Robust Optimization of Well Placement in Geologically Complex Reservoirs

Abstract

Abstract Optimal decisions about well placement and control in geologically complex reservoirs can reduce subsurface risks associated with field development plans. Especially, in deepwater developments, economic constraints impose stringent limits on the number of wells that can be drilled without compromising economic profitability. Thus, optimal placement and operation of wells often have a significant impact on project rewards. Reservoir management decisions are made based on evaluations of subsurface models that integrate all available data by use of geomodeling and reservoir simulation. It is important to recognize that uncertainty is inherent to reservoir models due to limited data/knowledge about subsurface. A robust reservoir management strategy calls for rigorous techniques that account for stratigraphic and structural uncertainty in geologically complex deepwater reservoirs. A robust well-location optimization method is developed particularly suitable for such reservoirs. A steepest-ascent algorithm that takes advantage of distributed parallel computation constitutes the computational core of the optimization method. The method operates in parallel on a number of probable reservoir models and maximizes the expected value of ultimate recovery factor while delivering minimum variance over the model uncertainty range. A realistic synthetic test case is evaluated for demonstrating the concept. The case uses alternative geologic models of a lobate turbidite reservoir with complex stratigraphic architecture. The results validate the auspicious features of the robust well location optimization method.