Lithium-Potassium Brine Reservoir Identification via Parameter Sensitivity
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Solution Overview
Problem
Current methods for identifying and predicting high-quality lithium-potassium-rich brine reservoirs in deep underground marine strata face challenges due to complex geological conditions and insufficient attention to water-bearing and brine-storage properties, leading to low identification accuracy and high prediction difficulty.
Innovation Solution
A method based on parameter sensitivity analysis, involving rock physics modeling, logging data analysis, and waveform phase-controlled inversion to establish identification markers and determine the distribution range of lithium-potassium-rich brine reservoirs, utilizing seismic and hydrochemical data to correlate seismic parameters with reservoir properties.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional identification methods are used for deep lithium-potassium-rich brine reservoirs, then the exploration process can be completed, but the identification accuracy remains low due to complex geological conditions and thin reservoir characteristics
Solution Approach 1:
The identification process is segmented into multiple independent analysis modules: water-bearing characteristic analysis, brine-storage property analysis, and sensitive parameter identification. Each module processes specific aspects of the reservoir characteristics separately, then integrates results to achieve high-accuracy identification despite complex geological conditions
Solution Approach 2:
The method transforms the identification approach by changing from conventional single-parameter analysis to multi-parameter sensitivity analysis. It identifies sensitive parameters (such as specific geophysical parameters) that show significant changes in response to brine reservoir characteristics, enabling accurate detection even when reservoir thickness is small and geological conditions are complex
2Measurement precision
If conventional identification methods focusing only on water-bearing characteristics are used, then the identification process is simple, but the inversion accuracy for thin reservoirs remains low and brine grade is unsatisfied
Solution Approach 1:
The method merges water-bearing characteristic analysis with brine-storage property analysis into a unified identification framework. By combining these two previously separate analysis approaches, the method achieves both high inversion accuracy for thin reservoirs and satisfactory brine grade evaluation, while managing complexity through systematic integration
Solution Approach 2:
The method adds a new dimension to conventional identification by incorporating brine-storage property analysis alongside traditional water-bearing characteristic analysis. This dimensional expansion enables simultaneous optimization of inversion accuracy and brine grade evaluation, transforming the identification process from two-dimensional to three-dimensional analysis
Data Source
AI summary
A method for identifying high-quality lithium-potassium-rich brine reservoirs based on parameter sensitivity analysis is provided. Basic characteristics of a brine reservoir area are determined. The sensitive parameter analysis of the brine reservoir area is performed by rock physics modeling and cross-plotting of logging curves to determine a rock physics parameter range and a logging parameter range of the brine reservoir area. The relationship between the wave impedance and the water saturation based on the rock physics model. A coordinate range of a water-rich reservoir is determined based on the inversion result of the water saturation. Within the coordinate range of the water-rich reservoir, a coordinate range of the lithium-potassium-rich brine reservoir is determined based on the natural gamma inversion result obtained by waveform phase-controlled inversion, so as to achieve geophysical identification and prediction of high-quality brine reservoirs in the marine strata.