Lithofacies Classification of Thin-Layered Turbidite Reservoirs Through the Integration of Core Data and Dielectric-Dispersion Log Measurements
- Marco Pirrone (Eni S.p.A.) | Alessandra Battigelli (Eni S.p.A.) | Livio Ruvo (Eni S.p.A.)
- Document ID
- Society of Petroleum Engineers
- SPE Reservoir Evaluation & Engineering
- Publication Date
- April 2016
- Document Type
- Journal Paper
- 226 - 238
- 2016.Society of Petroleum Engineers
- Dielectric Dispersion Modeling, Cluster Analysis, Probabilistic log-facies Classification, Core-facies classification, Thin Layered Reservoirs
- 2 in the last 30 days
- 296 since 2007
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Distal turbidites consist of thin laminations (inch scale), usually ranging from fine sand to clay-rich deposits and may represent major hydrocarbon reservoirs: Conventionally, they are studied by means of a log-based binary modeling that discriminates productive and nonproductive layers. Nevertheless, the binary model represents a major drawback when dealing with laminations in the silt-grain-size range, because their allotment to either end member can be extremely problematic. This paper deals with a novel, probabilistic, lithological facies- classification approach that integrates core data and a highresolution dielectric-dispersion wireline log: Its 1-in. vertical resolution and a related fit-for-purpose petrophysical model make the log tool’s response suitable to describe the lithological heterogeneity of these reservoirs. The approach is presented by means of a study performed on the cored section of a well drilled into a laminated gas-bearing Pleistocene reservoir in the Adriatic Basin. A core-based classification was first carried out with sedimentological descriptions, mineralogical analyses, cation-exchange-capacity (CEC) measurements, routine and special core analyses, and a statistical investigation of grain-size distributions: This allowed the identification of four lithofacies ranging from hemipelagite to coarse silt. Next, a log-based classification was carried out with a multivariate statistical numerical technique integrated in a Bayesian framework run on the dielectric-dispersion model curves. The outputs are the probability of log-facies, the most-probable facies scenario, and the associated uncertainty by means of entropy computation. In the end, a four-facies log-based classification was obtained that matches the core-based classification with an overall agreement in excess of 93%. Compared with the conventional methodology, the presented approach shows the added value of identifying intermediate lithologies, thus leading to a more-accurate quantification of the thickness of the potentially hydrocarbon-bearing net reservoir.
|File Size||1 MB||Number of Pages||13|
Bishop, Y. M., Fienberg, S. E., and Holland, P. W. 2007. Discrete Multivariate Analysis: Theory and Practice. Dordrecht, The Netherlands: Springer, 568 pp.
Bouma, A. H. 1962. Sedimentology of Some Flysch Deposits: A Graphic Approach to Facies Interpretation. New York: Elsevier Publishing Co., 168 pp.
Chelini, V., Galli, M. T., Mazzacca, A. et al. 2009. Petrophysical Characterization of Thin-Layered Reservoirs: A Case History From the Adriatic Basin. Presented at the Offshore Mediterranean Conference and Exhibition, Ravenna, Italy, 25–27 March. OMC-2009-020.
Doyen, P. M. 2007. Seismic Reservoir Characterization: An Earth Modelling Perspective. Houten, The Netherlands: EAGE Publications, 255 pp.
Folk, R. L. and Ward, W. C. 1957. Brazos River Bar: A Study in the Significance of Grain Size Parameters. J. Sediment. Research 27 (1): 3–26. http://dx.doi.org/10.1306/74D70646-2B21-11D7-8648000102C1865D.
Friedman, G. M. and Sanders, J. E. 1978. Principles of Sedimentology. New York: John Wiley & Sons, 792 pp.
Grana, D., Pirrone, M., and Mukerji, T. 2012. Quantitative Log Interpretation and Uncertainty Propagation of Petrophysical Properties and Facies Classification From Rock-physics Modeling and Formation Evaluation Analysis. Geophysics 77 (3): WA45–WA63. http://dx.doi.org/10.1190/geo2011-0272.1.
Hastie, T., Tibshirani, R., and Friedman, J. 2002. The Elements of Statistical Learning: Data Mining, Inference, and Prediction. New York: Springer, 739 pp.
Hizem, M., Budan, H., Deville, B. et al. 2008. Dielectric Dispersion: A New Wireline Petrophysical Measurement. Presented at the SPE Annual Technical Conference and Exhibition, Denver, USA, 21–24 September. SPE-116130-MS. http://dx.doi.org/10.2118/116130-MS.
Kaufman, L. and Rousseeuw, P. J. 1990. Finding Groups in Data: An Introduction to Cluster Analysis. New York: John Wiley & Sons, 342 pp.
Krumbein, W. C. 1934. Size Frequency Distributions of Sediments. J. Sediment. Research 4 (2): 65–77. http://dx.doi.org/10.1306/D4268EB9-2B26-11D7-8648000102C1865D.
Mukerji, T., Avseth, P., Mavko, G. et al. 2001. Statistical Rock Physics: Combining Rock Physics, Information Theory, and Geostatistics to Reduce Uncertainty in Seismic Reservoir Characterization. The Leading Edge 20: 313–319. http://dx.doi.org/10.1190/1.1438938.
Mutti, E., Tinterri, R., Ramacha, E. et al. 1999. An Introduction to the Analysis of Ancient Turbidite Basins From an Outcrop Perspective. AAPG Continuing Education Course Note Series No. 39, Tulsa, USA, 61 pp.
Pirrone, M., Bona, N., Galli, M. T. et al. 2011a. Experimental Investigation and Modeling of the Electrical Response of Thinly Layered Shaly-sands (20 MHz–1 GHz). Presented at the International Symposium of the Society of Core Analysts, Austin, Texas, USA, 18–21 September. Paper A016. SCA-2011-10.
Pirrone, M., Mei, H., Bona, N. et al. 2011b. A Novel Approach Based on Dielectric Dispersion Measurements to Evaluate the Quality of Complex Shaly-sand Reservoirs. Presented at the SPE Annual Technical Conference and Exhibition, Denver, USA, 30 October–2 November. SPE-147245-MS. http://dx.doi.org/10.2118/147245-MS.
Shannon, C. E. 1948. A Mathematical Theory of Communication. The Bell System Technical Journal 27: 379–423, 623–656. Monograph B-1598.
Stroud, D., Milton, G. W., and De, B. R. 1986. Analytical Model for the Dielectric Response of Brine-saturated rocks. Phys. Rev. B 34 (8): 5145–5153. http://dx.doi.org/10.1103/PhysRevB.34.5145.
Udden, J. A. 1914. Mechanical Composition of Clastic Sediments. Bull. Geol. Soc. Amer. 25: 655–744. http://dx.doi.org/10.1130/GSAB-25-655.
Ward, J. H. 1963. Hierarchical Grouping to Optimize an Objective Function. J. Amer. Stat. Ass. 58: 236–244. http://dx.doi.org/10.1080/01621459.1963.10500845.
Wentworth, C. K. 1922. A Scale of Grade and Class Terms for Clastic Sediments. J. Geol. 30: 377–392. http://dx.doi.org/10.1086/622910.