Permeability variation modeling and reservoir heterogeneity of Bangestan carbonate sequence, Mansouri oilfield, SW Iran

Soleimani, Bahman; Moradi, Mostafa; Ghabeishavi, Ali; Mousavi, Arash

doi:10.1007/s13146-018-0461-y

Cited by 9 publications

(8 citation statements)

References 33 publications

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“…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%

“…In the bottom of the last zone, a gradational variation from the Kazhdumi shale to Sarvak limestone indicates a regressive environmental condition. The porosity data variation indicated that the reservoir quality would improve from the base to the top 2,5,71,72,74,75 . Fig.…”

Section: General Stratigraphymentioning

confidence: 99%

“…3 for one of the drilled wells. The Bangestan reservoir was subdivided into nine zones in the studied field based on petrography, petrophysical parameters, and well-logging data 3,14,40,[71][72][73] . In Ilam Formation, the dominant lithology is White wackestone-mudstone, Brown packstone-wackestone, and White to brown limestone and argillaceous in some layers .…”

Section: General Stratigraphymentioning

confidence: 99%

“…Fig. 3 Stratigraphic column associated with reservoir zonation and selected petrographic thin sections 72 .…”

Section: General Stratigraphymentioning

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: Mgrc Clustering Methodsmentioning

confidence: 99%

Section: General Stratigraphymentioning

confidence: 99%

Section: General Stratigraphymentioning

confidence: 99%

“…Fig. 3 Stratigraphic column associated with reservoir zonation and selected petrographic thin sections 72 .…”

Section: General Stratigraphymentioning

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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“…2 for one of the drilled wells. The Bangestan reservoir was subdivided into nine zones in the studied field based on petrography, petrophysical parameters, and well-logging data 10,12,20,54 . The Asmari Formation geologically consists of sandstone, dolomite, limestone and low to average clay minerals.…”

Section: 2general Stratigraphymentioning

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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3D simulation of a giant oilfield in calcareous formations and scrutiny study of the interaction of the calculated parameters (Asmari formation in Maroon oilfield, Iran)

2021

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Permeability variation modeling and reservoir heterogeneity of Bangestan carbonate sequence, Mansouri oilfield, SW Iran

Cited by 9 publications

References 33 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

3D simulation of a giant oilfield in calcareous formations and scrutiny study of the interaction of the calculated parameters (Asmari formation in Maroon oilfield, Iran)

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