Seismic attributes supported by composite logs are the best way that can enable the interpreter to understand seismic data very well and generate a new view of the output results.Detection of the reservoir zone can be...Seismic attributes supported by composite logs are the best way that can enable the interpreter to understand seismic data very well and generate a new view of the output results.Detection of the reservoir zone can be enhanced by analyzing wells log data based on Gamma-ray,Resistivity,and Vp sonic logs respectively.Composite logs of Scarab-1,Scarab-De,Scarab-Da,Scarab-Dd,and Scarab-2 wells indicate the lateral and vertical variation of the gas reservoir in ElWastani Formation.However,there are several seismic attributes that can be used to support reservoirs identification.For enhancement the detection of the hydrocarbon reservoirs,it is important to carefully analyze the 2D seismic data,which in this study will be primarily prepared to enhance seismic attributes results for the identification of gas chimneys,gas zones as channels,enhance stratigraphic and structural interpretations.In this article,we have performed data conditioning,quality control and seismic well ties including the preliminary wavelet extractions to get accurate output.Then,we have extracted of several classes of physical,geometrical and complex attributes as a direct hydrocarbon indicator to identify the gas zones,channels and chimneys and to identify the faults and discontinuities.The main contribution of this work is to provide a more detailed seismic reflection image supported by several seismic attributes classes and well logs to show a visual and quantitative evidence to identify the gas channels and gas chimneys with improving the detection of the faults and discontinuities.展开更多
Derivative/volatility well-log attributes from very few commonly recorded well logs can assist in the prediction of total organic carbon(TOC)in shales and tight formations.This is of value where only limited suites of...Derivative/volatility well-log attributes from very few commonly recorded well logs can assist in the prediction of total organic carbon(TOC)in shales and tight formations.This is of value where only limited suites of well logs are recorded,and few laboratory measurements of TOC are conducted on rock samples.Data from two Lower-Barnett-Shale(LBS)wells(USA),including well logs and core analysis is considered.It demonstrates how well-log attributes can be exploited with machine learning(ML)to generate accurate TOC predictions.Six attributes are calculated for gamma-ray(GR),bulk-density(PB)and compressional-sonic(DT)logs.Used in combination with just one of those recorded logs,those attributes deliver more accurate TOC predictions with ML models than using all three recorded logs.When used in combination with two or three of the recorded logs,the attributes generate TOC prediction accuracy comparable with ML models using five recorded well logs.Multi-K-fold-cross-validation analysis reveals that the K-nearest-neighbour algorithm yields the most accurate TOC predictions for the LBS dataset.The extreme-gradient-boosting(XGB)algorithm also performs well.XGB is able to provide information about the relative importance of each well-log attribute used as an input variable.This facilitates feature selection making it possible to reduce the number of attributes required to generate accurate TOC predictions from just two or three recorded well logs.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 a part of the Egyptian General Petroleum Corporation(EGPC)project in west offshore Nile Delta,Egypt.
文摘Seismic attributes supported by composite logs are the best way that can enable the interpreter to understand seismic data very well and generate a new view of the output results.Detection of the reservoir zone can be enhanced by analyzing wells log data based on Gamma-ray,Resistivity,and Vp sonic logs respectively.Composite logs of Scarab-1,Scarab-De,Scarab-Da,Scarab-Dd,and Scarab-2 wells indicate the lateral and vertical variation of the gas reservoir in ElWastani Formation.However,there are several seismic attributes that can be used to support reservoirs identification.For enhancement the detection of the hydrocarbon reservoirs,it is important to carefully analyze the 2D seismic data,which in this study will be primarily prepared to enhance seismic attributes results for the identification of gas chimneys,gas zones as channels,enhance stratigraphic and structural interpretations.In this article,we have performed data conditioning,quality control and seismic well ties including the preliminary wavelet extractions to get accurate output.Then,we have extracted of several classes of physical,geometrical and complex attributes as a direct hydrocarbon indicator to identify the gas zones,channels and chimneys and to identify the faults and discontinuities.The main contribution of this work is to provide a more detailed seismic reflection image supported by several seismic attributes classes and well logs to show a visual and quantitative evidence to identify the gas channels and gas chimneys with improving the detection of the faults and discontinuities.
文摘Derivative/volatility well-log attributes from very few commonly recorded well logs can assist in the prediction of total organic carbon(TOC)in shales and tight formations.This is of value where only limited suites of well logs are recorded,and few laboratory measurements of TOC are conducted on rock samples.Data from two Lower-Barnett-Shale(LBS)wells(USA),including well logs and core analysis is considered.It demonstrates how well-log attributes can be exploited with machine learning(ML)to generate accurate TOC predictions.Six attributes are calculated for gamma-ray(GR),bulk-density(PB)and compressional-sonic(DT)logs.Used in combination with just one of those recorded logs,those attributes deliver more accurate TOC predictions with ML models than using all three recorded logs.When used in combination with two or three of the recorded logs,the attributes generate TOC prediction accuracy comparable with ML models using five recorded well logs.Multi-K-fold-cross-validation analysis reveals that the K-nearest-neighbour algorithm yields the most accurate TOC predictions for the LBS dataset.The extreme-gradient-boosting(XGB)algorithm also performs well.XGB is able to provide information about the relative importance of each well-log attribute used as an input variable.This facilitates feature selection making it possible to reduce the number of attributes required to generate accurate TOC predictions from just two or three recorded well logs.
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.
