In recent years,the Yanchang shale-oil formations of the Ordos Basin are rich in reserves with complex lithology and structure characteristics,low porosity and low permeability,and weak anomalies for oil and water dis...In recent years,the Yanchang shale-oil formations of the Ordos Basin are rich in reserves with complex lithology and structure characteristics,low porosity and low permeability,and weak anomalies for oil and water discriminations,have been the key targets of unconventional oil/gas resource exploration and development in the relevant areas.The joint acoustic-electrical(AE)properties can be used to interpret reservoir lithology,mineralogy,pore structure,and fluid saturation.To conduct tests of thin section analysis,X-ray diff raction,and ultrasonic and electrical experiments at diff erent pressures and saturation degrees,cores from the shale-oil formations in the Q area of the basin are collected.The variations in AE properties with respect to clay content,porosity,pressure(microfracture),and saturation are analyzed.The experimental results indicate that the rock physics behaviors of sandstones with diff erent clay contents vary significantly.The AE properties of clean sandstones are basically dependent on the microfractures(pressure),while for muddy sandstones,the clay content is an important factor affecting the responses.The target reservoir consists of interbedded sandstone and shale layers.The AE equivalent medium equations and the Gurevich theory are applied to establish the joint models for the diff erent lithologies and simulate the variations in AE properties with respect to fluid type,pore structure,and mineral components.The three-dimensional joint templates of clean and muddy sandstones,as well as shale,are developed based on the elastic and electrical attributes and then calibrated using the experimental and well-log data.The reservoir properties are estimated with the templates and validated by the log data.The results indicate that the joint templates based on lithology characteristics can eff ectively characterize the properties of interbedded sandstone and shale layers.Furthermore,the combined application of AE data provides more beneficial information for the assessment of rock properties,leading to precise estimates that conform with the actual formation conditions.展开更多
We propose an integrated method of data-driven and mechanism models for well logging formation evaluation,explicitly focusing on predicting reservoir parameters,such as porosity and water saturation.Accurately interpr...We propose an integrated method of data-driven and mechanism models for well logging formation evaluation,explicitly focusing on predicting reservoir parameters,such as porosity and water saturation.Accurately interpreting these parameters is crucial for effectively exploring and developing oil and gas.However,with the increasing complexity of geological conditions in this industry,there is a growing demand for improved accuracy in reservoir parameter prediction,leading to higher costs associated with manual interpretation.The conventional logging interpretation methods rely on empirical relationships between logging data and reservoir parameters,which suffer from low interpretation efficiency,intense subjectivity,and suitability for ideal conditions.The application of artificial intelligence in the interpretation of logging data provides a new solution to the problems existing in traditional methods.It is expected to improve the accuracy and efficiency of the interpretation.If large and high-quality datasets exist,data-driven models can reveal relationships of arbitrary complexity.Nevertheless,constructing sufficiently large logging datasets with reliable labels remains challenging,making it difficult to apply data-driven models effectively in logging data interpretation.Furthermore,data-driven models often act as“black boxes”without explaining their predictions or ensuring compliance with primary physical constraints.This paper proposes a machine learning method with strong physical constraints by integrating mechanism and data-driven models.Prior knowledge of logging data interpretation is embedded into machine learning regarding network structure,loss function,and optimization algorithm.We employ the Physically Informed Auto-Encoder(PIAE)to predict porosity and water saturation,which can be trained without labeled reservoir parameters using self-supervised learning techniques.This approach effectively achieves automated interpretation and facilitates generalization across diverse datasets.展开更多
Root zone soil moisture(RZSM)plays a critical role in land-atmosphere hydrological cycles and serves as the primary water source for vegetation growth.However,the correlations between RZSM and its associated variables...Root zone soil moisture(RZSM)plays a critical role in land-atmosphere hydrological cycles and serves as the primary water source for vegetation growth.However,the correlations between RZSM and its associated variables,including surface soil moisture(SSM),often exhibit nonlinearities that are challenging to identify and quantify using conventional statistical techniques.Therefore,this study presents a hybrid convolutional neural network(CNN)-long short-term memory neural network(LSTM)-attention(CLA)model for predicting RZSM.Owing to the scarcity of soil moisture(SM)observation data,the physical model Hydrus-1D was employed to simulate a comprehensive dataset of spatial-temporal SM.Meteorological data and moderate resolution imaging spectroradiometer vegetation characterization parameters were used as predictor variables for the training and validation of the CLA model.The results of the CLA model for SM prediction in the root zone were significantly enhanced compared with those of the traditional LSTM and CNN-LSTM models.This was particularly notable at the depth of 80–100 cm,where the fitness(R^(2))reached nearly 0.9298.Moreover,the root mean square error of the CLA model was reduced by 49%and 57%compared with those of the LSTM and CNN-LSTM models,respectively.This study demonstrates that the integration of physical modeling and deep learning methods provides a more comprehensive and accurate understanding of spatial-temporal SM variations in the root zone.展开更多
The excavation of deep tunnels crossing faults is highly prone to triggering rockburst disasters,which has become a significant engineering issue.In this study,taking the fault-slip rockbursts from a deep tunnel in so...The excavation of deep tunnels crossing faults is highly prone to triggering rockburst disasters,which has become a significant engineering issue.In this study,taking the fault-slip rockbursts from a deep tunnel in southwestern China as the engineering prototype,large-scale three-dimensional(3D)physical model tests were conducted on a 3D-printed complex geological model containing two faults.Based on the selfdeveloped 3D loading system and excavation device,the macroscopic failure of fault-slip rockbursts was simulated indoors.The stress,strain,and fracturing characteristics of the surrounding rock near the two faults were systematically evaluated during excavation and multistage loading.The test results effectively revealed the evolution and triggering mechanism of fault-slip rockbursts.After the excavation of a highstress tunnel,stress readjustment occurred.Owing to the presence of these two faults,stress continued to accumulate in the rock mass between them,leading to the accumulation of fractures.When the shear stress on a fault surface exceeded its shear strength,sudden fault slip and dislocation occurred,thus triggering rockbursts.Rockbursts occurred twice in the vault between the two faults,showing obvious intermittent characteristics.The rockburst pit was controlled by two faults.When the faults remained stable,tensile failure predominated in the surrounding rock.However,when the fault slip was triggered,shear failure in the surrounding rock increased.These findings provide valuable insights for enhancing the comprehension of fault-slip rockbursts.展开更多
Deep learning(DL)is making significant inroads into biomedical imaging as it provides novel and powerful ways of accurately and efficiently improving the image quality of photoacoustic microscopy(PAM).Off-the-shelf DL...Deep learning(DL)is making significant inroads into biomedical imaging as it provides novel and powerful ways of accurately and efficiently improving the image quality of photoacoustic microscopy(PAM).Off-the-shelf DL models,however,do not necessarily obey the fundamental governing laws of PAM physical systems,nor do they generalize well to scenarios on which they have not been trained.In this work,a physics-embedded degeneration learning(PEDL)approach is proposed to enhance the image quality of PAM with a self-attention enhanced U-Net network,which obtains greater physical consistency,improves data efficiency,and higher adaptability.The proposed method is demonstrated on both synthetic and real datasets,including animal experiments in vivo(blood vessels of mouse's ear and brain).And the results show that compared with previous DL methods,the PEDL algorithm exhibits good performance in recovering PAM images qualitatively and quantitatively.It overcomes the challenges related to training data,accuracy,and robustness which a typical data-driven approach encounters,whose exemplary application envisions to provide a new perspective for existing DL tools of enhanced PAM.展开更多
Brittleness analysis becomes important when looking for sweet spots in tightoil sandstone reservoirs. Hence, appropriate indices are required as accurate brittleness evaluation criteria. We construct a seismic rock ph...Brittleness analysis becomes important when looking for sweet spots in tightoil sandstone reservoirs. Hence, appropriate indices are required as accurate brittleness evaluation criteria. We construct a seismic rock physics model for tight-oil sandstone reservoirs with vertical fractures. Because of the complexities in lithology and pore structure and the anisotropic characteristics of tight-oil sandstone reservoirs, the proposed model is based on the solid components, pore connectivity, pore type, and fractures to better describe the sandstone reservoir microstructure. Using the model, we analyze the brittleness sensitivity of the elastic parameters in an anisotropic medium and establish a new brittleness index. We show the applicability of the proposed brittleness index for tight-oil sandstone reservoirs by considering the brittleness sensitivity, the rock physics response characteristics, and cross-plots. Compared with conventional brittleness indexes, the new brittleness index has high brittleness sensitivity and it is the highest in oil-bearing brittle zones with relatively high porosity. The results also suggest that the new brittleness index is much more sensitive to elastic properties variations, and thus can presumably better predict the brittleness characteristics of sweet spots in tight-oil sandstone reservoirs.展开更多
Due to the huge differences between the unconventional shale and conventional sand reservoirs in many aspects such as the types and the characteristics of minerals,matrix pores and fluids,the construction of shale roc...Due to the huge differences between the unconventional shale and conventional sand reservoirs in many aspects such as the types and the characteristics of minerals,matrix pores and fluids,the construction of shale rock physics model is significant for the exploration and development of shale reservoirs.To make a better characterization of shale gas-bearing reservoirs,we first propose a new but more suitable rock physics model to characterize the reservoirs.We then use a well A to demonstrate the feasibility and reliability of the proposed rock physics model of shale gas-bearing reservoirs.Moreover,we propose a new brittleness indicator for the high-porosity and organic-rich shale gas-bearing reservoirs.Based on the parameter analysis using the constructed rock physics model,we finally compare the new brittleness indicator with the commonly used Young’s modulus in the content of quartz and organic matter,the matrix porosity,and the types of filled fluids.We also propose a new shale brittleness index by integrating the proposed new brittleness indicator and the Poisson’s ratio.Tests on real data sets demonstrate that the new brittleness indicator and index are more sensitive than the commonly used Young’s modulus and brittleness index for the high-porosity and high-brittleness shale gas-bearing reservoirs.展开更多
Planetary gear set is the critical component in helicopter transmission train, and an important problem in condition monitoring and health management of planetary gear set is quantitative damage detection. In order to...Planetary gear set is the critical component in helicopter transmission train, and an important problem in condition monitoring and health management of planetary gear set is quantitative damage detection. In order to resolve this problem, an approach based on physical models is presented to detect damage quantitatively in planetary gear set. A particular emphasis is put on a feature generation and selection method, which is used for sun gear tooth breakage damage detection quantitatively in planetary gear box of helicopter transmission system. In this feature generation procedure, the pure torsional dynamical models of 2K-H planetary gear set is established for healthy case and sun gear tooth-breakage case. Then, a feature based on the spectrum of simulation signals of the dynamical models is generated. Aiming at selecting the best feature suitable for quantitative damage detection, a two-sample Z-test procedure is used to analyze the performance of features on damage evolution tracing. A feature named SR, which had better performance in tracking damage, is proposed to detect damage in planetary gear set. Meanwhile, the sun gear tooth-chipped seeded experiments with different severity are designed to validate the method above, and then the test vibration signal is picked up and used for damage detection. With the results of several experiments for quantitative damage detection, the feasibility and the effect of this approach are verified. The proposed method can supply an effective tool for degradation state identification in condition monitoring and health management of helicopter transmission system.展开更多
In most industrial fluidization units, two- or three-stage cyclone systems are used to clean the product gases. To return the solids to the bed, these cyclones are fitted with diplegs. By pass of gas from the bed thro...In most industrial fluidization units, two- or three-stage cyclone systems are used to clean the product gases. To return the solids to the bed, these cyclones are fitted with diplegs. By pass of gas from the bed through the dipleg is partially overcome by the back pressure build-up in the dipleg and by adding a trickle valve at the bottom of the dipleg. Diplegs of primary cyclones, operating at a high solid loading behave differently from diplegs of secondary and tertiary cyclones which operate at low solid loading. Both types have been investigated by pressure drop measurements, visual observation and by measurements of the air flow rate flowing up the riser. The primary dipleg was also studied using electrical capacitance tomography. The results are reported hereafter and will give a first indication towards the right design of the dipleg and the selection of the trickle valve. The influence of gas flow in the dipleg on the conversion in a catalytic fluidized bed reactor is found to be negligible.展开更多
The construction of a shale rock physics model and the selection of an appropriate brittleness index （B/） are two significant steps that can influence the accuracy of brittleness prediction. On one hand, the existin...The construction of a shale rock physics model and the selection of an appropriate brittleness index （B/） are two significant steps that can influence the accuracy of brittleness prediction. On one hand, the existing models of kerogen-rich shale are controversial, so a reasonable rock physics model needs to be built. On the other hand, several types of equations already exist for predicting the BI whose feasibility needs to be carefully considered. This study constructed a kerogen-rich rock physics model by performing the self- consistent approximation and the differential effective medium theory to model intercoupled clay and kerogen mixtures. The feasibility of our model was confirmed by comparison with classical models, showing better accuracy. Templates were constructed based on our model to link physical properties and the BL Different equations for the BI had different sensitivities, making them suitable for different types of formations. Equations based on Young＇s Modulus were sensitive to variations in lithology, while those using Lame＇s Coefficients were sensitive to porosity and pore fluids. Physical information must be considered to improve brittleness prediction.展开更多
In the manufacturing of thin wall components for aerospace industry,apart from the side wall contour error,the Remaining Bottom Thickness Error(RBTE)for the thin-wall pocket component(e.g.rocket shell)is of the same i...In the manufacturing of thin wall components for aerospace industry,apart from the side wall contour error,the Remaining Bottom Thickness Error(RBTE)for the thin-wall pocket component(e.g.rocket shell)is of the same importance but overlooked in current research.If the RBTE reduces by 30%,the weight reduction of the entire component will reach up to tens of kilograms while improving the dynamic balance performance of the large component.Current RBTE control requires the off-process measurement of limited discrete points on the component bottom to provide the reference value for compensation.This leads to incompleteness in the remaining bottom thickness control and redundant measurement in manufacturing.In this paper,the framework of data-driven physics based model is proposed and developed for the real-time prediction of critical quality for large components,which enables accurate prediction and compensation of RBTE value for the thin wall components.The physics based model considers the primary root cause,in terms of tool deflection and clamping stiffness induced Axial Material Removal Thickness(AMRT)variation,for the RBTE formation.And to incorporate the dynamic and inherent coupling of the complicated manufacturing system,the multi-feature fusion and machine learning algorithm,i.e.kernel Principal Component Analysis(kPCA)and kernel Support Vector Regression(kSVR),are incorporated with the physics based model.Therefore,the proposed data-driven physics based model combines both process mechanism and the system disturbance to achieve better prediction accuracy.The final verification experiment is implemented to validate the effectiveness of the proposed method for dimensional accuracy prediction in pocket milling,and the prediction accuracy of AMRT achieves 0.014 mm and 0.019 mm for straight and corner milling,respectively.展开更多
An ultrasonic sensitivity-improved fiber-optic Fabry-Perot interferometer （FPI） is proposed and employed for ultra- sonic imaging of seismic physical models （SPMs）. The FPI comprises a flexible ultra-thin gold fil...An ultrasonic sensitivity-improved fiber-optic Fabry-Perot interferometer （FPI） is proposed and employed for ultra- sonic imaging of seismic physical models （SPMs）. The FPI comprises a flexible ultra-thin gold film and the end face of a graded-index multimode fiber （MMF）, both of which are enclosed in a ceramic tube. The MMF in a specified length can collimate the diverged light beam and compensate for the light loss inside the air cavity, leading to an increased spectral fringe visibility and thus a steeper spectral slope. By using the spectral sideband filtering technique, the collimated FP1 shows an improved ultrasonic response. Moreover, two-dimensional images of two SPMs are achieved in air by recon- structing the pulse-echo signals through using the time-of-flight approach. The proposed sensor with easy fabrication and compact size can be a good candidate for high-sensitivity and high-precision nondestructive testing of SPMs.展开更多
A simplified geomechanical model was proposed by considering three typical neckingtype slopes;this model lays a foundation for the further investigation of the deformation behaviors of such slopes.Three physical model...A simplified geomechanical model was proposed by considering three typical neckingtype slopes;this model lays a foundation for the further investigation of the deformation behaviors of such slopes.Three physical models of necking-type slopes were built according to the geomechanical model with slope evolution stages.Finally,preliminary calculations related to the arching effect in the physical model were conducted.Three evolution stages of necking-type slopes,namely,the initial stage,compression stage,and failure stage,were presented based on the formation and disappearance of the arching effect within the slope.The specific parameters of the geomechanical model were given.In the setup of the tilting test,the failure angle of the necking-type slope model was calculated to be approximately 50°with a large lateral resistance coefficient.The proposed geomechanical model and physical models of necking-type slopes provide guidance for the establishment of geomechanical and physical models of landslides at specific sites.展开更多
To improve the ensemble prediction system of the tropical regional atmosphere model for the South China Sea(TREPS) in predicting landfalling tropical cyclones(TCs), the impacts of three new implementing strategies for...To improve the ensemble prediction system of the tropical regional atmosphere model for the South China Sea(TREPS) in predicting landfalling tropical cyclones(TCs), the impacts of three new implementing strategies for surface and model physics perturbations in TREPS were evaluated for 19 TCs making landfall in China during 2014–16. For sea surface temperature(SST) perturbations, spatially uncorrelated random perturbations were replaced with spatially correlated ones. The multiplier f, which is used to form perturbed tendency in the Stochastically Perturbed Parameterization Tendency(SPPT) scheme, was inflated in regions with evident convective activity(f-inflated SPPT). Lastly, the Stochastically Perturbed Parameterization(SPP) scheme with 14 perturbed parameters selected from the planetary boundary layer, surface layer, microphysics, and cumulus convection parameterizations was added. Overall, all these methods improved forecasts more significantly for non-intensifying than intensifying TCs. Compared with f-inflated SPPT,the spatially correlated SST perturbations generally showed comparable performance but were more(less) skillful for intensifying(non-intensifying) TCs. The advantages of the spatially correlated SST perturbations and f-inflated SPPT were mainly present in the deterministic guidance for both TC track and wind and in the probabilistic guidance for reliability of wind. For intensifying TCs, adding SPP led to mixed impacts with significant improvements in probability-matched mean of modest winds and in probabilistic forecasts of rainfall;while for non-intensifying TCs, adding SPP frequently led to positive impacts on the deterministic guidance for track, intensity, strong winds, and moderate rainfall and on the probabilistic guidance for wind and discrimination of rainfall.展开更多
The separation of rare earth elements using diatomite M45 from aqueous solutions was studied.The experimental isotherms for the adsorption of trivalent lanthanum,cerium,and neodymium cations on this adsorbent were qua...The separation of rare earth elements using diatomite M45 from aqueous solutions was studied.The experimental isotherms for the adsorption of trivalent lanthanum,cerium,and neodymium cations on this adsorbent were quantified under strongly acidic conditions(pH 2)at 298-328 K.The adsorption equilibria of these earth elements were analyzed using two statistical physics models(homogeneous and heterogeneous monolayer models).The results show that the adsorption of these ions implies a multiionic mechanism,which is exothermic.Si-containing functional groups are responsible for the adsorption of these rare-earth elements on the diatomite surface.A heterogeneous statistical physics model confirms that two Si-based functional groups participate in the separation of these cations.The calculated adsorption capacities at saturation follow the order:neodymium>cerium>lanthanum.Calculated interaction energies range from 28600 to 40100 J/mol,indicating physical adsorption on diatomite M45.This study demonstrates that diatomite M45 is a promising separation medium that can be used for the recovery of REEs dissolved in aqueous solutions via adsorption.展开更多
Based on the domain reduction method,this study employs an SEM-FEM hybrid workflow which integrates the advantages of the spectral element method(SEM)for flexible and highly efficient simulation of seismic wave propag...Based on the domain reduction method,this study employs an SEM-FEM hybrid workflow which integrates the advantages of the spectral element method(SEM)for flexible and highly efficient simulation of seismic wave propagation in a three-dimensional(3D)regional-scale geophysics model and the finite element method(FEM)for fine simulation of structural response including soil-structure interaction,and performs a physics-based simulation from initial fault rupture on an ancient wood structure.After verification of the hybrid workflow,a large-scale model of an ancient wood structure in the Beijing area,The Tower of Buddhist Incense,is established and its responses under the 1665 Tongxian earthquake and the 1730 Yiheyuan earthquake are simulated.The results from the simulated ground motion and seismic response of the wood structure under the two earthquakes demonstrate that this hybrid workflow can be employed to efficiently provide insight into the relationships between geophysical parameters and the structural response,and is of great significance toward accurate input for seismic simulation of structures under specific site and fault conditions.展开更多
Rock physics inversion is to use seismic elastic properties of underground strata for predicting reservoir petrophysical parameters.The Markov chain Monte Carlo(MCMC)algorithm is commonly used to solve rock physics in...Rock physics inversion is to use seismic elastic properties of underground strata for predicting reservoir petrophysical parameters.The Markov chain Monte Carlo(MCMC)algorithm is commonly used to solve rock physics inverse problems.However,all the parameters to be inverted are iterated simultaneously in the conventional MCMC algorithm.What is obtained is an optimal solution of combining the petrophysical parameters with being inverted.This study introduces the alternating direction(AD)method into the MCMC algorithm(i.e.the optimized MCMC algorithm)to ensure that each petrophysical parameter can get the optimal solution and improve the convergence of the inversion.Firstly,the Gassmann equations and Xu-White model are used to model shaly sandstone,and the theoretical relationship between seismic elastic properties and reservoir petrophysical parameters is established.Then,in the framework of Bayesian theory,the optimized MCMC algorithm is used to generate a Markov chain to obtain the optimal solution of each physical parameter to be inverted and obtain the maximum posterior density of the physical parameter.The proposed method is applied to actual logging and seismic data and the results show that the method can obtain more accurate porosity,saturation,and clay volume.展开更多
An improved coupling of numerical and physical models for simulating 2D wave propagation is developed in this paper. In the proposed model, an unstructured finite element model （FEM） based Boussinesq equations is ap...An improved coupling of numerical and physical models for simulating 2D wave propagation is developed in this paper. In the proposed model, an unstructured finite element model （FEM） based Boussinesq equations is applied for the numerical wave simulation, and a 2D piston-type wavemaker is used for the physical wave generation. An innovative scheme combining fourth-order Lagrange interpolation and Runge-Kutta scheme is described for solving the coupling equation. A Transfer function modulation method is presented to minimize the errors induced from the hydrodynamic invalidity of the coupling model and/or the mechanical capability of the wavemaker in area where nonlinearities or dispersion predominate. The overall performance and applicability of the coupling model has been experimentally validated by accounting for both regular and irregular waves and varying bathymetry. Experimental results show that the proposed numerical scheme and transfer function modulation method are efficient for the data transfer from the numerical model to the physical model up to a deterministic level.展开更多
A quantum chain model of multiple molecule motors is proposed as a mathematical physics theory for the microscopic modeling of classical force-velocity relation and tension transients in muscle fibers. The proposed mo...A quantum chain model of multiple molecule motors is proposed as a mathematical physics theory for the microscopic modeling of classical force-velocity relation and tension transients in muscle fibers. The proposed model was a quantum many-particle Hamiltonian to predict the force-velocity relation for the slow release of muscle fibers, which has not yet been empirically defined and was much more complicated than the hyperbolic relationships. Using the same Hamiltonian model, a mathematical force-velocity relationship was proposed to explain the tension observed when the muscle was stimulated with an alternative electric current. The discrepancy between input electric frequency and the muscle oscillation frequency could be explained physically by the Doppler effect in this quantum chain model. Further more, quantum physics phenomena were applied to explore the tension time course of cardiac muscle and insect flight muscle. Most of the experimental tension transient curves were found to correspond to the theoretical output of quantum two- and three-level models. Mathematical modeling electric stimulus as photons exciting a quantum three-level particle reproduced most of the tension transient curves of water bug Lethocerus maximus.展开更多
基金supported by the National Natural Science Foundation of China (Nos.41974123,42174161)the Jiangsu Innovation and Entrepreneurship Plan and the Jiangsu Province Science Fund for Distinguished Young Scholars (grant no.BK20200021).
文摘In recent years,the Yanchang shale-oil formations of the Ordos Basin are rich in reserves with complex lithology and structure characteristics,low porosity and low permeability,and weak anomalies for oil and water discriminations,have been the key targets of unconventional oil/gas resource exploration and development in the relevant areas.The joint acoustic-electrical(AE)properties can be used to interpret reservoir lithology,mineralogy,pore structure,and fluid saturation.To conduct tests of thin section analysis,X-ray diff raction,and ultrasonic and electrical experiments at diff erent pressures and saturation degrees,cores from the shale-oil formations in the Q area of the basin are collected.The variations in AE properties with respect to clay content,porosity,pressure(microfracture),and saturation are analyzed.The experimental results indicate that the rock physics behaviors of sandstones with diff erent clay contents vary significantly.The AE properties of clean sandstones are basically dependent on the microfractures(pressure),while for muddy sandstones,the clay content is an important factor affecting the responses.The target reservoir consists of interbedded sandstone and shale layers.The AE equivalent medium equations and the Gurevich theory are applied to establish the joint models for the diff erent lithologies and simulate the variations in AE properties with respect to fluid type,pore structure,and mineral components.The three-dimensional joint templates of clean and muddy sandstones,as well as shale,are developed based on the elastic and electrical attributes and then calibrated using the experimental and well-log data.The reservoir properties are estimated with the templates and validated by the log data.The results indicate that the joint templates based on lithology characteristics can eff ectively characterize the properties of interbedded sandstone and shale layers.Furthermore,the combined application of AE data provides more beneficial information for the assessment of rock properties,leading to precise estimates that conform with the actual formation conditions.
基金supported by National Key Research and Development Program (2019YFA0708301)National Natural Science Foundation of China (51974337)+2 种基金the Strategic Cooperation Projects of CNPC and CUPB (ZLZX2020-03)Science and Technology Innovation Fund of CNPC (2021DQ02-0403)Open Fund of Petroleum Exploration and Development Research Institute of CNPC (2022-KFKT-09)
文摘We propose an integrated method of data-driven and mechanism models for well logging formation evaluation,explicitly focusing on predicting reservoir parameters,such as porosity and water saturation.Accurately interpreting these parameters is crucial for effectively exploring and developing oil and gas.However,with the increasing complexity of geological conditions in this industry,there is a growing demand for improved accuracy in reservoir parameter prediction,leading to higher costs associated with manual interpretation.The conventional logging interpretation methods rely on empirical relationships between logging data and reservoir parameters,which suffer from low interpretation efficiency,intense subjectivity,and suitability for ideal conditions.The application of artificial intelligence in the interpretation of logging data provides a new solution to the problems existing in traditional methods.It is expected to improve the accuracy and efficiency of the interpretation.If large and high-quality datasets exist,data-driven models can reveal relationships of arbitrary complexity.Nevertheless,constructing sufficiently large logging datasets with reliable labels remains challenging,making it difficult to apply data-driven models effectively in logging data interpretation.Furthermore,data-driven models often act as“black boxes”without explaining their predictions or ensuring compliance with primary physical constraints.This paper proposes a machine learning method with strong physical constraints by integrating mechanism and data-driven models.Prior knowledge of logging data interpretation is embedded into machine learning regarding network structure,loss function,and optimization algorithm.We employ the Physically Informed Auto-Encoder(PIAE)to predict porosity and water saturation,which can be trained without labeled reservoir parameters using self-supervised learning techniques.This approach effectively achieves automated interpretation and facilitates generalization across diverse datasets.
基金supported by the National Natural Science Foundation of China(No.42061065)the Third Xinjiang Comprehensive Scientific Expedition,China(No.2022xjkk03010102).
文摘Root zone soil moisture(RZSM)plays a critical role in land-atmosphere hydrological cycles and serves as the primary water source for vegetation growth.However,the correlations between RZSM and its associated variables,including surface soil moisture(SSM),often exhibit nonlinearities that are challenging to identify and quantify using conventional statistical techniques.Therefore,this study presents a hybrid convolutional neural network(CNN)-long short-term memory neural network(LSTM)-attention(CLA)model for predicting RZSM.Owing to the scarcity of soil moisture(SM)observation data,the physical model Hydrus-1D was employed to simulate a comprehensive dataset of spatial-temporal SM.Meteorological data and moderate resolution imaging spectroradiometer vegetation characterization parameters were used as predictor variables for the training and validation of the CLA model.The results of the CLA model for SM prediction in the root zone were significantly enhanced compared with those of the traditional LSTM and CNN-LSTM models.This was particularly notable at the depth of 80–100 cm,where the fitness(R^(2))reached nearly 0.9298.Moreover,the root mean square error of the CLA model was reduced by 49%and 57%compared with those of the LSTM and CNN-LSTM models,respectively.This study demonstrates that the integration of physical modeling and deep learning methods provides a more comprehensive and accurate understanding of spatial-temporal SM variations in the root zone.
基金funding support from the National Natural Science Foundation of China(Grant Nos.42177136 and 52309126).
文摘The excavation of deep tunnels crossing faults is highly prone to triggering rockburst disasters,which has become a significant engineering issue.In this study,taking the fault-slip rockbursts from a deep tunnel in southwestern China as the engineering prototype,large-scale three-dimensional(3D)physical model tests were conducted on a 3D-printed complex geological model containing two faults.Based on the selfdeveloped 3D loading system and excavation device,the macroscopic failure of fault-slip rockbursts was simulated indoors.The stress,strain,and fracturing characteristics of the surrounding rock near the two faults were systematically evaluated during excavation and multistage loading.The test results effectively revealed the evolution and triggering mechanism of fault-slip rockbursts.After the excavation of a highstress tunnel,stress readjustment occurred.Owing to the presence of these two faults,stress continued to accumulate in the rock mass between them,leading to the accumulation of fractures.When the shear stress on a fault surface exceeded its shear strength,sudden fault slip and dislocation occurred,thus triggering rockbursts.Rockbursts occurred twice in the vault between the two faults,showing obvious intermittent characteristics.The rockburst pit was controlled by two faults.When the faults remained stable,tensile failure predominated in the surrounding rock.However,when the fault slip was triggered,shear failure in the surrounding rock increased.These findings provide valuable insights for enhancing the comprehension of fault-slip rockbursts.
基金supported by National Natural Science Foundation of China(62227818,12204239,62275121)Youth Foundation of Jiangsu Province(BK20220946)+1 种基金Fundamental Research Funds for the Central Universities(30923011024)Open Research Fund of Jiangsu Key Laboratory of Spectral Imaging&Intelligent Sense(JSGP202201).
文摘Deep learning(DL)is making significant inroads into biomedical imaging as it provides novel and powerful ways of accurately and efficiently improving the image quality of photoacoustic microscopy(PAM).Off-the-shelf DL models,however,do not necessarily obey the fundamental governing laws of PAM physical systems,nor do they generalize well to scenarios on which they have not been trained.In this work,a physics-embedded degeneration learning(PEDL)approach is proposed to enhance the image quality of PAM with a self-attention enhanced U-Net network,which obtains greater physical consistency,improves data efficiency,and higher adaptability.The proposed method is demonstrated on both synthetic and real datasets,including animal experiments in vivo(blood vessels of mouse's ear and brain).And the results show that compared with previous DL methods,the PEDL algorithm exhibits good performance in recovering PAM images qualitatively and quantitatively.It overcomes the challenges related to training data,accuracy,and robustness which a typical data-driven approach encounters,whose exemplary application envisions to provide a new perspective for existing DL tools of enhanced PAM.
基金supported by the National 973 project(Nos.2014CB239006 and 2011CB202402)the National Natural Science Foundation of China(Nos.41104069 and 41274124)+1 种基金Sinopec project(No.KJWX2014-05)the Fundamental Research Funds for the Central Universities(No.R1401005A)
文摘Brittleness analysis becomes important when looking for sweet spots in tightoil sandstone reservoirs. Hence, appropriate indices are required as accurate brittleness evaluation criteria. We construct a seismic rock physics model for tight-oil sandstone reservoirs with vertical fractures. Because of the complexities in lithology and pore structure and the anisotropic characteristics of tight-oil sandstone reservoirs, the proposed model is based on the solid components, pore connectivity, pore type, and fractures to better describe the sandstone reservoir microstructure. Using the model, we analyze the brittleness sensitivity of the elastic parameters in an anisotropic medium and establish a new brittleness index. We show the applicability of the proposed brittleness index for tight-oil sandstone reservoirs by considering the brittleness sensitivity, the rock physics response characteristics, and cross-plots. Compared with conventional brittleness indexes, the new brittleness index has high brittleness sensitivity and it is the highest in oil-bearing brittle zones with relatively high porosity. The results also suggest that the new brittleness index is much more sensitive to elastic properties variations, and thus can presumably better predict the brittleness characteristics of sweet spots in tight-oil sandstone reservoirs.
文摘Due to the huge differences between the unconventional shale and conventional sand reservoirs in many aspects such as the types and the characteristics of minerals,matrix pores and fluids,the construction of shale rock physics model is significant for the exploration and development of shale reservoirs.To make a better characterization of shale gas-bearing reservoirs,we first propose a new but more suitable rock physics model to characterize the reservoirs.We then use a well A to demonstrate the feasibility and reliability of the proposed rock physics model of shale gas-bearing reservoirs.Moreover,we propose a new brittleness indicator for the high-porosity and organic-rich shale gas-bearing reservoirs.Based on the parameter analysis using the constructed rock physics model,we finally compare the new brittleness indicator with the commonly used Young’s modulus in the content of quartz and organic matter,the matrix porosity,and the types of filled fluids.We also propose a new shale brittleness index by integrating the proposed new brittleness indicator and the Poisson’s ratio.Tests on real data sets demonstrate that the new brittleness indicator and index are more sensitive than the commonly used Young’s modulus and brittleness index for the high-porosity and high-brittleness shale gas-bearing reservoirs.
基金supported by National Natural Science Foundation of China (Grant No. 50905183)
文摘Planetary gear set is the critical component in helicopter transmission train, and an important problem in condition monitoring and health management of planetary gear set is quantitative damage detection. In order to resolve this problem, an approach based on physical models is presented to detect damage quantitatively in planetary gear set. A particular emphasis is put on a feature generation and selection method, which is used for sun gear tooth breakage damage detection quantitatively in planetary gear box of helicopter transmission system. In this feature generation procedure, the pure torsional dynamical models of 2K-H planetary gear set is established for healthy case and sun gear tooth-breakage case. Then, a feature based on the spectrum of simulation signals of the dynamical models is generated. Aiming at selecting the best feature suitable for quantitative damage detection, a two-sample Z-test procedure is used to analyze the performance of features on damage evolution tracing. A feature named SR, which had better performance in tracking damage, is proposed to detect damage in planetary gear set. Meanwhile, the sun gear tooth-chipped seeded experiments with different severity are designed to validate the method above, and then the test vibration signal is picked up and used for damage detection. With the results of several experiments for quantitative damage detection, the feasibility and the effect of this approach are verified. The proposed method can supply an effective tool for degradation state identification in condition monitoring and health management of helicopter transmission system.
文摘In most industrial fluidization units, two- or three-stage cyclone systems are used to clean the product gases. To return the solids to the bed, these cyclones are fitted with diplegs. By pass of gas from the bed through the dipleg is partially overcome by the back pressure build-up in the dipleg and by adding a trickle valve at the bottom of the dipleg. Diplegs of primary cyclones, operating at a high solid loading behave differently from diplegs of secondary and tertiary cyclones which operate at low solid loading. Both types have been investigated by pressure drop measurements, visual observation and by measurements of the air flow rate flowing up the riser. The primary dipleg was also studied using electrical capacitance tomography. The results are reported hereafter and will give a first indication towards the right design of the dipleg and the selection of the trickle valve. The influence of gas flow in the dipleg on the conversion in a catalytic fluidized bed reactor is found to be negligible.
基金supported by the NSFC and Sinopec Joint Key Project(No.U1663207)National Science and Technology Major Project(No.2017ZX05049-002)National 973 Program(No.2014CB239104)
文摘The construction of a shale rock physics model and the selection of an appropriate brittleness index （B/） are two significant steps that can influence the accuracy of brittleness prediction. On one hand, the existing models of kerogen-rich shale are controversial, so a reasonable rock physics model needs to be built. On the other hand, several types of equations already exist for predicting the BI whose feasibility needs to be carefully considered. This study constructed a kerogen-rich rock physics model by performing the self- consistent approximation and the differential effective medium theory to model intercoupled clay and kerogen mixtures. The feasibility of our model was confirmed by comparison with classical models, showing better accuracy. Templates were constructed based on our model to link physical properties and the BL Different equations for the BI had different sensitivities, making them suitable for different types of formations. Equations based on Young＇s Modulus were sensitive to variations in lithology, while those using Lame＇s Coefficients were sensitive to porosity and pore fluids. Physical information must be considered to improve brittleness prediction.
基金the Science and Technology Major Project of China(No.2019ZX04020001-004,2017ZX04007001)。
文摘In the manufacturing of thin wall components for aerospace industry,apart from the side wall contour error,the Remaining Bottom Thickness Error(RBTE)for the thin-wall pocket component(e.g.rocket shell)is of the same importance but overlooked in current research.If the RBTE reduces by 30%,the weight reduction of the entire component will reach up to tens of kilograms while improving the dynamic balance performance of the large component.Current RBTE control requires the off-process measurement of limited discrete points on the component bottom to provide the reference value for compensation.This leads to incompleteness in the remaining bottom thickness control and redundant measurement in manufacturing.In this paper,the framework of data-driven physics based model is proposed and developed for the real-time prediction of critical quality for large components,which enables accurate prediction and compensation of RBTE value for the thin wall components.The physics based model considers the primary root cause,in terms of tool deflection and clamping stiffness induced Axial Material Removal Thickness(AMRT)variation,for the RBTE formation.And to incorporate the dynamic and inherent coupling of the complicated manufacturing system,the multi-feature fusion and machine learning algorithm,i.e.kernel Principal Component Analysis(kPCA)and kernel Support Vector Regression(kSVR),are incorporated with the physics based model.Therefore,the proposed data-driven physics based model combines both process mechanism and the system disturbance to achieve better prediction accuracy.The final verification experiment is implemented to validate the effectiveness of the proposed method for dimensional accuracy prediction in pocket milling,and the prediction accuracy of AMRT achieves 0.014 mm and 0.019 mm for straight and corner milling,respectively.
基金Project supported by the National Natural Science Foundation of China(Grant Nos.61735014,61327012,and 61275088)the Scientific Research Program Funded by Shaanxi Provincial Education Department,China(Grant No.08JZ58)the Northwest University Graduate Innovation and Creativity Funds,China(Grant No.YZZ17088)
文摘An ultrasonic sensitivity-improved fiber-optic Fabry-Perot interferometer （FPI） is proposed and employed for ultra- sonic imaging of seismic physical models （SPMs）. The FPI comprises a flexible ultra-thin gold film and the end face of a graded-index multimode fiber （MMF）, both of which are enclosed in a ceramic tube. The MMF in a specified length can collimate the diverged light beam and compensate for the light loss inside the air cavity, leading to an increased spectral fringe visibility and thus a steeper spectral slope. By using the spectral sideband filtering technique, the collimated FP1 shows an improved ultrasonic response. Moreover, two-dimensional images of two SPMs are achieved in air by recon- structing the pulse-echo signals through using the time-of-flight approach. The proposed sensor with easy fabrication and compact size can be a good candidate for high-sensitivity and high-precision nondestructive testing of SPMs.
基金funded by the National Nature Science Foundation of China(No.42207216)the National Major Scientific Instruments and Equipment Development Projects of China(No.41827808)the National Key Research and Development Program of China(No.2017YFC1501305)。
文摘A simplified geomechanical model was proposed by considering three typical neckingtype slopes;this model lays a foundation for the further investigation of the deformation behaviors of such slopes.Three physical models of necking-type slopes were built according to the geomechanical model with slope evolution stages.Finally,preliminary calculations related to the arching effect in the physical model were conducted.Three evolution stages of necking-type slopes,namely,the initial stage,compression stage,and failure stage,were presented based on the formation and disappearance of the arching effect within the slope.The specific parameters of the geomechanical model were given.In the setup of the tilting test,the failure angle of the necking-type slope model was calculated to be approximately 50°with a large lateral resistance coefficient.The proposed geomechanical model and physical models of necking-type slopes provide guidance for the establishment of geomechanical and physical models of landslides at specific sites.
基金sponsored by the National Key R&D Program of China through Grant No. 2017YFC1501603the National Natural Science Foundation of China through Grant No. 41975136the Guangdong Basic and Applied Basic Research Foundation through Grant No. 2019A1515011118。
文摘To improve the ensemble prediction system of the tropical regional atmosphere model for the South China Sea(TREPS) in predicting landfalling tropical cyclones(TCs), the impacts of three new implementing strategies for surface and model physics perturbations in TREPS were evaluated for 19 TCs making landfall in China during 2014–16. For sea surface temperature(SST) perturbations, spatially uncorrelated random perturbations were replaced with spatially correlated ones. The multiplier f, which is used to form perturbed tendency in the Stochastically Perturbed Parameterization Tendency(SPPT) scheme, was inflated in regions with evident convective activity(f-inflated SPPT). Lastly, the Stochastically Perturbed Parameterization(SPP) scheme with 14 perturbed parameters selected from the planetary boundary layer, surface layer, microphysics, and cumulus convection parameterizations was added. Overall, all these methods improved forecasts more significantly for non-intensifying than intensifying TCs. Compared with f-inflated SPPT,the spatially correlated SST perturbations generally showed comparable performance but were more(less) skillful for intensifying(non-intensifying) TCs. The advantages of the spatially correlated SST perturbations and f-inflated SPPT were mainly present in the deterministic guidance for both TC track and wind and in the probabilistic guidance for reliability of wind. For intensifying TCs, adding SPP led to mixed impacts with significant improvements in probability-matched mean of modest winds and in probabilistic forecasts of rainfall;while for non-intensifying TCs, adding SPP frequently led to positive impacts on the deterministic guidance for track, intensity, strong winds, and moderate rainfall and on the probabilistic guidance for wind and discrimination of rainfall.
基金The Research Center for Advanced Materials Science (RCAMS)at King Khalid University,Saudi Arabia is acknowledged for funding this work under the grant number RCAMS/KKU/016-22。
文摘The separation of rare earth elements using diatomite M45 from aqueous solutions was studied.The experimental isotherms for the adsorption of trivalent lanthanum,cerium,and neodymium cations on this adsorbent were quantified under strongly acidic conditions(pH 2)at 298-328 K.The adsorption equilibria of these earth elements were analyzed using two statistical physics models(homogeneous and heterogeneous monolayer models).The results show that the adsorption of these ions implies a multiionic mechanism,which is exothermic.Si-containing functional groups are responsible for the adsorption of these rare-earth elements on the diatomite surface.A heterogeneous statistical physics model confirms that two Si-based functional groups participate in the separation of these cations.The calculated adsorption capacities at saturation follow the order:neodymium>cerium>lanthanum.Calculated interaction energies range from 28600 to 40100 J/mol,indicating physical adsorption on diatomite M45.This study demonstrates that diatomite M45 is a promising separation medium that can be used for the recovery of REEs dissolved in aqueous solutions via adsorption.
基金National Natural Science Foundation of China under Grant Nos.52108468 and 52178495。
文摘Based on the domain reduction method,this study employs an SEM-FEM hybrid workflow which integrates the advantages of the spectral element method(SEM)for flexible and highly efficient simulation of seismic wave propagation in a three-dimensional(3D)regional-scale geophysics model and the finite element method(FEM)for fine simulation of structural response including soil-structure interaction,and performs a physics-based simulation from initial fault rupture on an ancient wood structure.After verification of the hybrid workflow,a large-scale model of an ancient wood structure in the Beijing area,The Tower of Buddhist Incense,is established and its responses under the 1665 Tongxian earthquake and the 1730 Yiheyuan earthquake are simulated.The results from the simulated ground motion and seismic response of the wood structure under the two earthquakes demonstrate that this hybrid workflow can be employed to efficiently provide insight into the relationships between geophysical parameters and the structural response,and is of great significance toward accurate input for seismic simulation of structures under specific site and fault conditions.
基金supported by the National Natural Science Foundation of China(No.42174146)CNPC major forwardlooking basic science and technology projects(No.2021DJ0204).
文摘Rock physics inversion is to use seismic elastic properties of underground strata for predicting reservoir petrophysical parameters.The Markov chain Monte Carlo(MCMC)algorithm is commonly used to solve rock physics inverse problems.However,all the parameters to be inverted are iterated simultaneously in the conventional MCMC algorithm.What is obtained is an optimal solution of combining the petrophysical parameters with being inverted.This study introduces the alternating direction(AD)method into the MCMC algorithm(i.e.the optimized MCMC algorithm)to ensure that each petrophysical parameter can get the optimal solution and improve the convergence of the inversion.Firstly,the Gassmann equations and Xu-White model are used to model shaly sandstone,and the theoretical relationship between seismic elastic properties and reservoir petrophysical parameters is established.Then,in the framework of Bayesian theory,the optimized MCMC algorithm is used to generate a Markov chain to obtain the optimal solution of each physical parameter to be inverted and obtain the maximum posterior density of the physical parameter.The proposed method is applied to actual logging and seismic data and the results show that the method can obtain more accurate porosity,saturation,and clay volume.
基金supported by the National Natural Science Foundation of China(Grant Nos.51079023 and 51221961)the National Basic Research Program of China(973 Program,Grant Nos.2013CB036101 and 2011CB013703)
文摘An improved coupling of numerical and physical models for simulating 2D wave propagation is developed in this paper. In the proposed model, an unstructured finite element model （FEM） based Boussinesq equations is applied for the numerical wave simulation, and a 2D piston-type wavemaker is used for the physical wave generation. An innovative scheme combining fourth-order Lagrange interpolation and Runge-Kutta scheme is described for solving the coupling equation. A Transfer function modulation method is presented to minimize the errors induced from the hydrodynamic invalidity of the coupling model and/or the mechanical capability of the wavemaker in area where nonlinearities or dispersion predominate. The overall performance and applicability of the coupling model has been experimentally validated by accounting for both regular and irregular waves and varying bathymetry. Experimental results show that the proposed numerical scheme and transfer function modulation method are efficient for the data transfer from the numerical model to the physical model up to a deterministic level.
基金Project supported by the Fundamental Research Foundation for the Central Universities of China
文摘A quantum chain model of multiple molecule motors is proposed as a mathematical physics theory for the microscopic modeling of classical force-velocity relation and tension transients in muscle fibers. The proposed model was a quantum many-particle Hamiltonian to predict the force-velocity relation for the slow release of muscle fibers, which has not yet been empirically defined and was much more complicated than the hyperbolic relationships. Using the same Hamiltonian model, a mathematical force-velocity relationship was proposed to explain the tension observed when the muscle was stimulated with an alternative electric current. The discrepancy between input electric frequency and the muscle oscillation frequency could be explained physically by the Doppler effect in this quantum chain model. Further more, quantum physics phenomena were applied to explore the tension time course of cardiac muscle and insect flight muscle. Most of the experimental tension transient curves were found to correspond to the theoretical output of quantum two- and three-level models. Mathematical modeling electric stimulus as photons exciting a quantum three-level particle reproduced most of the tension transient curves of water bug Lethocerus maximus.
基金supported by the Mathematics and Physics Foundation of Beijing Polytechnic University and the National Natural Science Foundation of China (Grant No 40536029)
文摘Explicit solutions are derived for some nonlinear physical model equations by using a delicate way of two-step ansatz method.
