Waterflood performance evaluation in a chalk reservoir with an ensemble of tools

Good waterflood performance management requires an understanding of injector–producer connectivity. In this context, chalk reservoirs present unique challenges. Reservoir compaction, flow in long horizontal wells with transverse fractures, fracturing above the parting pressure, and water short-circuiting along the fault planes may not guarantee the expected fluid displacement. These reservoir attributes collectively contribute to flood management challenges. ime surveillance data form the basis of ongoing flood monitoring. This data interpretation improves the estimates for ultimate recovery by way of on-time well intervention. The data also helps to better define the future field development plan. Besides gathering real-time rate and bottomhole pressure (BHP) data, this study shows how time-lapse tracer, production logs, and 4D seismic data assists in gaining a credible history match with numerical-flow simulations. numerical modeling, this study used an array of analytical tools. These computationally inexpensive tools include both diagnosis and analysis. Amongst the diagnostic tools, the reciprocal-productivity index (RPI) provided crucial information on the degree of pressure support felt at a producer; the water–oil ratio (WOR) plot gave the clue on fluid displacement; and the modified-Hall plot helped understand matrix injection or the lack thereof. Combined rate/pressure data analysis with the capacitance–resistance model (CRM) provided quantitative measures of injector–producer connectivity. Where feasible, the rate-transient analysis (RTA) provided evolving reservoir pressure and the connected pore-volume information. The traditional decline-curve analysis (DCA) showed variability of the decline trend based upon the pressure-support and fluid-displacement scenarios. tudy underscores the importance of both real-time and time-lapse measurements in managing a waterflood in a challenging reservoir environment. The proposed workflow emphasizes learning from data diagnosis and analysis with analytical tools before embarking on history matching with numerical-flow simulations in the South Arne field, located in the Danish North Sea.