Fracture and Carbonate…

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

Deddy Hasanusi, Rahmat Wijaya, Indra Shahab, Bagus Endar B. Nurhandoko



The “T” 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 posses   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 “T” 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 , 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 the average 90 percent match with reservoir parameter in the existing wells.


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