The Clearwater Formation, Cold Lake, Alberta: a worldclass hydrocarbon reservoir hosted in a complex succession of tide-dominated deltaic deposits

2002 ◽  
Vol 50 (3) ◽  
pp. 370-392 ◽  
Author(s):  
G. G. McCrimmon
Keyword(s):  
Hydrocarbon Reservoir ◽  
Clearwater Formation ◽  
Cold Lake

Impulse Response of Seafloor Hydrocarbon Reservoir Model

PIERS Online ◽  
2007 ◽  
Vol 3 (8) ◽  
pp. 1334-1339
Author(s):  
Jingtian Tang ◽  
Weibin Luo
Keyword(s):  
Impulse Response ◽  
Reservoir Model ◽  
Hydrocarbon Reservoir

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

Energies ◽  
2021 ◽  
Vol 14 (5) ◽  
pp. 1513 ◽  
Author(s):  
Naser Golsanami ◽  
Xuepeng Zhang ◽  
Weichao Yan ◽  
Linjun Yu ◽  
Huaimin Dong ◽  
...  
Keyword(s):  
Elastic Wave ◽  
Wave Velocity ◽  
Transverse Relaxation Time ◽  
Seismic Attributes ◽  
Elastic Wave Velocity ◽  
Reservoir Rock ◽  
Elastic Response ◽  
Hydrocarbon Reservoir ◽  
Statistical Ensembles ◽  
Pore Types

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 (T2) curves were synthesized artificially. This task was done by dividing the area under the T2 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 T2 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.

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