Abstract
Nuclear magnetic resonance (NMR) log is a powerful tool for exploration and development of oil and gas fields since it can be applied to evaluate reservoir and nonreservoir horizons. Total porosity and free fluid porosity are two valuable outputs of NMR log which are accessible through processing of raw logs. In the present study, an attempt has been made to create a quantitative correlation between output parameters of NMR log and seismic attributes using linear regression method and artificial intelligent systems. An integration of 3D seismic data and well log data has been done to predict parameters of NMR log from a new source of data which is spread in the entire field and accessible in the primary stages of field development. For this purpose, the best seismic attributes were selected after extraction of acoustic impedance and sample- based attributes of 3D seismic data using stepwise linear regression method. Multivariate linear regression equations and correlation coefficients with target logs were determined. Finally, three different artificial intelligence systems including probabilistic neural network (PNN), multilayer feed-forward network (MLFN) and radial basis function network (RBFN) were designed and optimized. Results of correlation coefficients between real and predicted logs and also prediction error in the blind test showed that PNN performed better than MLFN and RBFN. At the last step, PNN was used to reconstruct the 3D model of NMR total porosity and free fluid porosity in the reservoir zone of the studied carbonate gas field in the south of Iran.
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References
Mohaghegh S., Richardson M., Ameri S.: Use of intelligent systems in reservoir characterization via synthetic magnetic resonance logs. J. Pet. Sci. Eng. 29, 189–204 (2001)
Labani M.M., Kadkhodaie-Ilkhchi A., Salahshoor K.: Estimation of NMR log parameters from conventional well log data using a committee machine with intelligent systems: a case study from the Iranian part of the South Pars gas field, Persian Gulf Basin. J. Pet. Sci. Eng. 72, 175–185 (2010)
Golsanami N., Kadkhodaie-Ilkhchi A., Sharghi Y., Zeinali M.: Estimating NMR T 2 distribution data from well log data with the use of a committee machine approach: a case study from the Asmari formation in the Zagros Basin, Iran. J. Pet. Sci. Eng. 114, 38–51 (2014)
Pan, J.G.-S.: Integrated 3D seismic inversion and volume visualization for reservoir characterization and reserve estimation. In: 2000 SEG Annual Meeting, Society of Exploration Geophysicists, (2000)
Özdemir, H.; Jensen, L.; Strudley, A.: Porosity and lithology mapping from seismic data. In: 54th EAEG Meeting (1992)
Cooke, D.; Sena, A.; O’Donnell, G.; Muryanto, T.; Ball, V.; Alaska, A.: What is the best seismic attribute for quantitative seismic reservoir characterization. In: Annual Meeting Abstracts, Society of Exploration Geophysicists, pp. 1588–1591. SEG, Tulsa (1999)
Berge T., Aminzadeh F., de Groot P., Oldenziel T.: Seismic inversion successfully predicts reservoir, porosity, and gas content in Ibhubesi Field, Orange Basin, South Africa. Lead. Edge 21, 338–348 (2002)
Latimer R.B., Davidson R., Van Riel P.: An interpreter’s guide to understanding and working with seismic-derived acoustic impedance data. Lead. Edge 19, 242–256 (2000)
Verwest, B.; Masters, R.; Sena, A.: Elastic impedance inversion. In: 2000 SEG Annual Meeting, Society of Exploration Geophysicists (2000)
Hart B.S., Balch R.S.: Approaches to defining reservoir physical properties from 3-D seismic attributes with limited well control: an example from the Jurassic Smackover Formation, Alabama. Geophysics 65, 368–376 (2000)
Hampson D.P., Schuelke J.S., Quirein J.A.: Use of multiattribute transforms to predict log properties from seismic data. Geophysics 66, 220–236 (2001)
Todorov, T.; Hampson, D.; Russell, B.: Sonic log predictions using seismic attributes. CREWES Res. Rep. 9, 1–39 (1997)
Walls J.D., Taner M.T., Taylor G., Smith M., Carr M., Derzhi N., Drummond J., McGuire D., Morris S., Bregar J.: Seismic reservoir characterization of a US Midcontinent fluvial system using rock physics, poststack seismic attributes, and neural networks. Lead. Edge 21, 428–436 (2002)
Leiphart D.J., Hart B.S.: Comparison of linear regression and a probabilistic neural network to predict porosity from 3-D seismic attributes in Lower Brushy Canyon channeled sandstones, southeast New Mexico. Geophysics 66, 1349–1358 (2001)
Khoshdel H., Riahi M.A.: Multi attribute transform and neural network in porosity estimation of an offshore oil field—a case study. J. Pet. Sci. Eng. 78, 740–747 (2011)
Flanagan H., Flanagan K., Tyler E.: Lithology and hydrocarbon mapping from multicomponent seismic data. Geophys. Prospect. 58, 297–306 (2010)
Coates G.R., Xiao L., Prammer M.G.: NMR Logging: Principles and Applications. Gulf Professional Publishing, Houston (1999)
Chen Q., Sidney S.: Seismic attribute technology for reservoir forecasting and monitoring. Lead. Edge 16, 445–448 (1997)
Coren F., Volpi V., Tinivella U.: Gas hydrate physical properties imaging by multi-attribute analysis—Blake Ridge BSR case history. Mar. Geol. 178, 197–210 (2001)
Sheriff R.E.: Encyclopedic Dictionary of Exploration Geophysics. Society of Exploration Geophysicists, Tulsa (1974)
Taner, M.T.: Seismic attributes. CSEG Rec. 26(7), 48–56 (2001)
Lawton D.C., Stewart R.R., Cordsen A., Hrycak S.: Advances in 3C-3D design for converted waves. CREWES Res. Rep. 7, 43.1–43.1 (1995)
McCulloch W.S., Pitts W.: A logical calculus of the ideas immanent in nervous activity. Bull. Math. Biophys. 5, 115–133 (1943)
Specht D.F.: Probabilistic neural networks. Neural Netw. 3, 109–118 (1990)
Timothy, M.: Signal and image processing with neural networks. In: Wiley (1994)
Broomhead, D.S.; Lowe, D.: Radial basis functions, multi-variable functional interpolation and adaptive networks. In: RSRE Memorandum No. 4148, Royal Signals and Radar Establishment. DTIC Document (1988)
Hatampour A., Schaffie M., Jafari S.: Hydraulic flow units, depositional facies and pore type of Kangan and Dalan Formations, South Pars Gas Field, Iran. J. Natural Gas Sci. Eng. 23, 171–183 (2015)
Meyer, A.; Boichard, R.; Azzam, I.; Al-Amoudi, A.: The upper Khuff formation, sedimentology and static core rock type approach-comparison of two offshore Abu Dhabi fields. In: Abu Dhabi International Conference and Exhibition, Society of Petroleum Engineers (2004)
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Hatampour, A., Schaffie, M. & Jafari, S. Estimation of NMR Total and Free Fluid Porosity from Seismic Attributes Using Intelligent Systems: A Case Study from an Iranian Carbonate Gas Reservoir. Arab J Sci Eng 42, 315–326 (2017). https://doi.org/10.1007/s13369-016-2107-5
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DOI: https://doi.org/10.1007/s13369-016-2107-5