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A Data-Driven Approach for Assessing in Situ Gas Content Using Machine Learning: Insights from Logging Data of Deep Coalbed Methane Reservoirs

https://doi.org/10.2139/ssrn.4765978
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Outgoing claims: the papers this work cites, builds on, or argues with.

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Evaluation of in situ stress changes with gas depletion of coalbed methane reservoirs
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Current status, challenges, and policy suggestions for coalbed methane industry development in China: A review
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Research on prediction model of geotechnical parameters based on BP neural network
inferredcites →https://doi.org/10.1007/s11053-021-09834-4openalex-extracted
Evaluation of Coal Body Structures and Their Distributions by Geophysical Logging Methods: Case Study in the Laochang Block, Eastern Yunnan, China
inferredcites →https://doi.org/10.1007/s11053-023-10233-0openalex-extracted
Accurate Prediction of the Macrolithotype of a High-Rank Coal Reservoir by Logging Interpretation: A Case Study of the Junlian Block, Sichuan Province, China
inferredcites →https://doi.org/10.1007/s11770-022-0997-4openalex-extracted
Selection of machine learning algorithms in coalbed methane content predictions
inferredcites →https://doi.org/10.1016/j.apenergy.2020.114575openalex-extracted
Modeling of multi-scale transport phenomena in shale gas production — A critical review
inferredcites →https://doi.org/10.1016/j.apenergy.2023.121499openalex-extracted
A data-driven approach for the prediction of coal seam gas content using machine learning techniques
inferredcites →https://doi.org/10.1016/j.coal.2009.10.017openalex-extracted
Variable gas saturation in coalbed methane reservoirs of the Black Warrior Basin: Implications for exploration and production
inferredcites →https://doi.org/10.1016/j.coal.2012.05.011openalex-extracted
Coalbed methane: A review
inferredcites →https://doi.org/10.1016/j.coal.2012.11.007openalex-extracted
Geology, spatial distribution of methane content and origin of coalbed gases in Upper Carboniferous (Upper Mississippian and Pennsylvanian) strata in the south-eastern part of the Upper Silesian Coal Basin, Poland
inferredcites →https://doi.org/10.1016/j.coal.2015.04.010openalex-extracted
Impact of geological modeling processes on spatial coalbed methane resource estimation
inferredcites →https://doi.org/10.1016/j.coal.2015.12.002openalex-extracted
Contribution of non-coal facies to the total gas-in-place in Mannville coal measures, Central Alberta
inferredcites →https://doi.org/10.1016/j.coal.2019.103261openalex-extracted
Application of nuclear magnetic resonance (NMR) in coalbed methane and shale reservoirs: A review
inferredcites →https://doi.org/10.1016/j.coal.2023.104369openalex-extracted
A comprehensive review of deep coalbed methane and recent developments in China
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A feature selection based on genetic algorithm for intrusion detection of industrial control systems
inferredcites →https://doi.org/10.1016/j.earscirev.2022.104134openalex-extracted
Oil content and resource quality evaluation methods for lacustrine shale: A review and a novel three-dimensional quality evaluation model
inferredcites →https://doi.org/10.1016/j.egyr.2022.01.187openalex-extracted
Surfactants employed in conventional and unconventional reservoirs for enhanced oil recovery—A review
inferredcites →https://doi.org/10.1016/j.enggeo.2006.02.007openalex-extracted
Application of back-propagation networks in debris flow prediction
inferredcites →https://doi.org/10.1016/j.eswa.2007.12.027openalex-extracted
Combining grey relation analysis with FMCGDM to evaluate financial performance of Taiwan container lines
inferredcites →https://doi.org/10.1016/j.eswa.2014.08.018openalex-extracted
Back propagation neural network with adaptive differential evolution algorithm for time series forecasting
inferredcites →https://doi.org/10.1016/j.fuel.2009.06.003openalex-extracted
Evaluation of gas content of coalbed methane reservoirs with the aid of geophysical logging technology
inferredcites →https://doi.org/10.1016/j.fuel.2019.116350openalex-extracted
Reservoir properties of Chinese tectonic coal: A review
inferredcites →https://doi.org/10.1016/j.fuel.2019.116917openalex-extracted
Evaluation of gas contents for a multi-seam deep coalbed methane reservoir and their geological controls: In situ direct method versus indirect method
inferredcites →https://doi.org/10.1016/j.fuel.2020.118246openalex-extracted
A new approach to estimating coal gas content for deep core sample
inferredcites →https://doi.org/10.1016/j.fuel.2022.127191openalex-extracted
Identification of coal structures by semi-supervised learning based on limited labeled logging data
inferredcites →https://doi.org/10.1016/j.fuel.2023.130578openalex-extracted
Modeling of methane and carbon dioxide sorption capacity in tight reservoirs using Machine learning techniques
inferredcites →https://doi.org/10.1016/j.gete.2023.100435openalex-extracted
Prediction of the shale gas permeability: A data mining approach
inferredcites →https://doi.org/10.1016/j.gr.2023.09.012openalex-extracted
Direct method to estimate the gas loss characteristics and in-situ gas contents of shale
inferredcites →https://doi.org/10.1016/j.gsf.2012.02.005openalex-extracted
The pore-fracture system properties of coalbed methane reservoirs in the Panguan Syncline, Guizhou, China
inferredcites →https://doi.org/10.1016/j.ijmst.2017.07.024openalex-extracted
Discussion on determination of gas content of coal and uncertainties of measurement
inferredcites →https://doi.org/10.1016/j.jngse.2015.08.020openalex-extracted
A new method for accurate and rapid measurement of underground coal seam gas content
inferredcites →https://doi.org/10.1016/j.jngse.2016.06.032openalex-extracted
Optimisation of gas mixture injection for enhanced coalbed methane recovery using a parallel genetic algorithm
inferredcites →https://doi.org/10.1016/j.jngse.2020.103455openalex-extracted
Vertical heterogeneity of permeability and gas content of ultra-high-thickness coalbed methane reservoirs in the southern margin of the Junggar Basin and its influence on gas production
inferredcites →https://doi.org/10.1016/j.oregeorev.2023.105599openalex-extracted
Distribution characteristics and enrichment model of valuable elements in coal: An example from the Nangou Mine, Ningwu Coalfield, northern China
inferredcites →https://doi.org/10.1016/j.petrol.2021.109431openalex-extracted
A new method to predict brittleness index for shale gas reservoirs: Insights from well logging data
inferredcites →https://doi.org/10.1016/j.procir.2016.10.092openalex-extracted
Research and Application of Improved Gas Concentration Prediction Model Based on Grey Theory and BP Neural Network in Digital Mine
inferredcites →https://doi.org/10.1016/j.psep.2019.10.002openalex-extracted
Prediction of coal and gas outburst: A method based on the BP neural network optimized by GASA
inferredcites →https://doi.org/10.1016/j.rser.2013.02.030openalex-extracted
Coal seam gas and associated water: A review paper
inferredcites →https://doi.org/10.1016/j.scitotenv.2024.169886openalex-extracted
An improved Back Propagation Neural Network framework and its application in the automatic calibration of Storm Water Management Model for an urban river watershed
inferredcites →https://doi.org/10.1016/s0166-5162(97)00040-2openalex-extracted
Measuring the gas content of coal: A review
inferredcites →https://doi.org/10.1016/s0166-5162(98)00030-5openalex-extracted
Geological controls on coalbed methane reservoir capacity and gas content
inferredcites →https://doi.org/10.1021/acs.energyfuels.3c01143openalex-extracted
New Data-Driven Method for In Situ Coalbed Methane Content Evolution: A BP Neural Network Prediction Model Optimized by Grey Relation Theory and Particle Swarm
inferredcites →https://doi.org/10.1021/acsomega.2c00519openalex-extracted
Coalbed Methane Production Model Based on Random Forests Optimized by a Genetic Algorithm
inferredcites →https://doi.org/10.1080/00206814.2017.1408034openalex-extracted
Resources and geology of coalbed methane in China: a review
inferredcites →https://doi.org/10.1080/10916466.2016.1153655openalex-extracted
Estimation of water content of natural gases using particle swarm optimization method
inferredcites →https://doi.org/10.1109/icicisys.2009.5357929openalex-extracted
Support vector machine regression model of CBM content and application
inferredcites →https://doi.org/10.1109/icmtma.2010.492openalex-extracted
Application and Comparison of BP Neural Network Algorithm in MATLAB
inferredcites →https://doi.org/10.1155/2017/1640303openalex-extracted
Route Optimization of Pipeline in Gas-Liquid Two-Phase Flow Based on Genetic Algorithm
inferredcites →https://doi.org/10.1306/02131312123openalex-extracted
Variable gas content, saturation, and accumulation characteristics of Weibei coalbed methane pilot-production field in the southeastern Ordos Basin, China
inferredcites →https://doi.org/10.1306/03231715171openalex-extracted
Coupling accumulation model with gas-bearing features to evaluate low-rank coalbed methane resource potential in the southern Junggar Basin, China
inferredcites →https://doi.org/10.1306/05110504114openalex-extracted
Volumetric strain associated with methane desorption and its impact on coalbed gas production from deep coal seams
inferredcites →https://doi.org/10.1306/06030302015openalex-extracted
Possible controls of hydrogeological and stress regimes on the producibility of coalbed methane in Upper Cretaceous–Tertiary strata of the Alberta basin, Canada
inferredcites →https://doi.org/10.2118/23025-msopenalex-extracted
Review of Research Efforts in Coalbed Methane Recovery
inferredcites →https://doi.org/10.2118/26196-msopenalex-extracted
Deep Coal Seams: An Overlooked Source for Long-Term Natural Gas Supplies
inferredcites →https://doi.org/10.2139/ssrn.4555118openalex-extracted
inferredcites →https://doi.org/10.3390/pr11020527openalex-extracted
CBM Gas Content Prediction Model Based on the Ensemble Tree Algorithm with Bayesian Hyper-Parameter Optimization Method: A Case Study of Zhengzhuang Block, Southern Qinshui Basin, North China

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inferred← citeshttps://doi.org/10.1016/b978-0-443-26510-5.00011-6openalex-snapshot
Diverse machine learning applications for coal property characterization of coalbed methane and mining resources
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