NMR-Based Study of the Pore Types’ Contribution to the Elastic Response of the Reservoir Rock
Seismic data and nuclear magnetic resonance (NMR) data are two of the highly trustable kinds of information in hydrocarbon reservoir engineering. Reservoir fluids influence the elastic wave velocity and also determine the NMR response of the reservoir. The current study investigates different pore t...
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doaj-53e46058a16f41a0a4a1c7733048c10e2021-03-10T00:06:25ZengMDPI AGEnergies1996-10732021-03-01141513151310.3390/en14051513NMR-Based Study of the Pore Types’ Contribution to the Elastic Response of the Reservoir RockNaser Golsanami0Xuepeng Zhang1Weichao Yan2Linjun Yu3Huaimin Dong4Xu Dong5Likai Cui6Madusanka Nirosh Jayasuriya7Shanilka Gimhan Fernando8Ehsan Barzgar9State Key Laboratory of Mining Disaster Prevention and Control, Shandong University of Science and Technology, Qingdao 266590, ChinaState Key Laboratory of Mining Disaster Prevention and Control, Shandong University of Science and Technology, Qingdao 266590, ChinaDepartment of Well Logging, School of Geosciences, China University of Petroleum (Huadong), Qingdao 266580, ChinaNo.12 Oil Production Plant, Changqing Oilfield Company, PetroChina, Xi’an 710200, ChinaDepartment of Well Logging, School of Geosciences, China University of Petroleum (Huadong), Qingdao 266580, ChinaKey Laboratory of Continental Shale Hydrocarbon Accumulation and Efficient Development, Ministry of Education, Northeast Petroleum University, Daqing 163318, ChinaInstitute of Unconventional Oil and Gas, Northeast Petroleum University, Daqing 163318, ChinaCollege of Energy and Mining Engineering, Shandong University of Science and Technology, Qingdao 266590, ChinaCollege of Energy and Mining Engineering, Shandong University of Science and Technology, Qingdao 266590, ChinaState Key Laboratory of Petroleum Resources and Prospecting, and Unconventional Petroleum Research Institute, China University of Petroleum, Beijing 102249, ChinaSeismic data and nuclear magnetic resonance (NMR) data are two of the highly trustable kinds of information in hydrocarbon reservoir engineering. Reservoir fluids influence the elastic wave velocity and also determine the NMR response of the reservoir. The current study investigates different pore types, i.e., micro, meso, and macropores’ contribution to the elastic wave velocity using the laboratory NMR and elastic experiments on coal core samples under different fluid saturations. Once a meaningful relationship was observed in the lab, the idea was applied in the field scale and the NMR transverse relaxation time (T<sub>2</sub>) curves were synthesized artificially. This task was done by dividing the area under the T<sub>2</sub> curve into eight porosity bins and estimating each bin’s value from the seismic attributes using neural networks (NN). Moreover, the functionality of two statistical ensembles, i.e., Bag and LSBoost, was investigated as an alternative tool to conventional estimation techniques of the petrophysical characteristics; and the results were compared with those from a deep learning network. Herein, NMR permeability was used as the estimation target and porosity was used as a benchmark to assess the reliability of the models. The final results indicated that by using the incremental porosity under the T<sub>2</sub> curve, this curve could be synthesized using the seismic attributes. The results also proved the functionality of the selected statistical ensembles as reliable tools in the petrophysical characterization of the hydrocarbon reservoirs.https://www.mdpi.com/1996-1073/14/5/1513NMR relaxationelastic responsestatistical ensemblesdeep learningcoalbed methane |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Naser Golsanami Xuepeng Zhang Weichao Yan Linjun Yu Huaimin Dong Xu Dong Likai Cui Madusanka Nirosh Jayasuriya Shanilka Gimhan Fernando Ehsan Barzgar |
spellingShingle |
Naser Golsanami Xuepeng Zhang Weichao Yan Linjun Yu Huaimin Dong Xu Dong Likai Cui Madusanka Nirosh Jayasuriya Shanilka Gimhan Fernando Ehsan Barzgar NMR-Based Study of the Pore Types’ Contribution to the Elastic Response of the Reservoir Rock Energies NMR relaxation elastic response statistical ensembles deep learning coalbed methane |
author_facet |
Naser Golsanami Xuepeng Zhang Weichao Yan Linjun Yu Huaimin Dong Xu Dong Likai Cui Madusanka Nirosh Jayasuriya Shanilka Gimhan Fernando Ehsan Barzgar |
author_sort |
Naser Golsanami |
title |
NMR-Based Study of the Pore Types’ Contribution to the Elastic Response of the Reservoir Rock |
title_short |
NMR-Based Study of the Pore Types’ Contribution to the Elastic Response of the Reservoir Rock |
title_full |
NMR-Based Study of the Pore Types’ Contribution to the Elastic Response of the Reservoir Rock |
title_fullStr |
NMR-Based Study of the Pore Types’ Contribution to the Elastic Response of the Reservoir Rock |
title_full_unstemmed |
NMR-Based Study of the Pore Types’ Contribution to the Elastic Response of the Reservoir Rock |
title_sort |
nmr-based study of the pore types’ contribution to the elastic response of the reservoir rock |
publisher |
MDPI AG |
series |
Energies |
issn |
1996-1073 |
publishDate |
2021-03-01 |
description |
Seismic data and nuclear magnetic resonance (NMR) data are two of the highly trustable kinds of information in hydrocarbon reservoir engineering. Reservoir fluids influence the elastic wave velocity and also determine the NMR response of the reservoir. The current study investigates different pore types, i.e., micro, meso, and macropores’ contribution to the elastic wave velocity using the laboratory NMR and elastic experiments on coal core samples under different fluid saturations. Once a meaningful relationship was observed in the lab, the idea was applied in the field scale and the NMR transverse relaxation time (T<sub>2</sub>) curves were synthesized artificially. This task was done by dividing the area under the T<sub>2</sub> curve into eight porosity bins and estimating each bin’s value from the seismic attributes using neural networks (NN). Moreover, the functionality of two statistical ensembles, i.e., Bag and LSBoost, was investigated as an alternative tool to conventional estimation techniques of the petrophysical characteristics; and the results were compared with those from a deep learning network. Herein, NMR permeability was used as the estimation target and porosity was used as a benchmark to assess the reliability of the models. The final results indicated that by using the incremental porosity under the T<sub>2</sub> curve, this curve could be synthesized using the seismic attributes. The results also proved the functionality of the selected statistical ensembles as reliable tools in the petrophysical characterization of the hydrocarbon reservoirs. |
topic |
NMR relaxation elastic response statistical ensembles deep learning coalbed methane |
url |
https://www.mdpi.com/1996-1073/14/5/1513 |
work_keys_str_mv |
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