Finetuning Lithofacies and Reservoir Modeling using Borehole Images and Neural Networks - Africa’s premier report on the oil, gas and energy landscape.

Finetuning Lithofacies and Reservoir Modeling using Borehole Images and Neural Networks

By Simone Di Santo’, Schlumberger Technical Services Inc., Angola, with Nilton Carvalho Rakesh Dhir Tank Gacem, Sonangol P&P.

The determination and definition of reservoir flow units is critical to developing an accurate and useful model of reservoir behaviour. Given that this model will ultimately guide significant capital expenditures such as interventions and infill drilling, it is crucial that the maximum accuracy be achieved. In this paper we describe how all available petrophysical data, with an emphasis on high resolution micro-image data, is used to delineate geological fades and ultimately define the reservoir flow units.

In our example we look at a deepwater field offshore West Africa with a number of wells, all with a variety of petrophysical data. We show how this data is integrated to first determine the range of fades to be identified and then to assign each metre of reservoir to a particular facies such that the resolution and accuracy of the flow unit determination is maximized.

We finally show how the results of the facies and flow unit identification are included in the reservoir simulation and reservoir modeling software to enhance reservoir understanding in order to optimize interventions and infield drilling.

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