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Fracture and Carbonate Reservoir Characterization using Sequential Hybrid Seismic Rock Physics, Statistic and Artificial Neural Network: Case Study of North Tiaka Field

Deddy Hasanusi1, Rahmat Wijaya1, Indra Shahab2, Bagus Endar B. Nurhandoko3
(1) JOB Pertamina-Medco Energy Tomori Sulawesi, Indonesia. (2) Pertamina Hulu Energi, Indonesia. (3) Rock Fluid Imaging Lab., Indonesia.

Tiaka field is located in the Senoro-Toili block at the eastern arm of Sulawesi, Indonesia. The main hydrocarbon bearing reservoir is a lower Miocene carbonate sequences which possess a dual porosity system both matrix and fracture. Actually, the carbonate rock characterization is quite complex, because of their matrix, pore system, and also consider of chemical reaction produced from fluid interaction in interior wall of their pores space. and also their wave propagation system through in carbonate reservoir. This carbonate complexity is required special treatment to precisely characterize the reservoir.

In this paper, the very latest technology for carbonate complex reservoir characterization using hybrid seismic rock physics, statistic and artificial neural network will be presented. This methodology enable in integrating a huge size of various data set to produce �coherence correlation� among input data and their target. The data set consist of core (i.e: lithology, lithofacies, fracture intensity, fracture width, porosity), well log (i.e. gamma ray, density, Sw, porositas, resistivitas etc.), multi-attribute either pre-stack or post-stack of a different vintages of 2 D seismic lines and seismic rock physics. The whole of input data was trained together using natural workflow which is also combined with statistic and artificial neural network. Afterwards it is used to predict several reservoir parameters.

This method is applied on North Tiaka Field to predict the lateral lithofacies, fracture, porosity, and their fluid or hydrocarbon distribution. In addition, the whole process of reservoir parameter prediction is done by using natural algorithm based on lithofacies prediction result, therefore the lithofacies is the first task which should be done before characterizing the other properties of reservoir. By using these approach , its can produce high accuracy on the reservoir parameter prediction. The accuracy of testing process show that predicted parameter reservoir on an average 90 percent match with reservoir parameter in the existing wells.


AAPG Search and Discovery Article #90141©2012, GEO-2012, 10th Middle East Geosciences Conference and Exhibition, 4-7 March 2012, Manama, Bahrain