Electrical Resistivity and Gamma-Ray Logs: Two Physics for Two Permeability 
Estimation Approaches in Abu Dhabi Carbonates

Alfarisi, Omar; Belgaied, Adel; Elhami, Mohamed; Kadada, Talal; Al-Jefri, Ghassan; Barkawi, Abdul

doi:10.2523/88687-ms

Cited by 8 publications

(6 citation statements)

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“…The choice of the machine learning algorithm is critical, because heterogeneous embeds complex interlinks within its features. Our heterogeneous system is Cretaceous carbonate rock fabric [4][5][6][7][8][9][10][11][12][13] where we target digitally classifying [14] it based on its physical and chemical properties using machine learning. The literature suggested that a decision tree-based algorithm performs better [15] than an artificial neural network-based algorithm, like Convolutional Neural Network (CNN), that works better with homogeneous systems [16,17].…”

Section: Introductionmentioning

confidence: 99%

Deducing Optimal Classification Algorithm for Heterogeneous Fabric

Alfarisi¹,

Aung²,

Sassi³

2022

Preprint

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For defining the optimal machine learning algorithm, the decision was not easy for which we shall choose. To help future researchers, we describe in this paper the optimal among the best of the algorithms. We built a synthetic data set and performed the supervised machine learning runs for five different algorithms. For heterogeneous rock fabric, we identified Random Forest, among others, to be the appropriate algorithm.

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Section: Introductionmentioning

confidence: 99%

Deducing Optimal Classification Algorithm for Heterogeneous Fabric

Alfarisi¹,

Aung²,

Sassi³

2022

Preprint

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“…Complex rock morphology like Cretaceous carbonate made rock typing [2][3][4][5][6][7] difficult for scientists. The critical determining properties for rock typing are pore throat size and network [8], capillary pressure [9][10][11][12][13][14], permeability [9,13,[15][16][17][18][19][20][21][22][23][24][25][26][27][28][29][30][31][32], and fluid saturations [33][34][35][36][37][38][39][40][41][42], which are also the most difficult to measure in heterogeneous fabric [43]. NMR came to provide a simultaneous solution for measuring fluid saturation of multiple phases and determining a lithologyindependent porosity [44][45][46], pore throat size and network, permeab...…”

Section: Introductionmentioning

confidence: 99%

Quantum Dipolar Coupling Thermal Correction for NMR Signal during Natural Rock Flooding by Melding Experimentation and Numerical Simulation (Th-CENS)

Alfarisi¹,

Zhang²,

Ouzzane³

et al. 2022

Preprint

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<p>Researchers have used NMR to measure multi-phase fluid saturation and distribution inside porousmedia of natural rock. However, the NMR signal amplitude suffers reduction with the increase of temperature. The main reason is the Transverse Overhauser Effect, where heating increases the freedom for ionic motion, affecting spinning behavior by having two spins go in two opposite directions to form the Dipolar Coupling. We approach solving NMR thermal effects correction by melding experimentation and numerical simulation method. We use NMR for Cretaceous carbonate rock multi-phase flow research. We conduct time step in-situ temperature measurement for four different sections of the flooding system at the inlet, center, and outlet along the flooding path. In addition, we conduct a temperature measurement at the NMR device radial axis, representing the permanent magnet temperature. We build a 3D cylindrical heat transfer model for the numerical simulator that simulates thermal effectdistribution on the NMR for optimally generating the correction model. The insight provided by the simulator improved the understanding of the thermal distribution at the natural rock core plug to produce a better thermal correction model that meld experimentation and simulation, a method we call Th-CENS. </p>

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Section: Introductionmentioning

confidence: 99%

Quantum Dipolar Coupling Thermal Correction for NMR Signal during Natural Rock Flooding by Melding Experimentation and Numerical Simulation (Th-CENS)

Alfarisi¹,

Zhang²,

Ouzzane³

et al. 2022

Preprint

View full text Add to dashboard Cite

Researchers have used NMR to measure multi-phase fluid saturation and distribution inside porous media of natural rock. However, the NMR signal amplitude suffers reduction with the increase of temperature. The main reason is the Transverse Overhauser Effect, where heating increases the freedom for ionic motion, affecting spinning behavior by having two spins go in two opposite directions to form the Dipolar Coupling. We approach solving NMR thermal effects correction by melding experimentation and numerical simulation method. We use NMR for Cretaceous carbonate rock multi-phase flow research. We conduct time step in-situ temperature measurement for four different sections of the flooding system at the inlet, center, and outlet along the flooding path. In addition, we conduct a temperature measurement at the NMR device radial axis, representing the permanent magnet temperature. We build a 3D cylindrical heat transfer model for the numerical simulator that simulates thermal effect distribution on the NMR for optimally generating the correction model. The insight provided by the simulator improved the understanding of the thermal distribution at the natural rock core plug to produce a better thermal correction model that meld experimentation and simulation, a method we call Th-CENS.

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Electrical Resistivity and Gamma-Ray Logs: Two Physics for Two Permeability Estimation Approaches in Abu Dhabi Carbonates

Cited by 8 publications

References 0 publications

Deducing Optimal Classification Algorithm for Heterogeneous Fabric

Deducing Optimal Classification Algorithm for Heterogeneous Fabric

Quantum Dipolar Coupling Thermal Correction for NMR Signal during Natural Rock Flooding by Melding Experimentation and Numerical Simulation (Th-CENS)

Quantum Dipolar Coupling Thermal Correction for NMR Signal during Natural Rock Flooding by Melding Experimentation and Numerical Simulation (Th-CENS)

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