Brine reservoir prediction and resource evaluation method for underground brine-type potash and lithium deposits
A technology of underground brine and evaluation method, which is applied in the directions of measurement, earthwork drilling, seismic signal processing, etc. It can solve the problems that the number of groundwater samples detected cannot meet the calculation of resource amount, there is no logging response characteristic, and various underground brine-type lithium mines cannot be solved. Parameter calculation and other issues
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example 1
[0056] Taking the first member of the Leikoupo Formation in the Moxi area of central Sichuan as an example, according to Lei Yi 1 The multi-well intersection analysis diagram of the sub-section shows that the gypsum rock has a high impedance value and is easy to distinguish from other lithologies. Dolomite and limestone are superimposed in terms of impedance and gamma, and it is difficult to distinguish them. The water content of the brine reservoir is relatively high, and the potassium-rich brine is rich in a large amount of electric ions. The formation shows low resistivity. The resistivity of the samples with porosity ≥ 2% is mostly less than 500Ω·m, and the resistivity of the brine reservoir is higher. Low, generally less than 200Ω·m. This shows that the brine reservoir has the petrophysical characteristics of low gamma, high porosity, low impedance and low resistivity, so the above characteristics can be used to identify the distribution of the brine reservoir.
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example 2
[0076] Taking the first member of the Leikoupo Formation in the Moxi area of central Sichuan as an example, Figure 5 Plots of positive correlation functions for potassium and lithium content are shown.
[0077] Firstly, according to the prediction method of the brine storage strata for underground brine-type potash and lithium ore, the plane distribution of the brine storage strata is predicted. The specific steps are as follows:
[0078] (1) Based on the logging data, the nonlinear relationship between wave impedance, natural gamma, porosity and resistivity is established.
[0079] (2) Based on the wave impedance of seismic inversion, the nonlinear mapping of wave impedance to natural gamma, porosity and resistivity is obtained by neural network, so as to obtain the wave impedance, natural gamma, porosity, and Physical inversion of resistivity.
[0080] (3) Using the three-dimensional multi-attribute fusion technology to describe the spatial distribution characteristics ...
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