Shielding materials are critical for downhole pulsed neutron tool design because they directly influence the accuracy of formation measurements.A well-designed shield configuration ensures that the response of the too...Shielding materials are critical for downhole pulsed neutron tool design because they directly influence the accuracy of formation measurements.A well-designed shield configuration ensures that the response of the tool is maximally representative of the formation without being affected by the tool and borehole environment.This study investigated the effects of boron-containing materials on neutron and gamma detectors based on a newly designed logging-while-drilling tool that is currently undergoing manufacturing.As the boron content increased,the ability to absorb thermal neutrons increased significantly.Through simulation,it was proven that boron carbide(B_(4)C)can be used as an effective boron shielding material for thermal neutrons,and is therefore employed in this work.To shield against thermal neutrons migrating from the mud pipes,the optimal shielding thicknesses for the near-and far-neutron detectors were determined to be 5 and 4 mm.At a porosity of 25 p.u.,near-neutron sensitivity exhibited a 5.6%increase.Furthermore,to shield the capture gamma generated by thermal neutrons once they enter the tool from the mud pipe and formation,internal and external shields for the gamma detector were evaluated.The results show that the internal shield requires a boron content of 75%,whereas the external shield has a thickness of 14.2 mm thickness and a boron content of 25%to minimize the tool effect.展开更多
Electrofacies are used to determine reservoir rock properties,especially permeability,to simulate fluid flow in porous media.These are determined based on classification of similar logs among different groups of loggi...Electrofacies are used to determine reservoir rock properties,especially permeability,to simulate fluid flow in porous media.These are determined based on classification of similar logs among different groups of logging data.Data classification is accomplished by different statistical analysis such as principal component analysis,cluster analysis and differential analysis.The aim of this study is to predict 3D FZI(flow zone index)and Electrofacies(EFACT)volumes from a large volume of 3D seismic data.This study is divided into two parts.In the first part of the study,in order to make the EFACT model,nuclear magnetic resonance(NMR)log parameters were employed for developing an Electrofacies diagram based on pore size distribution and porosity variations.Then,a graph-based clustering method,known as multi resolution graph-based clustering(MRGC),was employed to classify and obtain the optimum number of Electrofacies.Seismic attribute analysis was then applied to model each relaxation group in order to build the initial 3D model which was used to reach the final model by applying Probabilistic Neural Network(PNN).In the second part of the study,the FZI 3D model was created by multi attributes technique.Then,this model was improved by three different artificial intelligence systems including PNN,multilayer feed-forward network(MLFN)and radial basis function network(RBFN).Finally,models of FZI and EFACT were compared.Results obtained from this study revealed that the two models are in good agreement and PNN method is successful in modeling FZI and EFACT from 3D seismic data for which no Stoneley data or NMR log data are available.Moreover,they may be used to detect hydrocarbon-bearing zones and locate the exact place for producing wells for the future development plans.In addition,the result provides a geologically realistic spatial FZI and reservoir facies distribution which helps to understand the subsurface reservoirs heterogeneities in the study area.展开更多
基金supported by the Natural Science Foundation of China(Nos.U23B20151 and 52171253).
文摘Shielding materials are critical for downhole pulsed neutron tool design because they directly influence the accuracy of formation measurements.A well-designed shield configuration ensures that the response of the tool is maximally representative of the formation without being affected by the tool and borehole environment.This study investigated the effects of boron-containing materials on neutron and gamma detectors based on a newly designed logging-while-drilling tool that is currently undergoing manufacturing.As the boron content increased,the ability to absorb thermal neutrons increased significantly.Through simulation,it was proven that boron carbide(B_(4)C)can be used as an effective boron shielding material for thermal neutrons,and is therefore employed in this work.To shield against thermal neutrons migrating from the mud pipes,the optimal shielding thicknesses for the near-and far-neutron detectors were determined to be 5 and 4 mm.At a porosity of 25 p.u.,near-neutron sensitivity exhibited a 5.6%increase.Furthermore,to shield the capture gamma generated by thermal neutrons once they enter the tool from the mud pipe and formation,internal and external shields for the gamma detector were evaluated.The results show that the internal shield requires a boron content of 75%,whereas the external shield has a thickness of 14.2 mm thickness and a boron content of 25%to minimize the tool effect.
文摘Electrofacies are used to determine reservoir rock properties,especially permeability,to simulate fluid flow in porous media.These are determined based on classification of similar logs among different groups of logging data.Data classification is accomplished by different statistical analysis such as principal component analysis,cluster analysis and differential analysis.The aim of this study is to predict 3D FZI(flow zone index)and Electrofacies(EFACT)volumes from a large volume of 3D seismic data.This study is divided into two parts.In the first part of the study,in order to make the EFACT model,nuclear magnetic resonance(NMR)log parameters were employed for developing an Electrofacies diagram based on pore size distribution and porosity variations.Then,a graph-based clustering method,known as multi resolution graph-based clustering(MRGC),was employed to classify and obtain the optimum number of Electrofacies.Seismic attribute analysis was then applied to model each relaxation group in order to build the initial 3D model which was used to reach the final model by applying Probabilistic Neural Network(PNN).In the second part of the study,the FZI 3D model was created by multi attributes technique.Then,this model was improved by three different artificial intelligence systems including PNN,multilayer feed-forward network(MLFN)and radial basis function network(RBFN).Finally,models of FZI and EFACT were compared.Results obtained from this study revealed that the two models are in good agreement and PNN method is successful in modeling FZI and EFACT from 3D seismic data for which no Stoneley data or NMR log data are available.Moreover,they may be used to detect hydrocarbon-bearing zones and locate the exact place for producing wells for the future development plans.In addition,the result provides a geologically realistic spatial FZI and reservoir facies distribution which helps to understand the subsurface reservoirs heterogeneities in the study area.
