| Shale oil reservoirs are characterized by complex pore structures,strong heterogeneity,and diverse fluid occurrence states.Conventional logging methods are difficult to accurately evaluate reservoir parameters.Nuclear magnetic resonance（NMR）has irreplaceable advantages in complex reservoir evaluation by directly detecting fluid signals in reservoirs to obtain information such as reservoir porosity,permeability,and saturation.However,at present,most nuclear magnetic resonance data processing methods are mainly proposed for sandstone,tight sandstone,carbonate rock,and other reservoirs.These methods are less applicable or even difficult to apply in shale oil reservoirs with more complex reservoir conditions.Therefore,this article attempts to design a nuclear magnetic resonance data processing method suitable for shale oil reservoirs with complex reservoir characteristics,which can identify fluid components and conduct quantitative evaluation based on the characteristics of shale oil reservoirs and the existing nuclear magnetic resonance technology.In terms of one-dimensional nuclear magnetic resonance,this paper proposes an improved T2 spectral component decomposition method to solve the problem of poor applicability of conventional T2 spectral component decomposition methods in shale oil reservoirs.This method uses continuous wavelet transform for peak searching,and based on the actual distribution pattern of the nuclear magnetic resonance T2 spectrum of shale oil reservoirs,selects an asymmetric Gaussian function that can be adjusted according to the shape of the T2 spectrum to be fitted to split the original T2 spectrum.Finally,the corresponding fluid type is accurately identified based on the T2 relaxation time of each component spectrum obtained from the split.In numerical simulation experiments and nuclear magnetic resonance experimental analysis,this method can accurately identify fluids in different occurrence states in pores,such as clay bound water,capillary bound water,adsorbed oil,and free oil,and obtain the saturation of each fluid component.In the field of two-dimensional nuclear magnetic resonance,in order to more accurately and efficiently utilize two-dimensional nuclear magnetic resonance to quantitatively analyze reservoir fluids,this paper proposes a two-dimensional spectral component decomposition method based on morphological gray level reconstruction and Gaussian fitting.This method uses morphological gray level reconstruction algorithm to determine the distribution centers of different fluid signals in the two-dimensional spectrum,and on this basis,uses Gaussian fitting to obtain the two-dimensional component spectra of different fluid components,and then obtains fluid saturation based on the proportion of each two-dimensional component spectrum signal.Through numerical simulation experiments,the fluid component decomposition effects of K-means clustering,GMM clustering,and this method are compared and analyzed.The results show that this method has higher decomposition accuracy and better fluid partition effect.The processing results of T2-T1 two-dimensional spectrum in shale saturated with water and oil further indicate that this method can effectively identify various fluid components in shale pores and accurately calculate the saturation of each fluid component.Based on the one dimensional and two dimensional NMR data processing methods proposed in this paper,the NMR T2 spectral component decomposition software and the NMR two-dimensional spectral component decomposition software have been written and implemented respectively.The example application shows that the NMR T2 spectral component decomposition software has good application effects in actual shale oil core experiments and logging data,and can accurately identify oil layers;The 2D NMR spectral component decomposition software can accurately identify different fluid types and calculate fluid saturation in 2D NMR experimental analysis,providing a new technical means for NMR experimental analysis. |
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