The Experts below are selected from a list of 318 Experts worldwide ranked by ideXlab platform
Abdulazeez Abdulraheem - One of the best experts on this subject based on the ideXlab platform.
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Hybrid intelligent systems in Petroleum Reservoir characterization and modeling: the journey so far and the challenges ahead
Journal of Petroleum Exploration and Production Technology, 2017Co-Authors: Fatai Anifowose, Jane Labadin, Abdulazeez AbdulraheemAbstract:Computational intelligence (CI) techniques have positively impacted the Petroleum Reservoir characterization and modeling landscape. However, studies have showed that each CI technique has its strengths and weaknesses. Some of the techniques have the ability to handle datasets of high dimensionality and fast in execution, while others are limited in their ability to handle uncertainties, difficult to learn, and could not deal with datasets of high or low dimensionality. The “no free lunch” theorem also gives credence to this problem as it postulates that no technique or method can be applicable to all problems in all situations. A technique that worked well on a problem may not perform well in another problem domain just as a technique that was written off on one problem may be promising with another. There was the need for robust techniques that will make the best use of the strengths to overcome the weaknesses while producing the best results. The machine learning concepts of hybrid intelligent system (HIS) have been proposed to partly overcome this problem. In this review paper, the impact of HIS on the Petroleum Reservoir characterization process is enumerated, analyzed, and extensively discussed. It was concluded that HIS has huge potentials in the improvement of Petroleum Reservoir property predictions resulting in improved exploration, more efficient exploitation, increased production, and more effective management of energy resources. Lastly, a number of yet-to-be-explored hybrid possibilities were recommended.
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ensemble machine learning an untapped modeling paradigm for Petroleum Reservoir characterization
Journal of Petroleum Science and Engineering, 2017Co-Authors: Fatai Anifowose, Jane Labadin, Abdulazeez AbdulraheemAbstract:Abstract The successful applications of the conventional Computational Intelligence (CI) techniques and Hybrid Intelligent Systems (HIS) in Petroleum Reservoir characterization have been reported. However, these techniques are limited in their capability to handle a single hypothesis of a problem at a time. The major objective of the Reservoir characterization process is to produce models that are robust enough to help improve the accuracy of the predictions of Reservoir properties for use in full-field and large-scale simulation models. Research in CI continues to evolve new techniques and paradigms to meet this noble objective. It has been shown that there are uncertainties in the Reservoir characterization process as well as the optimal choice of CI/HIS models parameters. The main challenge is to develop models that are capable of handling multiple hypotheses to reduce the uncertainties thereby ensuring optimal solutions. The ensemble machine learning paradigm has been established to tackle this challenge. This new machine learning technology has not been adequately explored in handling some of the Petroleum engineering challenges. This paper rigorously reviews the concept of ensemble learning paradigm, presents successful applications outside Petroleum engineering and the geosciences, discusses a few successful attempts in Petroleum engineering and the geosciences, and concludes with some recommendations for the much-needed future applications.
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Integrating seismic and log data for improved Petroleum Reservoir properties estimation using non-linear feature-selection based hybrid computational intelligence models
Journal of Petroleum Science and Engineering, 2016Co-Authors: Fatai Anifowose, Suli Adeniye, Abdulazeez Abdulraheem, Abdullatif A. Al-shuhailAbstract:Abstract Various Petroleum Reservoir properties have been estimated in literature using only one of log, seismic or production data. The recent trend in data mining is integrating multi-modal and multi-dimensional data for improved Reservoir properties prediction. The objective of this paper is to employ hybrid machine learning and feature-selection based predictive models to estimate the permeability of carbonate Reservoirs from integrated seismic and well log data. Hybrid models of Type-2 Fuzzy Logic System (T2FLS) and Support Vector Machine (SVM) with Functional Networks (FN) as the non-linear feature selection algorithm are proposed. Five seismic attributes were integrated with six commonly used Well logs. Data were collected from 33 oil Wells but only 17 of them had a complete matching seismic-log pair. The performance of the hybrid models were compared to those of the individual models without the non-linear but with the conventional feature selection algorithm. The comparative results showed improved prediction accuracies with the hybrid models and more excellently with the FN-SVM model. A blind test on the models revealed that the FN-SVM hybrid model gave an (R-Square) of 0.82, root mean square error of 0.46, and mean absolute error of 0.42 compared to the lowest performing T2FLS model with 0.40, 0.77 and 0.65 respectively. This demonstrates the significance of the hybrid machine learning paradigm in solving Petroleum engineering problems with improved accuracies. The study presented some lessons learned from the data limitation challenges experienced in this work and proposed recommendations to chart further research directions.
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improving the prediction of Petroleum Reservoir characterization with a stacked generalization ensemble model of support vector machines
Applied Soft Computing, 2015Co-Authors: Fatai Anifowose, Jane Labadin, Abdulazeez AbdulraheemAbstract:Despite successful applications of ensembles, the Petroleum industry has not benefited enough.SVM is promising but its performance depends mostly on the regularization parameter.We propose an SVM ensemble with diverse opinions on the regularization parameter.The proposed model outperformed Random Forest but competitive with SVM Bagging.There is great potential for ensemble models in Petroleum Reservoir characterization. The ensemble learning paradigm has proved to be relevant to solving most challenging industrial problems. Despite its successful application especially in the Bioinformatics, the Petroleum industry has not benefited enough from the promises of this machine learning technology. The Petroleum industry, with its persistent quest for high-performance predictive models, is in great need of this new learning methodology. A marginal improvement in the prediction indices of Petroleum Reservoir properties could have huge positive impact on the success of exploration, drilling and the overall Reservoir management portfolio. Support vector machines (SVM) is one of the promising machine learning tools that have performed excellently well in most prediction problems. However, its performance is a function of the prudent choice of its tuning parameters most especially the regularization parameter, C. Reports have shown that this parameter has significant impact on the performance of SVM. Understandably, no specific value has been recommended for it. This paper proposes a stacked generalization ensemble model of SVM that incorporates different expert opinions on the optimal values of this parameter in the prediction of porosity and permeability of Petroleum Reservoirs using datasets from diverse geological formations. The performance of the proposed SVM ensemble was compared to that of conventional SVM technique, another SVM implemented with the bagging method, and Random Forest technique. The results showed that the proposed ensemble model, in most cases, outperformed the others with the highest correlation coefficient, and the lowest mean and absolute errors. The study indicated that there is a great potential for ensemble learning in Petroleum Reservoir characterization to improve the accuracy of Reservoir properties predictions for more successful explorations and increased production of Petroleum resources. The results also confirmed that ensemble models perform better than the conventional SVM implementation.
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MLSDA@AUS-AI - Predicting Petroleum Reservoir Properties from Downhole Sensor Data using an Ensemble Model of Neural Networks
Proceedings of Workshop on Machine Learning for Sensory Data Analysis - MLSDA '13, 2013Co-Authors: Fatai Anifowose, Jane Labadin, Abdulazeez AbdulraheemAbstract:The acquisition of huge sensor data has led to the advent of the smart field phenomenon in the Petroleum industry. A lot of data is acquired during drilling and production processes through logging tools equipped with sub-surface/down-hole sensors. Reservoir modeling has advanced from the use of empirical equations through statistical regression tools to the present embrace of Artificial Intelligence (AI) and its hybrid techniques. Due to the high dimensionality and heterogeneity of the sensor data, the capability of conventional AI techniques has become limited as they could not handle more than one hypothesis at a time. Ensemble learning method has the capability to combine several hypotheses to evolve a single ensemble solution to a problem. Despite its popular use, especially in Petroleum engineering, Artificial Neural Networks (ANN) has posed a number of challenges. One of such is the difficulty in determining the most suitable learning algorithm for optimal model performance. To save the cost, effort and time involved in the use of trial-and-error and evolutionary methods, this paper presents an ensemble model of ANN that combines the diverse performances of seven "weak" learning algorithms to evolve an ensemble solution in the prediction of porosity and permeability of Petroleum Reservoirs. When compared to the individual ANN, ANN-bagging and RandomForest, the proposed model performed best. This further confirms the great opportunities for ensemble modeling in Petroleum Reservoir characterization and other Petroleum engineering problems.
Fatai Anifowose - One of the best experts on this subject based on the ideXlab platform.
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Hybrid intelligent systems in Petroleum Reservoir characterization and modeling: the journey so far and the challenges ahead
Journal of Petroleum Exploration and Production Technology, 2017Co-Authors: Fatai Anifowose, Jane Labadin, Abdulazeez AbdulraheemAbstract:Computational intelligence (CI) techniques have positively impacted the Petroleum Reservoir characterization and modeling landscape. However, studies have showed that each CI technique has its strengths and weaknesses. Some of the techniques have the ability to handle datasets of high dimensionality and fast in execution, while others are limited in their ability to handle uncertainties, difficult to learn, and could not deal with datasets of high or low dimensionality. The “no free lunch” theorem also gives credence to this problem as it postulates that no technique or method can be applicable to all problems in all situations. A technique that worked well on a problem may not perform well in another problem domain just as a technique that was written off on one problem may be promising with another. There was the need for robust techniques that will make the best use of the strengths to overcome the weaknesses while producing the best results. The machine learning concepts of hybrid intelligent system (HIS) have been proposed to partly overcome this problem. In this review paper, the impact of HIS on the Petroleum Reservoir characterization process is enumerated, analyzed, and extensively discussed. It was concluded that HIS has huge potentials in the improvement of Petroleum Reservoir property predictions resulting in improved exploration, more efficient exploitation, increased production, and more effective management of energy resources. Lastly, a number of yet-to-be-explored hybrid possibilities were recommended.
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ensemble machine learning an untapped modeling paradigm for Petroleum Reservoir characterization
Journal of Petroleum Science and Engineering, 2017Co-Authors: Fatai Anifowose, Jane Labadin, Abdulazeez AbdulraheemAbstract:Abstract The successful applications of the conventional Computational Intelligence (CI) techniques and Hybrid Intelligent Systems (HIS) in Petroleum Reservoir characterization have been reported. However, these techniques are limited in their capability to handle a single hypothesis of a problem at a time. The major objective of the Reservoir characterization process is to produce models that are robust enough to help improve the accuracy of the predictions of Reservoir properties for use in full-field and large-scale simulation models. Research in CI continues to evolve new techniques and paradigms to meet this noble objective. It has been shown that there are uncertainties in the Reservoir characterization process as well as the optimal choice of CI/HIS models parameters. The main challenge is to develop models that are capable of handling multiple hypotheses to reduce the uncertainties thereby ensuring optimal solutions. The ensemble machine learning paradigm has been established to tackle this challenge. This new machine learning technology has not been adequately explored in handling some of the Petroleum engineering challenges. This paper rigorously reviews the concept of ensemble learning paradigm, presents successful applications outside Petroleum engineering and the geosciences, discusses a few successful attempts in Petroleum engineering and the geosciences, and concludes with some recommendations for the much-needed future applications.
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Integrating seismic and log data for improved Petroleum Reservoir properties estimation using non-linear feature-selection based hybrid computational intelligence models
Journal of Petroleum Science and Engineering, 2016Co-Authors: Fatai Anifowose, Suli Adeniye, Abdulazeez Abdulraheem, Abdullatif A. Al-shuhailAbstract:Abstract Various Petroleum Reservoir properties have been estimated in literature using only one of log, seismic or production data. The recent trend in data mining is integrating multi-modal and multi-dimensional data for improved Reservoir properties prediction. The objective of this paper is to employ hybrid machine learning and feature-selection based predictive models to estimate the permeability of carbonate Reservoirs from integrated seismic and well log data. Hybrid models of Type-2 Fuzzy Logic System (T2FLS) and Support Vector Machine (SVM) with Functional Networks (FN) as the non-linear feature selection algorithm are proposed. Five seismic attributes were integrated with six commonly used Well logs. Data were collected from 33 oil Wells but only 17 of them had a complete matching seismic-log pair. The performance of the hybrid models were compared to those of the individual models without the non-linear but with the conventional feature selection algorithm. The comparative results showed improved prediction accuracies with the hybrid models and more excellently with the FN-SVM model. A blind test on the models revealed that the FN-SVM hybrid model gave an (R-Square) of 0.82, root mean square error of 0.46, and mean absolute error of 0.42 compared to the lowest performing T2FLS model with 0.40, 0.77 and 0.65 respectively. This demonstrates the significance of the hybrid machine learning paradigm in solving Petroleum engineering problems with improved accuracies. The study presented some lessons learned from the data limitation challenges experienced in this work and proposed recommendations to chart further research directions.
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improving the prediction of Petroleum Reservoir characterization with a stacked generalization ensemble model of support vector machines
Applied Soft Computing, 2015Co-Authors: Fatai Anifowose, Jane Labadin, Abdulazeez AbdulraheemAbstract:Despite successful applications of ensembles, the Petroleum industry has not benefited enough.SVM is promising but its performance depends mostly on the regularization parameter.We propose an SVM ensemble with diverse opinions on the regularization parameter.The proposed model outperformed Random Forest but competitive with SVM Bagging.There is great potential for ensemble models in Petroleum Reservoir characterization. The ensemble learning paradigm has proved to be relevant to solving most challenging industrial problems. Despite its successful application especially in the Bioinformatics, the Petroleum industry has not benefited enough from the promises of this machine learning technology. The Petroleum industry, with its persistent quest for high-performance predictive models, is in great need of this new learning methodology. A marginal improvement in the prediction indices of Petroleum Reservoir properties could have huge positive impact on the success of exploration, drilling and the overall Reservoir management portfolio. Support vector machines (SVM) is one of the promising machine learning tools that have performed excellently well in most prediction problems. However, its performance is a function of the prudent choice of its tuning parameters most especially the regularization parameter, C. Reports have shown that this parameter has significant impact on the performance of SVM. Understandably, no specific value has been recommended for it. This paper proposes a stacked generalization ensemble model of SVM that incorporates different expert opinions on the optimal values of this parameter in the prediction of porosity and permeability of Petroleum Reservoirs using datasets from diverse geological formations. The performance of the proposed SVM ensemble was compared to that of conventional SVM technique, another SVM implemented with the bagging method, and Random Forest technique. The results showed that the proposed ensemble model, in most cases, outperformed the others with the highest correlation coefficient, and the lowest mean and absolute errors. The study indicated that there is a great potential for ensemble learning in Petroleum Reservoir characterization to improve the accuracy of Reservoir properties predictions for more successful explorations and increased production of Petroleum resources. The results also confirmed that ensemble models perform better than the conventional SVM implementation.
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MLSDA@AUS-AI - Predicting Petroleum Reservoir Properties from Downhole Sensor Data using an Ensemble Model of Neural Networks
Proceedings of Workshop on Machine Learning for Sensory Data Analysis - MLSDA '13, 2013Co-Authors: Fatai Anifowose, Jane Labadin, Abdulazeez AbdulraheemAbstract:The acquisition of huge sensor data has led to the advent of the smart field phenomenon in the Petroleum industry. A lot of data is acquired during drilling and production processes through logging tools equipped with sub-surface/down-hole sensors. Reservoir modeling has advanced from the use of empirical equations through statistical regression tools to the present embrace of Artificial Intelligence (AI) and its hybrid techniques. Due to the high dimensionality and heterogeneity of the sensor data, the capability of conventional AI techniques has become limited as they could not handle more than one hypothesis at a time. Ensemble learning method has the capability to combine several hypotheses to evolve a single ensemble solution to a problem. Despite its popular use, especially in Petroleum engineering, Artificial Neural Networks (ANN) has posed a number of challenges. One of such is the difficulty in determining the most suitable learning algorithm for optimal model performance. To save the cost, effort and time involved in the use of trial-and-error and evolutionary methods, this paper presents an ensemble model of ANN that combines the diverse performances of seven "weak" learning algorithms to evolve an ensemble solution in the prediction of porosity and permeability of Petroleum Reservoirs. When compared to the individual ANN, ANN-bagging and RandomForest, the proposed model performed best. This further confirms the great opportunities for ensemble modeling in Petroleum Reservoir characterization and other Petroleum engineering problems.
Fabrice Armougom - One of the best experts on this subject based on the ideXlab platform.
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A novel Thermotoga strain TFO isolated from a Californian Petroleum Reservoir phylogenetically related to Thermotoga petrophila and T. naphthophila, two thermophilic anaerobic isolates from a Japanese Reservoir: Taxonomic and genomic considerations
Systematic and Applied Microbiology, 2020Co-Authors: Zara Summers, Hassiba Belahbib, Nathalie Pradel, Manon Bartoli, Pooja Mishra, Christian Tamburini, Alain Dolla, Bernard Ollivier, Fabrice ArmougomAbstract:A novel Thermotoga strain TFO isolated from a Californian Petroleum Reservoir 1 phylogenetically related to Thermotoga petrophila and T. naphthophila, two 2 thermophilic anaerobic isolates from a Japanese Reservoir: taxonomic and genomic 3 considerations 4 5
Shizhong Yang - One of the best experts on this subject based on the ideXlab platform.
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iron oxides alter methanogenic pathways of acetate in production water of high temperature Petroleum Reservoir
Applied Microbiology and Biotechnology, 2017Co-Authors: Pan Pan, Serge Maurice Mbadinga, Jin-feng Liu, Bo Hong, Liying Wang, Shizhong YangAbstract:Acetate is a key intermediate in anaerobic crude oil biodegradation and also a precursor for methanogenesis in Petroleum Reservoirs. The impact of iron oxides, viz. β-FeOOH (akaganeite) and magnetite (Fe3O4), on the methanogenic acetate metabolism in production water of a high-temperature Petroleum Reservoir was investigated. Methane production was observed in all the treatments amended with acetate. In the microcosms amended with acetate solely about 30% of the acetate utilized was converted to methane, whereas methane production was stimulated in the presence of magnetite (Fe3O4) resulting in a 48.34% conversion to methane. Methane production in acetate-amended, β-FeOOH (akaganeite)-supplemented microcosms was much faster and acetate consumption was greatly improved compared to the other conditions in which the stoichiometric expected amounts of methane were not produced. Microbial community analysis showed that Thermacetogenium spp. (known syntrophic acetate oxidizers) and hydrogenotrophic methanogens closely related to Methanothermobacter spp. were enriched in acetate and acetate/magnetite (Fe3O4) microcosms suggesting that methanogenic acetate metabolism was through hydrogenotrophic methanogenesis fueled by syntrophic acetate oxidizers. The acetate/β-FeOOH (akaganeite) microcosms, however, differed by the dominance of archaea closely related to the acetoclastic Methanosaeta thermophila. These observations suggest that supplementation of β-FeOOH (akaganeite) accelerated the production of methane further, driven the alteration of the methanogenic community, and changed the pathway of acetate methanogenesis from hydrogenotrophic methanogenesis fueled by syntrophic acetate oxidizers to acetoclastic.
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Responses of Microbial Community Composition to Temperature Gradient and Carbon Steel Corrosion in Production Water of Petroleum Reservoir
Frontiers Media S.A., 2017Co-Authors: Tao Yang, Serge M. Mbadinga, Jin-feng Liu, Shizhong YangAbstract:Oil Reservoir production systems are usually associated with a temperature gradient and oil production facilities frequently suffer from pipeline corrosion failures. Both bacteria and archaea potentially contribute to biocorrosion of the oil production equipment. Here the response of microbial populations from the Petroleum Reservoir to temperature gradient and corrosion of carbon steel coupons were investigated under laboratory condition. Carbon steel coupons were exposed to production water from a depth of 1809 m of Jiangsu Petroleum Reservoir (China) and incubated for periods of 160 and 300 days. The incubation temperatures were set at 37, 55, and 65°C to monitoring mesophilic, thermophilic and hyperthermophilic microorganisms associated with anaerobic carbon steel corrosion. The results showed that corrosion rate at 55°C (0.162 ± 0.013 mm year-1) and 37°C (0.138 ± 0.008 mm year-1) were higher than that at 65°C (0.105 ± 0.007 mm year-1), and a dense biofilm was observed on the surface of coupons under all biotic incubations. The microbial community analysis suggests a high frequency of bacterial taxa associated with families Porphyromonadaceae, Enterobacteriaceae, and Spirochaetaceae at all three temperatures. While the majority of known sulfate-reducing bacteria, in particular Desulfotignum, Desulfobulbus and Desulfovibrio spp., were predominantly observed at 37°C; Desulfotomaculum spp., Thermotoga spp. and Thermanaeromonas spp. as well as archaeal members closely related to Thermococcus and Archaeoglobus spp. were substantially enriched at 65°C. Hydrogenotrophic methanogens of the family Methanobacteriaceae were dominant at both 37 and 55°C; acetoclastic Methanosaeta spp. and methyltrophic Methanolobus spp. were enriched at 37°C. These observations show that temperature changes significantly alter the microbial community structure in production fluids and also affected the biocorrosion of carbon steel under anaerobic conditions
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analysis of alkane dependent methanogenic community derived from production water of a high temperature Petroleum Reservoir
Applied Microbiology and Biotechnology, 2012Co-Authors: Serge Maurice Mbadinga, Lei Zhou, Shizhong Yang, Liying Wang, Jin-feng LiuAbstract:Microbial assemblage in an n-alkanes-dependent thermophilic methanogenic enrichment cultures derived from production waters of a high-temperature Petroleum Reservoir was investigated in this study. Substantially higher amounts of methane were generated from the enrichment cultures incubated at 55 °C for 528 days with a mixture of long-chain n-alkanes (C15–C20). Stoichiometric estimation showed that alkanes-dependent methanogenesis accounted for about 19.8% of the total amount of methane expected. Hydrogen was occasionally detected together with methane in the gas phase of the cultures. Chemical analysis of the liquid cultures resulted only in low concentrations of acetate and formate. Phylogenetic analysis of the enrichment revealed the presence of several bacterial taxa related to Firmicutes, Thermodesulfobiaceae, Thermotogaceae, Nitrospiraceae, Dictyoglomaceae, Candidate division OP8 and others without close cultured representatives, and Archaea predominantly related to uncultured members in the order Archaeoglobales and CO2-reducing methanogens. Screening of genomic DNA retrieved from the alkanes-amended enrichment cultures also suggested the presence of new alkylsuccinate synthase alpha-subunit (assA) homologues. These findings suggest the presence of poorly characterized (putative) anaerobic n-alkanes degraders in the thermophilic methanogenic enrichment cultures. Our results indicate that methanogenesis of alkanes under thermophilic condition is likely to proceed via syntrophic acetate and/or formate oxidation linked with hydrogenotrophic methanogenesis.
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molecular phylogenetic diversity of the microbial community associated with a high temperature Petroleum Reservoir at an offshore oilfield
FEMS Microbiology Ecology, 2007Co-Authors: Shizhong Yang, Zhaofeng Rong, Jie ZhangAbstract:The microbial community and its diversity in production water from a high-temperature, water-flooded Petroleum Reservoir of an offshore oilfield in China were characterized by 16S rRNA gene sequence analysis. The bacterial and archaeal 16S rRNA gene clone libraries were constructed from the community DNA and, using sequence analysis, 388 bacterial and 220 archaeal randomly selected clones were clustered with 60 and 28 phylotypes, respectively. The results showed that the 16S rRNA genes of bacterial clones belonged to the divisions Firmicutes, Thermotogae, Nitrospirae and Proteobacteria, whereas the archaeal library was dominated by methanogen-like rRNA genes (Methanothermobacter, Methanobacter, Methanobrevibacter and Methanococcus), with a lower percentage of clones belonging to Thermoprotei. Thermophilic microorganisms were found in the production water, as well as mesophilic microorganisms such as Pseudomonas and Acinetobacter-like clones. The thermophilic microorganisms may be common inhabitants of geothermally heated specialized subsurface environments, which have been isolated previously from a number of high-temperature Petroleum Reservoirs worldwide. The mesophilic microorganisms were probably introduced into the Reservoir as it was being exploited. The results of this work provide further insight into the composition of microbial communities of high-temperature Petroleum Reservoirs at offshore oilfields.
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Molecular phylogenetic diversity of the microbial community associated with a high‐temperature Petroleum Reservoir at an offshore oilfield
FEMS microbiology ecology, 2007Co-Authors: Shizhong Yang, Zhaofeng Rong, Jie ZhangAbstract:The microbial community and its diversity in production water from a high-temperature, water-flooded Petroleum Reservoir of an offshore oilfield in China were characterized by 16S rRNA gene sequence analysis. The bacterial and archaeal 16S rRNA gene clone libraries were constructed from the community DNA and, using sequence analysis, 388 bacterial and 220 archaeal randomly selected clones were clustered with 60 and 28 phylotypes, respectively. The results showed that the 16S rRNA genes of bacterial clones belonged to the divisions Firmicutes, Thermotogae, Nitrospirae and Proteobacteria, whereas the archaeal library was dominated by methanogen-like rRNA genes (Methanothermobacter, Methanobacter, Methanobrevibacter and Methanococcus), with a lower percentage of clones belonging to Thermoprotei. Thermophilic microorganisms were found in the production water, as well as mesophilic microorganisms such as Pseudomonas and Acinetobacter-like clones. The thermophilic microorganisms may be common inhabitants of geothermally heated specialized subsurface environments, which have been isolated previously from a number of high-temperature Petroleum Reservoirs worldwide. The mesophilic microorganisms were probably introduced into the Reservoir as it was being exploited. The results of this work provide further insight into the composition of microbial communities of high-temperature Petroleum Reservoirs at offshore oilfields.
Jie Zhang - One of the best experts on this subject based on the ideXlab platform.
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molecular phylogenetic diversity of the microbial community associated with a high temperature Petroleum Reservoir at an offshore oilfield
FEMS Microbiology Ecology, 2007Co-Authors: Shizhong Yang, Zhaofeng Rong, Jie ZhangAbstract:The microbial community and its diversity in production water from a high-temperature, water-flooded Petroleum Reservoir of an offshore oilfield in China were characterized by 16S rRNA gene sequence analysis. The bacterial and archaeal 16S rRNA gene clone libraries were constructed from the community DNA and, using sequence analysis, 388 bacterial and 220 archaeal randomly selected clones were clustered with 60 and 28 phylotypes, respectively. The results showed that the 16S rRNA genes of bacterial clones belonged to the divisions Firmicutes, Thermotogae, Nitrospirae and Proteobacteria, whereas the archaeal library was dominated by methanogen-like rRNA genes (Methanothermobacter, Methanobacter, Methanobrevibacter and Methanococcus), with a lower percentage of clones belonging to Thermoprotei. Thermophilic microorganisms were found in the production water, as well as mesophilic microorganisms such as Pseudomonas and Acinetobacter-like clones. The thermophilic microorganisms may be common inhabitants of geothermally heated specialized subsurface environments, which have been isolated previously from a number of high-temperature Petroleum Reservoirs worldwide. The mesophilic microorganisms were probably introduced into the Reservoir as it was being exploited. The results of this work provide further insight into the composition of microbial communities of high-temperature Petroleum Reservoirs at offshore oilfields.
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Molecular phylogenetic diversity of the microbial community associated with a high‐temperature Petroleum Reservoir at an offshore oilfield
FEMS microbiology ecology, 2007Co-Authors: Shizhong Yang, Zhaofeng Rong, Jie ZhangAbstract:The microbial community and its diversity in production water from a high-temperature, water-flooded Petroleum Reservoir of an offshore oilfield in China were characterized by 16S rRNA gene sequence analysis. The bacterial and archaeal 16S rRNA gene clone libraries were constructed from the community DNA and, using sequence analysis, 388 bacterial and 220 archaeal randomly selected clones were clustered with 60 and 28 phylotypes, respectively. The results showed that the 16S rRNA genes of bacterial clones belonged to the divisions Firmicutes, Thermotogae, Nitrospirae and Proteobacteria, whereas the archaeal library was dominated by methanogen-like rRNA genes (Methanothermobacter, Methanobacter, Methanobrevibacter and Methanococcus), with a lower percentage of clones belonging to Thermoprotei. Thermophilic microorganisms were found in the production water, as well as mesophilic microorganisms such as Pseudomonas and Acinetobacter-like clones. The thermophilic microorganisms may be common inhabitants of geothermally heated specialized subsurface environments, which have been isolated previously from a number of high-temperature Petroleum Reservoirs worldwide. The mesophilic microorganisms were probably introduced into the Reservoir as it was being exploited. The results of this work provide further insight into the composition of microbial communities of high-temperature Petroleum Reservoirs at offshore oilfields.
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molecular analysis of the bacterial community in a continental high temperature and water flooded Petroleum Reservoir
Fems Microbiology Letters, 2006Co-Authors: Shizhong Yang, Zhaofeng Rong, Jie ZhangAbstract:Water from a continental high-temperature, long-term water-flooded Petroleum Reservoir in Huabei Oilfield in China was analysed for its bacterial community and diversity. The bacteria were characterized by their 16S rRNA genes. A 16S rRNA gene clone library was constructed from the community DNA, and using restriction fragment length polymorphism analysis, 337 randomly selected clones were clustered with 74 operational taxonomic units. Sequencing and phylogenetic analyses showed that the screened clones were affiliated with Gammaproteobacteria (85.7%), Thermotogales (6.8%), Epsilonproteobacteria (2.4%), low-G+C Gram-positive (2.1%), high-G+C Gram-positive, Betaproteobacteria and Nitrospira (each <1.0%). Thermopilic bacteria were found in the high-temperature water from the flooded Petroleum Reservoir, as well as mesophilic bacteria such as Pseudomonas-like clones. The mesophilic bacteria were probably introduced into the Reservoir as it was being exploited. This work provides significant information on the structure of bacterial communities in high-temperature, long-term water-flooded Petroleum Reservoirs.
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Molecular analysis of the bacterial community in a continental high-temperature and water-flooded Petroleum Reservoir.
FEMS microbiology letters, 2006Co-Authors: Shizhong Yang, Zhaofeng Rong, Jie ZhangAbstract:Water from a continental high-temperature, long-term water-flooded Petroleum Reservoir in Huabei Oilfield in China was analysed for its bacterial community and diversity. The bacteria were characterized by their 16S rRNA genes. A 16S rRNA gene clone library was constructed from the community DNA, and using restriction fragment length polymorphism analysis, 337 randomly selected clones were clustered with 74 operational taxonomic units. Sequencing and phylogenetic analyses showed that the screened clones were affiliated with Gammaproteobacteria (85.7%), Thermotogales (6.8%), Epsilonproteobacteria (2.4%), low-G+C Gram-positive (2.1%), high-G+C Gram-positive, Betaproteobacteria and Nitrospira (each