Analysis of fractures in the Dalan and Kangan carbonate reservoirs using FMI logs: Sefid-Zakhur gas field in the Fars Province, Iran

Nabiei, M.; Yazdjerdi, Kouros; Soleimany, Bahman; Asadi, Abdolmajid

doi:10.1007/s13146-021-00686-w

Carbonates Evaporites

2021

DOI: 10.1007/s13146-021-00686-w

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Analysis of fractures in the Dalan and Kangan carbonate reservoirs using FMI logs: Sefid-Zakhur gas field in the Fars Province, Iran

M. Nabiei

Kouros Yazdjerdi

Bahman Soleimany³

et al.

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Cited by 4 publications

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“…Reservoir properties of the lateral parts are mainly associated with dolomitized carbonates, which mostly occur in the northwestern and southeastern sides of the deposit. For the Permo-Carboniferous deposit, special well logging methods included massive studies of fracturing using an azimuthal electric Formation Micro Imager (FMI) [35,36]. Fractures in FMI studies are displayed as sinusoids, which amplitude depends on the direction of their occurrence in relation to the well.…”

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Use of machine learning technology to model the distribution of lithotypes in the Permo-Carboniferous oil deposit of the Usinskoye field

Potekhin

Galkin

2023

PMI

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Permo-Carboniferous oil deposit of the Usinskoye field is characterized by an extremely complex type of the void space with intense cross-sectional distribution of cavernous and fractured rock. In this study, for this production site, the process of 3D geological modeling has been implemented. At the first stage, it provided for automated identification of reservoir volumes by comparing the data of core and well logging surveys; at the second stage, identification of rock lithotypes according to Dunham classification is performed on the basis of comparison of thin sections examination and well logging data. A large array of factual information enables the use of machine learning technology on the basis of Levenberg – Marquardt neural network apparatus toward achievement of our research goals. The prediction algorithms of reservoir and rock lithotype identification using well logging methods obtained on the basis of the training samples are applied to the wells without core sampling. The implemented approach enabled complementing the 3D geological model with information about rock permeability and porosity, taking into account the structural features of the identified lithotypes. For the Permo-Carboniferous oil deposit of the Usinskoye field, the volumetric zoning of the distribution of different rock lithotypes has been established. Taking into account the lithotypes identified based on machine learning algorithms, density and openness of fractures were determined, and fracture permeability in the deposit volume was calculated. In general, during the implementation, the machine learning errors remained within 3-5 %, which suggests reliability of the obtained predictive solutions. The results of the research are incorporated in the existing 3D digital geological and process model of the deposit under study.

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confidence: 99%

Use of machine learning technology to model the distribution of lithotypes in the Permo-Carboniferous oil deposit of the Usinskoye field

Potekhin

Galkin

2023

PMI

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Quantifying fracture density within the Asmari reservoir: an integrated analysis of borehole images, cores, and mud loss data to assess fracture-induced effects on oil production in the Southwestern Iranian Region

Taheri,

Alizadeh,

Askari

et al. 2024

Carbonates Evaporites

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Integrated fracture characterization of Asmari reservoir in Haftkel field

Khadivi

Alinaghi

Dehghani

et al. 2022

J Petrol Explor Prod Technol

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The Asmari reservoir in Haftkel field is one of the most prolific naturally fractured reservoirs in the Zagros folded zone in the southwest of Iran. The primary production was commenced in 1928 and continued until 1976 with a plateau rate of 200,000 bbl/day for several years. There was an initial gas cap on the oil column. Gas injection was commenced in June 1976 and so far, 28% of the initial oil in place have been recovered. As far as we concerned, fracture network is a key factor in sustaining oil production; therefore, it needs to be characterized and results be deployed in designing new wells to sustain future production. Multidisciplinary fracture evaluation from well to reservoir scale is a great privilege to improve model’s accuracy as well as enhancing reliability of future development plan in an efficient manner. Fracture identification and modeling usually establish at well scale and translate to reservoir using analytical or numerical algorithms with the limited tie-points between wells. Evaluating fracture network from production data can significantly improve conventional workflow where limited inter-well information is available. By incorporating those evidences, the fracture modeling workflow can be optimized further where lateral and vertical connectivity is a concern. This paper begins with the fracture characterization whereby all available data are evaluated to determine fracture patterns and extension of fracture network across the field. As results, a consistent correlation is obtained between the temperature gradient and productivity of wells, also convection phenomenon is confirmed. The findings of this section help us in better understanding fracture network, hydrodynamic communication and variation of temperature. Fracture modeling is the next step where characteristics of fractures are determined according to the structural geology and stress directions. Also, the fault’s related fractures and density of fractures are determined. Meanwhile, the results of data evaluation are deployed into the fracture model to control distribution and characteristics of fracture network, thereby a better representation is obtained that can be used for evaluating production data and optimizing development plan.

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Analysis of fractures in the Dalan and Kangan carbonate reservoirs using FMI logs: Sefid-Zakhur gas field in the Fars Province, Iran

Cited by 4 publications

References 56 publications

Use of machine learning technology to model the distribution of lithotypes in the Permo-Carboniferous oil deposit of the Usinskoye field

Use of machine learning technology to model the distribution of lithotypes in the Permo-Carboniferous oil deposit of the Usinskoye field

Quantifying fracture density within the Asmari reservoir: an integrated analysis of borehole images, cores, and mud loss data to assess fracture-induced effects on oil production in the Southwestern Iranian Region

Integrated fracture characterization of Asmari reservoir in Haftkel field

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