NMR-Based Study of the Pore Types’ Contribution to the Elastic Response of the Reservoir Rock

Naser Golsanami, Xuepeng Zhang, Weichao Yan, Linjun Yu, Huaimin Dong, Xu Dong, Likai Cui, Madusanka Nirosh Jayasuriya, Shanilka Gimhan Fernando and Ehsan Barzgar
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Naser Golsanami: State Key Laboratory of Mining Disaster Prevention and Control, Shandong University of Science and Technology, Qingdao 266590, China
Xuepeng Zhang: State Key Laboratory of Mining Disaster Prevention and Control, Shandong University of Science and Technology, Qingdao 266590, China
Weichao Yan: Department of Well Logging, School of Geosciences, China University of Petroleum (Huadong), Qingdao 266580, China
Linjun Yu: No.12 Oil Production Plant, Changqing Oilfield Company, PetroChina, Xi’an 710200, China
Huaimin Dong: Department of Well Logging, School of Geosciences, China University of Petroleum (Huadong), Qingdao 266580, China
Xu Dong: Key Laboratory of Continental Shale Hydrocarbon Accumulation and Efficient Development, Ministry of Education, Northeast Petroleum University, Daqing 163318, China
Likai Cui: Institute of Unconventional Oil and Gas, Northeast Petroleum University, Daqing 163318, China
Madusanka Nirosh Jayasuriya: College of Energy and Mining Engineering, Shandong University of Science and Technology, Qingdao 266590, China
Shanilka Gimhan Fernando: College of Energy and Mining Engineering, Shandong University of Science and Technology, Qingdao 266590, China
Ehsan Barzgar: State Key Laboratory of Petroleum Resources and Prospecting, and Unconventional Petroleum Research Institute, China University of Petroleum, Beijing 102249, China

Energies, 2021, vol. 14, issue 5, 1-26

Abstract: 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 2 ) curves were synthesized artificially. This task was done by dividing the area under the T 2 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 2 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.

Keywords: NMR relaxation; elastic response; statistical ensembles; deep learning; coalbed methane (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (5)

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