for predicting the minimum miscible pressure (MMP) of the CO2-crude oil system do not consider the impact of reservoir well spacing, leading to challenges in selecting appropriate pressures for CO2 injection development in actual reservoir conditions, and existing machine learning methods lack accuracy due to data requirements and model subjectivity.
Innovation Solution
A method that considers reservoir well spacing by conducting indoor slim tube experiments of varying lengths, analyzing crude oil components, and constructing a minimum miscible pressure prediction model using multi-stage swelling experiments and data fitting to account for well spacing effects.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If existing empirical formula methods are used to predict MMP, then the prediction process is simple and fast, but the prediction accuracy is insufficient because well spacing impact is not considered
Solution Approach 1:
The patent introduces well spacing as a new parameter into the MMP prediction model. By conducting slim tube experiments with different tube lengths (0.5m, 1.0m, 1.5m, 2.0m) to simulate different well spacing conditions, the method establishes a relationship between well spacing and MMP. This allows the prediction model to adapt to different reservoir conditions while maintaining the simplicity of empirical formula approaches.
2Measurement precision
If slim tube experiment method is used to predict MMP, then the prediction accuracy is high, but the operation cycle is long and resource consumption is significant
Solution Approach 1:
The patent segments the comprehensive MMP prediction into two parts: (1) base MMP values obtained from conventional slim tube experiments, and (2) correction values obtained from a limited number of additional experiments with different tube lengths. This segmentation allows the method to maintain high accuracy while reducing the total number of experiments needed compared to traditional comprehensive testing approaches.
Solution Approach 2:
The patent performs preliminary slim tube experiments to establish the baseline MMP and its relationship with well spacing before actual field application. By pre-establishing the correction relationship between well spacing and MMP through laboratory experiments, the method enables fast prediction for specific reservoir conditions without requiring extensive new experiments for each case.
3Productivity
If machine learning method is used to predict MMP, then the prediction speed is fast, but the accuracy is low due to data requirements and model subjectivity
Solution Approach 1:
The patent introduces well spacing as an intermediary parameter that connects the physical reservoir conditions with the MMP prediction. By establishing a clear physical relationship between well spacing and MMP through controlled experiments, the method provides a deterministic correction mechanism that enhances prediction accuracy without relying on complex machine learning models with subjective parameter selections.
Data Source
AI summary
A minimum miscible pressure prediction method for a CO2-crude oil system considering reservoir well spacing is provided, including: obtaining characteristic parameters of each component of crude oil; based on the characteristic parameters of each component of the crude oil, obtaining corresponding bubble point or dew point pressures, as well as corresponding oil-gas interfacial tension, oil phase and gas phase densities, and viscosities under different injected gas contents; based on the corresponding bubble point or dew point pressures, as well as the corresponding oil-gas interfacial tension, oil phase and gas phase densities, and viscosities under different injected gas contents, constructing a minimum miscible pressure prediction model for the CO2-crude oil system considering the reservoir well spacing; and based on the minimum miscible pressure prediction model for the CO2-crude oil system considering the reservoir well spacing, obtaining minimum miscible pressure values for the CO2-crude oil system under different reservoir well spacing.