纳米多孔介质中的流体流动

Fluid flow at nanoscale is closely related to many areas in nature and technology(e.g.,unconventional hydrocarbon recovery,carbon dioxide geo-storage,underground hydrocarbon storage,fuel cells,ocean desalination,and biomedicine).At nanoscale,interfacial forces dominate over bulk forces,and nonlinear effects are important,which significantly deviate from conventional theory.During the past decades,a series of experiments,theories,and simulations have been performed to investigate fluid flow at nanoscale,which has advanced our fundamental knowledge of this topic.However,a critical review is still lacking,which has seriously limited the basic understanding of this area.Therefore herein,we systematically review experimental,theoretical,and simulation works on single-and multi-phases fluid flow at nanoscale.We also clearly point out the current research gaps and future outlook.These insights will promote the significant development of nonlinear flow physics at nanoscale and will provide crucial guidance on the relevant areas.

Analysis on nonlinear effect of unsteady percolation in the inhomogeneous shale gas reservoir

The nonlinear effects of unsteady multi-scale shale gas percolation,such as desorption,slippage,diffusion,pressure-dependent viscosity,and compressibility,are investigated by numerical simulation.A new general mathematical model of the problem is built,in which the Gaussian distribution is used to describe the inhomogeneous intrinsic permeability.Based on the Boltzmann transformation,an efficient semi-analytical method is proposed.The problem is then converted into a nonlinear equation in an integral form for the pressure field,and a related explicit iteration scheme is constructed by numerical discretization.The validation examples show that the proposed method has good convergence,and the simulation results also agree well with the results obtained from both numerical and actual data of two vertical fractured test wells in the literature.Desorption,slippage,and diffusion have significant influence on shale gas flows.The accuracy of the usual technique that the product of viscosity and compressibility is approximated as its value at the average formation pressure is examined.

Investigation on nonlinear multi-scale effects of unsteady flow in hydraulic fractured horizontal shale gas wells

A unified mathematical model is established to simulate the nonlinear unsteady percolation of shale gas with the consideration of the nonlinear multi-scale effects such as slippage, diffusion, and desorption. The continuous inhomogeneous models of equivalent porosity and permeability are proposed for the whole shale gas reservoir includ- ing the hydraulic fracture, the micro-fracture, and the matrix regions. The corresponding semi-analytical method is developed by transforming the nonlinear partial differential governing equation into the integral equation and the numerical discretization. The nonlinear multi-scale effects of slippage and diffusion and the pressure dependent effect of desorption on the shale gas production are investigated.

Identification of carbonate sedimentary facies from well logs with machine learning Author links open overlay panel

Sedimentary facies identification is critical for carbonate oil and gas reservoir development.The traditional method of sedimentary facies identification not only be affected by the engineer's experience but also takes a long time.Identifying carbonate sedimentary facies based on machine learning is the trend of future development and has the advantages of short time consuming and reliable results without engineers'subjective influence.Although many references reported the application of machine learning to identify lithofacies,but identifying sedimentary facies of carbonate reservoirs is much more challenging due to the complex sedimentary environment and tectonic movement.This paper compares the performance of the carbonate sedimentary facies identification using four different machine learning models,and the optimal machine learning with the highest prediction accuracy is recommended.First,the carbonate sedimentary facies are classified into the lagoon,shallow sea,shoal,fore-shoal,and inter-shoal five tags based on the well loggings.Then,five well log curves including spectral gamma ray(SGR),uranium-free gamma ray(CGR),photoelectric absorption cross-section index(PE),true formation resistivity(RT),shallow lateral resistivity(RS)are used as the input,and the manual identified carbonate sedimentary facies are used as the output of the machine learning model.The performance of four different machine learning algorithms,including support vector machine(SVM),deep neural network(DNN),long short-term memory(LSTM)network,and random forest(RF)are compared.The other two wells are used for model validation.The research results show that the RF method has the highest accuracy of sedimentary facies prediction,and the average prediction accuracy is 78.81%;the average accuracy of sedimentary facies prediction using SVM is 77.93%.The sedimentary facies predictions using DNN and LSTM are less satisfying compared with RF and SVM,and the average accuracy is 69.94%and 73.05%,respectively.The predicted carbonate sedimentary facies by LSTM are more continuous compared with other machine learning models.This study is helpful for identifying compelx sedimentary facies of carbonate reservoirs from well logs.

Quantitative evaluation of organic richness from correlation of well logs and geochemical data: a case study of the Lower Permian Taiyuan shales in the southern North China Basin

Marine-continental transitional shale is a potential energy component in China and is expected to be a realistic field in terms of increasing reserves and enhancing the natural gas production.However,the complex lithology,constantly changing depositional environment and lithofacies make the quantitative determination of the total organic carbon(TOC)suitable for marine shales not necessarily applicable to transitional shales.Thus,the identification of marine-continental transitional organic-rich shales and the mechanism of organic matter enrichment need to be further studied.As a typical representative of transitional shale,samples from Well MY-1 in the Taiyuan Formation in the southern North China Basin,were selected for TOC prediction using a combination of experimental organic geochemical data and well logging data including natural gamma-ray(GR),density(DEN),acoustic(AC),neutron(CNL)and U spectral gamma-ray(U),and TH spectral gamma-ray(TH).The correlation coefficient,coefficient of determination,standard deviation,mean squared error(MSE)and root mean squared error(RMSE)were selected to conduct the error analysis of the evaluation of different well log-based prediction methods,involving U spectral gamma logging,ΔlogR,and multivariate fitting methods to obtain the optimal TOC prediction method for the Taiyuan transitional shale.The plots of TOC versus the remaining volatile hydrocarbon content and the generation potential from Rock Eval show good to excellent potentials for hydrocarbon generation.The integrated results obtained from the various log-based TOC estimation methods indicate that,the multivariate fitting method of GR-U-DEN-CNL combination is preferable,with the correlation coefficients of 0.78 and 0.97 for the entire and objective interval of the Taiyuan Formation respectively,and with the minimum MSE and RMSE values.Specifically,the U spectral gamma logging method based on single logging parameter is also a better choice for TOC prediction of the high-quality intervals.This study provides a reference for the exploration and development of unconventional shale gas such as transitional shale gas.