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Abstract

Ultralow-permeability (1.0- to 10- md) oil reservoirs are becoming primary targets offshore China. Reliable productivity evaluation is needed for such reservoirs, which have significant technical challenges and a narrow economical margin. Evaluation of wells drilled at high overbalance with water-based mud (WBM) presents several key challenges: severe near-wellbore formation damage, lack of porosity-permeability relationships because of the complex pore structures, large-scale permeability effects from pronounced heterogeneities, and big discrepancies among the permeability sources for which analysis is complicated by different measurement environments and interpretation methods. Parameter discrepancy creates significant bias for determining cut-offs and optimizing completion and stimulation strategies. More significantly, defining producible pay and consequently how it will produce are greatly complicated by capillary pressure and relative permeability conditions. Very different pictures may be drawn if these challenges are not addressed systematically. A comprehensive case study provided lessons learned for the productivity evaluation of an ultralow-permeability clastic oil reservoir drilled at high overbalance with WBM. An integrated approach was conducted to overcome challenges by using all available data through extensive data acquisition. Flow-based rock typing (FBRT) with a neural network used pore structure, capillary pressure, and relative permeability as key inputs or control parameters in addition to the routine parameters in conventional rock-typing methods. Different permeability sources with different scales were systematically integrated and up-scaled into a numerical model based on classified FBRTs and validated by history matching of dynamical tests. Pore structure delineation is identified as the key input for reliable productivity evaluation. FBRT presents an attractive solution if measurements of pore structure, capillary pressure, and relative permeability and dynamical data are carefully used. Optimizing productivity must start with minimizing formation damage and progress through optimizing completion and stimulation.

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/content/papers/10.3997/2214-4609-pdb.350.iptc16668
2013-03-26
2021-12-09
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http://instance.metastore.ingenta.com/content/papers/10.3997/2214-4609-pdb.350.iptc16668
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