The resistivity difference between oil and gas layers and the water layers in low contrast tight sandstone reservoirs is subtle. Fluid identification and saturation calculation based on conventional logging methods ar...The resistivity difference between oil and gas layers and the water layers in low contrast tight sandstone reservoirs is subtle. Fluid identification and saturation calculation based on conventional logging methods are facing challenges in such reservoirs. In this paper, a new method is proposed for fluid identification and saturation calculation in low contrast tight sandstone reservoirs. First, a model for calculating apparent formation water resistivity is constructed, which takes into account the influence of shale on the resistivity calculation and avoids apparent formation water resistivity abnormal values.Based on the distribution of the apparent formation water resistivity obtained by the new model, the water spectrum is determined for fluid identification in low contrast tight sandstone reservoirs.Following this, according to the average, standard deviation, and endpoints of the water spectrum, a new four-parameter model for calculating reservoir oil and gas saturation is built. The methods proposed in this paper are applied to the low contrast tight sandstone reservoirs in the Q4 formation of the X53 block and X70 block in the south of Songliao Basin, China. The results show that the water spectrum method can effectively distinguish oil-water layers and water layers in the study area. The standard deviation of the water spectrum in the oil-water layer is generally greater than that in the water layer. The new four-parameter model yields more accurate oil and gas saturation. These findings verify the effectiveness of the proposed methods.展开更多
In contrast to conventional gas-bearing rocks, gas shale has extremely low permeability due to its nano- scale pore networks. Organic matter which is dispersed in the shale matrix makes gas flow characteristics more c...In contrast to conventional gas-bearing rocks, gas shale has extremely low permeability due to its nano- scale pore networks. Organic matter which is dispersed in the shale matrix makes gas flow characteristics more complex. The traditional Darcy＇s law is unable to estimate matrix permeability due to the particular flow mechanisms of shale gas. Transport mechanisms and influence factors are studied to describe gas transport in extremely tight shale. Then Lattice Boltzmann simulation is used to establish a way to estimate the matrix permeability numerically. The results show that net desorption, diffu- sion, and slip flow are very sensitive to the pore scale. Pore pressure also plays an important role in mass fluxes of gas. Temperature variations only cause small changes in mass fluxes. The Lattice Boltzmann method can be used to study the flow field in the micropore spaces and then provides numerical solutions even in complex pore structure models. Understanding the transport characteristics and establishing a way to estimate potential gas flow is very important to guide shale gas t＇eserve estimation and recovery schemes.展开更多
In the petroleum industry,detection of multi-phase fluid flow is very important in both surface and down-hole measurements.Accurate measurement of high rate of water or gas multi-phase flow has always been an academic...In the petroleum industry,detection of multi-phase fluid flow is very important in both surface and down-hole measurements.Accurate measurement of high rate of water or gas multi-phase flow has always been an academic and industrial focus.NMR is an efficient and accurate technique for the detection of fluids;it is widely used in the determination of fluid compositions and properties.This paper is aimed to quantitatively detect multi-phase flow in oil and gas wells and pipelines and to propose an innovative method for online nuclear magnetic resonance(NMR)detection.The online NMR data acquisition,processing and interpretation methods are proposed to fill the blank of traditional methods.A full-bore straight tube design without pressure drop,a Halbach magnet structure design with zero magnetic leakage outside the probe,a separate antenna structure design without flowing effects on NMR measurement and automatic control technology will achieve unattended operation.Through the innovation of this work,the application of NMR for the real-time and quantitative detection of multi-phase flow in oil and gas wells and pipelines can be implemented.展开更多
Low-field(nuclear magnetic resonance)NMR has been widely used in petroleum industry,such as well logging and laboratory rock core analysis.However,the signal-to-noise ratio is low due to the low magnetic field strengt...Low-field(nuclear magnetic resonance)NMR has been widely used in petroleum industry,such as well logging and laboratory rock core analysis.However,the signal-to-noise ratio is low due to the low magnetic field strength of NMR tools and the complex petrophysical properties of detected samples.Suppressing the noise and highlighting the available NMR signals is very important for subsequent data processing.Most denoising methods are normally based on fixed mathematical transformation or handdesign feature selectors to suppress noise characteristics,which may not perform well because of their non-adaptive performance to different noisy signals.In this paper,we proposed a“data processing framework”to improve the quality of low field NMR echo data based on dictionary learning.Dictionary learning is a machine learning method based on redundancy and sparse representation theory.Available information in noisy NMR echo data can be adaptively extracted and reconstructed by dictionary learning.The advantages and application effectiveness of the proposed method were verified with a number of numerical simulations,NMR core data analyses,and NMR logging data processing.The results show that dictionary learning can significantly improve the quality of NMR echo data with high noise level and effectively improve the accuracy and reliability of inversion results.展开更多
A chronic phase following repetitive mild traumatic brain injury can present as chronic traumatic encephalopathy in some cases,which requires a neuropathological examination to make a definitive diagnosis.Positron emi...A chronic phase following repetitive mild traumatic brain injury can present as chronic traumatic encephalopathy in some cases,which requires a neuropathological examination to make a definitive diagnosis.Positron emission tomography(PET)is a molecular imaging modality that has high sensitivity for detecting even very small molecular changes,and can be used to quantitatively measure a range of molecular biological processes in the brain using different radioactive tracers.Functional changes have also been reported in patients with different forms of traumatic brain injury,especially mild traumatic brain injury and subsequent chronic traumatic encephalopathy.Thus,PET provides a novel approach for the further evaluation of mild traumatic brain injury at molecular levels.In this review,we discuss the recent advances in PET imaging with different radiotracers,including radioligands for PET imaging of glucose metabolism,tau,amyloid-beta,γ-aminobutyric acid type A receptors,and neuroinflammation,in the identification of altered neurological function.These novel radiolabeled ligands are likely to have widespread clinical application,and may be helpful for the treatment of mild traumatic brain injury.Moreover,PET functional imaging with different ligands can be used in the future to perform largescale and sequential studies exploring the time-dependent changes that occur in mild traumatic brain injury.展开更多
In the early exploration of many oilfields,low-resistivity-low-contrast(LRLC)pay zones are easily overlooked due to the resistivity similarity to the water zones.Existing identification methods are model-driven and ca...In the early exploration of many oilfields,low-resistivity-low-contrast(LRLC)pay zones are easily overlooked due to the resistivity similarity to the water zones.Existing identification methods are model-driven and cannot yield satisfactory results when the causes of LRLC pay zones are complicated.In this study,after analyzing a large number of core samples,main causes of LRLC pay zones in the study area are discerned,which include complex distribution of formation water salinity,high irreducible water saturation due to micropores,and high shale volume.Moreover,different oil testing layers may have different causes of LRLC pay zones.As a result,in addition to the well log data of oil testing layers,well log data of adjacent shale layers are also added to the original dataset as reference data.The densitybased spatial clustering algorithm with noise(DBSCAN)is used to cluster the original dataset into 49 clusters.A new dataset is ultimately projected into a feature space with 49 dimensions.The new dataset and oil testing results are respectively treated as input and output to train the multi-layer perceptron(MLP).A total of 3192 samples are used for stratified 8-fold cross-validation,and the accuracy of the MLP is found to be 85.53%.展开更多
In this paper,we proposed a novel method for low-field nuclear magnetic resonance(NMR)inversion based on low-rank and sparsity restraint(LRSR)of relaxation spectra,with which high quality construction is made possible...In this paper,we proposed a novel method for low-field nuclear magnetic resonance(NMR)inversion based on low-rank and sparsity restraint(LRSR)of relaxation spectra,with which high quality construction is made possible for one-and two-dimensional low-field and low signal to noise ratio NMR data.In this method,the low-rank and sparsity restraints are introduced into the objective function instead of the smoothing term.The low-rank features in relaxation spectra are extracted to ensure the local characteristics and morphology of spectra.The sparsity and residual term are contributed to the resolution and precision of spectra,with the elimination of the redundant relaxation components.Optimization process of the objective function is designed with alternating direction method of multiples,in which the objective function is decomposed into three subproblems to be independently solved.The optimum solution can be obtained by alternating iteration and updating process.At first,numerical simulations are conducted on synthetic echo data with different signal-to-noise ratios,to optimize the desirable regularization parameters and verify the feasibility and effectiveness of proposed method.Then,NMR experiments on solutions and artificial sandstone samples are conducted and analyzed,which validates the robustness and reliability of the proposed method.The results from simulations and experiments have demonstrated that the suggested method has unique advantages for improving the resolution of relaxation spectra and enhancing the ability of fluid quantitative identification.展开更多
Properties of shale in an acid environment are important when acid or CO2 is injected into geologic formations as a working fluid for enhanced oil and gas recovery,hydraulic fracturing and reduced fracture initiation ...Properties of shale in an acid environment are important when acid or CO2 is injected into geologic formations as a working fluid for enhanced oil and gas recovery,hydraulic fracturing and reduced fracture initiation pressure.It has previously been shown that acid fluids can enhance the formation conductivity and decrease the hardness of shale.However,less is known about the effect of dilute acid on the adhesion properties of shale.In the study,shale samples are characterized in detail with advanced analysis.Adhesion properties of shale via dilute acid treatment were revealed by atomic force microscopy(AFM)for the first time.Results indicate that acid treatment can greatly enhance adhesion forces of the shale surface.After acid treatment,the average adhesion forces show a platform-like growth with an increase in loading force.Through analysis of results from AFM,scanning electron microscopy,and X-ray diffraction,we affirm that the enhanced adhesion forces are mainly from increased specific surface area and reduced elastic modulus.The results presented in this work help understand the adhesion properties of shale oil/gas present in an acidic environment,which have great significance in unconventional resources development.展开更多
In this research,an integrated classification method based on principal component analysis-simulated annealing genetic algorithm-fuzzy cluster means(PCA-SAGA-FCM)was proposed for the unsupervised classification of tig...In this research,an integrated classification method based on principal component analysis-simulated annealing genetic algorithm-fuzzy cluster means(PCA-SAGA-FCM)was proposed for the unsupervised classification of tight sandstone reservoirs which lack the prior information and core experiments.A variety of evaluation parameters were selected,including lithology characteristic parameters,poro-permeability quality characteristic parameters,engineering quality characteristic parameters,and pore structure characteristic parameters.The PCA was used to reduce the dimension of the evaluation pa-rameters,and the low-dimensional data was used as input.The unsupervised reservoir classification of tight sandstone reservoir was carried out by the SAGA-FCM,the characteristics of reservoir at different categories were analyzed and compared with the lithological profiles.The analysis results of numerical simulation and actual logging data show that:1)compared with FCM algorithm,SAGA-FCM has stronger stability and higher accuracy;2)the proposed method can cluster the reservoir flexibly and effectively according to the degree of membership;3)the results of reservoir integrated classification match well with the lithologic profle,which demonstrates the reliability of the classification method.展开更多
基金funded by the National Natural Science Foundation of China (42174131)。
文摘The resistivity difference between oil and gas layers and the water layers in low contrast tight sandstone reservoirs is subtle. Fluid identification and saturation calculation based on conventional logging methods are facing challenges in such reservoirs. In this paper, a new method is proposed for fluid identification and saturation calculation in low contrast tight sandstone reservoirs. First, a model for calculating apparent formation water resistivity is constructed, which takes into account the influence of shale on the resistivity calculation and avoids apparent formation water resistivity abnormal values.Based on the distribution of the apparent formation water resistivity obtained by the new model, the water spectrum is determined for fluid identification in low contrast tight sandstone reservoirs.Following this, according to the average, standard deviation, and endpoints of the water spectrum, a new four-parameter model for calculating reservoir oil and gas saturation is built. The methods proposed in this paper are applied to the low contrast tight sandstone reservoirs in the Q4 formation of the X53 block and X70 block in the south of Songliao Basin, China. The results show that the water spectrum method can effectively distinguish oil-water layers and water layers in the study area. The standard deviation of the water spectrum in the oil-water layer is generally greater than that in the water layer. The new four-parameter model yields more accurate oil and gas saturation. These findings verify the effectiveness of the proposed methods.
基金supported by the National Natural Science Foundation of China(Grant No.41130417)‘‘111 Program’’(B13010)Shell Ph.D.Scholarship
文摘In contrast to conventional gas-bearing rocks, gas shale has extremely low permeability due to its nano- scale pore networks. Organic matter which is dispersed in the shale matrix makes gas flow characteristics more complex. The traditional Darcy＇s law is unable to estimate matrix permeability due to the particular flow mechanisms of shale gas. Transport mechanisms and influence factors are studied to describe gas transport in extremely tight shale. Then Lattice Boltzmann simulation is used to establish a way to estimate the matrix permeability numerically. The results show that net desorption, diffu- sion, and slip flow are very sensitive to the pore scale. Pore pressure also plays an important role in mass fluxes of gas. Temperature variations only cause small changes in mass fluxes. The Lattice Boltzmann method can be used to study the flow field in the micropore spaces and then provides numerical solutions even in complex pore structure models. Understanding the transport characteristics and establishing a way to estimate potential gas flow is very important to guide shale gas t＇eserve estimation and recovery schemes.
基金supported by the National Natural Science Foundation of China(Grant No.51704327)
文摘In the petroleum industry,detection of multi-phase fluid flow is very important in both surface and down-hole measurements.Accurate measurement of high rate of water or gas multi-phase flow has always been an academic and industrial focus.NMR is an efficient and accurate technique for the detection of fluids;it is widely used in the determination of fluid compositions and properties.This paper is aimed to quantitatively detect multi-phase flow in oil and gas wells and pipelines and to propose an innovative method for online nuclear magnetic resonance(NMR)detection.The online NMR data acquisition,processing and interpretation methods are proposed to fill the blank of traditional methods.A full-bore straight tube design without pressure drop,a Halbach magnet structure design with zero magnetic leakage outside the probe,a separate antenna structure design without flowing effects on NMR measurement and automatic control technology will achieve unattended operation.Through the innovation of this work,the application of NMR for the real-time and quantitative detection of multi-phase flow in oil and gas wells and pipelines can be implemented.
基金supported by Science Foundation of China University of Petroleum,Beijing(Grant Number ZX20210024)Chinese Postdoctoral Science Foundation(Grant Number 2021M700172)+1 种基金The Strategic Cooperation Technology Projects of CNPC and CUP(Grant Number ZLZX2020-03)National Natural Science Foundation of China(Grant Number 42004105)
文摘Low-field(nuclear magnetic resonance)NMR has been widely used in petroleum industry,such as well logging and laboratory rock core analysis.However,the signal-to-noise ratio is low due to the low magnetic field strength of NMR tools and the complex petrophysical properties of detected samples.Suppressing the noise and highlighting the available NMR signals is very important for subsequent data processing.Most denoising methods are normally based on fixed mathematical transformation or handdesign feature selectors to suppress noise characteristics,which may not perform well because of their non-adaptive performance to different noisy signals.In this paper,we proposed a“data processing framework”to improve the quality of low field NMR echo data based on dictionary learning.Dictionary learning is a machine learning method based on redundancy and sparse representation theory.Available information in noisy NMR echo data can be adaptively extracted and reconstructed by dictionary learning.The advantages and application effectiveness of the proposed method were verified with a number of numerical simulations,NMR core data analyses,and NMR logging data processing.The results show that dictionary learning can significantly improve the quality of NMR echo data with high noise level and effectively improve the accuracy and reliability of inversion results.
基金This work was supported by a grant from the National Natural Science Foundation of China,No.81671671Clinical Research Center for Medical Imaging in Hunan Province of China,No.2020SK4001(both to JL).
文摘A chronic phase following repetitive mild traumatic brain injury can present as chronic traumatic encephalopathy in some cases,which requires a neuropathological examination to make a definitive diagnosis.Positron emission tomography(PET)is a molecular imaging modality that has high sensitivity for detecting even very small molecular changes,and can be used to quantitatively measure a range of molecular biological processes in the brain using different radioactive tracers.Functional changes have also been reported in patients with different forms of traumatic brain injury,especially mild traumatic brain injury and subsequent chronic traumatic encephalopathy.Thus,PET provides a novel approach for the further evaluation of mild traumatic brain injury at molecular levels.In this review,we discuss the recent advances in PET imaging with different radiotracers,including radioligands for PET imaging of glucose metabolism,tau,amyloid-beta,γ-aminobutyric acid type A receptors,and neuroinflammation,in the identification of altered neurological function.These novel radiolabeled ligands are likely to have widespread clinical application,and may be helpful for the treatment of mild traumatic brain injury.Moreover,PET functional imaging with different ligands can be used in the future to perform largescale and sequential studies exploring the time-dependent changes that occur in mild traumatic brain injury.
基金funded by the Strategic Cooperation Technology Projects of CNPC and CUPB(ZLZX2020-03)
文摘In the early exploration of many oilfields,low-resistivity-low-contrast(LRLC)pay zones are easily overlooked due to the resistivity similarity to the water zones.Existing identification methods are model-driven and cannot yield satisfactory results when the causes of LRLC pay zones are complicated.In this study,after analyzing a large number of core samples,main causes of LRLC pay zones in the study area are discerned,which include complex distribution of formation water salinity,high irreducible water saturation due to micropores,and high shale volume.Moreover,different oil testing layers may have different causes of LRLC pay zones.As a result,in addition to the well log data of oil testing layers,well log data of adjacent shale layers are also added to the original dataset as reference data.The densitybased spatial clustering algorithm with noise(DBSCAN)is used to cluster the original dataset into 49 clusters.A new dataset is ultimately projected into a feature space with 49 dimensions.The new dataset and oil testing results are respectively treated as input and output to train the multi-layer perceptron(MLP).A total of 3192 samples are used for stratified 8-fold cross-validation,and the accuracy of the MLP is found to be 85.53%.
基金supported by “National Natural Science Foundation of China (Grant No. 42204106)”“China Postdoctoral Science Foundation (Grant No. 2021M700172)”+1 种基金“The Strategic Cooperation Technology Projects of CNPC and CUP (Grant No. ZLZX2020-03)”“Natural Science Foundation of the Jiangsu Higher Education Institutions of China (Grant No. 20KJD430002)”
文摘In this paper,we proposed a novel method for low-field nuclear magnetic resonance(NMR)inversion based on low-rank and sparsity restraint(LRSR)of relaxation spectra,with which high quality construction is made possible for one-and two-dimensional low-field and low signal to noise ratio NMR data.In this method,the low-rank and sparsity restraints are introduced into the objective function instead of the smoothing term.The low-rank features in relaxation spectra are extracted to ensure the local characteristics and morphology of spectra.The sparsity and residual term are contributed to the resolution and precision of spectra,with the elimination of the redundant relaxation components.Optimization process of the objective function is designed with alternating direction method of multiples,in which the objective function is decomposed into three subproblems to be independently solved.The optimum solution can be obtained by alternating iteration and updating process.At first,numerical simulations are conducted on synthetic echo data with different signal-to-noise ratios,to optimize the desirable regularization parameters and verify the feasibility and effectiveness of proposed method.Then,NMR experiments on solutions and artificial sandstone samples are conducted and analyzed,which validates the robustness and reliability of the proposed method.The results from simulations and experiments have demonstrated that the suggested method has unique advantages for improving the resolution of relaxation spectra and enhancing the ability of fluid quantitative identification.
基金supported by National Natural Science Foundation of China(No.51674275)National Science and Technology Major Project(2017ZX05009-003)PetroChina Innovation Foundation(2018D-5007-0308)
文摘Properties of shale in an acid environment are important when acid or CO2 is injected into geologic formations as a working fluid for enhanced oil and gas recovery,hydraulic fracturing and reduced fracture initiation pressure.It has previously been shown that acid fluids can enhance the formation conductivity and decrease the hardness of shale.However,less is known about the effect of dilute acid on the adhesion properties of shale.In the study,shale samples are characterized in detail with advanced analysis.Adhesion properties of shale via dilute acid treatment were revealed by atomic force microscopy(AFM)for the first time.Results indicate that acid treatment can greatly enhance adhesion forces of the shale surface.After acid treatment,the average adhesion forces show a platform-like growth with an increase in loading force.Through analysis of results from AFM,scanning electron microscopy,and X-ray diffraction,we affirm that the enhanced adhesion forces are mainly from increased specific surface area and reduced elastic modulus.The results presented in this work help understand the adhesion properties of shale oil/gas present in an acidic environment,which have great significance in unconventional resources development.
基金funded by the National Natural Science Foundation of China(42174131)the Strategic Cooperation Technology Projects of CNPC and CUPB(ZLZX2020-03).
文摘In this research,an integrated classification method based on principal component analysis-simulated annealing genetic algorithm-fuzzy cluster means(PCA-SAGA-FCM)was proposed for the unsupervised classification of tight sandstone reservoirs which lack the prior information and core experiments.A variety of evaluation parameters were selected,including lithology characteristic parameters,poro-permeability quality characteristic parameters,engineering quality characteristic parameters,and pore structure characteristic parameters.The PCA was used to reduce the dimension of the evaluation pa-rameters,and the low-dimensional data was used as input.The unsupervised reservoir classification of tight sandstone reservoir was carried out by the SAGA-FCM,the characteristics of reservoir at different categories were analyzed and compared with the lithological profiles.The analysis results of numerical simulation and actual logging data show that:1)compared with FCM algorithm,SAGA-FCM has stronger stability and higher accuracy;2)the proposed method can cluster the reservoir flexibly and effectively according to the degree of membership;3)the results of reservoir integrated classification match well with the lithologic profle,which demonstrates the reliability of the classification method.
