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
- 0 in the last 30 days
- 288 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.
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