Utica Shale Play Oil and Gas Brines: Geochemistry and Factors Influencing Wastewater Management

Blondes, Madalyn S.; Shelton, Jennifer L.; Engle, Mark A.; Trembly, Jason; Doolan, Colin A.; Jubb, Aaron M.; Chenault, Jessica; Rowan, Elisabeth L.; Haefner, Ralph J.; Mailot, Brian E.

doi:10.1021/acs.est.0c02461

Cited by 24 publications

(11 citation statements)

References 62 publications

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“…The Barnett shale produces both oil as well as dry gas while the Marcellus is primarily a dry gas play. The Utica shale is considered the major source rock in the Appalachian basin 35 and produces both oil and dry gas.…”

Section: Materials All Shale Samples Were Donated By Associate Profementioning

confidence: 99%

Kerogen nanoscale structure and CO2 adsorption in shale micropores

Gonciaruk

Hall

Fay

et al. 2021

Sci Rep

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Gas storage and recovery processes in shales critically depend on nano-scale porosity and chemical composition, but information about the nanoscale pore geometry and connectivity of kerogen, insoluble organic shale matter, is largely unavailable. Using adsorption microcalorimetry, we show that once strong adsorption sites within nanoscale network are taken, gas adsorption even at very low pressure is governed by pore width rather than chemical composition. A combination of focused ion beam with scanning electron microscopy and transmission electron microscopy reveal the nanoscale structure of kerogen includes not only the ubiquitous amorphous phase but also highly graphitized sheets, fiber- and onion-like structures creating nanoscale voids accessible for gas sorption. Nanoscale structures bridge the current gap between molecular size and macropore scale in existing models for kerogen, thus allowing accurate prediction of gas sorption, storage and diffusion properties in shales.

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Section: Materials All Shale Samples Were Donated By Associate Profementioning

confidence: 99%

Kerogen nanoscale structure and CO2 adsorption in shale micropores

Gonciaruk

Hall

Fay

et al. 2021

Sci Rep

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Section: Development and Testing Of ''Unknown'' Datasetmentioning

confidence: 99%

“…In addition to developing training sets with data in the USGS PWGD, we also used recently published data (Blondes et al, 2020;Engle, 2019;Engle et al, 2020) that is not currently included in the USGS PWGD as additional testing data for the models. The data are produced waters from the Eagle Ford (n = 39; Engle, 2019;Engle et al, 2020) and the Appalachian Basin, specifically the Utica Shale and Clinton sandstone (n = 52; Blondes et al, 2020). Because the data used in the validation sets may have originated from the same sampling campaigns as those data used in the training sets, i.e., both were from the same database, this may introduce bias into the TestPWGD7 and TestPWGD9 results.…”

Section: Development and Testing Of ''Unknown'' Datasetmentioning

confidence: 99%

“…The Random Forest ''extratrees'' model used on the unknown data (SI Table S4) predicted at 97% accuracy for the new Eagle Ford data, but only correctly classified 1.9% of the Appalachian produced water samples (see Supporting Information). Fifty-one of the 52 produced water samples that originated from the Appalachian basin (Blondes et al, 2020) were classified as Arkla waters. Only one sample from this 52 sample dataset was predicted to be from the Appalachian basin.…”

Section: Prediction On Unknown Datamentioning

confidence: 99%

“…Many producing formations or basins across the continental USA produce waters that have been previously well characterized, such as the Appalachian Basin (Blondes et al, 2020;McDevitt et al, 2020;Osborn et al, 2012;Tasker et al, 2020), the Permian Basin (Engle et al, 2016;Nicot et al, 2020), the Denver Basin (Rosenblum et al, 2017), the Williston Basin (Iampen & Rostron, 2000;McMahon et al, 2020;Varonka et al, 2020), and the Gulf Coast (Engle et al, 2020;McIntosh et al, 2010;Nicot et al, 2018). Although these regions produce waters that are geochemically well understood, few studies have analyzed trends in produced water chemistry across multiple basins or the continental USA (Birkle et al, 2019;Kharaka et al, 2019;Scanlon et al, 2020).…”

Section: Introductionmentioning

confidence: 99%

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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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Ion Exchange Processes for CO₂ Mineralization Using Industrial Waste Streams: Pilot Plant Demonstration and Life Cycle Assessment

Bustillos,

Christofides,

McDevitt

et al. 2024

ChemistrySelect

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An attractive technique for removing CO2 from the environment is sequestration within stable carbonate solids (e. g., calcite). However, continuous addition of alkalinity is required to achieve favorable conditions for carbonate precipitation (pH>8) from aqueous streams containing dissolved CO2 (pH<4.5) and Ca2+ ions. In this study, a pH‐swing process using ion exchange was demonstrated to process 300 L of produced water brine per day for CO2 mineralization. Proton titration capacities were quantified for aqueous streams in equilibrium with gas streams at various concentrations of CO2 (pCO2=0.03–0.20 atm) and at various flow rates (0.5–2.0 L min−1). Energy intensities for the process were determined to be between 30 and 65 kWh per tonne of CO2 sequestered depending on the composition of the brine stream. A life cycle assessment was performed to analyze the net carbon emissions of the technology which indicated a net CO2 reduction for pCO2≥0.12 atm (−0.06–−0.39 kg CO2e per kg precipitated CaCO3) utilizing calcium‐rich brines. The results from this study indicate the ion exchange process can be used as a scalable method to provide alkalinity necessary for the capture and storage of CO2 in Ca‐rich waste streams.

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Utica Shale Play Oil and Gas Brines: Geochemistry and Factors Influencing Wastewater Management

Cited by 24 publications

References 62 publications

Kerogen nanoscale structure and CO2 adsorption in shale micropores

Kerogen nanoscale structure and CO2 adsorption in shale micropores

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

Ion Exchange Processes for CO₂ Mineralization Using Industrial Waste Streams: Pilot Plant Demonstration and Life Cycle Assessment

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Utica Shale Play Oil and Gas Brines: Geochemistry and Factors Influencing Wastewater Management

Cited by 24 publications

References 62 publications

Kerogen nanoscale structure and CO2 adsorption in shale micropores

Kerogen nanoscale structure and CO2 adsorption in shale micropores

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

Ion Exchange Processes for CO2 Mineralization Using Industrial Waste Streams: Pilot Plant Demonstration and Life Cycle Assessment

Contact Info

Product

Resources

About

Ion Exchange Processes for CO₂ Mineralization Using Industrial Waste Streams: Pilot Plant Demonstration and Life Cycle Assessment