The study of the thin bed responses and correction methods in cased hole density logging can provide a theoretical basis for research to improve data processing methods. By using the Monte Carlo program MCNP, the chan...The study of the thin bed responses and correction methods in cased hole density logging can provide a theoretical basis for research to improve data processing methods. By using the Monte Carlo program MCNP, the change of detector count from thin beds with the vertical depth was calculated at different casing thicknesses. The calculation showed that with the low density thin bed moving upward, detector count first increased to a maximum then decreased. The responses of a thin bed with a high density were opposite to those of a thin bed with a low density. The change curve was symmetrical, and the maximums or minimums appeared at the midpoint between the detector and source. Besides, detector count increased with increasing thin bed thickness. At a specific thin bed thickness, further increase of thin bed thickness resulted in a slow increase of detector count then the count rate leveled off. In actual logging, the influence of adjacent formations on density log measurements can be ignored. Finally, based on numerical simulation correction methods for the dual influence of casing and thin beds are discussed.展开更多
Studying the ecology of ants can be a powerful tool for conservation, While the effect of logging is mainly investigated by the comparison of species richness and composition, the impact on individual species are ofte...Studying the ecology of ants can be a powerful tool for conservation, While the effect of logging is mainly investigated by the comparison of species richness and composition, the impact on individual species are often neglected. This study investigated the effect of selective logging on the nest density, foraging range and colony size on the ground-dwelling ant Aphaenogaster swammerdami in Kirindy forest--Madagascar. This ant is a common ground-dwelling species in Kirindy, a western dry deciduous forest of Madagascar. Sampling was done in two sites of the forest： One part that was selectively lodged and another that have not been logged. Here we show that selective logging led to a decrease in colony size and density, while the foraging range seemed to be unaffected. Higher desiccation stress and lower food availability in the logged forest are most likely to be responsible for these results.展开更多
The unconformity surface at the bottom of the Paleogene is one of the most important migration pathways in the Sikeshu Sag of the Junggar Basin,which consists of three layers：upper coarse clastic rock,lower weatherin...The unconformity surface at the bottom of the Paleogene is one of the most important migration pathways in the Sikeshu Sag of the Junggar Basin,which consists of three layers：upper coarse clastic rock,lower weathering crust and leached zone.The upper coarse clastic rock is characterized by higher density and lower SDT and gamma-ray logging parameters,while the lower weathering crust displays opposite features.The transport coefficient of the unconformity surface is controlled by its position in respect to the basal sandstone; it is higher in the ramp region but lower in the adjacent uplifted and sag areas.The content of saturated hydrocarbons increases with the decrease of the content of nonhydrocarbons and asphaltenes.The content of benzo[c] carbazole decreases as the content of benzo[a]carbazole and [alkyl carbazole]/[alkyl ＋ benzo carbazole] increases.This suggests that the unconformity surface is an efficient medium for the transportation of hydrocarbons.展开更多
Exploration and exploitation for hydrocarbon are associated with a lot of complexities, it is therefore necessary to integrate available geologic models for accurate hydrocarbon prospecting and risk analysis. This stu...Exploration and exploitation for hydrocarbon are associated with a lot of complexities, it is therefore necessary to integrate available geologic models for accurate hydrocarbon prospecting and risk analysis. This study is aimed at determining the structural, petrophysical and volumetric parameters for reservoir evaluation within the Rancho field. 3D seismic data was used for evaluating the hydrocarbon potential of the field. A suite of well logs but not limited to gamma ray logs (GR), deep resistivity log (DRES), neutron log (NPHI) and density log (RHOB) from four (4) wells were employed in characterising dynamic properties of the reservoirs. The GR log was used in lithology identification while the resistivity log was used in identifying probable hydrocarbon bearing sands. A correlation exercise was carried out to identify lateral continuity and discontinuity of facies across the wells. Thereafter petrophysical parameters were analysed from the suite of wire line logs. Major faults were mapped on the 3D seismic data and identified hydrocarbon bearing sand tops from the well logs were mapped as horizons on the seismic section, maps were generated and volumetric analysis was done. Nine (9) hydrocarbon sands (Sands A - I) were identified within the study area. The well log revealed an alternation of sand and shale layers as well as shale layers increased in thickness with depth, while the sand bodies reduced in thickness with depth which characterized the Abgada Formation of the Niger Delta. The effective porosities of the sands range from 21% - 31%, the permeability ranges from 28% - 44%, 70% - 80% for the net to gross, volume of shale range from 14% - 40% and hydrocarbon saturation ranges from 63% - 82%. Twelve (12) faults were mapped within the study area and the structural styles revealed a fault assisted closures. The volumetric analysis showed that Sand F had Stock Tank Oil Initially In Place (STOIIP) of 5,050,000,000 bbls of oil and Sand G had STOIIP of 17,870,000,000 bbls, these sands are proposed to be developd because of the volume of oil in them and area covered by the reservoir, calculated Gross Rock Volume (GRV) of 29.5 km3 and 104.5 km3 respectively.展开更多
Studying the response differences between neutron and density logging of gas reservoir for well-balanced and under-balanced logging will be of significance in evaluation of gas reservoir under the under-balanced condi...Studying the response differences between neutron and density logging of gas reservoir for well-balanced and under-balanced logging will be of significance in evaluation of gas reservoir under the under-balanced condition and application of logging data. With Monte Carlo simulation technique,the paper obtains the relationship between neutron and density logging measurement and borehole di-ameter,porosity or gas saturation for well-balanced and under-balanced logging. The conclusions show that the response trend of under-balanced logging to gas reservoirs agrees with that of well-balanced logging with small borehole,and under-balanced logging data can be used usually as well-balanced logging data. When borehole diameter is large,under-balanced logging data should be corrected for the influences of borehole.展开更多
In the petroleum industry,the analysis of petrophysical parameters is critical for efficient reservoir management,production optimization,development strategies,and accurate hydrocarbon reserve estimations.Over recent...In the petroleum industry,the analysis of petrophysical parameters is critical for efficient reservoir management,production optimization,development strategies,and accurate hydrocarbon reserve estimations.Over recent years,the integration of machine learning methodologies has revolutionized the field,addressing challenges in geology,geophysics,and petroleum engineering,even when confronted with limited or imperfect data.This study focuses on the prediction of density logs,a pivotal factor in evaluating reservoir hydrocarbon volumes.It is important to note that during well logging operations,log data for specific depths of interest may be missing or incorrect,presenting a significant challenge.To tackle this issue,we employed the Adaptive Neuro-Fuzzy Inference System(ANFIS)and Artificial Neural Networks(ANN)in combination with advanced optimization algorithms,including Particle Swarm Optimization(PSO),Imperialist Competitive Algorithms(ICA),and Genetic Algorithms(GA).These methods exhibit promising performance in predicting density logs from gamma-ray,neutron,sonic,and photoelectric log data.Remarkably,our results highlight that the Genetic Algorithms-based Artificial Neural Network(GA-ANN)approach outperforms all other methods,achieving an impressive Mean Squared Error(MSE)of 0.0013.In comparison,ANFIS records an MSE of 0.0015,ICA-ANN 0.0090,PSO-ANN 0.0093,and ANN 0.0183.展开更多
文摘The study of the thin bed responses and correction methods in cased hole density logging can provide a theoretical basis for research to improve data processing methods. By using the Monte Carlo program MCNP, the change of detector count from thin beds with the vertical depth was calculated at different casing thicknesses. The calculation showed that with the low density thin bed moving upward, detector count first increased to a maximum then decreased. The responses of a thin bed with a high density were opposite to those of a thin bed with a low density. The change curve was symmetrical, and the maximums or minimums appeared at the midpoint between the detector and source. Besides, detector count increased with increasing thin bed thickness. At a specific thin bed thickness, further increase of thin bed thickness resulted in a slow increase of detector count then the count rate leveled off. In actual logging, the influence of adjacent formations on density log measurements can be ignored. Finally, based on numerical simulation correction methods for the dual influence of casing and thin beds are discussed.
文摘Studying the ecology of ants can be a powerful tool for conservation, While the effect of logging is mainly investigated by the comparison of species richness and composition, the impact on individual species are often neglected. This study investigated the effect of selective logging on the nest density, foraging range and colony size on the ground-dwelling ant Aphaenogaster swammerdami in Kirindy forest--Madagascar. This ant is a common ground-dwelling species in Kirindy, a western dry deciduous forest of Madagascar. Sampling was done in two sites of the forest： One part that was selectively lodged and another that have not been logged. Here we show that selective logging led to a decrease in colony size and density, while the foraging range seemed to be unaffected. Higher desiccation stress and lower food availability in the logged forest are most likely to be responsible for these results.
基金fnancially supported by the National Key Project of Science and Technology for Development of Large-size Oil&Gas Fields and Coal-bed Gas(Grant No.2008ZX05003-002)by the State Key Laboratory of Petroleum Resources and Prospecting(No.prp2009-02)The study is a contribution to IGCP#592 Project
文摘The unconformity surface at the bottom of the Paleogene is one of the most important migration pathways in the Sikeshu Sag of the Junggar Basin,which consists of three layers：upper coarse clastic rock,lower weathering crust and leached zone.The upper coarse clastic rock is characterized by higher density and lower SDT and gamma-ray logging parameters,while the lower weathering crust displays opposite features.The transport coefficient of the unconformity surface is controlled by its position in respect to the basal sandstone; it is higher in the ramp region but lower in the adjacent uplifted and sag areas.The content of saturated hydrocarbons increases with the decrease of the content of nonhydrocarbons and asphaltenes.The content of benzo[c] carbazole decreases as the content of benzo[a]carbazole and [alkyl carbazole]/[alkyl ＋ benzo carbazole] increases.This suggests that the unconformity surface is an efficient medium for the transportation of hydrocarbons.
文摘Exploration and exploitation for hydrocarbon are associated with a lot of complexities, it is therefore necessary to integrate available geologic models for accurate hydrocarbon prospecting and risk analysis. This study is aimed at determining the structural, petrophysical and volumetric parameters for reservoir evaluation within the Rancho field. 3D seismic data was used for evaluating the hydrocarbon potential of the field. A suite of well logs but not limited to gamma ray logs (GR), deep resistivity log (DRES), neutron log (NPHI) and density log (RHOB) from four (4) wells were employed in characterising dynamic properties of the reservoirs. The GR log was used in lithology identification while the resistivity log was used in identifying probable hydrocarbon bearing sands. A correlation exercise was carried out to identify lateral continuity and discontinuity of facies across the wells. Thereafter petrophysical parameters were analysed from the suite of wire line logs. Major faults were mapped on the 3D seismic data and identified hydrocarbon bearing sand tops from the well logs were mapped as horizons on the seismic section, maps were generated and volumetric analysis was done. Nine (9) hydrocarbon sands (Sands A - I) were identified within the study area. The well log revealed an alternation of sand and shale layers as well as shale layers increased in thickness with depth, while the sand bodies reduced in thickness with depth which characterized the Abgada Formation of the Niger Delta. The effective porosities of the sands range from 21% - 31%, the permeability ranges from 28% - 44%, 70% - 80% for the net to gross, volume of shale range from 14% - 40% and hydrocarbon saturation ranges from 63% - 82%. Twelve (12) faults were mapped within the study area and the structural styles revealed a fault assisted closures. The volumetric analysis showed that Sand F had Stock Tank Oil Initially In Place (STOIIP) of 5,050,000,000 bbls of oil and Sand G had STOIIP of 17,870,000,000 bbls, these sands are proposed to be developd because of the volume of oil in them and area covered by the reservoir, calculated Gross Rock Volume (GRV) of 29.5 km3 and 104.5 km3 respectively.
基金Supported by the Major State Basic Research Development Program of China (973 Program) (Grant No.2006CB202306)
文摘Studying the response differences between neutron and density logging of gas reservoir for well-balanced and under-balanced logging will be of significance in evaluation of gas reservoir under the under-balanced condition and application of logging data. With Monte Carlo simulation technique,the paper obtains the relationship between neutron and density logging measurement and borehole di-ameter,porosity or gas saturation for well-balanced and under-balanced logging. The conclusions show that the response trend of under-balanced logging to gas reservoirs agrees with that of well-balanced logging with small borehole,and under-balanced logging data can be used usually as well-balanced logging data. When borehole diameter is large,under-balanced logging data should be corrected for the influences of borehole.
文摘In the petroleum industry,the analysis of petrophysical parameters is critical for efficient reservoir management,production optimization,development strategies,and accurate hydrocarbon reserve estimations.Over recent years,the integration of machine learning methodologies has revolutionized the field,addressing challenges in geology,geophysics,and petroleum engineering,even when confronted with limited or imperfect data.This study focuses on the prediction of density logs,a pivotal factor in evaluating reservoir hydrocarbon volumes.It is important to note that during well logging operations,log data for specific depths of interest may be missing or incorrect,presenting a significant challenge.To tackle this issue,we employed the Adaptive Neuro-Fuzzy Inference System(ANFIS)and Artificial Neural Networks(ANN)in combination with advanced optimization algorithms,including Particle Swarm Optimization(PSO),Imperialist Competitive Algorithms(ICA),and Genetic Algorithms(GA).These methods exhibit promising performance in predicting density logs from gamma-ray,neutron,sonic,and photoelectric log data.Remarkably,our results highlight that the Genetic Algorithms-based Artificial Neural Network(GA-ANN)approach outperforms all other methods,achieving an impressive Mean Squared Error(MSE)of 0.0013.In comparison,ANFIS records an MSE of 0.0015,ICA-ANN 0.0090,PSO-ANN 0.0093,and ANN 0.0183.
