Hydrocarbon potential evaluation in the Low Resistivity Pays (LRP) of Sarvak formation with combining Nuclear Magnetic Resonance (NMR) and seismic data, one of the hydrocarbon reservoirs in southwest of Iran

Amirnejad, Sina; Mohebian, Reza; Bahroudi, Abbas; Jahanbakhshi, Saman

doi:10.22059/ijmge.2023.361937.595082

Hydrocarbon potential evaluation in the Low Resistivity Pays (LRP) of Sarvak formation with combining Nuclear Magnetic Resonance (NMR) and seismic data, one of the hydrocarbon reservoirs in southwest of Iran

Document Type : Research Paper

Authors

School of Mining Engineering, College of Engineering, University of Tehran, Tehran, Iran.

10.22059/ijmge.2023.361937.595082

Abstract

The objective of petrophysical studies is to assess the quality of hydrocarbon reservoir layers and to zone the reservoir for identifying optimal zones for exploitation and informed development of oil fields. In some regions, there are zones that exhibit lower electrical resistivity values than their actual values. These low-resistivity zones are often identified through petrophysical investigations and conventional well logs, where water saturation levels are estimated higher due to their reduced resistivity. These zones, despite their hydrocarbon potential, are often neglected during production cycles. To overcome this challenge, nuclear magnetic resonance (NMR) logging tools can be employed to provide accurate estimations of free fluid saturation, irreducible fluid saturation, permeability, and effective porosity in such low-resistivity zones, making them more identifiable.
In this article, we utilized conventional well logs and NMR log from the A well in the Sarvak reservoir of one of the oil fields in southwestern Iran. Based on the obtained results, depth interval 9586 to 9783 ft in the Sarvak Formation, along with two intervals (10661-10815 ft) and (10830-11063 ft) in the Int zone, were identified as potential low-resistivity zones in the reservoir. By analyzing the high-resistivity logs, water saturation percentage was calculated for these zones, and the results from NMR logging confirmed their favorable reservoir potential (e.g., free fluid saturation, effective porosity, viscosity, and permeability).
Furthermore, to extend the petrophysical parameters, such as free fluid saturation and porosity, throughout the entire hydrocarbon field, various approaches including single- attribute methods, multi attribute methods, and neural networks were evaluated. The neural network method demonstrated higher accuracy in determining the parameters. Ultimately, the values of porosity and free fluid saturation in the study area were determined with 91% and 95.8% matching accuracy, respectively. The final results were validated using unseen data, and the high precision of the obtained results was confirmed.

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