Abstract
Multi-frequency dielectric scanning logging is an advanced method that plays a critical role in evaluating unconventional oil and gas reserves and residual oil distribution. This method provides higher accuracy compared to conventional logging and can obtain essential formation parameters such as formation water salinity, pore textural index, and dispersive phase volume fraction. Despite its advantages, the inversion of permittivity and conductivity measurements at multiple frequencies into formation properties remains a "black box" problem. This challenge makes it challenging to understand specific implementation methods and inversion techniques without purchasing expensive software and hardware from oil field service companies. To address this issue, this work proposes a publicly available and advanced intelligent optimization algorithm. Our approach reduces calculation complexity and achieves high accuracy in evaluating petrophysical properties, including shaly sand reservoirs, without relying on costly software services from oil field companies. Our method offers distinct advantages over traditional approaches, such as the ability to derive formation properties from dielectric logging information with confidence, without specialized equipment or software from commercial providers. Additionally, the open-source code is readily available in various programming languages, including Python, R, and Matlab, making our approach accessible and easy to implement.
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Data Availability
The data that support the findings of this study are available China University of Petroleum (Beijing). Restrictions apply to the availability of these data, which were used under license for the current study and so are not publicly available. However, data are available from the authors upon reasonable request and with permission from China University of Petroleum (Beijing).
Abbreviations
- S w :
-
Water porosity fraction
- S :
-
Formation water salinity
- MN :
-
Pore textural index
- p d :
-
Clay dispersion volume fraction
- ε r :
-
Dielectric constant
- σ :
-
Electrical conductivity
- ϕ :
-
Porosity
- ϕ w :
-
Water-filled porosity
- T :
-
Temperature
- ε ∞ :
-
High-frequency limit dielectric constant
- ε s :
-
Low-frequency limit dielectric constant
- ε 0 :
-
Permittivity of free space
- f c :
-
Critical frequency
- ε * w :
-
Water phase permittivity (complex form)
- σ w :
-
Water phase conductivity
- ε rw :
-
Water phase relative permittivity
- ε h :
-
Hydrocarbon permittivity
- ε m :
-
Matrix permittivity
- ε * :
-
Integrated permittivity
- σ d :
-
Dispersive phase conductivity (shaly sandstone)
- ε * d :
-
Permittivity (shaly sandstone)
- ε rm :
-
Matrix relative permittivity
- ε * c :
-
Integrated conductive phase dielectric constant (shaly sandstone)
- F1 :
-
The first optimization target
- F2 :
-
The second optimization target
- n:
-
Number of the emitted frequencies
- ε rtarget :
-
Target permittivity
- σ rtarget :
-
Target conductivity
- \(\overrightarrow{A}\) and \(\overrightarrow{C}\) :
-
Coefficient vectors in the grey wolf algorithm
- \(\overrightarrow{{r}_{1}}\) and \(\overrightarrow{{r}_{2}}\) :
-
Random vectors in the grey wolf algorithm
- \(\overrightarrow{a}\) :
-
Vector during the iteration process
- \(\overrightarrow{D}\) :
-
Distance vector
- \(\overrightarrow{X}\) :
-
Position vector
- t:
-
Iteration time
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Acknowledgements
This work is supported by the Science Foundation of China University of Petroleum, Beijing (2462021QNXZ004).
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Jia, B., Xian, C., Jia, W. et al. Improved Petrophysical Property Evaluation of Shaly Sand Reservoirs Using Modified Grey Wolf Intelligence Algorithm. Comput Geosci 27, 537–549 (2023). https://doi.org/10.1007/s10596-023-10217-2
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DOI: https://doi.org/10.1007/s10596-023-10217-2