Zobrazeno 1 - 10 of 17 pro vyhledávání: '"Syamil Mohd Razak"'
1
Physics-Guided Deep Learning for Improved Production Forecasting in Unconventional Reservoirs
Autor: Syamil Mohd Razak, Jodel Cornelio, Young Cho, Hui-Hai Liu, Ravimadhav Vaidya, Behnam Jafarpour
Publikováno v: SPE Journal. :1-23
Summary The complexity of physics-based modeling of fluid flow in hydraulically fractured unconventional reservoirs, together with the abundant data from repeated factory-style drilling and completion of these resources, has prompted the development
Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::ce1ec80a48e5859ecf1e6c018ca9327a
https://doi.org/10.2118/214663-pa
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2
Transfer Learning with Prior Data-Driven Models from Multiple Unconventional Fields
Autor: Jodel Cornelio, Syamil Mohd Razak, Young Cho, Hui-Hai Liu, Ravimadhav Vaidya, Behnam Jafarpour
Publikováno v: SPE Journal. :1-30
Summary Constructing reliable data-driven models to predict well production performance (e.g., estimated ultimate recovery, cumulative production, production curves, etc.) for unconventional reservoirs requires large amounts of data. However, when co
Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::edacbff0255033d8e9f12e5160f9d1f0
https://doi.org/10.2118/214312-pa
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3
Neural Network-Assisted Clustering for Improved Production Predictions in Unconventional Reservoirs
Autor: Jodel Cornelio, Syamil Mohd Razak, Young Cho, Hui-Hai Liu, Ravimadhav Vaidya, Behnam Jafarpour
Publikováno v: Day 2 Tue, May 23, 2023.
Given sufficiently extensive data, deep-learning models can effectively predict the behavior of unconventional reservoirs. However, current approaches in building the models do not directly reveal the causal effects of flow behavior, underlying physi
Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::602f64a193cc37c34f6a491d32ee54d7
https://doi.org/10.2118/212953-ms
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4
Dynamic Physics-Guided Deep Learning for Production Forecasting in Unconventional Reservoirs
Autor: Syamil Mohd Razak, Jodel Cornelio, Young Cho, Hui-Hai Liu, Ravimadhav Vaidya, Behnam Jafarpour
Publikováno v: Day 2 Tue, May 23, 2023.
Neural network predictive models are popular for production forecasting in unconventional reservoirs. They have the ability to learn complex input-output mapping between well properties and observed production responses from the large amount of data
Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::23ed80a5087db72afa0b63c1eff38402
https://doi.org/10.2118/212962-ms
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5
Deep Learning-Based Multiresolution Parameterization for Spatially Adaptive Model Updating
Autor: Mahammad Valiyev, Syamil Mohd Razak, Behnam Jafarpour
Publikováno v: Day 3 Thu, March 30, 2023.
This paper presents a new deep learning-based parameterization approach for model calibration with two important properties: spatial adaptivity and multiresolution representation. The method aims to establish a spatially adaptive multiresolution late
Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::2c2cf546b8103ef6e1a627a4d836754b
https://doi.org/10.2118/212167-ms
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6
Identifying and Ranking Multiple Source Models for Transfer Learning in Unconventional Reservoirs
Autor: Jodel Cornelio, Syamil Mohd Razak, Young Cho, Hui-Hai Liu, Ravimadhav Vaidya, Behnam Jafarpour
Publikováno v: Day 2 Mon, February 20, 2023.
When a limited number of wells are drilled at the early stages of developing unconventional fields, the available data is insufficient for developing data-driven models. To compensate for the lack of data in new fields, transfer learning may be adopt
Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::ead92fa654264454b77cba1a271a7d30
https://doi.org/10.2118/213349-ms
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7
A Dynamic Residual Learning Approach to Improve Physics-Constrained Neural Network Predictions in Unconventional Reservoirs
Autor: Syamil Mohd Razak, Jodel Cornelio, Young Cho, Hui-Hai Liu, Ravimadhav Vaidya, Behnam Jafarpour
Publikováno v: Day 2 Mon, February 20, 2023.
Predictive models that incorporate physical information or constraints are used for production prediction in subsurface systems. They come in many flavors; some include additional terms in the objective function, some directly embed physical function
Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::9cf60d4f26863ee1240f49a6af2625d2
https://doi.org/10.2118/213289-ms
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8
Deep Learning for Latent Space Data Assimilation in Subsurface Flow Systems
Autor: Syamil Mohd Razak, Atefeh Jahandideh, Ulugbek Djuraev, Behnam Jafarpour
Publikováno v: SPE Journal. 27:2820-2840
Summary We present a new deep learning architecture for efficient reduced-order implementation of ensemble data assimilation in learned low-dimensional latent spaces. Specifically, deep learning is used to improve two important aspects of data assimi
Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::1814e9fd9c5a535369e33f0704b48ce7
https://doi.org/10.2118/203997-pa
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10
Conditioning generative adversarial networks on nonlinear data for subsurface flow model calibration and uncertainty quantification
Autor: Behnam Jafarpour, Syamil Mohd Razak
Publikováno v: Computational Geosciences. 26:29-52
Conditioning complex subsurface flow models on nonlinear data is complicated by the need to preserve the expected geological connectivity patterns to maintain solution plausibility. Generative adversarial networks (GANs) have recently been proposed a
Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::90fbe2c419a6d9fab40d4ded49f5256a
https://doi.org/10.1007/s10596-021-10112-8
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