Abstract
AbstractAdvanced drilling technology has been widely and successfully applied to construct multilateral wells in reservoirs. This paper presents several field applications of the modeling of complex well architectures. A generalized semi-analytical segmented model, accounting for multilateral well systems in commingled layered reservoirs was used in several field applications. Cases include evaluating the flow efficiency of different configurations of wells with multiple laterals, analyzing an interference test between three horizontal wells in Al Rayyan oil field offshore Qatar, and analyzing a well test in a commingled multilateral well in a multilayer reservoir in the Dos Cuadras field offshore California.The model can predict the production performance under either constant-rate or constant-pressure conditions of a well system with any number of arbitrarily oriented laterals of any length and nonuniform formation damage. The reservoir layers, with different porosities, anisotropic permeabilities and drainage areas, are non-communicating except through the wellbore. The solution is valid for large reservoirs and when no-flow or constant-pressure boundaries affect the pressure behavior.Results of applying this method in the field cases showed that the model enabled us to predict multilateral well performance, to obtain information about reservoir connectivity, and to estimate well and reservoir properties in a multilayer system. Uncertainty, due to the large number of unknown parameters in such a complex system, represents the main challenge in using this method. It is recommended to use other means (e.g. production logging and PNC log) together with pressure transient data to reduce the uncertainty. Whenever possible, each lateral should be tested individually to provide more reliable estimates of reservoir and well properties.The presented model and the lessons learned from the field applications provide engineers a tool to use transient data collected from multilateral wells in multilayer systems for reservoir characterization and performance forecast.
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