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Abstract

The search for fractured reservoirs is one of our exploration focuses in Kuwait. Our deep carbonate reservoirs in the Study Area are highly fractured reservoirs. Mapping fault/fracture networks is key to unlocking the resource potential. This abstract illustrates the workflow from fault/fracture mapping to fractured reservoir modeling. It was demonstrated that seismic attributes are useful for fault/fracture mapping. The combined post-stack inversion and neural network analysis improved the accuracy of porosity estimation. Lithofacies classification was carried out, as fracture density is dependent on facies types. Velocity modeling was the key step in transforming seismically-derived properties (e.g. porosity volume) from time domain to depth domain. Through detailed well-by-well image data analysis, it was observed that there are 3 sets of open fractures in the Modeling Area. A Discrete Fracture Network (DFN) model was built based on these 3 sets, further controlled by fracture density, lithofacies, and layer thickness. Overall, a better understanding of the fracture network was achieved through the DFN modeling process.

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/content/papers/10.3997/2214-4609.20130806
2013-06-10
2020-10-20
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http://instance.metastore.ingenta.com/content/papers/10.3997/2214-4609.20130806
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