Petrophysical evaluation of low-resistivity sandstone reservoirs with nuclear magnetic resonance log

Hamada, G.M.; Al-Blehed, M.S.; Al-Awad, Musaed N.J.; Al-Saddique, Mohamed A.

doi:10.1016/s0920-4105(01)00095-x

Cited by 33 publications

(7 citation statements)

References 2 publications

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“…In addition Figures 9a, b also show the tendency for low surface area samples to approach minimum irreducible water saturation and for high surface area samples to remain more saturated. A relationship between specific surface and irreducible water saturation has been noted by several authors e. g. Hamada et al (2001) where authors reported an excellent correlation (R 2 =0.98) between irreducible water saturation and specific surface of pores.…”

Section: Specific Surface Areamentioning

confidence: 66%

Petrophysical properties of greensand as predicted from NMR measurements

Hossain

Grattoni

Solymar

et al. 2011

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Nuclear magnetic resonance (NMR) is a useful tool in reservoir evaluation. The objective of this study is to predict petrophysical properties from NMR T 2 distributions. A series of laboratory experiments including core analysis, capillary pressure measurements, NMR T 2 measurements and image analysis were done on sixteen greensand samples from two formations in the Nini field of the North Sea. Hermod Formation is weakly cemented, whereas Ty Formation is characterized by microcrystalline quartz cement. The surface area measured by BET method and the NMR derived surface relaxivity are associated with the micro-porous glauconite grains. The effective specific surface area as calculated from Kozeny's equation and as derived from petrographic image analysis of Backscattered Electron Micrograph's (BSE), as well as the estimated effective surface relaxivity is associated with macro-pores. Permeability may be predicted from NMR by using Kozeny's equation when surface relaxivity is known. Capillary pressure drainage curves may be predicted from NMR T 2 distribution when pore size distribution within a sample is homogeneous.

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Section: Specific Surface Areamentioning

confidence: 66%

Petrophysical properties of greensand as predicted from NMR measurements

Hossain

Grattoni

Solymar

et al. 2011

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“…First, the correction of the resistivity or the Archie formula based on the causes of low resistivity to calculate the water saturation more accurately, 13–15 such as the resistivity inversion 16 and the Simandoux Equation. Second, the identification of the low‐resistivity gas layer using the characteristic parameters besides resistivities, such as the relative value of natural gamma logging, neutron logging value, and equivalent elastic modulus 17–19 …”

Section: Introductionmentioning

confidence: 99%

Genetic analysis and identification of low‐resistivity gas reservoirs of southwestern Sulige gas field in China

Yang

Zhang

et al. 2022

Energy Science & Engineering

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Sulige gas field is the largest natural gas producing area in China. Due to the "water blocking" effect, the gas-water layer significantly influences the development of gas reservoirs. The existence of low-resistivity gas layers in the He 8 member of the Shihezi Formation in the southwestern Sulige gas field makes it challenging to distinguish the gas layers from the gas-water layers using conventional identification methods. To effectively identify the low-resistivity gas layers, their genetic mechanisms are analyzed by studying their lithology and pore structural characteristics based on the well logging and core experimental data. The low-resistivity gas layers are main affected by three causes: (1) electrical conductivity of argillaceous laminae in sandstone,(2) additional conductivity of clay minerals, and (3) high bound water saturation caused by the development of micropores and clay minerals in sandstone. Herein, to effectively distinguish the low-resistivity gas layer from the gas-water layer based on the genetic mechanism of the low-resistivity gas layer, first, a gas-bearing index was constructed to characterize the gas-bearing properties of the reservoir using the neutron logging and density logging curves after eliminating the influence of the dispersed shale. Second, a model to calculate the bound water saturation was constructed by selecting sensitive parameters with respect to the causes of bound water. Then, two plots, namely the gas-bearing index-natural gamma relative value cross-plot and the bound water content-porosity cross-plots, were constructed using the gas testing data, and the identification standard of the low-resistivity gas and gas-water layers was established. The interactive identification of the two cross-plots effectively distinguished the low-resistivity gas layer from the gaswater layer, thereby providing a basis for understanding the distribution of gas and water in the southwestern Sulige gas field, which may guide further exploration and the deployment of the development well pattern.

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“…However, the traditional fluid identification methods do not take into account the effect of changes in salinity. With the development of logging technology, some new fluid identification methods, such as nuclear magnetic resonance, array induction logging and imaging logging, can also accurately identify and interpret fluid types [15,16]. However, the high cost of testing and interpretation limit their use.…”

Section: Introductionmentioning

confidence: 99%

Using the Modified Resistivity–Porosity Cross Plot Method to Identify Formation Fluid Types in Tight Sandstone with Variable Water Salinity

Yang

Wang

et al. 2021

Energies

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It is generally difficult to identify fluid types in low-porosity and low-permeability reservoirs, and the Chang 8 Member in the Ordos Basin is a typical example. In the Chang 8 Member of Yanchang Formation in the Zhenyuan area of Ordos Basin, affected by lithology and physical properties, the resistivity of the oil layer and water layer are close, which brings great difficulties to fluid type identification. In this paper, we first analyzed the geological and petrophysical characteristics of the study area, and found that high clay content is one of the reasons for the low-resistivity oil pay layer. Then, the formation water types and characteristics of formation water salinity were studied. The water type was mainly CaCl2, and formation water salinity had a great difference in the study area ranging from 7510 ppm to 72,590 ppm, which is the main cause of the low-resistivity oil pay layer. According to the reservoir fluid logging response characteristics, the water saturation boundary of the oil layer, oil–water layer and water layer were determined to be 30%, 65% and 80%, respectively. We modified the traditional resistivity–porosity cross plot method based on Archie’s equations, and established three basic plates with variable formation water salinity, respectively. The above method was used to identify the fluid types of the reservoirs, and the application results indicate that the modified method agrees well with the perforation test data, which can effectively improve the accuracy of fluid identification. The accuracy of the plate is 88.1%. The findings of this study can help for a better understanding of fluid identification and formation evaluation.

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Petrophysical evaluation of low-resistivity sandstone reservoirs with nuclear magnetic resonance log

Cited by 33 publications

References 2 publications

Petrophysical properties of greensand as predicted from NMR measurements

Petrophysical properties of greensand as predicted from NMR measurements

Genetic analysis and identification of low‐resistivity gas reservoirs of southwestern Sulige gas field in China

Using the Modified Resistivity–Porosity Cross Plot Method to Identify Formation Fluid Types in Tight Sandstone with Variable Water Salinity

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