Facies analysis, depositional sequences and platform evolution of the Sarvak Formation (late Albian-Turonian) in the Zagros Basin, West of Iran

Moosa, Esfandyari; Mohseni, Hassan; Heidari, Mojtaba

doi:10.1016/j.jafrearsci.2022.104811

Cited by 11 publications

(9 citation statements)

References 75 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%

“…Therefore, identifying different rock facies of reservoir rock is a fundamental task in describing the characteristics of oil reservoirs. Detection of lithofacies in the repository is complex because the type and distribution of the facies are determined by the deposition system and are affected or altered by diagenesis and tectonics [15][16][17][18][19] . The most common method for determining lithofacies is the use of coring.…”

Section: Introductionmentioning

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: Introductionmentioning

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

View full text Add to dashboard Cite

show abstract

“…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 5,[13][14][15]49 . This study created an electric facies model utilizing a clustering method and well logs in a drilled well with coring in the Asmari Formation.…”

Section: 1estimating the Number Of Distinct Electrofaciesmentioning

confidence: 99%

“…Since these facies are determined only by pure mathematical processes in the cluster analysis method and no training or fitting function is considered, they are very accurate, and their application is not limited to a particular well or field. After identifying the log facies by assigning geological features to them, it can be applied to other wells on the surface of adjacent fields to match and predict the desired features 2,7,15,32,33 . An essential advantage of electrofacies over alternative types of facies classifications of rocks in the subsurface is that electrofacies can be defined solely based on well-log responses without reliance on cores, cuttings, or outcrops.…”

Section: Introductionmentioning

confidence: 99%

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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“…For geological modeling, lithofacies describe rock or sedimentary units with texture, structure, mineralogy, and rock properties, and these units can be employed to match important reservoir characteristics such as permeability and porosity ( Therefore, identifying different rock facies of reservoir rock is a fundamental task in describing the characteristics of oil reservoirs. Detection of lithofacies in the repository is complex because the type and distribution of the facies are determined by the deposition system and are affected or altered by diagenesis and tectonics (Esfandyari et al 2023; Mathis et al 2003;Mirkamali et al 2016;Shakiba et al 2015; Shoghi et al 2020). The most common method for determining lithofacies is the use of coring.…”

Section: Introductionmentioning

confidence: 99%

Determination of electrical facies using MRGC and ANN clustering methods

Eftekhari,

Memariani,

Maleki

et al. 2023

Preprint

View full text Add to dashboard Cite

The present research was performed on Iran's most crucial reservoir formation (Asmari Formation) in the Mansouri oil field in southwest Iran. This Formation is generally composed of carbonate lithologic, but it has sandstone layers in some zones. It is also the youngest reservoir rock in Iran. In the first step, the lithology of the Asmari Formation was determined by cross-lithology diagrams, and a combination of lime, dolomite, anhydrite, and sandstone with shaley layers was estimated. Also, in the sequence of this geological/reservoir zoning formation, five zones were identified with different geological/reservoir conditions, and the average of each of these petrophysical parameters was calculated in each zone, with the best reservoir quality in zones 3 and 5 with sandstone/shale lithology. Also, this study determines facies clustering methods MRGC and ANN and reading of gamma, neutron, density, acoustic and resistivity diagrams, facies having common geological/reservoir conditions in different subdivisions. For this purpose, the combined log readings of DT, RHOB, GR, Sw, NPHI, and PHIE were used. After applying the software (Geolog) method, an optimal model with 8 clusters (facies) was obtained. The reservoir features and plots in the software for better separation of rock species studied. Finally, five facies with different lithological compositions and reservoir conditions were identified in this study. The information obtained from determining electrical facies by clustering method determines the reservoir zone from non-reservoir and qualitatively (good, medium, and poor) of Mansouri oilfield were achieved.

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Facies analysis, depositional sequences and platform evolution of the Sarvak Formation (late Albian-Turonian) in the Zagros Basin, West of Iran

Cited by 11 publications

References 75 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

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

Determination of electrical facies using MRGC and ANN clustering methods

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