Equivalent Fluid Model Characterization Expedites Daily Surface Network Optimization in a Complex Reservoir Under Miscible WAG, Field Case

Abstract

Achieving history match for all three phases in a surface network model is not a straight forward task. Many variables are interacting in the process from sandface, well models, system architecture, measurements, and well testing until export meters. The objective of this study is to illustrate a methodology followed in a smart field located in Abu Dhabi, where it was possible to calibrate and match oil, gas, and water historical production rates in a top side model containing more than 55 strings among producers and injectors. Reservoir characterization played a very important role since the asset contents - a series of carbonate oil baring reservoirs with different properties - connected to a common surface network. The main reservoir has a strong compositional gradient starting from gas condensate to black oil fluids with saturation pressure and GOR varying in a wide range. Wells associated with the most prospective zone are placed in five inverted patterns as part of a miscible alternate water and gas injection (WAG) process which provide pressure support; others are under peripheral water injection with very stable bubble pressure and GOR across all areas. To combine in a single model all the complex and different fluid characteristics and dynamic interactions represented a challenge for the engineers. A surface network model was built with the purpose of creating online production optimization scenarios that consider all production system behavior using an economical model for boosting oil while minimizing gas production in order to reduce operating costs. After network calibration was performed, oil and water total rates matched. However, the resulting gas production was overestimated compared with the one reported in export meters, indicating that fluid properties need to be tuned since important compositional changes were expected to happen when pressure drops from subsurface to the processing plant. The present study demonstrates how to construct a representative fluid model for a group of complex reservoirs that honor actual production rates for all phases allowing network model history match, reducing computing time to accomplish comprehensive production optimization based on truly surface constraints.