To evaluate the accumulation conditions and gas-bearing properties of shale gas in the Lower Cambrian Niutitang Formation,northwestern Hunan Province,the first shale gas parameter well(Well Changye-1)that takes the Ni...To evaluate the accumulation conditions and gas-bearing properties of shale gas in the Lower Cambrian Niutitang Formation,northwestern Hunan Province,the first shale gas parameter well(Well Changye-1)that takes the Niutitang Formation as the target horizon in the Hunan Province was selected preferably and drilled,cumulatively revealing the thickest dark shale horizon of the Niutitang Formation among the single-well drillings in China,with a true vertical thickness of 674.5m.Through analyzing the core samples in terms of organic geochemistry,rock mineralogy and reservoir properties,the black shale horizons in the Niutitang Formation of Well Changye-1 have high organic carbon content(average 3.9%),moderate maturity(equivalent Ro average 2.6%),high brittle mineral content(quartz content average 50.1%),low clay mineral content(average 32.4%),low porosity(1.7%)and low permeability(1.32×10^(-3)mD),and well-developed meso to micro-pores and fractures,indicating good conditions for shale gas accumulation.Field desorption and laboratory isothermal adsorption measurements on core samples show that Well Changye-1 has good gas-bearing properties,and gas content generally increases with depth.The black shale horizons at the depth of 1100-1250m have an average organic carbon content up to 10.1%,total gas content of 0.5-2.1m^(3)/t and 3.7-6.4m^(3)/t,and this is the most favorable depth for shale gas development,indicating that the Niutitang Formation has good a prospect for shale gas exploration.Due to huge sedimentary thickness,the black shale in the middle-lower part of the Niutitang Formation should be given priority for exploration.展开更多
Evaluating the potential of shale gas reservoirs is inseparable from reservoir properties prediction.Accurate characterization of total organic carbon,porosity and permeability is necessary to understand shale gas res...Evaluating the potential of shale gas reservoirs is inseparable from reservoir properties prediction.Accurate characterization of total organic carbon,porosity and permeability is necessary to understand shale gas reservoirs.Seismic data can help to estimate these parameters in the area crossing-wells.We develop an improved deep learning method to achieve shale gas reservoir properties estimation.The rela-tionship between elastic attributes and reservoir properties is built up by training a deep bidirectional long short-term memory network,which is suitable for time/depth sequence prediction,on the logging and core data.Except some commonly used technologies,such as layer normalization and dropout,we also introduce attention mechanism to further enhance the prediction accuracy.Besides,we propose to carry on the normal scores transform on the input features,which aims to make the relationship between inputs and targets clear and easy to learn.During the training process,we construct quantile loss function,then use Adam algorithm to optimize the network.Not only the characterization results,but also the confidence interval can be output that is meaningful for uncertainty analysis.The well exper-iment indicates that the method is promising for reducing prediction errors when training samples are insufficient.After analyzing in wells,the established model is acted upon seismic inverted elastic attri-butes to characterize shale gas reservoirs in the whole studied area.The estimation results coincide well with the actual development results,showing the feasibility of the novel method on the characterization for shale gas reservoirs.展开更多
Environmental risks of organic chemicals have been greatly determined by their persistence,bioaccumulation, and toxicity（PBT） and physicochemical properties. Major regulations in different countries and regions iden...Environmental risks of organic chemicals have been greatly determined by their persistence,bioaccumulation, and toxicity（PBT） and physicochemical properties. Major regulations in different countries and regions identify chemicals according to their bioconcentration factor（BCF） and octanol–water partition coefficient（Kow）, which frequently displays a substantial correlation with the sediment sorption coefficient（Koc）. Half-life or degradability is crucial for the persistence evaluation of chemicals. Quantitative structure activity relationship（QSAR） estimation models are indispensable for predicting environmental fate and health effects in the absence of field-or laboratory-based data. In this study, 39 chemicals of high concern were chosen for half-life testing based on total organic carbon（TOC） degradation,and two widely accepted and highly used QSAR estimation models（i.e., EPI Suite and PBT Profiler） were adopted for environmental risk evaluation. The experimental results and estimated data, as well as the two model-based results were compared, based on the water solubility, Kow, Koc, BCF and half-life. Environmental risk assessment of the selected compounds was achieved by combining experimental data and estimation models. It was concluded that both EPI Suite and PBT Profiler were fairly accurate in measuring the physicochemical properties and degradation half-lives for water, soil, and sediment.However, the half-lives between the experimental and the estimated results were still not absolutely consistent. This suggests deficiencies of the prediction models in some ways, and the necessity to combine the experimental data and predicted results for the evaluation of environmental fate and risks of pollutants.展开更多
文摘To evaluate the accumulation conditions and gas-bearing properties of shale gas in the Lower Cambrian Niutitang Formation,northwestern Hunan Province,the first shale gas parameter well(Well Changye-1)that takes the Niutitang Formation as the target horizon in the Hunan Province was selected preferably and drilled,cumulatively revealing the thickest dark shale horizon of the Niutitang Formation among the single-well drillings in China,with a true vertical thickness of 674.5m.Through analyzing the core samples in terms of organic geochemistry,rock mineralogy and reservoir properties,the black shale horizons in the Niutitang Formation of Well Changye-1 have high organic carbon content(average 3.9%),moderate maturity(equivalent Ro average 2.6%),high brittle mineral content(quartz content average 50.1%),low clay mineral content(average 32.4%),low porosity(1.7%)and low permeability(1.32×10^(-3)mD),and well-developed meso to micro-pores and fractures,indicating good conditions for shale gas accumulation.Field desorption and laboratory isothermal adsorption measurements on core samples show that Well Changye-1 has good gas-bearing properties,and gas content generally increases with depth.The black shale horizons at the depth of 1100-1250m have an average organic carbon content up to 10.1%,total gas content of 0.5-2.1m^(3)/t and 3.7-6.4m^(3)/t,and this is the most favorable depth for shale gas development,indicating that the Niutitang Formation has good a prospect for shale gas exploration.Due to huge sedimentary thickness,the black shale in the middle-lower part of the Niutitang Formation should be given priority for exploration.
基金This work is financially supported by National Natural Science Foundation of China(No.42204136)Key R&D Projects of Sichuan Science and Technology Department of China(21ZDYF2939).
文摘Evaluating the potential of shale gas reservoirs is inseparable from reservoir properties prediction.Accurate characterization of total organic carbon,porosity and permeability is necessary to understand shale gas reservoirs.Seismic data can help to estimate these parameters in the area crossing-wells.We develop an improved deep learning method to achieve shale gas reservoir properties estimation.The rela-tionship between elastic attributes and reservoir properties is built up by training a deep bidirectional long short-term memory network,which is suitable for time/depth sequence prediction,on the logging and core data.Except some commonly used technologies,such as layer normalization and dropout,we also introduce attention mechanism to further enhance the prediction accuracy.Besides,we propose to carry on the normal scores transform on the input features,which aims to make the relationship between inputs and targets clear and easy to learn.During the training process,we construct quantile loss function,then use Adam algorithm to optimize the network.Not only the characterization results,but also the confidence interval can be output that is meaningful for uncertainty analysis.The well exper-iment indicates that the method is promising for reducing prediction errors when training samples are insufficient.After analyzing in wells,the established model is acted upon seismic inverted elastic attri-butes to characterize shale gas reservoirs in the whole studied area.The estimation results coincide well with the actual development results,showing the feasibility of the novel method on the characterization for shale gas reservoirs.
基金supported by the Special Scientific Research Funds for Environmental Protection Commonweal Section(No.201309027)the Xiamen Municipal Bureau of Science and Technology Program(No.3502Z20140013)
文摘Environmental risks of organic chemicals have been greatly determined by their persistence,bioaccumulation, and toxicity（PBT） and physicochemical properties. Major regulations in different countries and regions identify chemicals according to their bioconcentration factor（BCF） and octanol–water partition coefficient（Kow）, which frequently displays a substantial correlation with the sediment sorption coefficient（Koc）. Half-life or degradability is crucial for the persistence evaluation of chemicals. Quantitative structure activity relationship（QSAR） estimation models are indispensable for predicting environmental fate and health effects in the absence of field-or laboratory-based data. In this study, 39 chemicals of high concern were chosen for half-life testing based on total organic carbon（TOC） degradation,and two widely accepted and highly used QSAR estimation models（i.e., EPI Suite and PBT Profiler） were adopted for environmental risk evaluation. The experimental results and estimated data, as well as the two model-based results were compared, based on the water solubility, Kow, Koc, BCF and half-life. Environmental risk assessment of the selected compounds was achieved by combining experimental data and estimation models. It was concluded that both EPI Suite and PBT Profiler were fairly accurate in measuring the physicochemical properties and degradation half-lives for water, soil, and sediment.However, the half-lives between the experimental and the estimated results were still not absolutely consistent. This suggests deficiencies of the prediction models in some ways, and the necessity to combine the experimental data and predicted results for the evaluation of environmental fate and risks of pollutants.
