It is essential to characterize fluid flow in porous media to have a better understanding of petrophysical properties.Many approaches were developed to determine reservoir permeability among which the integrated analy...It is essential to characterize fluid flow in porous media to have a better understanding of petrophysical properties.Many approaches were developed to determine reservoir permeability among which the integrated analysis of hydraulic flow unit(HFU)and electrofacies(EF)is considered to be useful one.However,the application of HFU and EF analysis has not been totally understood with a limited data to develop correlation for less distance offset wells.In this study,an attempt was made to show the application of integrating HFU and EF for reliable estimation of permeability using core and wireline log data in one of the gas fields in Pakistan.The results obtained indicate that the integrated approach proposed in this study can be used,especially in less distance offset wells when a limited number of data are available for petrophysical characterization.展开更多
Efficient iterative unsupervised machine learning involving probabilistic clustering analysis with the expectation-maximization(EM)clustering algorithm is applied to categorize reservoir facies by exploiting latent an...Efficient iterative unsupervised machine learning involving probabilistic clustering analysis with the expectation-maximization(EM)clustering algorithm is applied to categorize reservoir facies by exploiting latent and observable well-log variables from a clastic reservoir in the Majnoon oilfield,southern Iraq.The observable well-log variables consist of conventional open-hole,well-log data and the computer-processed interpretation of gamma rays,bulk density,neutron porosity,compressional sonic,deep resistivity,shale volume,total porosity,and water saturation,from three wells located in the Nahr Umr reservoir.The latent variables include shale volume and water saturation.The EM algorithm efficiently characterizes electrofacies through iterative machine learning to identify the local maximum likelihood estimates(MLE)of the observable and latent variables in the studied dataset.The optimized EM model developed successfully predicts the core-derived facies classification in two of the studied wells.The EM model clusters the data into three distinctive reservoir electrofacies(F1,F2,and F3).F1 represents a gas-bearing electrofacies with low shale volume(Vsh)and water saturation(Sw)and high porosity and permeability values identifying it as an attractive reservoir target.The results of the EM model are validated using nuclear magnetic resonance(NMR)data from the third studied well for which no cores were recovered.The NMR results confirm the effectiveness and accuracy of the EM model in predicting electrofacies.The utilization of the EM algorithm for electrofacies classification/cluster analysis is innovative.Specifically,the clusters it establishes are less rigidly constrained than those derived from the more commonly used K-means clustering method.The EM methodology developed generates dependable electrofacies estimates in the studied reservoir intervals where core samples are not available.Therefore,once calibrated with core data in some wells,the model is suitable for application to other wells that lack core data.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
In this study, we used the multi-resolution graph-based clustering （MRGC） method for determining the electrofacies （EF） and lithofacies （LF） from well log data obtained from the intraplatform bank gas fields loc...In this study, we used the multi-resolution graph-based clustering （MRGC） method for determining the electrofacies （EF） and lithofacies （LF） from well log data obtained from the intraplatform bank gas fields located in the Amu Darya Basin. The MRGC could automatically determine the optimal number of clusters without prior knowledge about the structure or cluster numbers of the analyzed data set and allowed the users to control the level of detail actually needed to define the EF. Based on the LF identification and successful EF calibration using core data, an MRGC EF partition model including five clusters and a quantitative LF interpretation chart were constructed. The EF clusters 1 to 5 were interpreted as lagoon, anhydrite flat, interbank, low-energy bank, and high-energy bank, and the coincidence rate in the cored interval could reach 85%. We concluded that the MRGC could be accurately applied to predict the LF in non-cored but logged wells. Therefore, continuous EF clusters were partitioned and corresponding LF were characteristics ＆different LF were analyzed interpreted, and the distribution and petrophysical in the framework of sequence stratigraphy.展开更多
The sand intervals of the Early Cretaceous Lower Goru Formation are a conventional reservoir,generally distributed in the Middle and Lower Indus Basin of Pakistan.Lithostratigraphically formation is classified into tw...The sand intervals of the Early Cretaceous Lower Goru Formation are a conventional reservoir,generally distributed in the Middle and Lower Indus Basin of Pakistan.Lithostratigraphically formation is classified into two parts;the upper parts are predominantly composed of shale,siltstone,and thin layers of alternate shale and sandstone,while the lower parts are composed of sandstone with interlayering of shale and limestone.The sandstone of the Lower Goru Formation has been further divided into A,B,C,and D sand intervals based on reservoir quality.Detailed depositional facies and reservoir characteristics are essential for the evaluation of hydrocarbon exploration and development.This paper aims to evaluate the depositional environment and reservoir characterization of the siliciclastic reservoir of the Early Cretaceous Lower Goru Formation by integrating the gamma-ray log patterns and petrographic analysis and scanning electron microscopic(SEM)analysis.Petrographic characterization of the sand intervals and Gamma-ray log signatures were used for the interpretation of the depositional environment of the reservoir intervals.Petrographic analysis reveals that the sandstone of the Lower Goru Formation is fine-to medium-grained,well-sorted,arkose or feldspathic arenite.Primary intergranular macroporosity,secondary intragranular macropores,and Intercrystalline micropores were identified within the sandstone by the SEM analysis.The diagenetic analysis suggests that the sandstone possesses high porosity,low permeability,and has undergone significant alterations such as compaction,quartz cementation,feldspar dissolution,and clay minerals alteration.Five electrofacies are interpreted based on gamma-ray log patterns including(1)funnel shape(FA);(2)bell shape(FB);(3)cylindrical shape(FC);(4)bow shape(FD);and(5)serrated shape(FE)patterns.The interpreted facies results reveal shoreface environment for A-sand,Tidal flat for B-sand,mixed tidal flat for C sand,Tide dominated mixed for D-sand,and transgressive shelf for Esand.The present study will be helpful for the assessment of the reservoir quality of the Early Cretaceous Lower Goru Formation for further exploration and development in the Indus Basin of Pakistan.展开更多
The Oligo-Miocene Asmari Formation is one of the most important hydrocarbon reservoirs in the Middle East.The oilfield under study is one of the largest oilfields in the Zagros basin with the Asmari Formation being th...The Oligo-Miocene Asmari Formation is one of the most important hydrocarbon reservoirs in the Middle East.The oilfield under study is one of the largest oilfields in the Zagros basin with the Asmari Formation being the major reservoir rock.In this study,petrographic analyses,petrophysical data and neural network clustering techniques were used for identifying rock types in the Asmari reservoir.Facies analysis of the Asmari Formation in the study area has resulted in the definition of 1 clastic lithofacies and 14 carbonate microfacies types.Using petrophysical logs from 43 wells and their correlation with capillary pressure(Pc)curves,led to the recognition of 7 electrofacies(EF1-EF7).Microscopic evidence of Electrofacies group C1 and S1 show that the sedimentary facies of these electrofacies are most commonly found in restricted and shoal facies belts zone.Also,petrographic studies show that the sedimentary facies of C2,C3,C4,S2 and S3 were formed in the open marine,Lagoon,and Tidal flat facies belt zone of homoclinal ramp sedimentary environment during the Oligo-Miocene based on relative sea level changes respectively.The link between electrofacies and geological data indicated that both sedimentary and diagenetic processes controlled the reservoir quality of the Asmari Formation.Porosity,permeability and water saturation were used to estimate the reservoir quality of each electrofacies.EFs 1 and 2 with high porosity and permeability,low water saturation is considered as the best reservoir with regard to sedimentary textures(dolowackestone and dolograinstone)and the effect of diagenetic processes such as dolomitization processes.Vuggy,growth framework and interparticle porosities are major in EF-2,while the intercrystalline porosity is the major type in EF-1.EFs 3 and 4 show low values of porosity,permeability and high percentage of water saturations,which characterizes them as poor reservoir rocks.Finally,EF-5 is the only electrofacies in the siliciclastic parts of the Asmari reservoir,which is composed of rounded and well-sorted quartz grains that are slightly cemented.In sandstone electrofacies,electrofacies EF-5(S1),is the best type of sandstone reservoir rock and to move towards electrofacies EF-7(S3),will reduce reservoir quality.In carbonate electrofacies,also,electrofacies no 1,the best type of carbonate reservoir rock can be observed and move towards electrofacie number 4,lower quality of reservoir rocks is seen.展开更多
In view of the high accuracy and predictability, high-resolution sequence stratigraphy had been extensively applied to oil exploration and gotten prominent practicable results. This article takes the first layer, uppe...In view of the high accuracy and predictability, high-resolution sequence stratigraphy had been extensively applied to oil exploration and gotten prominent practicable results. This article takes the first layer, upper second submember, Shahejie （沙河街） Formation from Pucheng （濮城） oilfield as an example to analyze the application of high-resolution sequence stratigraphy in reservoir study on the basis of a comprehensive study of core log data. Firstly, facies analysis of this area reveals the corresponding terminal fan system occurring where sediment-laden streams decrease in size and vanish as a result of evaporation and transmission losses. The model includes a tripartite zonation of terminal fan into feeder, distributary, and basinal zones. Secondly, electrofacies were made by well-log analysis and then matched with sedimentary facies defined by core analysis. Four electrofacies characterizing the main sedimentary facies association and depositional environments within target area are defined （channel, lag deposit, lake or flood-plain, and overflow deposits）. Thirdly, related correlations based on high-resolution sequence stratigraphy were established. By observing the stacking arrangement of genetic sequences, different scales of stratigraphic cycle can be identified. Within scale and duration, the stratigraphic cycles are termed as genetic sequences, genetic sequence sets, and minor cycles.展开更多
文摘It is essential to characterize fluid flow in porous media to have a better understanding of petrophysical properties.Many approaches were developed to determine reservoir permeability among which the integrated analysis of hydraulic flow unit(HFU)and electrofacies(EF)is considered to be useful one.However,the application of HFU and EF analysis has not been totally understood with a limited data to develop correlation for less distance offset wells.In this study,an attempt was made to show the application of integrating HFU and EF for reliable estimation of permeability using core and wireline log data in one of the gas fields in Pakistan.The results obtained indicate that the integrated approach proposed in this study can be used,especially in less distance offset wells when a limited number of data are available for petrophysical characterization.
文摘Efficient iterative unsupervised machine learning involving probabilistic clustering analysis with the expectation-maximization(EM)clustering algorithm is applied to categorize reservoir facies by exploiting latent and observable well-log variables from a clastic reservoir in the Majnoon oilfield,southern Iraq.The observable well-log variables consist of conventional open-hole,well-log data and the computer-processed interpretation of gamma rays,bulk density,neutron porosity,compressional sonic,deep resistivity,shale volume,total porosity,and water saturation,from three wells located in the Nahr Umr reservoir.The latent variables include shale volume and water saturation.The EM algorithm efficiently characterizes electrofacies through iterative machine learning to identify the local maximum likelihood estimates(MLE)of the observable and latent variables in the studied dataset.The optimized EM model developed successfully predicts the core-derived facies classification in two of the studied wells.The EM model clusters the data into three distinctive reservoir electrofacies(F1,F2,and F3).F1 represents a gas-bearing electrofacies with low shale volume(Vsh)and water saturation(Sw)and high porosity and permeability values identifying it as an attractive reservoir target.The results of the EM model are validated using nuclear magnetic resonance(NMR)data from the third studied well for which no cores were recovered.The NMR results confirm the effectiveness and accuracy of the EM model in predicting electrofacies.The utilization of the EM algorithm for electrofacies classification/cluster analysis is innovative.Specifically,the clusters it establishes are less rigidly constrained than those derived from the more commonly used K-means clustering method.The EM methodology developed generates dependable electrofacies estimates in the studied reservoir intervals where core samples are not available.Therefore,once calibrated with core data in some wells,the model is suitable for application to other wells that lack core data.
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.
基金supported by the National Science and Technology Major Project of China(No.2011ZX05029-003)CNPC Science Research and Technology Development Project,China(No.2013D-0904)
文摘In this study, we used the multi-resolution graph-based clustering （MRGC） method for determining the electrofacies （EF） and lithofacies （LF） from well log data obtained from the intraplatform bank gas fields located in the Amu Darya Basin. The MRGC could automatically determine the optimal number of clusters without prior knowledge about the structure or cluster numbers of the analyzed data set and allowed the users to control the level of detail actually needed to define the EF. Based on the LF identification and successful EF calibration using core data, an MRGC EF partition model including five clusters and a quantitative LF interpretation chart were constructed. The EF clusters 1 to 5 were interpreted as lagoon, anhydrite flat, interbank, low-energy bank, and high-energy bank, and the coincidence rate in the cored interval could reach 85%. We concluded that the MRGC could be accurately applied to predict the LF in non-cored but logged wells. Therefore, continuous EF clusters were partitioned and corresponding LF were characteristics ＆different LF were analyzed interpreted, and the distribution and petrophysical in the framework of sequence stratigraphy.
基金the National Natural Science Foundation of China(grant No:41390451).
文摘The sand intervals of the Early Cretaceous Lower Goru Formation are a conventional reservoir,generally distributed in the Middle and Lower Indus Basin of Pakistan.Lithostratigraphically formation is classified into two parts;the upper parts are predominantly composed of shale,siltstone,and thin layers of alternate shale and sandstone,while the lower parts are composed of sandstone with interlayering of shale and limestone.The sandstone of the Lower Goru Formation has been further divided into A,B,C,and D sand intervals based on reservoir quality.Detailed depositional facies and reservoir characteristics are essential for the evaluation of hydrocarbon exploration and development.This paper aims to evaluate the depositional environment and reservoir characterization of the siliciclastic reservoir of the Early Cretaceous Lower Goru Formation by integrating the gamma-ray log patterns and petrographic analysis and scanning electron microscopic(SEM)analysis.Petrographic characterization of the sand intervals and Gamma-ray log signatures were used for the interpretation of the depositional environment of the reservoir intervals.Petrographic analysis reveals that the sandstone of the Lower Goru Formation is fine-to medium-grained,well-sorted,arkose or feldspathic arenite.Primary intergranular macroporosity,secondary intragranular macropores,and Intercrystalline micropores were identified within the sandstone by the SEM analysis.The diagenetic analysis suggests that the sandstone possesses high porosity,low permeability,and has undergone significant alterations such as compaction,quartz cementation,feldspar dissolution,and clay minerals alteration.Five electrofacies are interpreted based on gamma-ray log patterns including(1)funnel shape(FA);(2)bell shape(FB);(3)cylindrical shape(FC);(4)bow shape(FD);and(5)serrated shape(FE)patterns.The interpreted facies results reveal shoreface environment for A-sand,Tidal flat for B-sand,mixed tidal flat for C sand,Tide dominated mixed for D-sand,and transgressive shelf for Esand.The present study will be helpful for the assessment of the reservoir quality of the Early Cretaceous Lower Goru Formation for further exploration and development in the Indus Basin of Pakistan.
文摘The Oligo-Miocene Asmari Formation is one of the most important hydrocarbon reservoirs in the Middle East.The oilfield under study is one of the largest oilfields in the Zagros basin with the Asmari Formation being the major reservoir rock.In this study,petrographic analyses,petrophysical data and neural network clustering techniques were used for identifying rock types in the Asmari reservoir.Facies analysis of the Asmari Formation in the study area has resulted in the definition of 1 clastic lithofacies and 14 carbonate microfacies types.Using petrophysical logs from 43 wells and their correlation with capillary pressure(Pc)curves,led to the recognition of 7 electrofacies(EF1-EF7).Microscopic evidence of Electrofacies group C1 and S1 show that the sedimentary facies of these electrofacies are most commonly found in restricted and shoal facies belts zone.Also,petrographic studies show that the sedimentary facies of C2,C3,C4,S2 and S3 were formed in the open marine,Lagoon,and Tidal flat facies belt zone of homoclinal ramp sedimentary environment during the Oligo-Miocene based on relative sea level changes respectively.The link between electrofacies and geological data indicated that both sedimentary and diagenetic processes controlled the reservoir quality of the Asmari Formation.Porosity,permeability and water saturation were used to estimate the reservoir quality of each electrofacies.EFs 1 and 2 with high porosity and permeability,low water saturation is considered as the best reservoir with regard to sedimentary textures(dolowackestone and dolograinstone)and the effect of diagenetic processes such as dolomitization processes.Vuggy,growth framework and interparticle porosities are major in EF-2,while the intercrystalline porosity is the major type in EF-1.EFs 3 and 4 show low values of porosity,permeability and high percentage of water saturations,which characterizes them as poor reservoir rocks.Finally,EF-5 is the only electrofacies in the siliciclastic parts of the Asmari reservoir,which is composed of rounded and well-sorted quartz grains that are slightly cemented.In sandstone electrofacies,electrofacies EF-5(S1),is the best type of sandstone reservoir rock and to move towards electrofacies EF-7(S3),will reduce reservoir quality.In carbonate electrofacies,also,electrofacies no 1,the best type of carbonate reservoir rock can be observed and move towards electrofacie number 4,lower quality of reservoir rocks is seen.
基金supported by the National Key Technology R&D Program (No. 2006BAC18B05)
文摘In view of the high accuracy and predictability, high-resolution sequence stratigraphy had been extensively applied to oil exploration and gotten prominent practicable results. This article takes the first layer, upper second submember, Shahejie （沙河街） Formation from Pucheng （濮城） oilfield as an example to analyze the application of high-resolution sequence stratigraphy in reservoir study on the basis of a comprehensive study of core log data. Firstly, facies analysis of this area reveals the corresponding terminal fan system occurring where sediment-laden streams decrease in size and vanish as a result of evaporation and transmission losses. The model includes a tripartite zonation of terminal fan into feeder, distributary, and basinal zones. Secondly, electrofacies were made by well-log analysis and then matched with sedimentary facies defined by core analysis. Four electrofacies characterizing the main sedimentary facies association and depositional environments within target area are defined （channel, lag deposit, lake or flood-plain, and overflow deposits）. Thirdly, related correlations based on high-resolution sequence stratigraphy were established. By observing the stacking arrangement of genetic sequences, different scales of stratigraphic cycle can be identified. Within scale and duration, the stratigraphic cycles are termed as genetic sequences, genetic sequence sets, and minor cycles.
