We investigated the quantity and quality 0f fallen l0gs in different Tsuga l0ngibracteata f0rest c0mmunities in the Tianba0yan Nati0nal Nature Reserve. We used redundancy analysis t0 determine the spatial distributi0n...We investigated the quantity and quality 0f fallen l0gs in different Tsuga l0ngibracteata f0rest c0mmunities in the Tianba0yan Nati0nal Nature Reserve. We used redundancy analysis t0 determine the spatial distributi0n 0f fallen l0gs in the different f0rest c0mmunities and t0 analyze the relati0nships am0ng stand structure, t0p0graphic fact0rs and human disturbance. The v0lume, c0vered area, mean l0g length and number 0f fallen l0gs differed significantly am0ng f0rest types （P 〈 0.05）, but mean diameter at breast height sh0wed n0 significant difference （P 〉 0.05）. The l0g v0lume and c0vered area in different f0rest types sh0wed the f0ll0wing trend： T. l0ngibracteata pure f0rest 〈 T. l0ngibracteata ＋ Olig0staehyum scabrifl0rur 〈 T. l0ngibraeteata ＋ hardw00d 〈 Rh0d0dendr0n simiarum ＋ T. l0ngibraeteata 〈 T. l0ngibraeteata ＋ Phyll0stachys heter0cycla pubescens. The spatial distributi0n patterns 0f l0gs quantity and quality indicated that l0g v0lume and c0vered area were str0ngly affected by envir0nmental fact0rs in the f0ll0wing 0rder： human disturbance 〉 elevati0n 〉 sl0pe p0siti0n 〉 b0le height 〉 tree height 〉 sl0pe aspect 〉 density 〉 basal area 〉 sl0pe gradient. The relative c0ntributi0n 0f envir0nmental variables 0n the t0tal variance was t0p0graphy （76%） 〉 disturbance （42%） 〉 stand structure （35%）. T0p0graphy and disturbance c0mbined explained 8.2% 0f the variance. Fallen l0~s auantitv and aualitvwere negatively related t0 elevati0n and sl0pe p0siti0n, and p0sitively ass0ciated t0 human disturbance. The l0g v0lume decreased fr0m n0rthern t0 s0uthern sl0pes. Envir0nmental fact0rs had the highest impact 0n class I （slightly decayed）, and l0west impact 0n class V （highly decayed）.展开更多
选择 X 射线作为检测源透射原木,根据检测透过被检物体后的射线强度差异,判断被检测原木内部是否存在缺陷和检测缺陷细节。检测过程中应用计算机数字图像处理技术对原始的 X 射线图像进行中值滤波、图像增强、差分和边缘检测,使得处理...选择 X 射线作为检测源透射原木,根据检测透过被检物体后的射线强度差异,判断被检测原木内部是否存在缺陷和检测缺陷细节。检测过程中应用计算机数字图像处理技术对原始的 X 射线图像进行中值滤波、图像增强、差分和边缘检测,使得处理后的图像更加清晰,图像中的目标易于人眼识别。实验结果表明这种方法行之有效。展开更多
This paper deals with the comparison of models for predicting porosity and permeability of oil reservoirs by coupling a machine learning concept and petrophysical logs.Different machine learning methods including conv...This paper deals with the comparison of models for predicting porosity and permeability of oil reservoirs by coupling a machine learning concept and petrophysical logs.Different machine learning methods including conventional artificial neural network,genetic algorithm,fuzzy decision tree,the imperialist competitive algorithm(ICA),particle swarm optimization(PSO),and a hybrid of those ones are employed to have a comprehensive comparison.The machine learning approach was constructed and tested via data samples recorded from northern Persian Gulf oil reservoirs.The results gained from the machine learning models used in this paper are compared to the relevant real petrophysical data and the outputs achieved by other methods employed in our previous studies.The average relative absolute deviation between the approach estimations and the relevant actual data is found to be less than 1%for the hybridized approaches.The results reported in this paper indicate that implication of hybridized machine learning methods in porosity and permeability estimations can lead to the construction of more reliable static reservoir models in simulation plans.展开更多
Assessment of reservoir and fracture parameters is necessary to optimize oil production,especially in heterogeneous reservoirs.Core and image logs are regarded as two of the best methods for this aim.However,due to co...Assessment of reservoir and fracture parameters is necessary to optimize oil production,especially in heterogeneous reservoirs.Core and image logs are regarded as two of the best methods for this aim.However,due to core limitations,using image log is considered as the best method.This study aims to use electrical image logs in the carbonate Asmari Formation reservoir in Zagros Basin,SW Iran,in order to evaluate natural fractures,porosity system,permeability profile and heterogeneity index and accordingly compare the results with core and well data.The results indicated that the electrical image logs are reliable for evaluating fracture and reservoir parameters,when there is no core available for a well.Based on the results from formation micro-imager(FMI)and electrical micro-imager(EMI),Asmari was recognized as a completely fractured reservoir in studied field and the reservoir parameters are mainly controlled by fractures.Furthermore,core and image logs indicated that the secondary porosity varies from 0%to 10%.The permeability indicator indicates that zones 3 and 5 have higher permeability index.Image log permeability index shows a very reasonable permeability profile after scaling against core and modular dynamics tester mobility,mud loss and production index which vary between 1 and 1000 md.In addition,no relationship was observed between core porosity and permeability,while the permeability relied heavily on fracture aperture.Therefore,fracture aperture was considered as the most important parameter for the determination of permeability.Sudden changes were also observed at zones 1-1 and 5 in the permeability trend,due to the high fracture aperture.It can be concluded that the electrical image logs(FMI and EMI)are usable for evaluating both reservoir and fracture parameters in wells with no core data in the Zagros Basin,SW Iran.展开更多
The Ordovician Majiagou Formation is one of the main gas-producing strata in the Ordos Basin,China.The identification of hydrocarbon-bearing intervals via conventional well logs is a challenging task.This study descri...The Ordovician Majiagou Formation is one of the main gas-producing strata in the Ordos Basin,China.The identification of hydrocarbon-bearing intervals via conventional well logs is a challenging task.This study describes the litholog of Ma 5(Member 5 of Majiagou Formation)dolostones,and then analyzes the responses of various conventional well logs to the presences of natural gas.The lithology of the gas bearing layers is dominantly of the dolomicrite to fine to medium crystalline dolomite.Natural gas can be produced from the low resistivity layers,and the dry layers are characterized by high resistivities.Neutron-density crossovers are not sensitive to the presences of natural gas.In addition,there are no significant increases in sonic transit times in natural gas bearing layers.NMR(nuclear magnetic resonance)logs,DSI(Dipole Sonic Imager)logs and borehole image logs(XRMI)are introduced to discriminate the fluid property in Majiagou dolostone reservoirs.The gas bearing intervals have broad NMR T2(transverse relaxation time)spectrum with tail distributions as well as large T2gm(T2 logarithmic mean values)values,and the T2 spectrum commonly display polymodal behaviors.In contrast,the dry layers and water layers have low T2gm values and very narrow T2 spectrum without tails.The gas bearing layers are characterized by low Vp/Vs ratios,low Poisson’s ratios and low P-wave impedances,therefore the fluid property can be discriminated using DSI logs,and the interpretation results show good matches with the gas test data.The apparent formation water resistivity(AFWR)spectrum can be derived from XRMI image logs by using the Archie’s formula in the flushed zone.The gas bearing layers have broad apparent formation water resistivity spectrum and tail distributions compared with the dry and water layers,and also the interpretation results from the image logs exhibit good agreement with the gas test data.The fluid property in Majiagou dolostone reservoirs can be discriminated through NMR logs,DSI logs and borehole image logs.展开更多
Reef-bank reservoirs are an important target for petroleum exploration in marine carbonates and also an essential supplemental area for oil and gas production in China. Due to the diversity of reservoirs and the extre...Reef-bank reservoirs are an important target for petroleum exploration in marine carbonates and also an essential supplemental area for oil and gas production in China. Due to the diversity of reservoirs and the extreme heterogeneity of reef-banks, it is very difficult to discriminate the sedimentary facies and lithologies in reef-bank reservoirs using conventional well logs. The borehole image log provides clear identification of sedimentary structures and textures and is an ideal tool for discriminating sedimentary facies and lithologies. After examining a large number of borehole images and cores, we propose nine typical patterns for borehole image interpretation and a method that uses these patterns to discriminate sedimentary facies and lithologies in reeI^bank reservoirs automatically. We also develop software with user-friendly interface. The results of applications in reef-bank reservoirs in the middle Tarim Basin and northeast Sichuan have proved that the proposed method and the corresponding software are quite effective.展开更多
The authors applied a the combination of Continuous Wavelet Transform (CWT) and Fast Fourier Transform (FFT) methods to gamma ray well-log data from the Q3, G1 and D2 wells. This high-resolution stratigraphic study wa...The authors applied a the combination of Continuous Wavelet Transform (CWT) and Fast Fourier Transform (FFT) methods to gamma ray well-log data from the Q3, G1 and D2 wells. This high-resolution stratigraphic study was based on Milankovitch's orbital cycle theory. It was found that the CWT scale factors, ‘a,’ of 12, 24 and 60 match the ratios of the periodicities of precession, obliquity and eccentricity very well. Nine intervals of the Permo-carboniferous strata were recognized to have Milankovitch cycles in them. For example, section A of well Q3 has 29 precession cycles, 15 obliquity cycles and 7 short eccentricity cycles. The wavelengths are 2.7, 4.4 and 7.8 m for precession, obliquity and eccentricity, respectively. Important geological parameters such as the stratigraphic completeness and the accumulation rate were also estimated. These results provide basic information for further cyclostratigraphic correlation studies in the area. They are of great significance for the study of ancient and future climate change.展开更多
Gas-bearing volcanic reservoirs have been found in the deep Songliao Basin, China. Choosing proper interpretation parameters for log evaluation is difficult due to complicated mineral compositions and variable mineral...Gas-bearing volcanic reservoirs have been found in the deep Songliao Basin, China. Choosing proper interpretation parameters for log evaluation is difficult due to complicated mineral compositions and variable mineral contents. Based on the QAPF classification scheme given by IUGS, we propose a method to determine the mineral contents of volcanic rocks using log data and a genetic algorithm. According to the QAPF scheme, minerals in volcanic rocks are divided into five groups： Q（quartz）, A （Alkaline feldspar）, P （plagioclase）, M （mafic） and F （feldspathoid）. We propose a model called QAPM including porosity for the volumetric analysis of reservoirs. The log response equations for density, apparent neutron porosity, transit time, gamma ray and volume photoelectrical cross section index were first established with the mineral parameters obtained from the Schlumberger handbook of log mineral parameters. Then the volumes of the four minerals in the matrix were calculated using the genetic algorithm （GA）. The calculated porosity, based on the interpretation parameters, can be compared with core porosity, and the rock names given in the paper based on QAPF classification according to the four mineral contents are compatible with those from the chemical analysis of the core samples.展开更多
Shear wave velocity has numerous applications in geomechanical, petrophysical and geophysical studies of hydrocarbon reserves. However, data related to shear wave velocity isn’t available for all wells, especially ol...Shear wave velocity has numerous applications in geomechanical, petrophysical and geophysical studies of hydrocarbon reserves. However, data related to shear wave velocity isn’t available for all wells, especially old wells and it is very important to estimate this parameter using other well logging. Hence, lots of methods have been developed to estimate these data using other available information of reservoir. In this study, after processing and removing inappropriate petrophysical data, we estimated petrophysical properties affecting shear wave velocity of the reservoir and statistical methods were used to establish relationship between effective petrophysical properties and shear wave velocity. To predict (VS), first we used empirical relationships and then multivariate regression methods and neural networks were used. Multiple regression method is a powerful method that uses correlation between available information and desired parameter. Using this method, we can identify parameters affecting estimation of shear wave velocity. Neural networks can also be trained quickly and present a stable model for predicting shear wave velocity. For this reason, this method is known as “dynamic regression” compared with multiple regression. Neural network used in this study is not like a black box because we have used the results of multiple regression that can easily modify prediction of shear wave velocity through appropriate combination of data. The same information that was intended for multiple regression was used as input in neural networks, and shear wave velocity was obtained using compressional wave velocity and well logging data (neutron, density, gamma and deep resistivity) in carbonate rocks. The results show that methods applied in this carbonate reservoir was successful, so that shear wave velocity was predicted with about 92 and 95 percents of correlation coefficient in multiple regression and neural network method, respectively. Therefore, we propose using these methods to estimate shear wave veloci展开更多
The Liangshan and Qixia formations in the Sichuan Basin of central China were formed in the earlier middle Permian. Based on outcrop observation of the Changjianggou section at Shangsi, Guangyuan region and 3 rd -orde...The Liangshan and Qixia formations in the Sichuan Basin of central China were formed in the earlier middle Permian. Based on outcrop observation of the Changjianggou section at Shangsi, Guangyuan region and 3 rd -order sequence division in typical drillings, one-dimensional spectrum analysis has been used to choose the better curve between the natural gamma ray spectrometry log（ln （Th/K）） in Well-Long17 and the gamma ray log（GR） in Well-Wujia1, respectively, for identifying Milankovitch cycles in Sequence PSQ1 which comprises the Liangshan and Qixia formations, and then to identify the variation in the Milankovitch cycle sequences. On this basis, the system tract and 4 th -order sequence interfaces in Sequence PSQ1 were found via two-dimensional spectral analysis and digital filtering. Finally, a high-frequency sequence division program was established. Among these cycles, long eccentricity （413.0 ka） and short eccentricity （123.0 ka） are the most unambiguous, and they are separately the major control factors in forming 4 th -order （parasequence sets） and 5 th -order （parasequences） sequences, with the average thicknesses corresponding to the main cycles being 11.47 m and 3.32 m in Well-Long17, and 14.21 m and 3.79 m in Well-Wujia1, respectively. In other words, the deposition rate in the beach subfacies is faster than that of the inner ramp facies. The ln（Th/K） curve is more sensitive than the GR as the index of relatively ancient water depth in carbonate deposition. One-dimensional spectrum analysis of ln（Th/K） curve could distinguish the Milankovitch cycle sequences that arose from the Precession cycle （20.90 ka）, with a much higher credibility. Sequence PSQ1 in Well-Long17 contains 10 4 th -order sequences, and the growth span of Sequence PSQ1 consisting of the Liangshan and Qixia formations is about 4.13 Ma. The single deposition thickness of the long eccentricity cycle sequence has the characteristics of thinning and then thickening in the two-dimensional spectrum, wh展开更多
Sedimentary facies identification is critical for carbonate oil and gas reservoir development.The traditional method of sedimentary facies identification not only be affected by the engineer's experience but also ...Sedimentary facies identification is critical for carbonate oil and gas reservoir development.The traditional method of sedimentary facies identification not only be affected by the engineer's experience but also takes a long time.Identifying carbonate sedimentary facies based on machine learning is the trend of future development and has the advantages of short time consuming and reliable results without engineers'subjective influence.Although many references reported the application of machine learning to identify lithofacies,but identifying sedimentary facies of carbonate reservoirs is much more challenging due to the complex sedimentary environment and tectonic movement.This paper compares the performance of the carbonate sedimentary facies identification using four different machine learning models,and the optimal machine learning with the highest prediction accuracy is recommended.First,the carbonate sedimentary facies are classified into the lagoon,shallow sea,shoal,fore-shoal,and inter-shoal five tags based on the well loggings.Then,five well log curves including spectral gamma ray(SGR),uranium-free gamma ray(CGR),photoelectric absorption cross-section index(PE),true formation resistivity(RT),shallow lateral resistivity(RS)are used as the input,and the manual identified carbonate sedimentary facies are used as the output of the machine learning model.The performance of four different machine learning algorithms,including support vector machine(SVM),deep neural network(DNN),long short-term memory(LSTM)network,and random forest(RF)are compared.The other two wells are used for model validation.The research results show that the RF method has the highest accuracy of sedimentary facies prediction,and the average prediction accuracy is 78.81%;the average accuracy of sedimentary facies prediction using SVM is 77.93%.The sedimentary facies predictions using DNN and LSTM are less satisfying compared with RF and SVM,and the average accuracy is 69.94%and 73.05%,respectively.The predicted carbonate sedimentary facies展开更多
基金supported by the National Natural Science Foundation of China (Grant No.31370624)the Specialized Research Fund for the Doctoral Program of Higher Education (Grant No. 20103515110005)the Natural Science Foundation of Fujian, China (Grant No. 2011J01071)
文摘We investigated the quantity and quality 0f fallen l0gs in different Tsuga l0ngibracteata f0rest c0mmunities in the Tianba0yan Nati0nal Nature Reserve. We used redundancy analysis t0 determine the spatial distributi0n 0f fallen l0gs in the different f0rest c0mmunities and t0 analyze the relati0nships am0ng stand structure, t0p0graphic fact0rs and human disturbance. The v0lume, c0vered area, mean l0g length and number 0f fallen l0gs differed significantly am0ng f0rest types （P 〈 0.05）, but mean diameter at breast height sh0wed n0 significant difference （P 〉 0.05）. The l0g v0lume and c0vered area in different f0rest types sh0wed the f0ll0wing trend： T. l0ngibracteata pure f0rest 〈 T. l0ngibracteata ＋ Olig0staehyum scabrifl0rur 〈 T. l0ngibraeteata ＋ hardw00d 〈 Rh0d0dendr0n simiarum ＋ T. l0ngibraeteata 〈 T. l0ngibraeteata ＋ Phyll0stachys heter0cycla pubescens. The spatial distributi0n patterns 0f l0gs quantity and quality indicated that l0g v0lume and c0vered area were str0ngly affected by envir0nmental fact0rs in the f0ll0wing 0rder： human disturbance 〉 elevati0n 〉 sl0pe p0siti0n 〉 b0le height 〉 tree height 〉 sl0pe aspect 〉 density 〉 basal area 〉 sl0pe gradient. The relative c0ntributi0n 0f envir0nmental variables 0n the t0tal variance was t0p0graphy （76%） 〉 disturbance （42%） 〉 stand structure （35%）. T0p0graphy and disturbance c0mbined explained 8.2% 0f the variance. Fallen l0~s auantitv and aualitvwere negatively related t0 elevati0n and sl0pe p0siti0n, and p0sitively ass0ciated t0 human disturbance. The l0g v0lume decreased fr0m n0rthern t0 s0uthern sl0pes. Envir0nmental fact0rs had the highest impact 0n class I （slightly decayed）, and l0west impact 0n class V （highly decayed）.
文摘选择 X 射线作为检测源透射原木,根据检测透过被检物体后的射线强度差异,判断被检测原木内部是否存在缺陷和检测缺陷细节。检测过程中应用计算机数字图像处理技术对原始的 X 射线图像进行中值滤波、图像增强、差分和边缘检测,使得处理后的图像更加清晰,图像中的目标易于人眼识别。实验结果表明这种方法行之有效。
文摘This paper deals with the comparison of models for predicting porosity and permeability of oil reservoirs by coupling a machine learning concept and petrophysical logs.Different machine learning methods including conventional artificial neural network,genetic algorithm,fuzzy decision tree,the imperialist competitive algorithm(ICA),particle swarm optimization(PSO),and a hybrid of those ones are employed to have a comprehensive comparison.The machine learning approach was constructed and tested via data samples recorded from northern Persian Gulf oil reservoirs.The results gained from the machine learning models used in this paper are compared to the relevant real petrophysical data and the outputs achieved by other methods employed in our previous studies.The average relative absolute deviation between the approach estimations and the relevant actual data is found to be less than 1%for the hybridized approaches.The results reported in this paper indicate that implication of hybridized machine learning methods in porosity and permeability estimations can lead to the construction of more reliable static reservoir models in simulation plans.
基金financial and data support from NISOC Oil Company.
文摘Assessment of reservoir and fracture parameters is necessary to optimize oil production,especially in heterogeneous reservoirs.Core and image logs are regarded as two of the best methods for this aim.However,due to core limitations,using image log is considered as the best method.This study aims to use electrical image logs in the carbonate Asmari Formation reservoir in Zagros Basin,SW Iran,in order to evaluate natural fractures,porosity system,permeability profile and heterogeneity index and accordingly compare the results with core and well data.The results indicated that the electrical image logs are reliable for evaluating fracture and reservoir parameters,when there is no core available for a well.Based on the results from formation micro-imager(FMI)and electrical micro-imager(EMI),Asmari was recognized as a completely fractured reservoir in studied field and the reservoir parameters are mainly controlled by fractures.Furthermore,core and image logs indicated that the secondary porosity varies from 0%to 10%.The permeability indicator indicates that zones 3 and 5 have higher permeability index.Image log permeability index shows a very reasonable permeability profile after scaling against core and modular dynamics tester mobility,mud loss and production index which vary between 1 and 1000 md.In addition,no relationship was observed between core porosity and permeability,while the permeability relied heavily on fracture aperture.Therefore,fracture aperture was considered as the most important parameter for the determination of permeability.Sudden changes were also observed at zones 1-1 and 5 in the permeability trend,due to the high fracture aperture.It can be concluded that the electrical image logs(FMI and EMI)are usable for evaluating both reservoir and fracture parameters in wells with no core data in the Zagros Basin,SW Iran.
基金This work is financially supported by the Science Foundation of China University of Petroleum, Beijing (Grant No. 2462017YJRC023)the Fundamental Research Funds for the Central Universities and the Opening Fund of Key Laboratory of Deep Oil & Gas (Grant No. 20CX02116A)
文摘The Ordovician Majiagou Formation is one of the main gas-producing strata in the Ordos Basin,China.The identification of hydrocarbon-bearing intervals via conventional well logs is a challenging task.This study describes the litholog of Ma 5(Member 5 of Majiagou Formation)dolostones,and then analyzes the responses of various conventional well logs to the presences of natural gas.The lithology of the gas bearing layers is dominantly of the dolomicrite to fine to medium crystalline dolomite.Natural gas can be produced from the low resistivity layers,and the dry layers are characterized by high resistivities.Neutron-density crossovers are not sensitive to the presences of natural gas.In addition,there are no significant increases in sonic transit times in natural gas bearing layers.NMR(nuclear magnetic resonance)logs,DSI(Dipole Sonic Imager)logs and borehole image logs(XRMI)are introduced to discriminate the fluid property in Majiagou dolostone reservoirs.The gas bearing intervals have broad NMR T2(transverse relaxation time)spectrum with tail distributions as well as large T2gm(T2 logarithmic mean values)values,and the T2 spectrum commonly display polymodal behaviors.In contrast,the dry layers and water layers have low T2gm values and very narrow T2 spectrum without tails.The gas bearing layers are characterized by low Vp/Vs ratios,low Poisson’s ratios and low P-wave impedances,therefore the fluid property can be discriminated using DSI logs,and the interpretation results show good matches with the gas test data.The apparent formation water resistivity(AFWR)spectrum can be derived from XRMI image logs by using the Archie’s formula in the flushed zone.The gas bearing layers have broad apparent formation water resistivity spectrum and tail distributions compared with the dry and water layers,and also the interpretation results from the image logs exhibit good agreement with the gas test data.The fluid property in Majiagou dolostone reservoirs can be discriminated through NMR logs,DSI logs and borehole image logs.
基金sponsored by the National S&T Major Special Project(No.2008ZX05020-01)
文摘Reef-bank reservoirs are an important target for petroleum exploration in marine carbonates and also an essential supplemental area for oil and gas production in China. Due to the diversity of reservoirs and the extreme heterogeneity of reef-banks, it is very difficult to discriminate the sedimentary facies and lithologies in reef-bank reservoirs using conventional well logs. The borehole image log provides clear identification of sedimentary structures and textures and is an ideal tool for discriminating sedimentary facies and lithologies. After examining a large number of borehole images and cores, we propose nine typical patterns for borehole image interpretation and a method that uses these patterns to discriminate sedimentary facies and lithologies in reeI^bank reservoirs automatically. We also develop software with user-friendly interface. The results of applications in reef-bank reservoirs in the middle Tarim Basin and northeast Sichuan have proved that the proposed method and the corresponding software are quite effective.
基金supported by the Project Sponsored by the Scientific Research Foundation for the Re-turned Overseas Chinese Scholars, State Education Ministry (2006331) National Basic Research Program of China (2003CB214608)
文摘The authors applied a the combination of Continuous Wavelet Transform (CWT) and Fast Fourier Transform (FFT) methods to gamma ray well-log data from the Q3, G1 and D2 wells. This high-resolution stratigraphic study was based on Milankovitch's orbital cycle theory. It was found that the CWT scale factors, ‘a,’ of 12, 24 and 60 match the ratios of the periodicities of precession, obliquity and eccentricity very well. Nine intervals of the Permo-carboniferous strata were recognized to have Milankovitch cycles in them. For example, section A of well Q3 has 29 precession cycles, 15 obliquity cycles and 7 short eccentricity cycles. The wavelengths are 2.7, 4.4 and 7.8 m for precession, obliquity and eccentricity, respectively. Important geological parameters such as the stratigraphic completeness and the accumulation rate were also estimated. These results provide basic information for further cyclostratigraphic correlation studies in the area. They are of great significance for the study of ancient and future climate change.
基金National Natural Science Foundation of China (No. 49894194-4)
文摘Gas-bearing volcanic reservoirs have been found in the deep Songliao Basin, China. Choosing proper interpretation parameters for log evaluation is difficult due to complicated mineral compositions and variable mineral contents. Based on the QAPF classification scheme given by IUGS, we propose a method to determine the mineral contents of volcanic rocks using log data and a genetic algorithm. According to the QAPF scheme, minerals in volcanic rocks are divided into five groups： Q（quartz）, A （Alkaline feldspar）, P （plagioclase）, M （mafic） and F （feldspathoid）. We propose a model called QAPM including porosity for the volumetric analysis of reservoirs. The log response equations for density, apparent neutron porosity, transit time, gamma ray and volume photoelectrical cross section index were first established with the mineral parameters obtained from the Schlumberger handbook of log mineral parameters. Then the volumes of the four minerals in the matrix were calculated using the genetic algorithm （GA）. The calculated porosity, based on the interpretation parameters, can be compared with core porosity, and the rock names given in the paper based on QAPF classification according to the four mineral contents are compatible with those from the chemical analysis of the core samples.
文摘Shear wave velocity has numerous applications in geomechanical, petrophysical and geophysical studies of hydrocarbon reserves. However, data related to shear wave velocity isn’t available for all wells, especially old wells and it is very important to estimate this parameter using other well logging. Hence, lots of methods have been developed to estimate these data using other available information of reservoir. In this study, after processing and removing inappropriate petrophysical data, we estimated petrophysical properties affecting shear wave velocity of the reservoir and statistical methods were used to establish relationship between effective petrophysical properties and shear wave velocity. To predict (VS), first we used empirical relationships and then multivariate regression methods and neural networks were used. Multiple regression method is a powerful method that uses correlation between available information and desired parameter. Using this method, we can identify parameters affecting estimation of shear wave velocity. Neural networks can also be trained quickly and present a stable model for predicting shear wave velocity. For this reason, this method is known as “dynamic regression” compared with multiple regression. Neural network used in this study is not like a black box because we have used the results of multiple regression that can easily modify prediction of shear wave velocity through appropriate combination of data. The same information that was intended for multiple regression was used as input in neural networks, and shear wave velocity was obtained using compressional wave velocity and well logging data (neutron, density, gamma and deep resistivity) in carbonate rocks. The results show that methods applied in this carbonate reservoir was successful, so that shear wave velocity was predicted with about 92 and 95 percents of correlation coefficient in multiple regression and neural network method, respectively. Therefore, we propose using these methods to estimate shear wave veloci
基金part of a key project carried out in 2008-2011 and financially supported by the National Major Special Science and Technology Project (No.2008ZX05004-001)a Major Special Issue of the China National Petroleum Corporation (No. 2008E-0702)
文摘The Liangshan and Qixia formations in the Sichuan Basin of central China were formed in the earlier middle Permian. Based on outcrop observation of the Changjianggou section at Shangsi, Guangyuan region and 3 rd -order sequence division in typical drillings, one-dimensional spectrum analysis has been used to choose the better curve between the natural gamma ray spectrometry log（ln （Th/K）） in Well-Long17 and the gamma ray log（GR） in Well-Wujia1, respectively, for identifying Milankovitch cycles in Sequence PSQ1 which comprises the Liangshan and Qixia formations, and then to identify the variation in the Milankovitch cycle sequences. On this basis, the system tract and 4 th -order sequence interfaces in Sequence PSQ1 were found via two-dimensional spectral analysis and digital filtering. Finally, a high-frequency sequence division program was established. Among these cycles, long eccentricity （413.0 ka） and short eccentricity （123.0 ka） are the most unambiguous, and they are separately the major control factors in forming 4 th -order （parasequence sets） and 5 th -order （parasequences） sequences, with the average thicknesses corresponding to the main cycles being 11.47 m and 3.32 m in Well-Long17, and 14.21 m and 3.79 m in Well-Wujia1, respectively. In other words, the deposition rate in the beach subfacies is faster than that of the inner ramp facies. The ln（Th/K） curve is more sensitive than the GR as the index of relatively ancient water depth in carbonate deposition. One-dimensional spectrum analysis of ln（Th/K） curve could distinguish the Milankovitch cycle sequences that arose from the Precession cycle （20.90 ka）, with a much higher credibility. Sequence PSQ1 in Well-Long17 contains 10 4 th -order sequences, and the growth span of Sequence PSQ1 consisting of the Liangshan and Qixia formations is about 4.13 Ma. The single deposition thickness of the long eccentricity cycle sequence has the characteristics of thinning and then thickening in the two-dimensional spectrum, wh
文摘Sedimentary facies identification is critical for carbonate oil and gas reservoir development.The traditional method of sedimentary facies identification not only be affected by the engineer's experience but also takes a long time.Identifying carbonate sedimentary facies based on machine learning is the trend of future development and has the advantages of short time consuming and reliable results without engineers'subjective influence.Although many references reported the application of machine learning to identify lithofacies,but identifying sedimentary facies of carbonate reservoirs is much more challenging due to the complex sedimentary environment and tectonic movement.This paper compares the performance of the carbonate sedimentary facies identification using four different machine learning models,and the optimal machine learning with the highest prediction accuracy is recommended.First,the carbonate sedimentary facies are classified into the lagoon,shallow sea,shoal,fore-shoal,and inter-shoal five tags based on the well loggings.Then,five well log curves including spectral gamma ray(SGR),uranium-free gamma ray(CGR),photoelectric absorption cross-section index(PE),true formation resistivity(RT),shallow lateral resistivity(RS)are used as the input,and the manual identified carbonate sedimentary facies are used as the output of the machine learning model.The performance of four different machine learning algorithms,including support vector machine(SVM),deep neural network(DNN),long short-term memory(LSTM)network,and random forest(RF)are compared.The other two wells are used for model validation.The research results show that the RF method has the highest accuracy of sedimentary facies prediction,and the average prediction accuracy is 78.81%;the average accuracy of sedimentary facies prediction using SVM is 77.93%.The sedimentary facies predictions using DNN and LSTM are less satisfying compared with RF and SVM,and the average accuracy is 69.94%and 73.05%,respectively.The predicted carbonate sedimentary facies
