Intuitively,the solvation structure featuring stronger interacted sheath in deep eutectic solution(DES)electrolyte would result in sluggish interfacial charge transfer and intense polarization,which obstructs its prac...Intuitively,the solvation structure featuring stronger interacted sheath in deep eutectic solution(DES)electrolyte would result in sluggish interfacial charge transfer and intense polarization,which obstructs its practical application in emerging Zn based batteries.Unexpectedly,here we discover a Zn‖organic battery with exceptional kinetics properties enabled by a hydrated DES electrolyte,which can render higher discharge capacity,smaller voltage polarization,and faster kinetics of charge transfer in comparison with conventional aqueous 3 M ZnCl_(2)electrolyte,though its viscosity is two orders of magnitude higher than the latter.The improved kinetics of charge transfer and ion diffusion is demonstrated to originate from the local electron structure regulation of cathode in hydrated DES electrolyte.Furthermore,the DES electrolyte has also been shown to restrict parasitic reaction associated with active water by preferential urea-molecular adsorption on Zn surface and stronger water trapping in solvation structure,giving rise to long-term stable dendrite-free Zn plating/stripping.This work provides a new rationale for understanding electrochemical behaviors of organic cathodes in DES electrolyte,which is conducive to the development of high-performance Zn‖organic batteries.展开更多
Grating-based X-ray phase-contrast imaging enhances the contrast of imaged objects,particularly soft tissues.However,the radiation dose in computed tomography(CT)is generally excessive owing to the complex collection ...Grating-based X-ray phase-contrast imaging enhances the contrast of imaged objects,particularly soft tissues.However,the radiation dose in computed tomography(CT)is generally excessive owing to the complex collection scheme.Sparse-view CT collection reduces the radiation dose,but with reduced resolution and reconstructed artifacts particularly in analytical reconstruction methods.Recently,deep learning has been employed in sparse-view CT reconstruction and achieved stateof-the-art results.Nevertheless,its low generalization performance and requirement for abundant training datasets have hindered the practical application of deep learning in phase-contrast CT.In this study,a CT model was used to generate a substantial number of simulated training datasets,thereby circumventing the need for experimental datasets.By training a network with simulated training datasets,the proposed method achieves high generalization performance in attenuationbased CT and phase-contrast CT,despite the lack of sufficient experimental datasets.In experiments utilizing only half of the CT data,our proposed method obtained an image quality comparable to that of the filtered back-projection algorithm with full-view projection.The proposed method simultaneously addresses two challenges in phase-contrast three-dimensional imaging,namely the lack of experimental datasets and the high exposure dose,through model-driven deep learning.This method significantly accelerates the practical application of phase-contrast CT.展开更多
Recent studies employing deep learning to solve the traveling salesman problem(TSP)have mainly focused on learning construction heuristics.Such methods can improve TSP solutions,but still depend on additional programs...Recent studies employing deep learning to solve the traveling salesman problem(TSP)have mainly focused on learning construction heuristics.Such methods can improve TSP solutions,but still depend on additional programs.However,methods that focus on learning improvement heuristics to iteratively refine solutions remain insufficient.Traditional improvement heuristics are guided by a manually designed search strategy and may only achieve limited improvements.This paper proposes a novel framework for learning improvement heuristics,which automatically discovers better improvement policies for heuristics to iteratively solve the TSP.Our framework first designs a new architecture based on a transformer model to make the policy network parameterized,which introduces an action-dropout layer to prevent action selection from overfitting.It then proposes a deep reinforcement learning approach integrating a simulated annealing mechanism(named RL-SA)to learn the pairwise selected policy,aiming to improve the 2-opt algorithm's performance.The RL-SA leverages the whale optimization algorithm to generate initial solutions for better sampling efficiency and uses the Gaussian perturbation strategy to tackle the sparse reward problem of reinforcement learning.The experiment results show that the proposed approach is significantly superior to the state-of-the-art learning-based methods,and further reduces the gap between learning-based methods and highly optimized solvers in the benchmark datasets.Moreover,our pre-trained model M can be applied to guide the SA algorithm(named M-SA(ours)),which performs better than existing deep models in small-,medium-,and large-scale TSPLIB datasets.Additionally,the M-SA(ours)achieves excellent generalization performance in a real-world dataset on global liner shipping routes,with the optimization percentages in distance reduction ranging from3.52%to 17.99%.展开更多
Static adsorption and dynamic damage experiments were carried out on typical 8#deep coal rock of the Carboniferous Benxi Formation in the Ordos Basin,NW China,to evaluate the adsorption capacity of hydroxypropyl guar ...Static adsorption and dynamic damage experiments were carried out on typical 8#deep coal rock of the Carboniferous Benxi Formation in the Ordos Basin,NW China,to evaluate the adsorption capacity of hydroxypropyl guar gum and polyacrylamide as fracturing fluid thickeners on deep coal rock surface and the permeability damage caused by adsorption.The adsorption morphology of the thickener was quantitatively characterized by atomic force microscopy,and the main controlling factors of the thickener adsorption were analyzed.Meanwhile,the adsorption mechanism of the thickener was revealed by Zeta potential,Fourier infrared spectroscopy and X-ray photoelectron spectroscopy.The results show that the adsorption capacity of hydroxypropyl guar gum on deep coal surface is 3.86 mg/g,and the permeability of coal rock after adsorption decreases by 35.24%–37.01%.The adsorption capacity of polyacrylamide is 3.29 mg/g,and the permeability of coal rock after adsorption decreases by 14.31%–21.93%.The thickness of the thickener adsorption layer is positively correlated with the mass fraction of thickener and negatively correlated with temperature,and a decrease in pH will reduce the thickness of the hydroxypropyl guar gum adsorption layer and make the distribution frequency of the thickness of polyacrylamide adsorption layer more concentrated.Functional group condensation and intermolecular force are chemical and physical forces for adsorbing fracturing fluid thickener in deep coal rock.Optimization of thickener mass fraction,chemical modification of thickener molecular,oxidative thermal degradation of polymer and addition of desorption agent can reduce the potential damages on micro-nano pores and cracks in coal rock.展开更多
In the era of coronavirus disease 2019(COVID-19)pandemic,imported COVID-19 cases pose great challenges to many countries.Chest CT examination is considered to be complementary to nucleic acid test for COVID-19 detecti...In the era of coronavirus disease 2019(COVID-19)pandemic,imported COVID-19 cases pose great challenges to many countries.Chest CT examination is considered to be complementary to nucleic acid test for COVID-19 detection and diagnosis.Wie report the first community infected COVID-19 patient by an imported case in Beijing,which manifested as nodular lesions on chest CT imaging at the early stage.Deep Learning(DL)-based diagnostic systems quantitatively monitored the progress of pulmonary lesions in 6 days and timely made alert for suspected pneumonia,so that prompt medical isolation was taken.The patient was confirmed as COVID-19 case after nucleic acid test,for which the community transmission was prevented timely.The roles of DL-assisted diagnosis in helping radiologists screening suspected COVID cases were discussed.展开更多
Focused on the lost circulation control in deep naturally fractured reservoirs, the multiscale structure of fracture plugging zone is proposed based on the theory of granular matter mechanics, and the structural failu...Focused on the lost circulation control in deep naturally fractured reservoirs, the multiscale structure of fracture plugging zone is proposed based on the theory of granular matter mechanics, and the structural failure pattern of plugging zone is developed to reveal the plugging zone failure mechanisms in deep, high temperature, high pressure, and high in-situ stress environment. Based on the fracture plugging zone strength model, key performance parameters are determined for the optimal selection of loss control material(LCM). Laboratory fracture plugging experiments with new LCM are carried out to evaluate the effect of the key performance parameters of LCM on fracture plugging quality. LCM selection strategy for fractured reservoirs is developed. The results show that the force chain formed by LCMs determines the pressure stabilization of macro-scale fracture plugging zone. Friction failure and shear failure are the two major failure patterns of fracture plugging zone. The strength of force chain depends on the performance of micro-scale LCM, and the LCM key performance parameters include particle size distribution, fiber aspect ratio, friction coefficient, compressive strength, soluble ability and high temperature resistance. Results of lab experiments and field test show that lost circulation control quality can be effectively improved with the optimal material selection based on the extracted key performance parameters of LCMs.展开更多
Pure shales in the first member of Qingshankou Formation(simplified as Qing 1 Member)in the southern Songliao Basin,i.e.,the semi-deep and deep lacustrine shales,are characterized by a high content of clay minerals an...Pure shales in the first member of Qingshankou Formation(simplified as Qing 1 Member)in the southern Songliao Basin,i.e.,the semi-deep and deep lacustrine shales,are characterized by a high content of clay minerals and poor hydrocarbon mobility,making the development of shale oil difficult.According to the drilling and testing results,the shale of Qing 1 Member can be classified into 3 lithofacies,i.e.,bedded argillaceous shale,laminated diamictite shale,and interbedded felsic shale.The TOC and brittle minerals control the enrichment of shale oil,of them,TOC controls the total oil content,in other words,the total oil content increases with the increase of TOC;while the laminae made up of brittle minerals contain a large number of bigger intergranular pores which are favorable enrichment space for movable shale oil.In consideration of the origins of the 3 lithofacies,two shale oil enrichment models are classified,i.e.,the deep lacustrine high-TOC bedded argillaceous shale(Model-I)and the semi-deep lacustrine moderate-high-TOC laminated diamictite shale(Model-II).In the Model-I,the shale is characterized by high hydrocarbon generation ability,high total oil content,abundant horizontal bedding fractures,and vertical and high angle fractures locally;the complex fracture network formed by horizontal bedding fractures and vertical fractures improve the storage capacity and permeability of the shale reservoir,increase the enrichment space for movable oil.In the Model-II,the shale is characterized by good hydrocarbon generation ability and fairly high total oil content,and as the brittle laminae contain large intergranular pores,the shale has a higher movable oil content.Based on the two models,shale oil sweet-spot areas of 2880 km2 in the southern Songliao Basin are favorable for further exploration.Aimed at the difficulties in reservoir fracturing of the lacustrine shale with a high content of clay minerals,the composite fracturing technology with supercritical carbon dioxide was used in the shale oil reservoir for the first time,realizing large-scale volume fracturing in shale with a high content of clay minerals and strong heterogeneity,marking a breakthrough of oil exploration in continental shale with a high content of clay minerals in China.展开更多
We consider an image semantic communication system in a time-varying fading Gaussian MIMO channel,with a finite number of channel states.A deep learning-aided broadcast approach scheme is proposed to benefit the adapt...We consider an image semantic communication system in a time-varying fading Gaussian MIMO channel,with a finite number of channel states.A deep learning-aided broadcast approach scheme is proposed to benefit the adaptive semantic transmission in terms of different channel states.We combine the classic broadcast approach with the image transformer to implement this adaptive joint source and channel coding(JSCC)scheme.Specifically,we utilize the neural network(NN)to jointly optimize the hierarchical image compression and superposition code mapping within this scheme.The learned transformers and codebooks allow recovering of the image with an adaptive quality and low error rate at the receiver side,in each channel state.The simulation results exhibit our proposed scheme can dynamically adapt the coding to the current channel state and outperform some existing intelligent schemes with the fixed coding block.展开更多
Characterizing and control the chemical compositions of multi-element particles as single metal nanoparticles(mNPs) on the surfaces of catalytic metal oxide supports is challenging.This can be attributed to the hetero...Characterizing and control the chemical compositions of multi-element particles as single metal nanoparticles(mNPs) on the surfaces of catalytic metal oxide supports is challenging.This can be attributed to the heterogeneity and large size at the nanoscale,the poorly defined catalyst nanostructure,and thermodynamic immiscibility of the strongly repelling metallic elements.To address these challenges,an ultrasonic-assisted coincident electro-oxidation-reduction-precipitation(U-SEO-P) is presented to fabricate ultra-stable PtRuAgCoCuP NPs,which produces numerous active intermediates and induces strong metal-support interactions.To sort the active high-entropy mNPs,individual NPs are described on the support surface and the role of deep learning in understanding/predicting the features of PtRuAgCoCu@TiO_(x) catalysts is explained.Notably,this deep learning approach required minimal to no human input.The as-prepared PtRuAgCoCu@TiO_(x) catalysts can be used to catalyze various important chemical reactions,such as a high reduction conversion(100% in 30 s),with no loss of catalytic activity even after 20 cycles of nitroarene and ketone/aldehyde,which is several times higher than commercial Pt@TiO_(x) owing to individual PtRuAgCoCuP NPs on TiO_(x) surface.In this study,we present the "Totally Defined Catalysis" concept,which has enormous potential for the advancement of high-activity catalysts in the reduction of organic compounds.展开更多
Deep and ultra-deep reservoirs have gradually become the primary focus of hydrocarbon exploration as a result of a series of significant discoveries in deep hydrocarbon exploration worldwide.These reservoirs present u...Deep and ultra-deep reservoirs have gradually become the primary focus of hydrocarbon exploration as a result of a series of significant discoveries in deep hydrocarbon exploration worldwide.These reservoirs present unique challenges due to their deep burial depth(4500-8882 m),low matrix permeability,complex crustal stress conditions,high temperature and pressure(HTHP,150-200℃,105-155 MPa),coupled with high salinity of formation water.Consequently,the costs associated with their exploitation and development are exceptionally high.In deep and ultra-deep reservoirs,hydraulic fracturing is commonly used to achieve high and stable production.During hydraulic fracturing,a substantial volume of fluid is injected into the reservoir.However,statistical analysis reveals that the flowback rate is typically less than 30%,leaving the majority of the fluid trapped within the reservoir.Therefore,hydraulic fracturing in deep reservoirs not only enhances the reservoir permeability by creating artificial fractures but also damages reservoirs due to the fracturing fluids involved.The challenging“three-high”environment of a deep reservoir,characterized by high temperature,high pressure,and high salinity,exacerbates conventional forms of damage,including water sensitivity,retention of fracturing fluids,rock creep,and proppant breakage.In addition,specific damage mechanisms come into play,such as fracturing fluid decomposition at elevated temperatures and proppant diagenetic reactions at HTHP conditions.Presently,the foremost concern in deep oil and gas development lies in effectively assessing the damage inflicted on these reservoirs by hydraulic fracturing,comprehending the underlying mechanisms,and selecting appropriate solutions.It's noteworthy that the majority of existing studies on reservoir damage primarily focus on conventional reservoirs,with limited attention given to deep reservoirs and a lack of systematic summaries.In light of this,our approach entails initially summarizing the current knowledge pertaining to the types of fracturing fluids employed in deep and ultra-deep reservoirs.Subsequently,we delve into a systematic examination of the damage processes and mechanisms caused by fracturing fluids within the context of hydraulic fracturing in deep reservoirs,taking into account the unique reservoir characteristics of high temperature,high pressure,and high in-situ stress.In addition,we provide an overview of research progress related to high-temperature deep reservoir fracturing fluid and the damage of aqueous fracturing fluids to rock matrix,both artificial and natural fractures,and sand-packed fractures.We conclude by offering a summary of current research advancements and future directions,which hold significant potential for facilitating the efficient development of deep oil and gas reservoirs while effectively mitigating reservoir damage.展开更多
Based on the global basement reservoir database and the dissection of basement reservoirs in China,the characteristics of hydrocarbon accumulation in basement reservoirs are analyzed,and the favorable conditions for h...Based on the global basement reservoir database and the dissection of basement reservoirs in China,the characteristics of hydrocarbon accumulation in basement reservoirs are analyzed,and the favorable conditions for hydrocarbon accumulation in deep basement reservoirs are investigated to highlight the exploration targets.The discovered basement reservoirs worldwide are mainly buried in the Archean and Precambrian granitic and metamorphic formations with depths less than 4500 m,and the relatively large reservoirs have been found in rift,back-arc and foreland basins in tectonic active zones of the Meso-Cenozoic plates.The hydrocarbon accumulation in basement reservoirs exhibits the characteristics in three aspects.First,the porous-fractured reservoirs with low porosity and ultra-low permeability are dominant,where extensive hydrocarbon accumulation occurred during the weathering denudation and later tectonic reworking of the basin basement.High resistance to compaction allows the physical properties of these highly heterogeneous reservoirs to be independent of the buried depth.Second,the hydrocarbons were sourced from the formations outside the basement.The source-reservoir assemblages are divided into contacted source rock-basement and separated source rock-basement patterns.Third,the abnormal high pressure in the source rock and the normal–low pressure in the basement reservoirs cause a large pressure difference between the source rock and the reservoirs,which is conducive to the pumping effect of hydrocarbons in the deep basement.The deep basement prospects are mainly evaluated by the factors such as tectonic activity of basement,source-reservoir combination,development of large deep faults(especially strike-slip faults),and regional seals.The Precambrian crystalline basements at the margin of the intracontinental rifts in cratonic basins,as well as the Paleozoic folded basements and the Meso-Cenozoic fault-block basements adjacent to the hydrocarbon generation depressions,have favorable conditions for hydrocarbon accumulation,and thus they are considered as the main targets for future exploration of deep basement reservoirs.展开更多
基金financial support from the National Natural Science Foundation of China(NSFC No.52202253,52072173)Natural Science Foundation of Jiangsu Province(No.BK20220914)+1 种基金Fundamental Research Funds for the Central Universities(No.ILA22061,ILA22075)Large Instrument and Equipment Sharing Fund of NUAA.
文摘Intuitively,the solvation structure featuring stronger interacted sheath in deep eutectic solution(DES)electrolyte would result in sluggish interfacial charge transfer and intense polarization,which obstructs its practical application in emerging Zn based batteries.Unexpectedly,here we discover a Zn‖organic battery with exceptional kinetics properties enabled by a hydrated DES electrolyte,which can render higher discharge capacity,smaller voltage polarization,and faster kinetics of charge transfer in comparison with conventional aqueous 3 M ZnCl_(2)electrolyte,though its viscosity is two orders of magnitude higher than the latter.The improved kinetics of charge transfer and ion diffusion is demonstrated to originate from the local electron structure regulation of cathode in hydrated DES electrolyte.Furthermore,the DES electrolyte has also been shown to restrict parasitic reaction associated with active water by preferential urea-molecular adsorption on Zn surface and stronger water trapping in solvation structure,giving rise to long-term stable dendrite-free Zn plating/stripping.This work provides a new rationale for understanding electrochemical behaviors of organic cathodes in DES electrolyte,which is conducive to the development of high-performance Zn‖organic batteries.
基金supported by the National Natural Science Foundation of China(Nos.U2032148,U2032157,11775224)USTC Research Funds of the Double First-Class Initiative(No.YD2310002008)the National Key Research and Development Program of China(No.2017YFA0402904),the Youth Innovation Promotion Association,CAS(No.2020457)。
文摘Grating-based X-ray phase-contrast imaging enhances the contrast of imaged objects,particularly soft tissues.However,the radiation dose in computed tomography(CT)is generally excessive owing to the complex collection scheme.Sparse-view CT collection reduces the radiation dose,but with reduced resolution and reconstructed artifacts particularly in analytical reconstruction methods.Recently,deep learning has been employed in sparse-view CT reconstruction and achieved stateof-the-art results.Nevertheless,its low generalization performance and requirement for abundant training datasets have hindered the practical application of deep learning in phase-contrast CT.In this study,a CT model was used to generate a substantial number of simulated training datasets,thereby circumventing the need for experimental datasets.By training a network with simulated training datasets,the proposed method achieves high generalization performance in attenuationbased CT and phase-contrast CT,despite the lack of sufficient experimental datasets.In experiments utilizing only half of the CT data,our proposed method obtained an image quality comparable to that of the filtered back-projection algorithm with full-view projection.The proposed method simultaneously addresses two challenges in phase-contrast three-dimensional imaging,namely the lack of experimental datasets and the high exposure dose,through model-driven deep learning.This method significantly accelerates the practical application of phase-contrast CT.
基金Project supported by the National Natural Science Foundation of China(Grant Nos.72101046 and 61672128)。
文摘Recent studies employing deep learning to solve the traveling salesman problem(TSP)have mainly focused on learning construction heuristics.Such methods can improve TSP solutions,but still depend on additional programs.However,methods that focus on learning improvement heuristics to iteratively refine solutions remain insufficient.Traditional improvement heuristics are guided by a manually designed search strategy and may only achieve limited improvements.This paper proposes a novel framework for learning improvement heuristics,which automatically discovers better improvement policies for heuristics to iteratively solve the TSP.Our framework first designs a new architecture based on a transformer model to make the policy network parameterized,which introduces an action-dropout layer to prevent action selection from overfitting.It then proposes a deep reinforcement learning approach integrating a simulated annealing mechanism(named RL-SA)to learn the pairwise selected policy,aiming to improve the 2-opt algorithm's performance.The RL-SA leverages the whale optimization algorithm to generate initial solutions for better sampling efficiency and uses the Gaussian perturbation strategy to tackle the sparse reward problem of reinforcement learning.The experiment results show that the proposed approach is significantly superior to the state-of-the-art learning-based methods,and further reduces the gap between learning-based methods and highly optimized solvers in the benchmark datasets.Moreover,our pre-trained model M can be applied to guide the SA algorithm(named M-SA(ours)),which performs better than existing deep models in small-,medium-,and large-scale TSPLIB datasets.Additionally,the M-SA(ours)achieves excellent generalization performance in a real-world dataset on global liner shipping routes,with the optimization percentages in distance reduction ranging from3.52%to 17.99%.
基金Supported by National Natural Science Foundation of China(51674209)Sichuan Province Youth Science and Technology Innovation Team(2021JDTD0017).
文摘Static adsorption and dynamic damage experiments were carried out on typical 8#deep coal rock of the Carboniferous Benxi Formation in the Ordos Basin,NW China,to evaluate the adsorption capacity of hydroxypropyl guar gum and polyacrylamide as fracturing fluid thickeners on deep coal rock surface and the permeability damage caused by adsorption.The adsorption morphology of the thickener was quantitatively characterized by atomic force microscopy,and the main controlling factors of the thickener adsorption were analyzed.Meanwhile,the adsorption mechanism of the thickener was revealed by Zeta potential,Fourier infrared spectroscopy and X-ray photoelectron spectroscopy.The results show that the adsorption capacity of hydroxypropyl guar gum on deep coal surface is 3.86 mg/g,and the permeability of coal rock after adsorption decreases by 35.24%–37.01%.The adsorption capacity of polyacrylamide is 3.29 mg/g,and the permeability of coal rock after adsorption decreases by 14.31%–21.93%.The thickness of the thickener adsorption layer is positively correlated with the mass fraction of thickener and negatively correlated with temperature,and a decrease in pH will reduce the thickness of the hydroxypropyl guar gum adsorption layer and make the distribution frequency of the thickness of polyacrylamide adsorption layer more concentrated.Functional group condensation and intermolecular force are chemical and physical forces for adsorbing fracturing fluid thickener in deep coal rock.Optimization of thickener mass fraction,chemical modification of thickener molecular,oxidative thermal degradation of polymer and addition of desorption agent can reduce the potential damages on micro-nano pores and cracks in coal rock.
文摘In the era of coronavirus disease 2019(COVID-19)pandemic,imported COVID-19 cases pose great challenges to many countries.Chest CT examination is considered to be complementary to nucleic acid test for COVID-19 detection and diagnosis.Wie report the first community infected COVID-19 patient by an imported case in Beijing,which manifested as nodular lesions on chest CT imaging at the early stage.Deep Learning(DL)-based diagnostic systems quantitatively monitored the progress of pulmonary lesions in 6 days and timely made alert for suspected pneumonia,so that prompt medical isolation was taken.The patient was confirmed as COVID-19 case after nucleic acid test,for which the community transmission was prevented timely.The roles of DL-assisted diagnosis in helping radiologists screening suspected COVID cases were discussed.
基金Supported by the National Natural Science Foundation of China(Grant No.51604236)Science and Technology Program of Sichuan Province(Grant No.2018JY0436)the Sichuan Province Youth Science and Technology Innovation Team Project(Grant No.2016TD0016)
文摘Focused on the lost circulation control in deep naturally fractured reservoirs, the multiscale structure of fracture plugging zone is proposed based on the theory of granular matter mechanics, and the structural failure pattern of plugging zone is developed to reveal the plugging zone failure mechanisms in deep, high temperature, high pressure, and high in-situ stress environment. Based on the fracture plugging zone strength model, key performance parameters are determined for the optimal selection of loss control material(LCM). Laboratory fracture plugging experiments with new LCM are carried out to evaluate the effect of the key performance parameters of LCM on fracture plugging quality. LCM selection strategy for fractured reservoirs is developed. The results show that the force chain formed by LCMs determines the pressure stabilization of macro-scale fracture plugging zone. Friction failure and shear failure are the two major failure patterns of fracture plugging zone. The strength of force chain depends on the performance of micro-scale LCM, and the LCM key performance parameters include particle size distribution, fiber aspect ratio, friction coefficient, compressive strength, soluble ability and high temperature resistance. Results of lab experiments and field test show that lost circulation control quality can be effectively improved with the optimal material selection based on the extracted key performance parameters of LCMs.
基金Supported by the China Geological Survey Project(DD20190115)
文摘Pure shales in the first member of Qingshankou Formation(simplified as Qing 1 Member)in the southern Songliao Basin,i.e.,the semi-deep and deep lacustrine shales,are characterized by a high content of clay minerals and poor hydrocarbon mobility,making the development of shale oil difficult.According to the drilling and testing results,the shale of Qing 1 Member can be classified into 3 lithofacies,i.e.,bedded argillaceous shale,laminated diamictite shale,and interbedded felsic shale.The TOC and brittle minerals control the enrichment of shale oil,of them,TOC controls the total oil content,in other words,the total oil content increases with the increase of TOC;while the laminae made up of brittle minerals contain a large number of bigger intergranular pores which are favorable enrichment space for movable shale oil.In consideration of the origins of the 3 lithofacies,two shale oil enrichment models are classified,i.e.,the deep lacustrine high-TOC bedded argillaceous shale(Model-I)and the semi-deep lacustrine moderate-high-TOC laminated diamictite shale(Model-II).In the Model-I,the shale is characterized by high hydrocarbon generation ability,high total oil content,abundant horizontal bedding fractures,and vertical and high angle fractures locally;the complex fracture network formed by horizontal bedding fractures and vertical fractures improve the storage capacity and permeability of the shale reservoir,increase the enrichment space for movable oil.In the Model-II,the shale is characterized by good hydrocarbon generation ability and fairly high total oil content,and as the brittle laminae contain large intergranular pores,the shale has a higher movable oil content.Based on the two models,shale oil sweet-spot areas of 2880 km2 in the southern Songliao Basin are favorable for further exploration.Aimed at the difficulties in reservoir fracturing of the lacustrine shale with a high content of clay minerals,the composite fracturing technology with supercritical carbon dioxide was used in the shale oil reservoir for the first time,realizing large-scale volume fracturing in shale with a high content of clay minerals and strong heterogeneity,marking a breakthrough of oil exploration in continental shale with a high content of clay minerals in China.
基金supported in part by the National Key R&D Project of China under Grant 2020YFA0712300National Natural Science Foundation of China under Grant NSFC-62231022,12031011supported in part by the NSF of China under Grant 62125108。
文摘We consider an image semantic communication system in a time-varying fading Gaussian MIMO channel,with a finite number of channel states.A deep learning-aided broadcast approach scheme is proposed to benefit the adaptive semantic transmission in terms of different channel states.We combine the classic broadcast approach with the image transformer to implement this adaptive joint source and channel coding(JSCC)scheme.Specifically,we utilize the neural network(NN)to jointly optimize the hierarchical image compression and superposition code mapping within this scheme.The learned transformers and codebooks allow recovering of the image with an adaptive quality and low error rate at the receiver side,in each channel state.The simulation results exhibit our proposed scheme can dynamically adapt the coding to the current channel state and outperform some existing intelligent schemes with the fixed coding block.
基金National Research Foundation (NRF) of South Korea (NRF-2022R1A2C1004392)Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (IRIS RS-202300240109)。
文摘Characterizing and control the chemical compositions of multi-element particles as single metal nanoparticles(mNPs) on the surfaces of catalytic metal oxide supports is challenging.This can be attributed to the heterogeneity and large size at the nanoscale,the poorly defined catalyst nanostructure,and thermodynamic immiscibility of the strongly repelling metallic elements.To address these challenges,an ultrasonic-assisted coincident electro-oxidation-reduction-precipitation(U-SEO-P) is presented to fabricate ultra-stable PtRuAgCoCuP NPs,which produces numerous active intermediates and induces strong metal-support interactions.To sort the active high-entropy mNPs,individual NPs are described on the support surface and the role of deep learning in understanding/predicting the features of PtRuAgCoCu@TiO_(x) catalysts is explained.Notably,this deep learning approach required minimal to no human input.The as-prepared PtRuAgCoCu@TiO_(x) catalysts can be used to catalyze various important chemical reactions,such as a high reduction conversion(100% in 30 s),with no loss of catalytic activity even after 20 cycles of nitroarene and ketone/aldehyde,which is several times higher than commercial Pt@TiO_(x) owing to individual PtRuAgCoCuP NPs on TiO_(x) surface.In this study,we present the "Totally Defined Catalysis" concept,which has enormous potential for the advancement of high-activity catalysts in the reduction of organic compounds.
基金Dao-Bing Wang was supported by the Beijing Natural Science Foundation Project(No.3222030)the National Natural Science Foundation of China(No.52274002)+1 种基金the PetroChina Science and Technology Innovation Foundation Project(No.2021DQ02-0201)Fu-Jian Zhou was supported by the National Natural Science Foundation of China(No.52174045).
文摘Deep and ultra-deep reservoirs have gradually become the primary focus of hydrocarbon exploration as a result of a series of significant discoveries in deep hydrocarbon exploration worldwide.These reservoirs present unique challenges due to their deep burial depth(4500-8882 m),low matrix permeability,complex crustal stress conditions,high temperature and pressure(HTHP,150-200℃,105-155 MPa),coupled with high salinity of formation water.Consequently,the costs associated with their exploitation and development are exceptionally high.In deep and ultra-deep reservoirs,hydraulic fracturing is commonly used to achieve high and stable production.During hydraulic fracturing,a substantial volume of fluid is injected into the reservoir.However,statistical analysis reveals that the flowback rate is typically less than 30%,leaving the majority of the fluid trapped within the reservoir.Therefore,hydraulic fracturing in deep reservoirs not only enhances the reservoir permeability by creating artificial fractures but also damages reservoirs due to the fracturing fluids involved.The challenging“three-high”environment of a deep reservoir,characterized by high temperature,high pressure,and high salinity,exacerbates conventional forms of damage,including water sensitivity,retention of fracturing fluids,rock creep,and proppant breakage.In addition,specific damage mechanisms come into play,such as fracturing fluid decomposition at elevated temperatures and proppant diagenetic reactions at HTHP conditions.Presently,the foremost concern in deep oil and gas development lies in effectively assessing the damage inflicted on these reservoirs by hydraulic fracturing,comprehending the underlying mechanisms,and selecting appropriate solutions.It's noteworthy that the majority of existing studies on reservoir damage primarily focus on conventional reservoirs,with limited attention given to deep reservoirs and a lack of systematic summaries.In light of this,our approach entails initially summarizing the current knowledge pertaining to the types of fracturing fluids employed in deep and ultra-deep reservoirs.Subsequently,we delve into a systematic examination of the damage processes and mechanisms caused by fracturing fluids within the context of hydraulic fracturing in deep reservoirs,taking into account the unique reservoir characteristics of high temperature,high pressure,and high in-situ stress.In addition,we provide an overview of research progress related to high-temperature deep reservoir fracturing fluid and the damage of aqueous fracturing fluids to rock matrix,both artificial and natural fractures,and sand-packed fractures.We conclude by offering a summary of current research advancements and future directions,which hold significant potential for facilitating the efficient development of deep oil and gas reservoirs while effectively mitigating reservoir damage.
基金Supported by the Science and Technology Project of China National Petroleum Corporation(2021DJ02).
文摘Based on the global basement reservoir database and the dissection of basement reservoirs in China,the characteristics of hydrocarbon accumulation in basement reservoirs are analyzed,and the favorable conditions for hydrocarbon accumulation in deep basement reservoirs are investigated to highlight the exploration targets.The discovered basement reservoirs worldwide are mainly buried in the Archean and Precambrian granitic and metamorphic formations with depths less than 4500 m,and the relatively large reservoirs have been found in rift,back-arc and foreland basins in tectonic active zones of the Meso-Cenozoic plates.The hydrocarbon accumulation in basement reservoirs exhibits the characteristics in three aspects.First,the porous-fractured reservoirs with low porosity and ultra-low permeability are dominant,where extensive hydrocarbon accumulation occurred during the weathering denudation and later tectonic reworking of the basin basement.High resistance to compaction allows the physical properties of these highly heterogeneous reservoirs to be independent of the buried depth.Second,the hydrocarbons were sourced from the formations outside the basement.The source-reservoir assemblages are divided into contacted source rock-basement and separated source rock-basement patterns.Third,the abnormal high pressure in the source rock and the normal–low pressure in the basement reservoirs cause a large pressure difference between the source rock and the reservoirs,which is conducive to the pumping effect of hydrocarbons in the deep basement.The deep basement prospects are mainly evaluated by the factors such as tectonic activity of basement,source-reservoir combination,development of large deep faults(especially strike-slip faults),and regional seals.The Precambrian crystalline basements at the margin of the intracontinental rifts in cratonic basins,as well as the Paleozoic folded basements and the Meso-Cenozoic fault-block basements adjacent to the hydrocarbon generation depressions,have favorable conditions for hydrocarbon accumulation,and thus they are considered as the main targets for future exploration of deep basement reservoirs.
