Methods of Well Radiometry

Barsukov, O. A.; Blinova, N.M.; Vybornykh, S.F.; Gulin, Yu.A.; Dakhnov, V.N.; Larionov, Viktor; Kholin, A.I.

doi:10.1016/b978-0-08-010001-2.50007-2

Cited by 1 publication

(4 citation statements)

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“…There are many methods and algorithms to calculate total porosity, like a combination of neutron‐density cross plot, cores and density log. Among these, estimation from density log ( ρ b , RHOB) is preferred if fluid and matrix densities are known (Anselmetti & Eberli, 1999; Asquith, 1985; Larionov, 1969). This relationship is written as

\begin{equation}{\phi }_{{\rm{total}}} = {\phi }_{\rm{D}}\ = \frac{{{\rho }_{{\rm{m}}\ - \ }{\rho }_{\rm{b}}}}{{{\rho }_{{\rm{m}}\ - }\ {\rho }_{\rm{f}}}},\end{equation}

where ρ m is the matrix density, ρ f is the fluid density, and ρ b is the bulk density (measured density log data).…”

Section: Methodsmentioning

confidence: 99%

“…There are various methods for obtaining shale volume, and in this research, we followed the method of Larionov's equations. Larionov (1969) introduced a method for shale volume calculation in tertiary or younger rocks using the following equations:

\begin{equation}{I}_{{\rm{GR}}} = \frac{{{\rm{GR}}_{{\rm{log}}} - {\rm{GR}}_{{\rm{min}}}}}{{{\rm{GR}}_{{\rm{max}}} - {\rm{GR}}_{{\rm{min}}}}},\end{equation}

\begin{equation}{V}_{{\rm{sh}}} = \ 0.33\left( {{2}^{[2{I}_{{\rm{GR}}}]} - 1} \right)\,{\rm{for}}\,{\rm{older}}\,{\rm{rocks,}}\end{equation}

\begin{equation}{V}_{{\rm{sh}}} = \ 0.083\left( {{2}^{[3.7{I}_{{\rm{GR}}}]} - 1} \right)\,{\rm{for}}\,{\rm{younger}}\,{\rm{rocks,}}\end{equation}

where I GR is the gamma ray index, GR log is the measured gamma‐ray, GR min and GR max are observed minimum and maximum gamma‐ray, or clean rock and shaly (non‐organic) interval, respectively.…”

Section: Methodsmentioning

confidence: 99%

“…There are many methods and algorithms to calculate total porosity, like a combination of neutron-density cross plot, cores and density log. Among these, estimation from density log (ρ b , RHOB) is preferred if fluid and matrix densities are known (Anselmetti & Eberli, 1999;Asquith, 1985;Larionov, 1969). This relationship is written as…”

Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

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A novel approach for fracture porosity estimation of carbonate reservoirs

Sharifi

Moghaddas

Saberi³

et al. 2023

Geophysical Prospecting

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The quality of carbonate reservoirs is linked to their rock properties and surrounding stress settings which can play a vital role in their production optimization and development programs. Imposing various stresses can increase porosity and permeability by creating joints and cracks across the rock mass. Therefore, developing methods to identify and accurately evaluate fractures in carbonate reservoirs is very significant in reservoir characterization. To deal with this necessity, we developed a fracture porosity estimation method using empirical and analytical solutions based on the wireline data considering stress conditions. The proposed methodology was applied to a carbonate reservoir as the case study. As fracture porosity is sensitive to stress, experimental tests were conducted on the core samples using triaxial hydrostatic tests. These tests are needed to simulate reservoir conditions to investigate the relationships between fracture volume fraction and the applied stress. A substantial porosity loss was observed during the hydrostatic test, which can be linked to the crack porosity closure. The reliability of our results was validated by thin section, computed tomography-scan images, velocity deviation method, elastic boundary laws, and image logs. Eventually, we introduced an equation to estimate in situ fracture porosity from the relationship between porosity and effective confining pressure. We verified the repeatability and generality of our proposed methodology using a blind well, and the results indicate a comparable level of accuracy. Our findings are applicable for estimating crack porosity for rock physics modelling purposes of detecting fracture zones in the absence of image logs and core data. Moreover, by calibrating and optimizing the constant parameters in our proposed relationship, one may further estimate crack density in any other given carbonate reservoirs.

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