Role of biogeochemistry in efficient shale oil and gas production

Agrawal, Vikas; Agrawal, Vikas; Akondi, Rawlings N.

doi:10.1016/j.fuel.2019.116207

Cited by 27 publications

(14 citation statements)

References 189 publications

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“…The depletion and fall of conventional oil and gas resources will lead to a shortage in the supply of the world’s energy needs . Therefore, unconventional resources, in particular shale gas, are gaining popularity in the recent era of oil and gas. − Organic-rich shale is one of the most vital sources of unconventional oil and gas.…”

Section: Introductionmentioning

confidence: 99%

Data-Driven Approaches to Predict Thermal Maturity Indices of Organic Matter Using Artificial Neural Networks

et al. 2020

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Prediction of thermal maturity index parameters in organic shales plays a critical role in defining the hydrocarbon prospect and proper economic evaluation of the field. Hydrocarbon potential in shales is evaluated using the percentage of organic indices such as total organic carbon (TOC), thermal maturity temperature, source potentials, and hydrogen and oxygen indices. Direct measurement of these parameters in the laboratory is the most accurate way to obtain a representative value, but, at the same time, it is very expensive. In the absence of such facilities, other approaches such as analytical solutions and empirical correlations are used to estimate the organic indices in shale. The objective of this study is to develop data-driven machine learning-based models to predict continuous profiles of geochemical logs of organic shale formation. The machine learning models are trained using the petrophysical wireline logs as input and the corresponding laboratory-measured core data as a target for Barnett shale formations. More than 400 log data and the corresponding core data were collected for this purpose. The petrophysical wireline logs are γ-ray, bulk density, neutron porosity, sonic transient time, spontaneous potential, and shallow resistivity logs. The corresponding core data includes the experimental results from the Rock-Eval pyrolysis and Leco TOC measurements. A backpropagation artificial neural network coupled with a particle swarm optimization algorithm was used in this work. In addition to the development of optimized PSO-ANN models, explicit empirical correlations are also extracted from the fine-tuned weights and biases of the optimized models. The proposed models work with a higher accuracy within the range of the data set on which the models are trained. The proposed models can give real-time quantification of the organic matter maturity that can be linked with the real-time drilling operations and help identify the hotspots of mature organic matter in the drilled section.

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Section: Introductionmentioning

confidence: 99%

Data-Driven Approaches to Predict Thermal Maturity Indices of Organic Matter Using Artificial Neural Networks

et al. 2020

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“…Unconventional hydrocarbon resources, such as shale oil and gas, have garnered more attention and are being considered as an alternative energy resource to conventional oil and gas [1,2]. Shale oil and gas reservoirs require additional treatments, i.e., volumetric hydraulic fracturing, to achieve commercial production from tight formations due to their ultra-low permeability and ultra-low porosity [3][4][5].…”

Section: Introductionmentioning

confidence: 99%

Turbulent Drag Reduction with an Ultra-High-Molecular-Weight Water-Soluble Polymer in Slick-Water Hydrofracking

et al. 2022

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Water-soluble polymers as drag reducers have been widely utilized in slick-water for fracturing shale oil and gas reservoirs. However, the low viscosity characteristics, high operating costs, and freshwater consumption of conventional friction reducers limit their practical use in deeper oil and gas reservoirs. Therefore, a high viscosity water-soluble friction reducer (HVFR), poly-(acrylamide-co-acrylic acid-co-2-acrylamido-2-methylpropanesulphonic acid), was synthesized via free radical polymerization in aqueous solution. The molecular weight, solubility, rheological behavior, and drag reduction performance of HVFR were thoroughly investigated. The results showed that the viscosity-average molecular weight of HVFR is 23.2 × 106 g⋅mol−1. The HVFR powder could be quickly dissolved in water within 240 s under 700 rpm. The storage modulus (G′) and loss modulus (G″) as well as viscosity of the solutions increased with an increase in polymer concentration. At a concentration of 1700 mg⋅L−1, HVFR solution shows 67% viscosity retention rate after heating from 30 to 90 °C, and the viscosity retention rate of HVFR solution when increasing CNaCl to 21,000 mg⋅L−1 is 66%. HVFR exhibits significant drag reduction performance for both low viscosity and high viscosity. A maximum drag reduction of 80.2% is attained from HVFR at 400 mg⋅L−1 with 5.0 mPa⋅s, and drag reduction of HVFR is 75.1% at 1700 mg⋅L−1 with 30.2 mPa⋅s. These findings not only indicate the prospective use of HVFR in slick-water hydrofracking, but also shed light on the design of novel friction reducers utilized in the oil and gas industry.

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“…Main text paragraph. Oil plays an indispensable role in global energy infrastructure [1]. Most cities establish large-scale oil storage tank farms to meet the rapidly growing demand for oil [2].…”

Section: Introductionmentioning

confidence: 99%

Thermal Decomposition Properties of Industrial Oily Sludge under Different Atmospheres

Zhang¹,

Jiang²,

Xu³

et al. 2022

Pol. J. Environ. Stud.

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The environmentally friendly degradation of and energy recovery from oily sludge is a common concern in both research and industry. In this study, industrial oily sludge was collected after wastewater treatment by oil storage corporations. First, the physical and chemical properties of the samples were observed. Then, samples were analyzed using thermogravimetry-gas chromatography-mass spectrometry under air atmosphere and under nitrogen atmosphere. The characteristics of the residue after thermal decomposition were determined through Fourier-transform infrared spectroscopy, scanning electron microscopy, and energy dispersive spectrometry. The results revealed that the thermal degradation of oily sludge is divided into three steps under nitrogen atmosphere but four steps under air atmosphere. Moreover, thermal decomposition was mainly exothermic under nitrogen atmosphere but endothermic under air atmosphere. The mass loss of oily sludge was greater under air atmosphere. The final residue was 26.6% under an air atmosphere, which was lower than the 35.8% observed under nitrogen atmosphere. The gaseous products of oily sludge included gaseous fuels under nitrogen atmosphere but were only combustion products under air atmosphere. In both atmospheres, the solid products remaining in the residue were the metallic elements Fe, Al, Mg, Na, and Ca, which can be reused in desulfurization applications.

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Role of biogeochemistry in efficient shale oil and gas production

Cited by 27 publications

References 189 publications

Data-Driven Approaches to Predict Thermal Maturity Indices of Organic Matter Using Artificial Neural Networks

Data-Driven Approaches to Predict Thermal Maturity Indices of Organic Matter Using Artificial Neural Networks

Turbulent Drag Reduction with an Ultra-High-Molecular-Weight Water-Soluble Polymer in Slick-Water Hydrofracking

Thermal Decomposition Properties of Industrial Oily Sludge under Different Atmospheres

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