Advanced Applied Geology

Advanced Applied Geology

Assessment of geological heterogeneities and their impact on flow performance of the Fahliyan reservoir using FMI image logs: A case study from southwestern Iran

Authors
1 Geology Department, Faculty of Earth Sciences, Shahid Chamran University of Ahvaz, Ahvaz, Iran.
2 Department of Earth Sciences, College of Science, Shiraz University, Shiraz, Iran.
Abstract
The development of hydrocarbon fields necessitates a detailed understanding of geological features influencing reservoir heterogeneity and production rates. In this study, the application of FMI (Formation Micro Imager) logs, integrated with conventional petrophysical data, was investigated to identify and analyze geological features within the Fahliyan reservoir in a southwestern Iranian oil field. Raw FMI data were processed through speed correction and normalization to generate static and dynamic images. The results revealed 241 bedding boundaries with a dominant dip of 5° to 25° toward the northeast. Additionally, 83 conductive solution seams and 51 dense streaks were identified, exhibiting trends consistent with the bedding planes. Fracture analysis led to the identification of 173 open fractures and a single closed fracture, primarily concentrated in the Upper Fahliyan member. Based on their geometric relationship with bedding, open fractures were classified into longitudinal and oblique sets. The findings indicate that open fractures significantly enhance secondary permeability and production performance in the upper reservoir section. The results of this study are instrumental for accurate heterogeneity modeling and optimizing future drilling locations in similar carbonate reservoirs.
Keywords
Subjects

 
Aghanabati, S.A., 2004. Geology of Iran. Geological Survey and Mining Exploration of Iran, Tehran, p.586.
Aghli, G., Moussavi-Harami, R., Mohammadian, R., 2020. Reservoir heterogeneity and fracture parameter determination using electrical image logs and petrophysical data (a case study, carbonate Asmari Formation, Zagros Basin, SW Iran). Petroleum Science 17, 51–69. https://doi.org/10.1007/s12182-019-00413-0
Aghli, G., Motra, H.B., Saberi, M.R., Nikbin, M., Ostadhassan, M., 2025. Fracture Development and Characterization from Field Data in a Dolomite-Limestone Sequence: G. Aghli et al. Rock Mechanics and Rock Engineering 11, 1–25. https://doi.org/10.1007/s00603-025-05125-z
Ahr, W.M., 2011. Geology of carbonate reservoirs: the identification, description and characterization of hydrocarbon reservoirs in carbonate rocks, John Wiley, p. 296.
Asgarinezhad, Y., Asgarinezhad, M., Tokhmechi, B., Kamkar-Rouhani, A., Sherkati, S., 2016. Detecting vuggy porosity in carbonate reservoirs by well logs. Journal of Petroleum Exploration and Production Technology 6, 25–31. https://doi.org/10.1007/s13202-015-0169-7
Babadagli, T., Al-Salmi, S., 2004. A review of permeability-prediction methods for carbonate reservoirs using well-log data. SPE Reservoir Evaluation & Engineering 7, 75–88. https://doi.org/10.2118/87824-PA
Burgess, C.J., Peter, C.K., 1985. Formation, distribution, and prediction of stylolites as permeability barriers in the Thamama Group, Abu Dhabi, in: Middle East Oil Technical Conference and Exhibition. OnePetro. https://doi.org/10.2118/13698-MS
Ezati, M., Azizzadeh, M., Riahi, M.A., Fattahpour, V., Honarmand, J., 2018. Characterization of micro-fractures in carbonate Sarvak reservoir, using petrophysical and geological data, SW Iran. Journal of Petroleum Science and Engineering 170, 675–695. https://doi.org/10.1016/j.petrol.2018.06.058
Farshi, M., Moussavi-Harami, R., Mahboubi, A., Khanehbad, M., Golafshani, T., 2019. Reservoir rock typing using integrating geological and petrophysical properties for the Asmari Formation in the Gachsaran oil field, Zagros basin. Journal of Petroleum Science and Engineering 176, 161–171. https://doi.org/10.1016/j.petrol.2018.12.068
Jamshidian, M., Hadian, M., Zadeh, M.M., Kazempoor, Z., Bazargan, P., Salehi, H., 2015. Prediction of free flowing porosity and permeability based on conventional well logging data using artificial neural networks optimized by imperialist competitive algorithm–a case study in the South Pars Gas field. Journal of Natural Gas Science and Engineering 24, 89–98. https://doi.org/10.1016/j.jngse.2015.02.026
Kadkhodaie‐Ilkhchi, A., Amini, A., 2009. A fuzzy logic approach to estimating hydraulic flow units from well log data: A case study from the Ahwaz oilfield, South Iran. Journal of Petroleum Geology 32, 67–78.  https://doi.org/10.1111/j.1747-5457.2009.00435.x
Khoshbakht, F., Memarian, H., Mohammadnia, M., 2009. Comparison of Asmari, Pabdeh and Gurpi formation’s fractures, derived from image log. Journal of Petroleum science and Engineering 67, 65–74. https://doi.org/10.1016/j.petrol.2009.02.011
Khoshbakht, F., Rasaie, M.R., Shekarifard, A., 2016. Investigating induction log response in the presence of natural fractures. Journal of Petroleum Science and Engineering 145, 357–369. https://doi.org/10.1016/j.petrol.2016.05.023
Lee, H., Lee, S.G., Doyle, P.S., 2015. Photopatterned oil-reservoir micromodels with tailored wetting properties. Lab on a Chip 15, 3047–3055. https://doi.org/10.1039/C5LC00277J
Liu, Yisheng, Liu, Yuetian, Zhang, Q., Li, C., Feng, Y., Wang, Y., Xue, Y., Ma, H., 2019. Petrophysical static rock typing for carbonate reservoirs based on mercury injection capillary pressure curves using principal component analysis. Journal of Petroleum Science and Engineering 181, 106175. https://doi.org/10.1016/j.petrol.2019.06.039
Mirzaei-Paiaman, A., Ostadhassan, M., Rezaee, R., Saboorian-Jooybari, H., Chen, Z., 2018. A new approach in petrophysical rock typing. Journal of Petroleum Science and Engineering 166, 445–464. https://doi.org/10.1016/j.petrol.2018.03.075
Mohsenipour, A., Soleimani, B., Zahmatkesh, I., Veisi, I., 2022a. Determination of reservoir rock typing using integrating geological and petrophysical methods in one of the oil field in south-west of Iran. Carbonates and Evaporites 37, 1–15. https://doi.org/10.1007/s13146-022-00782-5
Mohsenipour, A., Soleimani, B., Zahmatkesh, I., Veisi, I., 2021. Estimation of nuclear magnetic resonance (NMR) log permeability by integrating artificial neural network and imperialist competition optimization algorithm (ICA) in one of the oil fields of southwestern Iran. Arabian Journal of Geosciences 14, 1–18. https://doi.org/10.1007/s12517-021-08821-6
Mohsenipour, A., Zahmatkesh, I., Soleimani, B., 2022b. Fractures Analysis Using Core Data and Image Logs: A Case Study in the Dalan–Kangan Reservoir of South Pars Gas Field, Iran. Iran. Iranian Journal of Science and Technology, Transactions A: Science 46, 819–828. https://doi.org/10.1007/s40995-022-01288-4
Motiei, H., 1993. Stratigraphy of Zagros. Geological Survey of Iran Publication, Tehran, p.536.
Oden, C.P., LoCoco, J.J., 2000. Variable frequency monopole-dipole sonic logging for mechanical and hydrogeologic properties, in: Symposium on the Application of Geophysics to Engineering and Environmental Problems 2000.  13th EEGS Symposium on the Application of Geophysics to Engineering and Environmental Problems, pp, 493–502. https://doi.org/10.3997/2214-4609-pdb.200.2000_058
Ostad, M.N., Asghari, O., Emery, X., Azizzadeh, M., Khoshbakht, F., 2016. Fracture network modeling using petrophysical data, an approach based on geostatistical concepts. Journal of Natural Gas Science and Engineering 31, 758–768. https://doi.org/10.1016/j.jngse.2016.03.084
Payton, E., Khubchandani, J., Thompson, A., Price, J.H., 2017. Parents’ expectations of high schools in firearm violence prevention. Journal of community health 42, 1118–1126. https://doi.org/10.1007/s10900-017-0360-5
Rafik, B., Kamel, B., 2017. Prediction of permeability and porosity from well log data using the nonparametric regression with multivariate analysis and neural network, Hassi R’Mel Field, Algeria. Egyptian journal of petroleum 26, 763–778. https://doi.org/10.1016/j.ejpe.2016.10.013
Rajabi, M., Sherkati, S., Bohloli, B., Tingay, M., 2010. Subsurface fracture analysis and determination of in-situ stress direction using FMI logs: An example from the Santonian carbonates (Ilam Formation) in the Abadan Plain, Iran. Tectonophysics 492, 192–200. https://doi.org/10.1016/j.tecto.2010.06.014
Sadeghi, R., Moussavi-Harami, R., kadkhodaie, A., Mahboubi, A., Ashtari, A., 2021. Reservoir rock typing of the Asmari Formation using integrating geological and petrophysical data for unraveling the reservoir heterogeneity: a case study from the Ramshir oilfield, southwest Iran. Carbonates and Evaporites 36, 1–28. https://doi.org/10.1007/S13146-021-00692-Y
Schlumberger, 2002. FMI-Borehole Geology, Geomechanics and 3D Reservoir Modeling. Current, p.12.
Sherkati, S., Letouzey, J., 2004. Variation of structural style and basin evolution in the central Zagros (Izeh zone and Dezful Embayment), Iran. Marine and petroleum geology 21, 535–554. https://doi.org/10.1016/j.marpetgeo.2004.01.007
Singh, N.P., 2019. Permeability prediction from wireline logging and core data: a case study from Assam-Arakan basin. Journal of Petroleum Exploration and Production Technology 9, 297–305. https://doi.org/10.1007/s13202-018-0459-y
Tadayoni, M., Khalilbeyg, M., Bin Junin, R., 2020. A new approach to heterogeneity analysis in a highly complex carbonate reservoir by using borehole image and conventional log data. Journal of Petroleum Exploration and Production Technology 10, 2613–2629. https://doi.org/10.1007/s13202-020-00930-4
Tayeb, S.H., 2012. No TGeological supplementary report of Azadegan exploratory well-4. Exploration management of National Iranian Oil Company.
Zahmatkesh, I., Soleimani, B., Kadkhodaie, A., Golalzadeh, A., Abdollahi, A.-M., 2017. Estimation of DSI log parameters from conventional well log data using a hybrid particle swarm optimization–adaptive neuro-fuzzy inference system. Journal of Petroleum Science and Engineering 157, 842–859. https://doi.org/10.1016/j.petrol.2017.08.002
Zhang, Z., Zhang, H., Li, J., Cai, Z., 2021. Permeability and porosity prediction using logging data in a heterogeneous dolomite reservoir: An integrated approach. Journal of Natural Gas Science and Engineering 86, 103743. https://doi.org/10.1016/J.JNGSE.2020.103743