Sand hazards are serious at the Danghe Reservoir of Dunhuang,and efforts to control sand are ineffective because disaster-causing mechanisms are currently unclear.The source of sandy materials,dynamic environment of b...Sand hazards are serious at the Danghe Reservoir of Dunhuang,and efforts to control sand are ineffective because disaster-causing mechanisms are currently unclear.The source of sandy materials,dynamic environment of blown sand,and controlling measures of the reservoir area are investigated using different methods,such as granularity analyses,wind regime and sand transport field observation and analyses,sand drift potential calculation.Accordingly,the sandy materials are found to derive chiefly from the Mingsha Mountain on the north side of the reservoir area.In addition,sand grain in the range of 0.50-0.25 mm and 0.25-0.10 mm are dominant.The prevailing sand-moving wind originates from the N direction,accounting for 15.38% of the yearly total,which coincides in the same direction with sand source,thereby increasing the severity of sand hazards in the reservoir area.The yearly sand DP is 1386.59 VU,the yearly RDP is 567.31 VU,the yearly RDP/DP is 0.41,and the yearly RDD is 183.15°.In the windy season(mainly in summer),sand materials are blown by wind from north to south,and then blocked by the Danghe River.The sand materials then move with an approximate east-west trend into the river channel and produce sediment,thereby causing adisaster.We propose that the sand-controlling pattern of the Danghe Reservoir is dominated by sand blocking in the outer fringe and sand fixing in the inner fringe.Applying windbreak and sand fixation to control sandy material movement into the river channel plays an important role in retarding sedimentation and extending the useful life of the Danghe Reservoir.展开更多
The Jianggang Harbour-centered radial sand ridge(RSR) is the largest sand body in the Yellow Sea. Its formation and evolution are of interest for scientists of various fields; however, the sediment provenance is uncer...The Jianggang Harbour-centered radial sand ridge(RSR) is the largest sand body in the Yellow Sea. Its formation and evolution are of interest for scientists of various fields; however, the sediment provenance is uncertain. In this study, rare earth element(REE) geochemical compositions of the RSR sediments together with their potential sources are investigated to identify the provenance of the RSR sediments. The typical parameters((La/Yb)_N,(La/Sm)_N and(Gd/Yb)_N) as well as the upper continental crust-normalized patterns of REEs can only be associated with source rocks, and thus can be used as effective tracers for the origin and sources of sediments. However, the REE contents of sediments are affected by many factors, such as particle sorting and chemical weathering. Onshore RSR sediments are different in REE geochemical composition from offshore RSR sediments to some extent, suggesting that not all of the offshore RSR sediments have the same sources as the onshore RSR sediments. Meanwhile, the sediments adjacent to the northeast of Cheju Island and at Lian Island near the Lianyun Harbour were not the source of the RSR sediments due to their distinctive REE patterns, dEu,(La/Yb)_N,(Gd/Yb)_N and(La/Sm)_N. The Korean river sediments could be dispersed to the Jiangsu Coast slightly impacting the fine fractions of the RSR sediments, particularly the offshore RSR sediments. Additionally, geochemical comparisons show that the modern Yellow River was responsible for the onshore RSR sediments, whereas the sediment loads from the Yangtze River could serve as a major contributor to the RSR, particularly the offshore RSR. In addition, the offshore RSR could also be partly fed by an unknown source due to some high values of(La/Yb)_N,(La/Sm)_N and La contents differing from those of the Chinese and Korean river sediments.展开更多
This study focuses on predicting acoustic and mechanical rock properties using random forest and feed forward neural network models to evaluate the likelihood of developing efficient ways of handling absence of rock p...This study focuses on predicting acoustic and mechanical rock properties using random forest and feed forward neural network models to evaluate the likelihood of developing efficient ways of handling absence of rock properties at offset locations.The Random Forest algorithm was used for direct prediction of the sonic data without considering the depth range of the facies;while Feed forward Neural network was used to predict the sonic data with emphasis on the lithofacies depths.The accuracy of these approaches was used in choosing the best and the most robust model for predicting sonic data when estimating formation strength and mechnical properties.Acoustic log was predicted after training a combination of caliper log,gamma log,depth,density log and resistivity log from offset wells.5 hidden layers that accounts for the data structural complexities was included in the model architecture.A multilayer perceptron network was adopted for the Random forest algorithm to handle linear combinations of input data set.Diverse error computations were used to evaluate the performance of the model.Lastly,mechanical properties and sanding potential was evaluated using standard relations and appropriate depositional conditions.Random forest algorithm gave the best prediction accuracy of more than 96%,but the Feed forward network has the lower mean absolute error and mean squared error of 2.75 and 5.93 respectively.Generally,the predicted compressive and shear wave velocity show increase of values with depth,a behavior that is capable of identifying payzone characteristics.This was validated by the distinction seen within the 200 feet gas sand formation in the deeper portion of the studied well(9600e9800 feet).Potential failure portions of the wells,a common feature in the field,were inferred from the sanding potential computed using the predicted mechanical properties value.展开更多
基金funded by the National Natural Science Foundation of China (Grant No.41401611)one of Special Fund for Forest Scientific Research in the Public Welfare (201504401)+1 种基金the China Postdoctoral Science Foundation (Grant Nos.2014M560817 and 2015T81069)the science and technology program of Gansu Province (Grant No.145RJZA118)
文摘Sand hazards are serious at the Danghe Reservoir of Dunhuang,and efforts to control sand are ineffective because disaster-causing mechanisms are currently unclear.The source of sandy materials,dynamic environment of blown sand,and controlling measures of the reservoir area are investigated using different methods,such as granularity analyses,wind regime and sand transport field observation and analyses,sand drift potential calculation.Accordingly,the sandy materials are found to derive chiefly from the Mingsha Mountain on the north side of the reservoir area.In addition,sand grain in the range of 0.50-0.25 mm and 0.25-0.10 mm are dominant.The prevailing sand-moving wind originates from the N direction,accounting for 15.38% of the yearly total,which coincides in the same direction with sand source,thereby increasing the severity of sand hazards in the reservoir area.The yearly sand DP is 1386.59 VU,the yearly RDP is 567.31 VU,the yearly RDP/DP is 0.41,and the yearly RDD is 183.15°.In the windy season(mainly in summer),sand materials are blown by wind from north to south,and then blocked by the Danghe River.The sand materials then move with an approximate east-west trend into the river channel and produce sediment,thereby causing adisaster.We propose that the sand-controlling pattern of the Danghe Reservoir is dominated by sand blocking in the outer fringe and sand fixing in the inner fringe.Applying windbreak and sand fixation to control sandy material movement into the river channel plays an important role in retarding sedimentation and extending the useful life of the Danghe Reservoir.
基金Under the auspices of National Key Technology Research and Development Program(No.2012BAB03B01)National Natural Science Foundation of China(No.41273015,51278172,51478167)
文摘The Jianggang Harbour-centered radial sand ridge(RSR) is the largest sand body in the Yellow Sea. Its formation and evolution are of interest for scientists of various fields; however, the sediment provenance is uncertain. In this study, rare earth element(REE) geochemical compositions of the RSR sediments together with their potential sources are investigated to identify the provenance of the RSR sediments. The typical parameters((La/Yb)_N,(La/Sm)_N and(Gd/Yb)_N) as well as the upper continental crust-normalized patterns of REEs can only be associated with source rocks, and thus can be used as effective tracers for the origin and sources of sediments. However, the REE contents of sediments are affected by many factors, such as particle sorting and chemical weathering. Onshore RSR sediments are different in REE geochemical composition from offshore RSR sediments to some extent, suggesting that not all of the offshore RSR sediments have the same sources as the onshore RSR sediments. Meanwhile, the sediments adjacent to the northeast of Cheju Island and at Lian Island near the Lianyun Harbour were not the source of the RSR sediments due to their distinctive REE patterns, dEu,(La/Yb)_N,(Gd/Yb)_N and(La/Sm)_N. The Korean river sediments could be dispersed to the Jiangsu Coast slightly impacting the fine fractions of the RSR sediments, particularly the offshore RSR sediments. Additionally, geochemical comparisons show that the modern Yellow River was responsible for the onshore RSR sediments, whereas the sediment loads from the Yangtze River could serve as a major contributor to the RSR, particularly the offshore RSR. In addition, the offshore RSR could also be partly fed by an unknown source due to some high values of(La/Yb)_N,(La/Sm)_N and La contents differing from those of the Chinese and Korean river sediments.
基金The authors would like to thank Covenant University centre for Research Innovation and Discovery Ota,Nigeria for its support in making the publication of this research possible.
文摘This study focuses on predicting acoustic and mechanical rock properties using random forest and feed forward neural network models to evaluate the likelihood of developing efficient ways of handling absence of rock properties at offset locations.The Random Forest algorithm was used for direct prediction of the sonic data without considering the depth range of the facies;while Feed forward Neural network was used to predict the sonic data with emphasis on the lithofacies depths.The accuracy of these approaches was used in choosing the best and the most robust model for predicting sonic data when estimating formation strength and mechnical properties.Acoustic log was predicted after training a combination of caliper log,gamma log,depth,density log and resistivity log from offset wells.5 hidden layers that accounts for the data structural complexities was included in the model architecture.A multilayer perceptron network was adopted for the Random forest algorithm to handle linear combinations of input data set.Diverse error computations were used to evaluate the performance of the model.Lastly,mechanical properties and sanding potential was evaluated using standard relations and appropriate depositional conditions.Random forest algorithm gave the best prediction accuracy of more than 96%,but the Feed forward network has the lower mean absolute error and mean squared error of 2.75 and 5.93 respectively.Generally,the predicted compressive and shear wave velocity show increase of values with depth,a behavior that is capable of identifying payzone characteristics.This was validated by the distinction seen within the 200 feet gas sand formation in the deeper portion of the studied well(9600e9800 feet).Potential failure portions of the wells,a common feature in the field,were inferred from the sanding potential computed using the predicted mechanical properties value.
