Ecosystems along the eastern margin of the Qinghai-Tibet Plateau(EQTP)are highly fragile and extremely sensitive to climate change and human disturbances.To quantitatively assess climate-induced ecosystem responses,th...Ecosystems along the eastern margin of the Qinghai-Tibet Plateau(EQTP)are highly fragile and extremely sensitive to climate change and human disturbances.To quantitatively assess climate-induced ecosystem responses,this study proposes a Climate-Induced Productivity Index(CIPI)based on the Super Slack-Based Measure(Super-SBM)model using remote sensing data from 2001 to 2020.The results reveal persistently low CIPI values(0.47-0.53)across major ecosystem types,indicating widespread vulnerability to climatic variability.Among these ecosystems,forests exhibit the highest CIPI(0.55),followed by shrublands(0.54),croplands(0.53),grasslands(0.51),and barelands(0.43).The Theil index analysis further demonstrates significant intra-group disparities,suggesting that extreme climatic events amplify CIPI heterogeneity.Moreover,the dominant environmental drivers differ among ecosystem types:the Palmer Drought Severity Index(PDSI)primarily constrains grassland productivity,solar radiation(SRAD)strongly influences shrub and cropland systems,whereas subsurface factors exert greater control in forested regions.This study provides a quantitative framework for evaluating climate-ecosystem interactions and offers a scientific basis for long-term ecological monitoring and security planning across the EQTP.展开更多
Vegetation in terrestrial ecosystems as a carbon sink is a crucial factor in mitigating global warming and reaching carbon neutrality targets,although the drivers of net ecosystem productivity(NEP)under combined human...Vegetation in terrestrial ecosystems as a carbon sink is a crucial factor in mitigating global warming and reaching carbon neutrality targets,although the drivers of net ecosystem productivity(NEP)under combined human and environmental pressures remain poorly understood.In this study,we analyzed the spatiotemporal evolution of NEP in the Horqin Sandy Land,China from 2000 to 2020,and observed the variation in NEP across different land use types.We further identified and quantified the effects of human activities,topographical features,climatic conditions,and soil properties on NEP through the application of structural equation modeling(SEM)and boosted regression trees(BRT).The results showed that the multi-year average NEP ranged from–137.79 to 461.96 g C/m^(2) in the Horqin Sandy Land,with 88.21%of the area showing a significant increasing trend.Among different land use types,forestland exhibited the highest NEP values,followed by cropland,grassland,impervious land,and unused land.The NEP in carbon sink areas was primarily regulated by potential evapotranspiration(negatively correlated)and precipitation(positively correlated).Slope was identified as the most significant positive determinant in carbon source areas.Forestland exhibited climate–topography interactions driving NEP,whereas cropland and grassland relied on temperature;unused land and impervious land were susceptible to land use/cover change and human footprint.This study has significant implications for maintaining the carbon sink function and promoting ecological engineering programs that aim to enhance the capacity of terrestrial carbon sinks in the semi-arid agro-pastoral ecotone.展开更多
Gross primary production(GPP)is closely associated with processes such as photosynthesis and transpiration within ecosystems,which is a vital component of the global carbon-water-energy cycle.Accurate prediction of GP...Gross primary production(GPP)is closely associated with processes such as photosynthesis and transpiration within ecosystems,which is a vital component of the global carbon-water-energy cycle.Accurate prediction of GPP in terrestrial ecosystems is essential for evaluating terrestrial carbon cycle processes.Machine learning(ML)models provide significant technical support in this domain.Presently,there is a deficiency of high-precision and robust GPP prediction variables and models.Challenges such as unclear contributions of predictive variables,extended model training durations,and limited robustness must be addressed.Solar-induced chlorophyll fluorescence(SIF),optimized multilayer perceptron neural networks,and ensemble learning models show the potential to overcome these challenges.This study aimed to develop an optimized multilayer perceptron neural network model and an ensemble learning model,while objectively assessing the capacity of SIF to predict GPP.Identifying robust models capable of enhancing the accuracy of GPP predictions was the ultimate goal.This study utilized continuous observations of SIF and meteorological data collected from 2020 to 2021 at a designated research observation station within the Populus plantation ecosystem of the Huanghuaihai agricultural protective forest system in Henan Province,China.By optimizing and evaluating the predictive accuracy and robustness of the models across different temporal scales(half-hourly and daily scales),a multi-layer perceptron(MLP)neural network optimization model based on the back propagation(BP)neural network(BPNN)algorithm(BP/MLP)and MLP and random forest(RF)integration(MLP-RF)ensemble models were constructed,utilizing SIF as the primary predictive variable for GPP.Both the BP/MLP(half-hourly scale model R^(2)=0.885,daily scale model R^(2)=0.921)and the MLP-RF(half-hourly scale model R^(2)=0.845,daily scale model R^(2)=0.914)models showed superior accuracy compared to the BPNN(half-hourly scale model R^(2)=0.841,daily scale model R^(2)=0.918)and the traditional RF(half-hourly scale model R^(2)=0.798,daily scale model R^(2)=0.867)models,with the BP/MLP model consistently outperforming the MLP-RF model.The BP/MLP model,which was optimized through particle swarm optimization(PSO),significantly enhanced the robustness of GPP predictions on a half-hourly scale and daily scale.Considering both half-hourly scale and daily scale in the PSO-BP/MLP modeling,the four indicators,light-use efficiency(LUE),photosynthetically active radiation(PAR),absorbed photosynthetically active radiation(APAR),and the variation in SIF with NIRvP(fSIF(NIRvP)),exhibited the potential for enhancing the accuracy of GPP predictions.This study employed a series of model optimization techniques to develop a GPP prediction model with enhanced performance that objectively evaluated the contributions of the predictive variables.This approach provided an innovative and effective method for assessing the carbon cycle in terrestrial ecosystems.展开更多
Increasing basic farmland soil productivity has significance in reducing fertilizer application and maintaining high yield of crops. In this study, we defined that the basic soil productivity （BSP） is the production...Increasing basic farmland soil productivity has significance in reducing fertilizer application and maintaining high yield of crops. In this study, we defined that the basic soil productivity （BSP） is the production capacity of a farmland soil with its own physical and chemical properties for a specific crop season under local environment and field management. Based on 22-yr （1990-2011） long-term experimental data on black soil （Typic hapludoll） in Gongzhuling, Jilin Province, Northeast China, the decision support system for an agro-technology transfer （DSSAT）-CERES-Maize model was applied to simulate the yield by BSP of spring maize （Zea mays L.） to examine the effects of long-term fertilization on changes of BSP and explore the mechanisms of BSP increasing. Five treatments were examined： （1） no-fertilization control （control）; （2） chemical nitrogen, phosphorus, and potassium （NPK）; （3） NPK plus farmyard manure （NPKM）; （4） 1.5 time of NPKM （1.5NPKM） and （5） NPK plus straw （NPKS）. Results showed that after 22-yr fertilization, the yield by BSP of spring maize significantly increased 78.0, 101.2, and 69.4% under the NPKM, 1.5NPKM and NPKS, respectively, compared to the initial value （in 1992）, but not significant under NPK （26.9% increase） and the control （8.9% decrease）. The contribution percentage of BSP showed a significant rising trend （P〈0.05） under 1.5NPKM. The average contribution percentage of BSP among fertilizations ranged from 74.4 to 84.7%, and ranked as 1.5NPKM〉NPKM〉NPK〉NPKS, indicating that organic manure combined with chemical fertilizers （I.5NPKM and NPKM） could more effectively increase BSP compared with the inorganic fertilizer application alone （NPK） in the black soil. This study showed that soil organic matter （SOM） was the key factor among various fertility factors that could affect BSP in the black soil, and total N, total P and/or available P also played important role in BSP increasing. Compared with the chemical fertilization, a balanced chemical plus manure or straw fertilization （NPKM or NPKS） not only increased the concentrations of soil nutrient, but also improved the soil physical properties, and structure and diversity of soil microbial population, resulting in an iincrease of BSP. We recommend that a balanced chemical plus manure or straw fertilization （NPKM or NPKS） should be the fertilization practices to enhance spring maize yield and improve BSP in the black soil of Northeast China.展开更多
A Nutrient -Phytoplankton -Zooplankton（NPZD） type of ecological model is developed to reflect the biochemical process, and further coupled to a primitive equation ocean model, an irradiation model as well as a river...A Nutrient -Phytoplankton -Zooplankton（NPZD） type of ecological model is developed to reflect the biochemical process, and further coupled to a primitive equation ocean model, an irradiation model as well as a river discharge model to reproduce ecosystem dynamics in the Bohai Sea. Modeled primary production shows reasonable consistency with observations quantitatively and qualitatively; in addition, f-ratio is examined in detail in the first time, which is also within the range reported in other studies and reveals some meaningful insight into the relative contributions of ammonium and nitrate to the growth of phytoplankton in the Bohai Sea.展开更多
Spatial scaling for net primary productivity （NPP） refers to the transferring process of establishing quantitative correlation between simulated NPP derived from data at different spatial resolutions. How to transfe...Spatial scaling for net primary productivity （NPP） refers to the transferring process of establishing quantitative correlation between simulated NPP derived from data at different spatial resolutions. How to transfer NPP at one scale by the algorithm with smaller error to at another is the urgent problem. Nonlinearity and effects from land cover type are two main problems in NPP scaling. In this paper, the contextural approach based on mixed pixels and support vector machine （SVM） algorithm are used to make the scaling model from the fine resolution （TM） to the coarse resolution （MODIS）. Spatial scaling from NPP retrieved from fine resolution data to NPP derived from coarse resolution images is performed, and the correction of scale effect to NPP retrieved from coarse resolution data of MODIS is accomplished. The result shows that the correlation between Rj_coereted of the correction factor for scale effect and 1-Fmiddle dessity grassland estimated by SVM regression model is higher （R2=0.81）. Before the correction for scale effect, the correlation between NPPMODIS and NPPTM is lower （R2=0.69; RMSE=3.47）, while the correlation between NPPTM and corrected NPPMODIS_corrected is higher （R2=0.84; RMSE= 1.87）. Therefore, NPP corrected for scale effect has been greatly improved in both correlation and error.展开更多
Fire is a global phenomenon and a major source of aerosols from the terrestrial biosphere to the atmosphere.Most previous studies quantified the effect of fire aerosols on climate and atmospheric circulation,or on the...Fire is a global phenomenon and a major source of aerosols from the terrestrial biosphere to the atmosphere.Most previous studies quantified the effect of fire aerosols on climate and atmospheric circulation,or on the regional and site-scale terrestrial ecosystem productivity.So far,only one work has quantified their global impacts on terrestrial ecosystem productivity based on offline simulations,which,however,did not consider the impacts of aerosol–cloud interactions and aerosol–climate feedbacks.This study quantitatively assesses the influence of fire aerosols on the global annual gross primary productivity(GPP)of terrestrial ecosystems using simulations with the fully coupled global Earth system model CESM1.2.Results show that fire aerosols generally decrease GPP in vegetated areas,with a global total of−1.6 Pg C yr^−1,mainly because fire aerosols cool and dry the land surface and weaken the direct photosynthetically active radiation(PAR).The exception to this is the Amazon region,which is mainly due to a fire-aerosol-induced wetter land surface and increased diffuse PAR.This study emphasizes the importance of the influence of fire aerosols on climate in quantifying global-scale fire aerosols’impacts on terrestrial ecosystem productivity.展开更多
A model of a potentially effective type energy-resource-saving of optimization of agro-technologies, based on the principle subordination of synergetics, was established. There was developed computer system energy-res...A model of a potentially effective type energy-resource-saving of optimization of agro-technologies, based on the principle subordination of synergetics, was established. There was developed computer system energy-resource-saving optimization of agricultural technologies. The main feature of crop production is provided by the plants which themselves are self-organizing organisms. This allows us to adopt the principle of subordination of synergetics as the basis of the model. The value of free energy at the input into plants, estimated by the process of photosynthesis, is equal to the value of “radiation exergy for plant growth”. Assessment of the use of radiation energy is carried out based on the energy-converting characteristics of plants, which were obtained in climate chambers under controlled conditions. We used the model based on the principle of subordination of synergetics to develop common quantitative mutually agreed definitions of the main agroecological variables: Agroclimatic and Meliorative potentials of lands, their fertility, and potential (maximum) productivity of plants under different environment conditions.展开更多
Compared with conventional well, herringbone-like laterals wells can increase the area of oil release, and can reduce the number of wellhead slots of platforms,?and?also can greatly improve the development efficiency....Compared with conventional well, herringbone-like laterals wells can increase the area of oil release, and can reduce the number of wellhead slots of platforms,?and?also can greatly improve the development efficiency. Based on threshold pressure gradient in heavy oil reservoir,?and?the applied principle of mirror reflection and superposition, the pressure distribution equation of herringbone-like laterals wells is obtained in heavy oil reservoir. Productivity model of herringbone-like laterals wells is proposed by reservoir-wellbore steady seepage. The example shows that the productivity model is great accuracy?to?predict the productivity of herringbone-like laterals wells. The model is used to analyze the branching length, branching angle, branching symmetry, branching position and spacing and their effects on productivity of herringbone-like laterals wells. The principle of optimizing the well shape of herringbone-like laterals wells is proposed.展开更多
The hydrocarbon deposits have stimulated worldwide efforts to understand gas production from hydrate dissociation in hydrate reservoirs well. This paper deals with the potential of gas hydrates as a source of energy w...The hydrocarbon deposits have stimulated worldwide efforts to understand gas production from hydrate dissociation in hydrate reservoirs well. This paper deals with the potential of gas hydrates as a source of energy which is widely available in permafrost and oceanic sediments. It discusses methods for gas production from natural gas hydrates. Authors provide a detailed methodology used to model gas productivity recovery from hydrate reservoir well. The mathematical modelling of gas dissociation from hydrate reservoir as a tool for evaluating the potential of gas hydrates for natural gas production. The simulation results show that the process of natural gas production in a hydrate reservoir is a sensitive function of reservoir temperature and hydrate zone permeability. The model couples nth order decomposition kinetics with gas flow through porous media. The models provide a simple and useful tool for hydrate reservoir analysis.展开更多
[Objective]The aim was to establish the linear regression prediction models between sowing time and plant productivity, biological yield of forage sorghum in autumn idle land.[Method]The relationships between sowing t...[Objective]The aim was to establish the linear regression prediction models between sowing time and plant productivity, biological yield of forage sorghum in autumn idle land.[Method]The relationships between sowing time and plant productivity, biological yield of forage sorghum were simulated and compared by using field experiment and linear regression analysis.[Result] The sowing time had an important influence on the plant productivity and biological yield of forage sorghum in autumn idle land. The plant productivity and biological yield of forage sorghum both decreased with the delay of sowing time.The regression model between plant fresh weight and sowing time was ？fresh=0.618-0.015x; the regression model between plant dry weight and sowing time was ？dry=0.184-0.005x; and the regression model between biological yield and sowing time was yield=29 126.461-711.448x. During July 23rd to August 30th, when the sowing time was delayed by 1 day, the plant fresh weight of forage sorghum was reduced by 0.015 g, the plant dry weight was reduced by 0.005 g, and the yield was reduced by 711.448 kg/hm2. [Conclusion] The three regression models established in this study will provide theoretical support for the production of forage sorghum.展开更多
The generation method of three-dimensional fractal discrete fracture network(FDFN)based on multiplicative cascade process was developed.The complex multi-scale fracture system in shale after fracturing was characteriz...The generation method of three-dimensional fractal discrete fracture network(FDFN)based on multiplicative cascade process was developed.The complex multi-scale fracture system in shale after fracturing was characterized by coupling the artificial fracture model and the natural fracture model.Based on an assisted history matching(AHM)using multiple-proxy-based Markov chain Monte Carlo algorithm(MCMC),an embedded discrete fracture modeling(EDFM)incorporated with reservoir simulator was used to predict productivity of shale gas well.When using the natural fracture generation method,the distribution of natural fracture network can be controlled by fractal parameters,and the natural fracture network generated coupling with artificial fractures can characterize the complex system of different-scale fractures in shale after fracturing.The EDFM,with fewer grids and less computation time consumption,can characterize the attributes of natural fractures and artificial fractures flexibly,and simulate the details of mass transfer between matrix cells and fractures while reducing computation significantly.The combination of AMH and EDFM can lower the uncertainty of reservoir and fracture parameters,and realize effective inversion of key reservoir and fracture parameters and the productivity forecast of shale gas wells.Application demonstrates the results from the proposed productivity prediction model integrating FDFN,EDFM and AHM have high credibility.展开更多
Proton Exchange Membrane Water Electrolyzers(PEMWE)are efficient and sustainable hydrogen production devices.This article analyzes their static and dynamic electrical models integrated with degradation mechanisms.Stat...Proton Exchange Membrane Water Electrolyzers(PEMWE)are efficient and sustainable hydrogen production devices.This article analyzes their static and dynamic electrical models integrated with degradation mechanisms.Static models reveal steady-state behavior,while dynamic models capture transient responses to input variations.The developed modeling approach combines the activation and diffusion phenomena,resulting in a novel PEMWE model that closely reflects real-world conditions and enables fast simulations.The electrical model is integrated with the aging model through two key ratios,surface degradation ratio and membrane degradation ratio,which characterize degradation mechanisms affecting electrode and membrane performance.The linear model using second-order Taylor approximation enables the development of a diagnosis approach that can contribute to estimating the remaining useful life of PEMWEs.By associating aging models with electrical models through the proposed ratios,a deeper understanding is achieved regarding how degra-dation phenomena evolve and influence electrolyzer efficiency and durability.The integrated framework enables predictive maintenance strategies,making it valuable for industrial hydrogen production applications.展开更多
Green technology innovation has gradually become an important driving force to promote new quality productivity.This paper constructs a quantitative index system of new quality productivity based on the three major el...Green technology innovation has gradually become an important driving force to promote new quality productivity.This paper constructs a quantitative index system of new quality productivity based on the three major elements of workers,labour objects and labour tools,and empirically analyses the impact of green technology innovation on the level of new quality productivity using spatial econometric model and VAR model.The result shows that:(1)The level of new quality productivity is not only affected by its own factors,but also by the significant spatial spillover effect between regions,especially in the case of strong geographic proximity,the interregional economic activities and resource allocation have a strong interaction and dependence.(2)The direct effect of green technology innovation is negative,mainly due to the high R&D investment and the short-term cost increase brought about by technological transformation,but its indirect effect is positive,showing that green technology has a positive effect on the new quality productivity enhancement of neighbouring regions through technology diffusion and cooperative innovation.(3)The eastern and western regions are affected by high upfront costs and transformation challenges,showing negative effects;while the central and northeastern regions benefit from policy support and industrial upgrading,showing positive effects.(4)Impulse response function analysis shows that the short-term impact of green technological innovation on new quality productivity is negative,but the long-term potential is significant,and the negative effect gradually diminishes over time.Based on this,this paper puts forward the suggestions of optimising the green innovation input structure,formulating regional differentiated policies and strengthening regional synergistic cooperation,which provide the theoretical basis and practical path for realising the green transformation and high-quality development of the economy.展开更多
In the realm of subsurface flow simulations,deep-learning-based surrogate models have emerged as a promising alternative to traditional simulation methods,especially in addressing complex optimization problems.However...In the realm of subsurface flow simulations,deep-learning-based surrogate models have emerged as a promising alternative to traditional simulation methods,especially in addressing complex optimization problems.However,a significant challenge lies in the necessity of numerous high-fidelity training simulations to construct these deep-learning models,which limits their application to field-scale problems.To overcome this limitation,we introduce a training procedure that leverages transfer learning with multi-fidelity training data to construct surrogate models efficiently.The procedure begins with the pre-training of the surrogate model using a relatively larger amount of data that can be efficiently generated from upscaled coarse-scale models.Subsequently,the model parameters are finetuned with a much smaller set of high-fidelity simulation data.For the cases considered in this study,this method leads to about a 75%reduction in total computational cost,in comparison with the traditional training approach,without any sacrifice of prediction accuracy.In addition,a dedicated well-control embedding model is introduced to the traditional U-Net architecture to improve the surrogate model's prediction accuracy,which is shown to be particularly effective when dealing with large-scale reservoir models under time-varying well control parameters.Comprehensive results and analyses are presented for the prediction of well rates,pressure and saturation states of a 3D synthetic reservoir system.Finally,the proposed procedure is applied to a field-scale production optimization problem.The trained surrogate model is shown to provide excellent generalization capabilities during the optimization process,in which the final optimized net-present-value is much higher than those from the training data ranges.展开更多
Smart cities,a new kind of urbanization,offer a means of achieving the condition in which environmental conservation and economic growth are mutually beneficial.As a result,it is important to think about whether and h...Smart cities,a new kind of urbanization,offer a means of achieving the condition in which environmental conservation and economic growth are mutually beneficial.As a result,it is important to think about whether and how the development of smart cities might support the high-quality growth of urban economies.Based on the panel data of 163 prefecture-level cities in China from 2009–2018,the green total factor productivity(GTFP)of each prefecture-level city is measured using the SBM-GML model,and the appropriate spatial econometric model is screened by various types of tests.The spatial effect of smart city construction on GFTP is studied,and it is concluded that the pilot cities have a significant positive spatial spillover effect.The decomposition econometric model also shows that the pilot cities have a significant positive spatial spillover effect,and it also indicating that the smart city construction can also drive the surrounding cities to jointly improve the quality of economic development.Finally,the robustness of the spatial effect of smart city policy is also verified by changing the spatial measurement model and the type of spatial weight matrix,which also shows that the results of the spatial spillover effect of smart city construction are reliable.展开更多
This study introduces a novel approach to addressing the challenges of high-dimensional variables and strong nonlinearity in reservoir production and layer configuration optimization.For the first time,relational mach...This study introduces a novel approach to addressing the challenges of high-dimensional variables and strong nonlinearity in reservoir production and layer configuration optimization.For the first time,relational machine learning models are applied in reservoir development optimization.Traditional regression-based models often struggle in complex scenarios,but the proposed relational and regression-based composite differential evolution(RRCODE)method combines a Gaussian naive Bayes relational model with a radial basis function network regression model.This integration effectively captures complex relationships in the optimization process,improving both accuracy and convergence speed.Experimental tests on a multi-layer multi-channel reservoir model,the Egg reservoir model,and a real-field reservoir model(the S reservoir)demonstrate that RRCODE significantly reduces water injection and production volumes while increasing economic returns and cumulative oil recovery.Moreover,the surrogate models employed in RRCODE exhibit lightweight characteristics with low computational overhead.These results highlight RRCODE's superior performance in the integrated optimization of reservoir production and layer configurations,offering more efficient and economically viable solutions for oilfield development.展开更多
Background: Predictive models shed light on aboveground fungal yield dynamics and can assist decision-making in forestry by integrating this valuable non-wood forest product into forest management planning. However, t...Background: Predictive models shed light on aboveground fungal yield dynamics and can assist decision-making in forestry by integrating this valuable non-wood forest product into forest management planning. However, the currently existing models are based on rather local data and, thus, there is a lack of predictive tools to monitor mushroom yields on larger scales.Results: This work presents the first empirical models for predicting the annual yields of ectomycorrhizal mushrooms and related ecosystem services in Pinus sylvestris and Pinus pinaster stands in northern Spain, using a long-term dataset suitable to account for the combined effect of meteorological conditions and stand structure.Models were fitted for the following groups of fungi separately: all ectomycorrhizal mushrooms, edible mushrooms and marketed mushrooms. Our results show the influence of the weather variables(mainly precipitation) on mushroom yields as well as the relevance of the basal area of the forest stand that follows a right-skewed unimodal curve with maximum predicted yields at stand basal areas of 30–40 m2·ha-1.Conclusion: These models are the first empirical models for predicting the annual yields of ectomycorrhizal mushrooms in Pinus sylvestris and Pinus pinaster stands in northern Spain, being of the highest resolution developed to date and enable predictions of mushrooms productivity by taking into account weather conditions and forests’ location, composition and structure.展开更多
Productivity prediction plays an important role in the efficient and rational development of shale gas reservoirs.Current research on the productivity of multistage fractured horizontal wells in shale gas reservoirs d...Productivity prediction plays an important role in the efficient and rational development of shale gas reservoirs.Current research on the productivity of multistage fractured horizontal wells in shale gas reservoirs does not consider the stress-sensitive effects of natural fractures,hydraulic fracture morphology,and seepage characteristics in the same capacity model.Therefore,we considered the adsorption,desorption,and diffusion mechanisms(pseudo-steady state and transient diffusion)of shale gas in reservoirs and the stress-sensitive effects of natural fractures based on the dual-medium seepage theory model.Thefinite conductivity of the hydraulic fracture and hydraulic fracture azimuth were considered in the hydraulic fracture model.The source function method was used to discretize the crack,and the hydraulic fracture model was superimposed.Finally,the two models were coupled to obtain the unstable seepage and productivity models of the multistage fractured hor-izontal well in a shale gas reservoir.According to the established horizontal well production model of shale gas fracturing,the production characteristic curve was calculated by programming,and the simulation results were compared with thefield data of shale gas wells to verify the accuracy of the model.We used the model to analyze the effects of fracture conductivity,fracture half-length,fracture spacing,skin factor,storage ratio and leakage coefficient on productivity.展开更多
Improving the quality of human resources through edu-cation and training is of great strategic significance.However, few people have done in depth and systematicstudies on the relationship between education and pro-du...Improving the quality of human resources through edu-cation and training is of great strategic significance.However, few people have done in depth and systematicstudies on the relationship between education and pro-ducttvity in China. This study developed an educationproductivity transform model and empirically tested themodel using data on the most districts of China. The ba-sic notion is that the effect depends both on the capabilityresulted from education, and on the environment inwhich this capability can be exercised, the effect is a re-sult of two factors combined.展开更多
基金National Key R&D Program of China,No.2022YFF1302401National Natural Science Foundation of China,No.42271007。
文摘Ecosystems along the eastern margin of the Qinghai-Tibet Plateau(EQTP)are highly fragile and extremely sensitive to climate change and human disturbances.To quantitatively assess climate-induced ecosystem responses,this study proposes a Climate-Induced Productivity Index(CIPI)based on the Super Slack-Based Measure(Super-SBM)model using remote sensing data from 2001 to 2020.The results reveal persistently low CIPI values(0.47-0.53)across major ecosystem types,indicating widespread vulnerability to climatic variability.Among these ecosystems,forests exhibit the highest CIPI(0.55),followed by shrublands(0.54),croplands(0.53),grasslands(0.51),and barelands(0.43).The Theil index analysis further demonstrates significant intra-group disparities,suggesting that extreme climatic events amplify CIPI heterogeneity.Moreover,the dominant environmental drivers differ among ecosystem types:the Palmer Drought Severity Index(PDSI)primarily constrains grassland productivity,solar radiation(SRAD)strongly influences shrub and cropland systems,whereas subsurface factors exert greater control in forested regions.This study provides a quantitative framework for evaluating climate-ecosystem interactions and offers a scientific basis for long-term ecological monitoring and security planning across the EQTP.
基金funded by the National Major Science and Technology Program for Water Pollution Control and Treatment(2017ZX07101-002)the Discipline Construction Program of ZHANG Huayong,Distinguished Professor of School of Life Sciences,Shandong University(61200082363001).
文摘Vegetation in terrestrial ecosystems as a carbon sink is a crucial factor in mitigating global warming and reaching carbon neutrality targets,although the drivers of net ecosystem productivity(NEP)under combined human and environmental pressures remain poorly understood.In this study,we analyzed the spatiotemporal evolution of NEP in the Horqin Sandy Land,China from 2000 to 2020,and observed the variation in NEP across different land use types.We further identified and quantified the effects of human activities,topographical features,climatic conditions,and soil properties on NEP through the application of structural equation modeling(SEM)and boosted regression trees(BRT).The results showed that the multi-year average NEP ranged from–137.79 to 461.96 g C/m^(2) in the Horqin Sandy Land,with 88.21%of the area showing a significant increasing trend.Among different land use types,forestland exhibited the highest NEP values,followed by cropland,grassland,impervious land,and unused land.The NEP in carbon sink areas was primarily regulated by potential evapotranspiration(negatively correlated)and precipitation(positively correlated).Slope was identified as the most significant positive determinant in carbon source areas.Forestland exhibited climate–topography interactions driving NEP,whereas cropland and grassland relied on temperature;unused land and impervious land were susceptible to land use/cover change and human footprint.This study has significant implications for maintaining the carbon sink function and promoting ecological engineering programs that aim to enhance the capacity of terrestrial carbon sinks in the semi-arid agro-pastoral ecotone.
基金supported by the National Key R&D Program of China(No.2023YFD2200400-01)the Fundamental Scientific Research Operation of Central-level Public Welfare Scientific Research Institutes(No.CAFYBB2023MA001).
文摘Gross primary production(GPP)is closely associated with processes such as photosynthesis and transpiration within ecosystems,which is a vital component of the global carbon-water-energy cycle.Accurate prediction of GPP in terrestrial ecosystems is essential for evaluating terrestrial carbon cycle processes.Machine learning(ML)models provide significant technical support in this domain.Presently,there is a deficiency of high-precision and robust GPP prediction variables and models.Challenges such as unclear contributions of predictive variables,extended model training durations,and limited robustness must be addressed.Solar-induced chlorophyll fluorescence(SIF),optimized multilayer perceptron neural networks,and ensemble learning models show the potential to overcome these challenges.This study aimed to develop an optimized multilayer perceptron neural network model and an ensemble learning model,while objectively assessing the capacity of SIF to predict GPP.Identifying robust models capable of enhancing the accuracy of GPP predictions was the ultimate goal.This study utilized continuous observations of SIF and meteorological data collected from 2020 to 2021 at a designated research observation station within the Populus plantation ecosystem of the Huanghuaihai agricultural protective forest system in Henan Province,China.By optimizing and evaluating the predictive accuracy and robustness of the models across different temporal scales(half-hourly and daily scales),a multi-layer perceptron(MLP)neural network optimization model based on the back propagation(BP)neural network(BPNN)algorithm(BP/MLP)and MLP and random forest(RF)integration(MLP-RF)ensemble models were constructed,utilizing SIF as the primary predictive variable for GPP.Both the BP/MLP(half-hourly scale model R^(2)=0.885,daily scale model R^(2)=0.921)and the MLP-RF(half-hourly scale model R^(2)=0.845,daily scale model R^(2)=0.914)models showed superior accuracy compared to the BPNN(half-hourly scale model R^(2)=0.841,daily scale model R^(2)=0.918)and the traditional RF(half-hourly scale model R^(2)=0.798,daily scale model R^(2)=0.867)models,with the BP/MLP model consistently outperforming the MLP-RF model.The BP/MLP model,which was optimized through particle swarm optimization(PSO),significantly enhanced the robustness of GPP predictions on a half-hourly scale and daily scale.Considering both half-hourly scale and daily scale in the PSO-BP/MLP modeling,the four indicators,light-use efficiency(LUE),photosynthetically active radiation(PAR),absorbed photosynthetically active radiation(APAR),and the variation in SIF with NIRvP(fSIF(NIRvP)),exhibited the potential for enhancing the accuracy of GPP predictions.This study employed a series of model optimization techniques to develop a GPP prediction model with enhanced performance that objectively evaluated the contributions of the predictive variables.This approach provided an innovative and effective method for assessing the carbon cycle in terrestrial ecosystems.
基金supported by the National 973 Program of China (2011CB100501)the National 863 Program of China(2013AA102901)+1 种基金the Special Fund for Agro-Scientific Research in the Public Interest, China (201203077)the Science and Technology Project for Grain Production, China (2011BAD16B15)
文摘Increasing basic farmland soil productivity has significance in reducing fertilizer application and maintaining high yield of crops. In this study, we defined that the basic soil productivity （BSP） is the production capacity of a farmland soil with its own physical and chemical properties for a specific crop season under local environment and field management. Based on 22-yr （1990-2011） long-term experimental data on black soil （Typic hapludoll） in Gongzhuling, Jilin Province, Northeast China, the decision support system for an agro-technology transfer （DSSAT）-CERES-Maize model was applied to simulate the yield by BSP of spring maize （Zea mays L.） to examine the effects of long-term fertilization on changes of BSP and explore the mechanisms of BSP increasing. Five treatments were examined： （1） no-fertilization control （control）; （2） chemical nitrogen, phosphorus, and potassium （NPK）; （3） NPK plus farmyard manure （NPKM）; （4） 1.5 time of NPKM （1.5NPKM） and （5） NPK plus straw （NPKS）. Results showed that after 22-yr fertilization, the yield by BSP of spring maize significantly increased 78.0, 101.2, and 69.4% under the NPKM, 1.5NPKM and NPKS, respectively, compared to the initial value （in 1992）, but not significant under NPK （26.9% increase） and the control （8.9% decrease）. The contribution percentage of BSP showed a significant rising trend （P〈0.05） under 1.5NPKM. The average contribution percentage of BSP among fertilizations ranged from 74.4 to 84.7%, and ranked as 1.5NPKM〉NPKM〉NPK〉NPKS, indicating that organic manure combined with chemical fertilizers （I.5NPKM and NPKM） could more effectively increase BSP compared with the inorganic fertilizer application alone （NPK） in the black soil. This study showed that soil organic matter （SOM） was the key factor among various fertility factors that could affect BSP in the black soil, and total N, total P and/or available P also played important role in BSP increasing. Compared with the chemical fertilization, a balanced chemical plus manure or straw fertilization （NPKM or NPKS） not only increased the concentrations of soil nutrient, but also improved the soil physical properties, and structure and diversity of soil microbial population, resulting in an iincrease of BSP. We recommend that a balanced chemical plus manure or straw fertilization （NPKM or NPKS） should be the fertilization practices to enhance spring maize yield and improve BSP in the black soil of Northeast China.
基金by the National Natural 0ur studies were supported Science Foundation of China under contract No.50339040the National Basic Research Program of China under contract No.2005CB422301.
文摘A Nutrient -Phytoplankton -Zooplankton（NPZD） type of ecological model is developed to reflect the biochemical process, and further coupled to a primitive equation ocean model, an irradiation model as well as a river discharge model to reproduce ecosystem dynamics in the Bohai Sea. Modeled primary production shows reasonable consistency with observations quantitatively and qualitatively; in addition, f-ratio is examined in detail in the first time, which is also within the range reported in other studies and reveals some meaningful insight into the relative contributions of ammonium and nitrate to the growth of phytoplankton in the Bohai Sea.
基金Foundation： Chinese Liaoning Province Education Bureau General Science Research Project （No. L2010226） Chinese Education Ministry Humanities and Social Sciences Key Research Base Project （No.08JJD790142）+1 种基金 Chinese Liaoning Province Education Bureau Innovation Team Project （No. 2007T095） Chinese Special Funds for Major State Basic Research Project （No. 2007CB714406）.
文摘Spatial scaling for net primary productivity （NPP） refers to the transferring process of establishing quantitative correlation between simulated NPP derived from data at different spatial resolutions. How to transfer NPP at one scale by the algorithm with smaller error to at another is the urgent problem. Nonlinearity and effects from land cover type are two main problems in NPP scaling. In this paper, the contextural approach based on mixed pixels and support vector machine （SVM） algorithm are used to make the scaling model from the fine resolution （TM） to the coarse resolution （MODIS）. Spatial scaling from NPP retrieved from fine resolution data to NPP derived from coarse resolution images is performed, and the correction of scale effect to NPP retrieved from coarse resolution data of MODIS is accomplished. The result shows that the correlation between Rj_coereted of the correction factor for scale effect and 1-Fmiddle dessity grassland estimated by SVM regression model is higher （R2=0.81）. Before the correction for scale effect, the correlation between NPPMODIS and NPPTM is lower （R2=0.69; RMSE=3.47）, while the correlation between NPPTM and corrected NPPMODIS_corrected is higher （R2=0.84; RMSE= 1.87）. Therefore, NPP corrected for scale effect has been greatly improved in both correlation and error.
基金This study was co-supported by the National Key R&D Program of China[grant number 2017YFA0604302]the National Natural Science Foundation of China[grant numbers 41475099 and 41875137]the Chinese Academy of Sciences Key Research Program of Frontier Sciences[grant number QYZDY-SSW-DQC002].
文摘Fire is a global phenomenon and a major source of aerosols from the terrestrial biosphere to the atmosphere.Most previous studies quantified the effect of fire aerosols on climate and atmospheric circulation,or on the regional and site-scale terrestrial ecosystem productivity.So far,only one work has quantified their global impacts on terrestrial ecosystem productivity based on offline simulations,which,however,did not consider the impacts of aerosol–cloud interactions and aerosol–climate feedbacks.This study quantitatively assesses the influence of fire aerosols on the global annual gross primary productivity(GPP)of terrestrial ecosystems using simulations with the fully coupled global Earth system model CESM1.2.Results show that fire aerosols generally decrease GPP in vegetated areas,with a global total of−1.6 Pg C yr^−1,mainly because fire aerosols cool and dry the land surface and weaken the direct photosynthetically active radiation(PAR).The exception to this is the Amazon region,which is mainly due to a fire-aerosol-induced wetter land surface and increased diffuse PAR.This study emphasizes the importance of the influence of fire aerosols on climate in quantifying global-scale fire aerosols’impacts on terrestrial ecosystem productivity.
文摘A model of a potentially effective type energy-resource-saving of optimization of agro-technologies, based on the principle subordination of synergetics, was established. There was developed computer system energy-resource-saving optimization of agricultural technologies. The main feature of crop production is provided by the plants which themselves are self-organizing organisms. This allows us to adopt the principle of subordination of synergetics as the basis of the model. The value of free energy at the input into plants, estimated by the process of photosynthesis, is equal to the value of “radiation exergy for plant growth”. Assessment of the use of radiation energy is carried out based on the energy-converting characteristics of plants, which were obtained in climate chambers under controlled conditions. We used the model based on the principle of subordination of synergetics to develop common quantitative mutually agreed definitions of the main agroecological variables: Agroclimatic and Meliorative potentials of lands, their fertility, and potential (maximum) productivity of plants under different environment conditions.
文摘Compared with conventional well, herringbone-like laterals wells can increase the area of oil release, and can reduce the number of wellhead slots of platforms,?and?also can greatly improve the development efficiency. Based on threshold pressure gradient in heavy oil reservoir,?and?the applied principle of mirror reflection and superposition, the pressure distribution equation of herringbone-like laterals wells is obtained in heavy oil reservoir. Productivity model of herringbone-like laterals wells is proposed by reservoir-wellbore steady seepage. The example shows that the productivity model is great accuracy?to?predict the productivity of herringbone-like laterals wells. The model is used to analyze the branching length, branching angle, branching symmetry, branching position and spacing and their effects on productivity of herringbone-like laterals wells. The principle of optimizing the well shape of herringbone-like laterals wells is proposed.
文摘The hydrocarbon deposits have stimulated worldwide efforts to understand gas production from hydrate dissociation in hydrate reservoirs well. This paper deals with the potential of gas hydrates as a source of energy which is widely available in permafrost and oceanic sediments. It discusses methods for gas production from natural gas hydrates. Authors provide a detailed methodology used to model gas productivity recovery from hydrate reservoir well. The mathematical modelling of gas dissociation from hydrate reservoir as a tool for evaluating the potential of gas hydrates for natural gas production. The simulation results show that the process of natural gas production in a hydrate reservoir is a sensitive function of reservoir temperature and hydrate zone permeability. The model couples nth order decomposition kinetics with gas flow through porous media. The models provide a simple and useful tool for hydrate reservoir analysis.
文摘[Objective]The aim was to establish the linear regression prediction models between sowing time and plant productivity, biological yield of forage sorghum in autumn idle land.[Method]The relationships between sowing time and plant productivity, biological yield of forage sorghum were simulated and compared by using field experiment and linear regression analysis.[Result] The sowing time had an important influence on the plant productivity and biological yield of forage sorghum in autumn idle land. The plant productivity and biological yield of forage sorghum both decreased with the delay of sowing time.The regression model between plant fresh weight and sowing time was ？fresh=0.618-0.015x; the regression model between plant dry weight and sowing time was ？dry=0.184-0.005x; and the regression model between biological yield and sowing time was yield=29 126.461-711.448x. During July 23rd to August 30th, when the sowing time was delayed by 1 day, the plant fresh weight of forage sorghum was reduced by 0.015 g, the plant dry weight was reduced by 0.005 g, and the yield was reduced by 711.448 kg/hm2. [Conclusion] The three regression models established in this study will provide theoretical support for the production of forage sorghum.
基金Supported by the National Science and Technology Major Project(2017ZX05063-005)Science and Technology Development Project of PetroChina Research Institute of Petroleum Exploration and Development(YGJ2019-12-04)。
文摘The generation method of three-dimensional fractal discrete fracture network(FDFN)based on multiplicative cascade process was developed.The complex multi-scale fracture system in shale after fracturing was characterized by coupling the artificial fracture model and the natural fracture model.Based on an assisted history matching(AHM)using multiple-proxy-based Markov chain Monte Carlo algorithm(MCMC),an embedded discrete fracture modeling(EDFM)incorporated with reservoir simulator was used to predict productivity of shale gas well.When using the natural fracture generation method,the distribution of natural fracture network can be controlled by fractal parameters,and the natural fracture network generated coupling with artificial fractures can characterize the complex system of different-scale fractures in shale after fracturing.The EDFM,with fewer grids and less computation time consumption,can characterize the attributes of natural fractures and artificial fractures flexibly,and simulate the details of mass transfer between matrix cells and fractures while reducing computation significantly.The combination of AMH and EDFM can lower the uncertainty of reservoir and fracture parameters,and realize effective inversion of key reservoir and fracture parameters and the productivity forecast of shale gas wells.Application demonstrates the results from the proposed productivity prediction model integrating FDFN,EDFM and AHM have high credibility.
文摘Proton Exchange Membrane Water Electrolyzers(PEMWE)are efficient and sustainable hydrogen production devices.This article analyzes their static and dynamic electrical models integrated with degradation mechanisms.Static models reveal steady-state behavior,while dynamic models capture transient responses to input variations.The developed modeling approach combines the activation and diffusion phenomena,resulting in a novel PEMWE model that closely reflects real-world conditions and enables fast simulations.The electrical model is integrated with the aging model through two key ratios,surface degradation ratio and membrane degradation ratio,which characterize degradation mechanisms affecting electrode and membrane performance.The linear model using second-order Taylor approximation enables the development of a diagnosis approach that can contribute to estimating the remaining useful life of PEMWEs.By associating aging models with electrical models through the proposed ratios,a deeper understanding is achieved regarding how degra-dation phenomena evolve and influence electrolyzer efficiency and durability.The integrated framework enables predictive maintenance strategies,making it valuable for industrial hydrogen production applications.
基金supported by the project of Non-Tax Revenue of Yunnan Provincial Department of Finance(Grant No.FSYJ202119).
文摘Green technology innovation has gradually become an important driving force to promote new quality productivity.This paper constructs a quantitative index system of new quality productivity based on the three major elements of workers,labour objects and labour tools,and empirically analyses the impact of green technology innovation on the level of new quality productivity using spatial econometric model and VAR model.The result shows that:(1)The level of new quality productivity is not only affected by its own factors,but also by the significant spatial spillover effect between regions,especially in the case of strong geographic proximity,the interregional economic activities and resource allocation have a strong interaction and dependence.(2)The direct effect of green technology innovation is negative,mainly due to the high R&D investment and the short-term cost increase brought about by technological transformation,but its indirect effect is positive,showing that green technology has a positive effect on the new quality productivity enhancement of neighbouring regions through technology diffusion and cooperative innovation.(3)The eastern and western regions are affected by high upfront costs and transformation challenges,showing negative effects;while the central and northeastern regions benefit from policy support and industrial upgrading,showing positive effects.(4)Impulse response function analysis shows that the short-term impact of green technological innovation on new quality productivity is negative,but the long-term potential is significant,and the negative effect gradually diminishes over time.Based on this,this paper puts forward the suggestions of optimising the green innovation input structure,formulating regional differentiated policies and strengthening regional synergistic cooperation,which provide the theoretical basis and practical path for realising the green transformation and high-quality development of the economy.
基金funding support from the National Natural Science Foundation of China(No.52204065,No.ZX20230398)supported by a grant from the Human Resources Development Program(No.20216110100070)of the Korea Institute of Energy Technology Evaluation and Planning(KETEP)。
文摘In the realm of subsurface flow simulations,deep-learning-based surrogate models have emerged as a promising alternative to traditional simulation methods,especially in addressing complex optimization problems.However,a significant challenge lies in the necessity of numerous high-fidelity training simulations to construct these deep-learning models,which limits their application to field-scale problems.To overcome this limitation,we introduce a training procedure that leverages transfer learning with multi-fidelity training data to construct surrogate models efficiently.The procedure begins with the pre-training of the surrogate model using a relatively larger amount of data that can be efficiently generated from upscaled coarse-scale models.Subsequently,the model parameters are finetuned with a much smaller set of high-fidelity simulation data.For the cases considered in this study,this method leads to about a 75%reduction in total computational cost,in comparison with the traditional training approach,without any sacrifice of prediction accuracy.In addition,a dedicated well-control embedding model is introduced to the traditional U-Net architecture to improve the surrogate model's prediction accuracy,which is shown to be particularly effective when dealing with large-scale reservoir models under time-varying well control parameters.Comprehensive results and analyses are presented for the prediction of well rates,pressure and saturation states of a 3D synthetic reservoir system.Finally,the proposed procedure is applied to a field-scale production optimization problem.The trained surrogate model is shown to provide excellent generalization capabilities during the optimization process,in which the final optimized net-present-value is much higher than those from the training data ranges.
基金Jilin Province Social Science Project:Path Analysis and Empirical Research on Empowering Rural Industry Integration with Digital Economy in Jilin Province 2023B40Key Project of Education Science Planning in Jilin Province:Exploration of Talent Training Model for Economic Statistics Majors in Universities Based on OBE Theory-Taking Jilin Jianzhu University as an Example.ZD22028.
文摘Smart cities,a new kind of urbanization,offer a means of achieving the condition in which environmental conservation and economic growth are mutually beneficial.As a result,it is important to think about whether and how the development of smart cities might support the high-quality growth of urban economies.Based on the panel data of 163 prefecture-level cities in China from 2009–2018,the green total factor productivity(GTFP)of each prefecture-level city is measured using the SBM-GML model,and the appropriate spatial econometric model is screened by various types of tests.The spatial effect of smart city construction on GFTP is studied,and it is concluded that the pilot cities have a significant positive spatial spillover effect.The decomposition econometric model also shows that the pilot cities have a significant positive spatial spillover effect,and it also indicating that the smart city construction can also drive the surrounding cities to jointly improve the quality of economic development.Finally,the robustness of the spatial effect of smart city policy is also verified by changing the spatial measurement model and the type of spatial weight matrix,which also shows that the results of the spatial spillover effect of smart city construction are reliable.
基金supported by the National Natural Science Foundation of China under Grant 52325402,52274057,and 52074340the National Key R&D Program of China under Grant 2023YFB4104200+2 种基金the Major Scientific and Technological Projects of CNOOC under Grant CCL2022RCPS0397RSN111 Project under Grant B08028China Scholarship Council under Grant 202306450108.
文摘This study introduces a novel approach to addressing the challenges of high-dimensional variables and strong nonlinearity in reservoir production and layer configuration optimization.For the first time,relational machine learning models are applied in reservoir development optimization.Traditional regression-based models often struggle in complex scenarios,but the proposed relational and regression-based composite differential evolution(RRCODE)method combines a Gaussian naive Bayes relational model with a radial basis function network regression model.This integration effectively captures complex relationships in the optimization process,improving both accuracy and convergence speed.Experimental tests on a multi-layer multi-channel reservoir model,the Egg reservoir model,and a real-field reservoir model(the S reservoir)demonstrate that RRCODE significantly reduces water injection and production volumes while increasing economic returns and cumulative oil recovery.Moreover,the surrogate models employed in RRCODE exhibit lightweight characteristics with low computational overhead.These results highlight RRCODE's superior performance in the integrated optimization of reservoir production and layer configurations,offering more efficient and economically viable solutions for oilfield development.
基金partially supported by the Spanish Ministry of Science,Innovation and Universities(grant number RTI2018-099315-A-I00)by the Spanish Ministry of Economy and Competitivity(MINECO)(Grant number AGL2015–66001-C3)+1 种基金by the Cost action FP1203:European Non-Wood Forest Products Networkby the European project Star Tree–Multipurpose trees and non-wood forest products(Grant number 311919)a Serra-Húnter Fellowship provided by the Generalitat of Catalunya
文摘Background: Predictive models shed light on aboveground fungal yield dynamics and can assist decision-making in forestry by integrating this valuable non-wood forest product into forest management planning. However, the currently existing models are based on rather local data and, thus, there is a lack of predictive tools to monitor mushroom yields on larger scales.Results: This work presents the first empirical models for predicting the annual yields of ectomycorrhizal mushrooms and related ecosystem services in Pinus sylvestris and Pinus pinaster stands in northern Spain, using a long-term dataset suitable to account for the combined effect of meteorological conditions and stand structure.Models were fitted for the following groups of fungi separately: all ectomycorrhizal mushrooms, edible mushrooms and marketed mushrooms. Our results show the influence of the weather variables(mainly precipitation) on mushroom yields as well as the relevance of the basal area of the forest stand that follows a right-skewed unimodal curve with maximum predicted yields at stand basal areas of 30–40 m2·ha-1.Conclusion: These models are the first empirical models for predicting the annual yields of ectomycorrhizal mushrooms in Pinus sylvestris and Pinus pinaster stands in northern Spain, being of the highest resolution developed to date and enable predictions of mushrooms productivity by taking into account weather conditions and forests’ location, composition and structure.
基金supported by the National Natural Science Foundation of China(Grant no.U19A2043)the Key Research and Development Projects of Science and Technology Plan of Sichuan Province,China(2018JZ0079).
文摘Productivity prediction plays an important role in the efficient and rational development of shale gas reservoirs.Current research on the productivity of multistage fractured horizontal wells in shale gas reservoirs does not consider the stress-sensitive effects of natural fractures,hydraulic fracture morphology,and seepage characteristics in the same capacity model.Therefore,we considered the adsorption,desorption,and diffusion mechanisms(pseudo-steady state and transient diffusion)of shale gas in reservoirs and the stress-sensitive effects of natural fractures based on the dual-medium seepage theory model.Thefinite conductivity of the hydraulic fracture and hydraulic fracture azimuth were considered in the hydraulic fracture model.The source function method was used to discretize the crack,and the hydraulic fracture model was superimposed.Finally,the two models were coupled to obtain the unstable seepage and productivity models of the multistage fractured hor-izontal well in a shale gas reservoir.According to the established horizontal well production model of shale gas fracturing,the production characteristic curve was calculated by programming,and the simulation results were compared with thefield data of shale gas wells to verify the accuracy of the model.We used the model to analyze the effects of fracture conductivity,fracture half-length,fracture spacing,skin factor,storage ratio and leakage coefficient on productivity.
基金This paper is one result of the reaserch project of the China National Natural Sciencd Fund and is published of the Fourth Intemational Meeting of DSI(1997,sydusy,Australia)
文摘Improving the quality of human resources through edu-cation and training is of great strategic significance.However, few people have done in depth and systematicstudies on the relationship between education and pro-ducttvity in China. This study developed an educationproductivity transform model and empirically tested themodel using data on the most districts of China. The ba-sic notion is that the effect depends both on the capabilityresulted from education, and on the environment inwhich this capability can be exercised, the effect is a re-sult of two factors combined.
