Výsledky vyhledávání - "Hamid Rahmanifard"

Hybrid compressed air energy storage, wind and geothermal energy systems in Alberta: Feasibility simulation and economic assessment

Autor: Tatyana Plaksina, Hamid Rahmanifard

Publikováno v: Renewable Energy. 143:453-470

Fossil fuels are the major source of electric power production in Alberta, Canada (about 90%). This makes electricity generation sector of Alberta an intensive source of CO2 emissions and a good candidate for greenhouse gas (GHG) emission reduction b

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::3575201bcdba9ae68857809c2bd1d33a
https://doi.org/10.1016/j.renene.2019.05.001

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Wavelet Modelling Approach for Reservoir Model Classification

Autor: Shengnan Chen, Hamid Rahmanifard, Hassan Karimi, Hamzeh Alimohammadi

Publikováno v: Day 1 Tue, December 01, 2020.

In oil and gas industry it is crucial to have reliable information on well, reservoir and boundary types and properties. Detailed information can be extracted from a proper interpretation of pressure and rate transients of well testing data. Though,

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::54bf336f0371b9e141ccbfe2c27304e2
https://doi.org/10.2118/200555-ms

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Multivariate Time Series Modelling Approach for Production Forecasting in Unconventional Resources

Autor: Nancy Chen, Hamzeh Alimohammadi, Hamid Rahmanifard

Publikováno v: Day 2 Tue, October 27, 2020.

Evaluating the potential of the unconventional resources is a key for the development of this type of reservoirs. The currently adopted models for the well production forecast including decline curve analysis often fail to capture the complexity of f

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::34997e9171a5209a49ade9e2bee82e38
https://doi.org/10.2118/201571-ms

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Well Performance Prediction in Montney Formation Using Machine Learning Approaches

Autor: Ian D. Gates, Hamid Rahmanifard, Hamzeh Alimohammadi

Publikováno v: Proceedings of the 8th Unconventional Resources Technology Conference.

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::3ee96a518c3f3bdb32e109447ed3da84
https://doi.org/10.15530/urtec-2020-2465

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Application of fast analytical approach and AI optimization techniques to hydraulic fracture stage placement in shale gas reservoirs

Autor: Hamid Rahmanifard, Tatyana Plaksina

Publikováno v: Journal of Natural Gas Science and Engineering. 52:367-378

In the last decades, natural gas from unconventional reservoirs has become a major portion of total gas supply due to advances in horizontal well drilling and multi-stage hydraulic fracturing as well as reduction of operational costs and capital expe

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::dcf55a8347f34466f8e3fb58aa1c1223
https://doi.org/10.1016/j.jngse.2018.01.047

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Application of artificial intelligence techniques in the petroleum industry: a review

Autor: Hamid Rahmanifard, Tatyana Plaksina

Publikováno v: Artificial Intelligence Review. 52:2295-2318

In recent years, artificial intelligence (AI) has been widely applied to optimization problems in the petroleum exploration and production industry. This survey offers a detailed literature review based on different types of AI algorithms, their appl

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::2f3dfa5cb9ddd0e7f6c69bea8815c5cb
https://doi.org/10.1007/s10462-018-9612-8

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The application of supervised machine learning techniques for multivariate modelling of gas component viscosity: A comparative study

Autor: Tatyana Plaksina, Hamid Rahmanifard, Hamzeh Alimohamadi, Paiman Maroufi, Ian D. Gates

Publikováno v: Fuel. 285:119146

Accurate prediction of gas component viscosity is crucial in gas processing, heat and mass transfer and flow calculations, as well as gas reserves estimation. Many models have been proposed to predict the viscosity of gas components, but they have li

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::44f321bcb94f0dc87fab14a53b9877cc
https://doi.org/10.1016/j.fuel.2020.119146

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On improving the hydrogen and methanol production using an auto-thermal double-membrane reactor: Model prediction and optimisation

Autor: Reza Vakili, Masoud Babaei, Mohammad Reza Rahimpour, Xiaolei Fan, Tatyana Plaksina, Hamid Rahmanifard

Publikováno v: Rahmanifard, H, Vakili, R, Plaksina, T, Rahimpour, M R, Babaei, M & Fan, X 2018, ' On improving the hydrogen and methanol production using an auto-thermal double-membrane reactor: Model prediction and optimisation ', COMPUTERS & CHEMICAL ENGINEERING, vol. 119, pp. 258-269 . https://doi.org/10.1016/j.compchemeng.2018.09.006

The concentric configured thermally-coupled double-membrane reactor (TCDMR) was optimised to improve the co-production of hydrogen and methanol. Using a detailed approach, we identified the non-linear differential evolution (DE) algorithm as the most

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::2beb9ab3bf4a49e775fbe064e18f2fb6
https://doi.org/10.1016/j.compchemeng.2018.09.006

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