Stratigraphic influences on reservoir heterogeneities of the Mid-Cretaceous carbonates in southwest Iran: Insight from an integrated stratigraphic, diagenetic and seismic attribute study

Sabouhi, Mostafa; Moussavi‐Harami, Reza; Kadkhodaie, Ali; Rezaee, Payman; Jalali, Mahmoud; Wood, David A.

doi:10.1016/j.jseaes.2022.105514

Cited by 17 publications

(8 citation statements)

References 54 publications

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“…It has a variable width of up to 6 kilometers in the middle of the field and an average of 4.5 kilometers, which decreases to the east and west slopes. The dimensions of the reservoir at the contact surface of water and oil (2272 m below sea level) are 30 km long and 3.5 km wide, stretching northwest-southeast (Aleali et al, 2013;Hosseini et al, 2023a;Kianoush et al, 2023b;Kianoush et al, 2023a;Michael and Gupta, 2022;NISCO, 2022;Sabouhi et al, 2023). In addition to the Asmari reservoir and sandstone section of Ahwaz, the Bangestan reservoir (Ilam and Sarvak Formations) are also present in this field (AbdollahieFard et al, 2019;Motiei, 1995;Talaie et al, 2023;Tavakkoli and Amini, 2006).…”

Section: Geological Settingmentioning

confidence: 95%

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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Section: Geological Settingmentioning

confidence: 95%

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

show abstract

“…It has a variable width of up to 6 kilometers in the middle of the field and an average of 4.5 kilometers, which decreases to the east and west slopes. The dimensions of the reservoir at the contact surface of water and oil (2272 m below sea level) are 30 km long and 3.5 km wide, stretching northwest-southeast 2,9,23,[64][65][66][67][68][69][70][71] . In addition to the Asmari reservoir and sandstone section of Ahwaz, the Bangestan reservoir (Ilam and Sarvak Formations) are also present in this field 39,62,65,66 .…”

Section: Geological Setting 21 Structural Geologymentioning

confidence: 99%

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: Geological Setting 21structural Geologymentioning

confidence: 99%

“…The broad meaning of "facies" is the characteristics, appearance, and aspects of a rock unit, usually imaging the circumstances of its origin, particularly determining the unit from neighboring or associated units [1][2][3][4] . Electrofacies are numerical mixtures of petrophysical log reactions that reflect a rock interval's compositional characteristics and specific physicals.…”

Section: Introductionmentioning

confidence: 99%

“…The easy determination and application of this zoning and its reliance on log data make it possible to quickly and accurately distinguish reservoir segments from non-reservoir segments. With overgrowing the world, the cluster analysis method is utilized as the principal direction analysis of the log facies identification 4,7,[29][30][31] . It is employed as an essential tool for logging in advanced petroleum software.…”

Section: Introductionmentioning

confidence: 99%

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Electrical facies of the Asmari Formation in the Mansouri oilfield, an application of multi-resolution graph-based and artificial neural network clustering methods

Eftekhari,

Memariani,

Maleki

et al. 2023

Preprint

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Electrofacies analysis conducted the distribution effects throughout the reservoir despite the difficulty of characterizing stratigraphic relationships. Clustering methods quantitatively define the reservoir zone from non-reservoir considering electrofacies. Asmari Formation is the most significant reservoir of the Mansouri oilfield in SW Iran, generally composed of carbonate and sandstone layers. The stratigraphical study is determined by employing 250 core samples from one exploratory well in the studied field. Five zones with the best reservoir quality in zones 3 and 5 containing sandstone/shale are determined. Moreover, multi-resolution graph-based and artificial neural network clustering involving six logs are employed. Utilizing Geolog software, an optimal model with eight clusters with better rock separation is obtained. Eventually, five electrofacies with different lithological compositions and reservoir conditions are identified and based on lithofacies describing thin sections, sandstone, and shale in zones 3 and 5 show high reservoir quality. According to the depth related to these zones, most of the facies that exist in these depths include sandstone and dolomite facies, and this is affected by the two factors of the primary sedimentary texture and the effect of the diagenesis process on them. Results can compared to the clustering zone determination in other nearby sandstone reservoirs without cores.

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Stratigraphic influences on reservoir heterogeneities of the Mid-Cretaceous carbonates in southwest Iran: Insight from an integrated stratigraphic, diagenetic and seismic attribute study

Cited by 17 publications

References 54 publications

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

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

Electrical facies of the Asmari Formation in the Mansouri oilfield, an application of multi-resolution graph-based and artificial neural network clustering methods

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