Electrofacies Estimation of Carbonate Reservoir in the Scotian Offshore Basin, Canada Using the Multi-resolution Graph-Based Clustering (MRGC) to Develop the Rock Property Models

Maurya, S. P.; Singh, Kumar Hemant

doi:10.1007/s13369-022-07521-x

Cited by 10 publications

(18 citation statements)

References 39 publications

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“…This information is available only in a very limited number of cored wells. One geological concern is preparing and explaining the sedimentary model with this limited information 16,52,61,[78][79][80] . This study created an electric facies model utilizing a clustering method and well logs in the cored (or non-cored) wells.…”

Section: Methodsmentioning

confidence: 99%

“…MRGC is one of the few non-parametric and very suitable methods for studying and analyzing data clusters obtained from well logs and drilling cores. This method has several benefits, such as the capability to identify natural patterns in the logs, no need for prior knowledge about the data, an automatic suggestion of the best number of clusters, the lowest parameters and insensitivity to their changes, and no restrictions on the type and number of data and clusters 4,8,46,50,52,72 . To determine the electrofacies with this method, first in the FACIMAGE™ section of the Geolog software, among the petrophysical logs, those logs that were most related to the results target, which include: gamma ray log (CGR), sonic log (DT), density log (RHOB), neutron log (NPHI) and water saturation (SW), is selected.…”

Section: Mgrc Clustering Methodsmentioning

confidence: 99%

“…Kernel functions allow mapping non-linearly separable points into a (generally) higher dimensional feature space so that the inner product in the new space can be computed without needing to compute the exact feature maps bringing further computational benefits. Although most algorithms in the broader field of machine learning have been developed for the linear case, real-world data often requires nonlinear models capable of unveiling the underlying complex relationships towards improving the performance of downstream tasks 50,52,56,84 . To use kernel functions for learning node representations via matrix factorization: Let (X, d X ) be a metric space and H be a Hilbert space of real-valued functions defined on X.…”

Section: Mgrc Clustering Methodsmentioning

confidence: 99%

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Determining the electrical facies utilizing multi-resolution graph-based and artificial neural network clustering methods in an Oilfield, SW Iran

Eftekhari,

Memariani,

Maleki

et al. 2023

Preprint

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Asmari Formation is the most significant reservoir of the Mansouri oil field in SW Iran. This Formation is generally composed of carbonate lithologic but has sandstone layers in some zones. It is also the youngest reservoir rock in Iran. Cross-lithology diagrams determined the lithology of the Asmari Formation by applying 280 core samples from one drilled well in the studied reservoir, and a combination of lime, dolomite, anhydrite, and sandstone with shaley layers was estimated. Also, five zones were identified with different geological/reservoir conditions, with the best reservoir quality in zones 3 and 5 with sandstone/shale lithology. Furthermore, this study determines multi-resolution graph-based clustering (MRGC) and artificial neural network (ANN) facies clustering methods. For this purpose, the combined log readings of DT, RHOB, GR, Sw, NPHI, and PHIE were used. Lithology was evaluated and estimated in each sequence using corrected and edited logs and lithology cross-sections. After applying the Geolog software, an optimal model with 8 clusters (facies) with better separation of rock species was obtained. Finally, five facies with different lithological compositions and reservoir conditions were identified. The information obtained from determining electrical facies by clustering method defines the reservoir zone from non-reservoir and qualitatively (good, medium, and poor).

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Section: Methodsmentioning

confidence: 99%

Section: Mgrc Clustering Methodsmentioning

confidence: 99%

Section: Mgrc Clustering Methodsmentioning

confidence: 99%

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Determining the electrical facies utilizing multi-resolution graph-based and artificial neural network clustering methods in an Oilfield, SW Iran

Eftekhari,

Memariani,

Maleki

et al. 2023

Preprint

View full text Add to dashboard Cite

show abstract

“…Compilation of core porosity and permeability are used to identify these units. Yasmaniar et al (2018) utilized Artificial Neural Network (ANN) to determine the permeability of different Rock Type Using the Hydraulic Flow Unit Concept (Ding et al, 2022;Kharrat et al, 2009;Kianoush et al, 2023c;Mahadasu and Singh, 2022;Masroor et al, 2023;Rafik and Kamel, 2017). Oliveira et al (2020) demonstrated that an inter-clustering process is recommended when selecting data points associated with representative volumes and local spots characterizing HFUs.…”

Section: Introductionmentioning

confidence: 99%

Determining the petrophysical rock types utilizing the Fuzzy C-means Clustering technique and the concept of hydraulic flow units

Eftekhari,

Memariani,

Maleki

et al. 2023

Preprint

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Rock types are the reservoir's most essential properties and show special facies with a defined range of porosity and permeability. This study used the fuzzy c-means clustering technique to identify rock types in 280 core samples from one of the wells drilled in the Asmari reservoir in the Mansouri field, SW Iran. Four hydraulic flow units were determined for studied data after classifying the flow zone index with histogram analysis, normal probability analysis, and the sum of square error methods. Then the two methods of flow zone index and fuzzy c-means clustering were used to determine the rock types in given wells according to the results obtained from the implementation of these two methods in-depth, and continuity index acts, the fuzzy c-means methods with continuity number 3.12 compared to flow zone index with continuity number 2.77 shows more continuity in depth. The relationship between porosity and permeability improved using hydraulic flow unit techniques significantly. In this study, the correlation coefficient between porosity and permeability improves and increases in each hydraulic flow unit using the flow zone index method. So that in the general case, all samples increased from 0.55 to 0.81 in the first hydraulic flow unit and finally 0.94 in the fourth hydraulic flow unit. The samples were characterized by similar flow properties in a hydraulic flow unit. In comparison, the correlation coefficient is obtained less than the general case in the fuzzy c-means method in all hydraulic flow units.

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“…Compilation of core porosity and permeability are used to identify these units. Yasmaniar et al (2018) utilized Artificial Neural Network (ANN) to determine the permeability of different Rock Type Using the Hydraulic Flow Unit Concept (Ding et al, 2022;Kharrat et al, 2009;Kianoush et al, 2023c;Mahadasu & Singh, 2022;Masroor et al, 2023;Rafik & Kamel, 2017). Oliveira et al (2020) demonstrated that an inter-clustering process is recommended when selecting data points associated with representative volumes and local spots characterizing HFUs.…”

Section: Introductionmentioning

confidence: 99%

Determining the petrophysical rock types utilizing the Fuzzy C-means Clustering technique and the concept of hydraulic flow units

Eftekhari,

Memariani,

Maleki

et al. 2023

Preprint

View full text Add to dashboard Cite

Rock types are the reservoir's most essential properties and show special facies with a defined range of porosity and permeability. This study used the fuzzy c-means clustering technique to identify rock types in 280 core samples obtained from one of the wells drilled in the Asmari reservoir located in the Mansouri field. Four hydraulic flow units were determined for studied data after classifying the flow zone index with histogram analysis, normal probability analysis, and the sum of square error methods. Then the two methods of flow zone index and fuzzy c-means clustering were used to determine the rock types in given wells according to the results obtained from the implementation of these two methods in-depth, and continuity index acts, the fuzzy c-means methods with continuity number 3.12 compared to flow zone index with continuity number 2.77 shows more continuity in depth. Amounts of porosity and permeability of the different reservoir rock samples have high dispersion; the relationship between these two parameters improves by using hydraulic flow unit techniques significantly. In this study, the relationship between porosity and permeability of correlation coefficient improves and increases in each hydraulic flow unit by using the flow zone index method so that in the general case for all samples increased from 0.55 to 0.81 in the first hydraulic flow unit, 0.94 in the second hydraulic flow unit, 0.85 in the third hydraulic flow unit and 0.94 in the fourth hydraulic flow unit that this is because the samples were characterized by similar flow properties in a hydraulic flow unit. In comparison, the correlation coefficient is obtained less than the general case in the fuzzy c-means method in all hydraulic flow units.

show abstract

Electrofacies Estimation of Carbonate Reservoir in the Scotian Offshore Basin, Canada Using the Multi-resolution Graph-Based Clustering (MRGC) to Develop the Rock Property Models

Cited by 10 publications

References 39 publications

Determining the electrical facies utilizing multi-resolution graph-based and artificial neural network clustering methods in an Oilfield, SW Iran

Determining the electrical facies utilizing multi-resolution graph-based and artificial neural network clustering methods in an Oilfield, SW Iran

Determining the petrophysical rock types utilizing the Fuzzy C-means Clustering technique and the concept of hydraulic flow units

Determining the petrophysical rock types utilizing the Fuzzy C-means Clustering technique and the concept of hydraulic flow units

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