Fate of injected CO 2 in the Wilcox Group, Louisiana, Gulf Coast Basin: Chemical and isotopic tracers of microbial–brine–rock–CO 2 interactions

Shelton, Jennifer L.; McIntosh, Jennifer C.; Warwick, Peter D.; Yi, Amelia Lee Zhi

doi:10.1016/j.apgeochem.2014.09.015

Cited by 19 publications

(43 citation statements)

References 71 publications

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“…We justified the use of this complimentary, older dataset (oil and gas data from 2011/2012) due to slow rates of crude oil biodegradation and subsequent methanogenesis (e.g., Larter et al, 2003 , 2006 ), and because chemical water analyses from this sampling campaign produced values similar to those from 2011/2012 (Shelton et al, 2014 ). The sampled wells spanned a wide range of geochemical parameters, including temperature, pH, and salinity (Table 1 ), and formation fluids with varying extents of both crude oil biodegradation and microbial methanogenesis (McIntosh et al, 2010 ; Shelton et al, 2014 , 2016 ). One oil field sampled, the Olla Field, has been previously suggested to be a hot spot of microbial methanogenesis in the region (McIntosh et al, 2010 ), later attributed to ideal geochemical conditions for microbial methanogenesis (Shelton et al, 2014 ).…”

Section: Study Site and Methodsmentioning

confidence: 92%

“…Biomass, produced water, and 4 oil samples were collected in this 2014 sampling campaign. These data were supplemented by produced gas and oil data collected from a 2011/2012 sampling campaign (Shelton et al, 2014 , 2016 ). We justified the use of this complimentary, older dataset (oil and gas data from 2011/2012) due to slow rates of crude oil biodegradation and subsequent methanogenesis (e.g., Larter et al, 2003 , 2006 ), and because chemical water analyses from this sampling campaign produced values similar to those from 2011/2012 (Shelton et al, 2014 ).…”

Section: Study Site and Methodsmentioning

confidence: 99%

“…These data were supplemented by produced gas and oil data collected from a 2011/2012 sampling campaign (Shelton et al, 2014 , 2016 ). We justified the use of this complimentary, older dataset (oil and gas data from 2011/2012) due to slow rates of crude oil biodegradation and subsequent methanogenesis (e.g., Larter et al, 2003 , 2006 ), and because chemical water analyses from this sampling campaign produced values similar to those from 2011/2012 (Shelton et al, 2014 ). The sampled wells spanned a wide range of geochemical parameters, including temperature, pH, and salinity (Table 1 ), and formation fluids with varying extents of both crude oil biodegradation and microbial methanogenesis (McIntosh et al, 2010 ; Shelton et al, 2014 , 2016 ).…”

Section: Study Site and Methodsmentioning

confidence: 99%

“…To accomplish this objective, we characterized microbial communities from 22 different oil wells from 6 different oil fields across a transect; all wells produce from the Wilcox Group of north-central Louisiana, USA. Previous work (Warwick et al, 2008 ; McIntosh et al, 2010 ; Shelton et al, 2014 , 2016 ) established the amount of crude oil biodegradation, relative amount of microbial methanogenesis, and the formation gas and water geochemistry across north-central Louisiana. The new microbial data, combined with previous analyses of fluid chemistry, enabled us to explore the environmental conditions and microbial populations most conducive to methanogenic crude oil biodegradation.…”

Section: Introductionmentioning

confidence: 99%

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Environmental Drivers of Differences in Microbial Community Structure in Crude Oil Reservoirs across a Methanogenic Gradient

et al. 2016

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Stimulating in situ microbial communities in oil reservoirs to produce natural gas is a potentially viable strategy for recovering additional fossil fuel resources following traditional recovery operations. Little is known about what geochemical parameters drive microbial population dynamics in biodegraded, methanogenic oil reservoirs. We investigated if microbial community structure was significantly impacted by the extent of crude oil biodegradation, extent of biogenic methane production, and formation water chemistry. Twenty-two oil production wells from north central Louisiana, USA, were sampled for analysis of microbial community structure and fluid geochemistry. Archaea were the dominant microbial community in the majority of the wells sampled. Methanogens, including hydrogenotrophic and methylotrophic organisms, were numerically dominant in every well, accounting for, on average, over 98% of the total Archaea present. The dominant Bacteria groups were Pseudomonas, Acinetobacter, Enterobacteriaceae, and Clostridiales, which have also been identified in other microbially-altered oil reservoirs. Comparing microbial community structure to fluid (gas, water, and oil) geochemistry revealed that the relative extent of biodegradation, salinity, and spatial location were the major drivers of microbial diversity. Archaeal relative abundance was independent of the extent of methanogenesis, but closely correlated to the extent of crude oil biodegradation; therefore, microbial community structure is likely not a good sole predictor of methanogenic activity, but may predict the extent of crude oil biodegradation. However, when the shallow, highly biodegraded, low salinity wells were excluded from the statistical analysis, no environmental parameters could explain the differences in microbial community structure. This suggests that the microbial community structure of the 5 shallow, up-dip wells was different than the 17 deeper, down-dip wells. Also, the 17 down-dip wells had statistically similar microbial communities despite significant changes in environmental parameters between oil fields. Together, this implies that no single microbial population is a reliable indicator of a reservoir's ability to degrade crude oil to methane, and that geochemistry may be a more important indicator for selecting a reservoir suitable for microbial enhancement of natural gas generation.

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Section: Study Site and Methodsmentioning

confidence: 92%

Section: Study Site and Methodsmentioning

confidence: 99%

Section: Study Site and Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Environmental Drivers of Differences in Microbial Community Structure in Crude Oil Reservoirs across a Methanogenic Gradient

et al. 2016

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“…Laboratory studies have also found that bicarbonate did not stimulate methane production in coal slurries, but the addition of Zonyl FSN surfactant did enhance methanogenesis from coal (Papendick et al, 2011). It has also been suggested that using coal seams as CO 2 sequestration reservoirs might also increase methanogenesis (Budwill et al, 2003;Faison and Crawford, 1993;Mayumi et al, 2013;McIntosh et al, 2010;Scott, 1999;Stricker et al, 2008;Tang et al, 2012), although others have suggested that the addition of CO 2 may have no effect on methanogenesis because these systems are H 2 limited rather than CO 2 limited (Kirk, 2011;Papendick et al, 2011;Shelton et al, 2014). Research into the effect of changing reservoir conditions has been primarily laboratory based.…”

Section: In Situ Reservoir Conditions For Microbial Cbm Generationmentioning

confidence: 99%

Enhanced microbial coalbed methane generation: A review of research, commercial activity, and remaining challenges

Ritter

Vinson

Barnhart

et al. 2015

International Journal of Coal Geology

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239

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Machine Learning Can Assign Geologic Basin to Produced Water Samples Using Major Ion Geochemistry

et al. 2021

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Understanding the geochemistry of waters produced during petroleum extraction is essential to informing the best treatment and reuse options, which can potentially be optimized for a given geologic basin. Here, we used the US Geological Survey’s National Produced Waters Geochemical Database (PWGD) to determine if major ion chemistry could be used to classify accurately a produced water sample to a given geologic basin based on similarities to a given training dataset. Two datasets were derived from the PWGD: one with seven features but more samples (PWGD7), and another with nine features but fewer samples (PWGD9). The seven-feature dataset, prior to randomly generating a training and testing (i.e., validation) dataset, had 58,541 samples, 20 basins, and was classified based on total dissolved solids (TDS), bicarbonate (HCO3), Ca, Na, Cl, Mg, and sulfate (SO4). The nine-feature dataset, prior to randomly splitting into a training and testing (i.e., validation) dataset, contained 33,271 samples, 19 basins, and was classified based on TDS, HCO3, Ca, Na, Cl, Mg, SO4, pH, and specific gravity. Three supervised machine learning algorithms—Random Forest, k-Nearest Neighbors, and Naïve Bayes—were used to develop multi-class classification models to predict a basin of origin for produced waters using major ion chemistry. After training, the models were tested on three different datasets: Validation7, Validation9, and one based on data absent from the PWGD. Prediction accuracies across the models ranged from 23.5 to 73.5% when tested on the two PWGD-based datasets. A model using the Random Forest algorithm predicted most accurately compared to all other models tested. The models generally predicted basin of origin more accurately on the PWGD7-based dataset than on the PWGD9-based dataset. An additional dataset, which contained data not in the PWGD, was used to test the most accurate model; results suggest that some basins may lack geochemical diversity or may not be well described, while others may be geochemically diverse or are well described. A compelling result of this work is that a produced water basin of origin can be determined using major ions alone and, therefore, deep basinal fluid compositions may not be as variable within a given basin as previously thought. Applications include predicting the geochemistry of produced fluid prior to drilling at different intervals and assigning historical produced water data to a producing basin.

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Fate of injected CO 2 in the Wilcox Group, Louisiana, Gulf Coast Basin: Chemical and isotopic tracers of microbial–brine–rock–CO 2 interactions

Cited by 19 publications

References 71 publications

Environmental Drivers of Differences in Microbial Community Structure in Crude Oil Reservoirs across a Methanogenic Gradient

Environmental Drivers of Differences in Microbial Community Structure in Crude Oil Reservoirs across a Methanogenic Gradient

Enhanced microbial coalbed methane generation: A review of research, commercial activity, and remaining challenges

Machine Learning Can Assign Geologic Basin to Produced Water Samples Using Major Ion Geochemistry

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