Well deliverability is a critical reservoir parameter that defines well performance. Accurate knowledge of deliverability is very important for reserve booking and production facility design. Reliable well deliverability can be obtained from DST (Drill Stem Test) on selective perforation of individual layers and/or straddle tests for commingled producers. However, these methods are both time and cost consuming. Simple single well analytical model can be built to calculate well deliverability. But it often does not count for certain factors such as: formation heterogeneity & anisotropy, initial oil saturation, reservoir with bottom or edge-drive aquifers, as well as capillary force and relative permeability. Therefore the result can be unreliable. Under this situation, numerical model is the solution.

This paper presents a method to forecast well deliverability with a comprehensive approach to factor in various reservoir parameters. A single well numerical model is developed by integrating Nuclear Magnetic Resonance (NMR), borehole image, open-hole logs, well test and/or formation tester, and core data together. The workflow of the method includes four steps: 1) perform petrophysical interpretation and lithofacies characterization and hydraulic flow unit (HFU) classification, 2) calibrate NMR derived permeability, capillary and relative permeability curves with special core analysis, 3) construct a dynamic model and calibrate it with well testing and/or/ formation testing data, and 4) forecast well deliverability with the calibrate numerical model under different completion and well management options.

A case study is presented to illustrate this integrated approach. The single well model was calibrated with well test data for a tested zone, then the calibrated model was used to forecast well deliverability for the rest zones where no well tests were performed. Subsequently sensitivity analysis was performed on formation damage. Also bottom/edge water drive effect on well deliverability were analyzed. This case study shows that the well deliverability forecasted with this workflow has greatly reduced the uncertainty and enhanced the understanding of the reservoir performance for decision making.

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