DETERMINING HYDRAULIC FLOW UNITS USING A HYBRID NEURAL NETWORK AND MULTI-RESOLUTION GRAPH-BASED CLUSTERING METHOD: CASE STUDY FROM SOUTH PARS GASFIELD, IRAN
M. Nouri-Taleghani1, A. Kadkhodaie-llkhchi2 * and M. Karimi-Khaledi3
1 Faculty of Mining Engineering, University of Tehran, Tehran, Iran.
2 Department of Earth Science, Faculty of Natural Science, University of Tabriz, Tabriz, NW Iran.
3 Abadan Institute of Technology, Petroleum University of Technology, Abadan, Iran.
* author for correspondence, email: email@example.com
Hydraulic flow units are defined as reservoir units with lateral continuity whose geological properties controlling fluid flow are consistent and different from those of other flow units. Because pore-throat size is the ultimate control on fluid flow, each flow unit has a relatively similar pore-throat size distribution resulting in consistent flow behaviour. The relations between porosity and permeability in terms of hydraulic flow units can be used to characterize heterogeneous carbonate reservoirs.
In this study, a quantitative correlation is made between hydraulic flow units and well logs in South Pars gasfield, offshore southern Iran, by integrating intelligent and clustering methods of data analysis. For this purpose, a supervised artificial neural network model was integrated with multi-resolution graph-based clustering (MRGC) to identify hydraulic flow units from well log data. The hybrid model provides a more precise definition of flow units compared to definitions based only on a neural network. There is a good agreement between the results of well log analyses and core-derived flow units. The synthesized flow units derived from the well log data are sufficiently reliable to be considered as inputs in the construction of a 3D reservoir model of the South Pars field.
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