Introducing an integrated strategy in fault modelling with multi-attributes in 3D seismic data in a field from Persian Gulf

Document Type : Research Article


1 M.Sc. Graduated, Department of Petroleum Engineering and Geophysics, Faculty of Mining, Petroleum and Geophysics Engineering, Shahrood University of Technology, Shahrood, Iran

2 Assistant Professor, Department of Petroleum Engineering and Geophysics, Faculty of Mining, Petroleum and Geophysics Engineering, Shahrood University of Technology, Shahrood, Iran

3 Associate Professor, Department of Petroleum Engineering and Geophysics, Faculty of Mining, Petroleum and Geophysics Engineering, Shahrood University of Technology, Shahrood, Iran

4 M.Sc., Iranian Offshore Oil Company, Tehran, Iran


Fault and fracture modelling is an important step in reservoir engineering which is required for any reservoir characterization and production management. There are various types of methods and strategies for building such models, however, each has its own advantages and drawbacks. The most important issue that should be considered is the ability to model both large- and small-scale faults, simultaneously. It is important, as large faults define geological frameworks of the reservoir, while small faults influence fluid movement in the reservoir. In this study, we introduce an integrated strategy for modelling small- and large-scale faults by seismic data, using multi-attributes. Large faults are defined by hand picking from seismic data using attributes, and small faults are modelled by an automatic ant tracking algorithm. Then, two separated models are integrated to build a unique, but multi-scale fault model. Result of each step of modelling is evaluated by well data. The methodology is applied on a hydrocarbon reservoir from the Persian Gulf. Results show that the multi scale fault model is accurate when evaluated by well data.
Integrated modelling of faults of fractures to obtain a unique multi-scale model is an interesting topic in reservoir engineering. Normally fractured reservoirs are divided into several production zones based on division made by large faults, while fluid movement in each zone is controlled by small fracturs and faults. Thus, obtaining a unique model which contain information of faults in several scale is under investigation. However, conventional methods use separate sources of information for modelling faults in various scales. Large scale faults are normally modelled by seismic data while well data are used for modelling small faults. Ozkaya (2019) stated that modelling of faults both with seismic and well data would reduce uncertainty in reservoir fracture modelling. Cao et al. (2019) introduced an integrated strategy for modeling faults with two scales in 2D seismic data, but using seismic and well data. Kurison et al. (2019) have modelled faults and fractures in reservoir with 3D seismic data and well data, but in separate manners. But their final interpretation has shown that using both types of model would result in better reservoir modelling. Xu et al. (2019) introduced an integrated strategy for modelling faults and fractures in two scales simultaneously using seismic and well data. In this study, we introduce an integrated strategy for multi-scale fault modelling using only seismic data, which could be used in reservoirs which lack of well data.
The proposed strategy introduced here, initiates with a geological model building. Subsequently, large faults can be defined on seismic data and related attributes. Simultaneously, small scale faults can be modelled by an ant tracking algorithm in an automatic manner, then it would be refined by interpreter to remove other lineaments than fault that was modelled by the algorithm. Each model then would be evaluated by well data and in case of any error in the model, they would be removed by more ant tracking parameter optimizations and also deeper investigation by the interpreter. In the final step, both fault model would be integrated to build a unique informative multi-scale fault model which contains information of all faults in various sizes. Other characteristics of faults in the integrated model would be investigated for further analysis.
Large scale fault model showed major faults with northwest-southeast trending acting in the center of the reservoir, which has a dome shaped structure, and some minor faults with various trending around the major one. Through this modeling curvature, chaos and variance attributes were used for better fault detection. Small faults obtained by ant tracking distributed around the center of the field. Ant tracking algorithm parameter were optimized through sensitivity analysis prior to application. Afterwards, fault model was refined to remove non-fault lineament. Both models were evaluated by a fullbore formation microimager (FMI) log which proved fractures and faults that were obtained by seismic data. One fault that was detected by the proposed strategy were also captured by well. Then both fault models were integrated to a unique model and faults were modeled by deterministic method.
The integrated fault model obtained by the proposed strategy revealed the importance of a multi-scale fault model in reservoir engineering. Large faults of the study reservoir showed different zones of fractures in the formation reservoir, while small faults in the same model built a discrete network of fractures which provides canals for fluid movement. The integrated model shows that large faults in the study field are mostly in the center of the reservoir, while small faults are distributed through the edges of the formation reservoir, which could be used for further investigation of locating for production and/or injection wells.


Main Subjects

وارسته، ع.، سیاه کوهی، ح.، خامه‌چی، ا. و نوروزی، س.، 1391، کاربرد نشانگرهای لرزه‌ای همدوسی در توصیف گسل‌ها و شکستگی‌های مخزن، مجله پژوهش نفت، 69، 64-72.
Abul Khair, H., Cooke, D., Backé, G., King, R., Hand, M., Tingay, M. and Holford, S., 2012, Subsurface mapping of natural fracture networks; A major challenge to be solved. Case study from the shale intervals in the copper basin, south Australia, SGP, TR 194.
Avseth, P., Mukerji, T. and Mavko, G., 2010, Quantitative seismic interpretation: applying rock physics tools to reduce interpretation risk, Cambridge University Press, ISBN 0-521-15135-X.
Brown, A., 2001, Understanding seismic attributes, Geophysics, 66, 47-49. 1.1444919
Cao, R., Fang, S., Jia, P., Cheng, L., and Rao, X., 2019, An efficient embedded discrete-fracture model for 2D anisotropic reservoir simulation. Journal of Petroleum Science and Engineering, 174, 115-130.
Chen, L., Xiao, C., Li, X., Wang, Z., and Huo, S., 2018, A seismic fault recognition method based on ant colony optimization. Journal of Applied Geophysics, 152, Pages 1-8, j.jappgeo.2018.02.009
Chopra, S. and Marfurt, K.J., 2005, Seismic attributes A historical perspective, Geophysics, 70 (5), 3-28.
Chopra, S. and Marfurt, K.J., 2007, Volumetric curvature attributes add value to 3D seismic data interpretation, Proceedings, The Leading Edge, 26(7), 856-867.
Dorigo, M., and Stützle, T., 2004, Ant colony optimization, MIT Press, ISBN: 9780262042192
Hale, D., 2013, Methods to compute fault
images, extract fault surfaces, and
estimate fault throws from 3D seismic images, Geophysics, 78 (2), 33-43.
Hashemi Shahdani, H., and Javaherian, A., 2009, Seismic attribute redundancy reduction using statistical feature extraction technique, 1st EAGE International Petroleum Conference and Exhibition, Session: Seismic Interpretation-Attribute Analysis, Shiraz,
Hashemi Shahdani, H., Hadiloo, S., Mirzaee, S., and Beiranvand, B., 2017, SeisART software: seismic facies analysis by contributing interpreter and computer. Arabian Journal of Geosciences, 10 (23), 519.
Hu, J.L., Kang, Z.H., and Yuan, L.L., 2014, Automatic fracture identification using ant tracking in Tahe oilfield, Advanced Materials Research, 962, 556-559, AMR.962-965.556
Hunt, L., Reynolds, S., Broen, T., And Hadley, S., 2010, Quantitative estimate of fracture density variations in the Nordegg with azimuthal AVO and curvature: A case study, The Leading Edge, 1122-1137.
Jansen, K., 2005, Seismic investigation of wrench faulting and fracturing at Rulison field Master’s Thesis, Colorado School of Mines.
Konyuhov, A. I., Maleki, B., 2006, The Persian Gulf Basin: Geological history, sedimentary formations, and petroleum potential. Lithology and Mineral Resources. 41(4), 344–361 S0024490206040055
Kurison, C., Kuleli, H. S., Mubarak, A., Al-Sultan, A., and Shehri, S. J., 2019. Reducing uncertainty in unconventional reservoir hydraulic fracture modeling: A case study in Saudi Arabia. Journal of Natural Gas Science and Engineering, 71, 102948,
Li, J., Mitra, S., and Qi, J., 2020, Seismic analysis of polygonal fault systems in the Great South Basin, New Zealand. Marine and Petroleum Geology, 111, 638-649, .052
Mahdavi Basir, H., Javaherian, A. and Tavakoli, M., 2013, Multi-attribute ant-tracking and neural network for fault detection: a case study of an Iranian oilfield, Journal of Geophysics and Engineering. 10, .
Marfurt, k., 2007, Seismic Attributes for Prospect Identification and Reservoir Characterization, vol.1 EAGE, 45-71
Maerten., Legrand, X., Castagnac, C., Lefranc, M., Joonnekindt, J. P., and Maerten, F., 2019. Fault-related fracture modeling in the complex tectonic environment of the Malay Basin, offshore Malaysia: An integrated 4D geomechanical approach. Marine and Petroleum Geology, 105, 222-237, 
Negri, A. P., Tamunobereton-ari, I., and Amakiri, A. R. C., 2015, Ant-tracker attributes: an effective approach to enhancing fault identification and interpretation, IOSR Journal, 5, 67-73.
Noori, M., Hassani, H., Javaherian, A., Amindavar, H., and Torabie, S., 2019, Automatic fault detection in seismic data using Gaussian process regression, Journal of Applied Geophysics, 163, 117-131,
Odoh, B.I., Ilechukwa, J.N. and Okoli, N.I., 2014, The use of seismic attributes to enhance fault interpretation of OT field, Niger delta, International Journal of Geosciences, 5, 826-834.
Özkaya, S. I., 2019, Fracture modeling from borehole image logs and water invasion in carbonate reservoirs with layer-bound fractures and fracture corridors. Journal of Petroleum Science and Engineering, 179, 199-209.
Pedersen, S. I., Randen, T., Sonnelan, L., and Steen, O., 2002, Automatic fault extraction using artificial ants, SEG Int'l Exposition and 72nd Annual Meeting, Salt Lake City,
Pereira, L.A.G.R., 2009, Seismic attributes in hydrocarbon reservoirs characterization: Master Thesis, The Department of Geosciences of the University of Aveiro, Portugal
Ren, J., and Guo, P., 2019. A novel semi-analytical model for finite-conductivity multiple fractured horizontal wells in shale gas reservoirs. Journal of Natural Gas Science and Engineering, 24, 35-51.
Roberts, A., 2001, Curvature attributes and their application to 3D interpreted horizons, First Break, 19, 85-100.
Souche, L., Astratti, D., Aarre, V., Clerc, N., Clark, A., Al Dayyni, T. N. A. and Mahmoud, S. L., 2012, A dual representation of multiscale fracture network modelling: application to a giant UAE carbonate field, First Break 30, 43-52.
Xu, S., Feng, O., Li, Y., and Wang, S., 2019, An integrated workflow for fracture propagation and reservoir simulation in tight oil. Journal of Petroleum Science and Engineering, 179, 1159-1172, 2019.05.007.
Yan, Z., Gu, H., and Cai, C., 2013, Automatic fault tracking based on ant colony algorithms, Computers and Geosciences, 51, 269-281,
Yao, X., Chen, W. Hu, G., Zou, W., and Li, Z., 2014, A fault surface extraction and reconstruction method based on 3D seismic image, SEG Denver Annual Meeting, 1543–1547.