Beyond One-Dimensional Evaluations: The Search for Genetic Reservoir Regions through Time & Space

Abstract The infill potential of one of the most complex fractured carbonate reservoirs in the Sultanate of Oman has been evaluated through the integration, visualization and analysis of different data sources. The field has been split into different simplified genetic geobodies which contain the culmination of facies changes that define rock quality, fluid fill, oil saturation distribution and fracture network, amongst other properties that affect fluid flow. The long production history of more than 45 years, along with the large number of logged long horizontal wells scattered around the field, were key enabler for the analytical methodology. Production data, coupled with resistivity logs in horizontal wells, viewed through time were the backbone of the analysis. Data analysis was achieved by combining these data in a single platform and performing the analysis at different slices of time. At each timeslice, different interpretations were inferred that explain the observed production behaviour and remaining oil saturation from the logged wells. The interpretations were narrowed down into a minimum number of realizations by combining interpretations from the same area gathered from different slices of time. The analysis has resulted in the identification of four genetic performance regions in the field. Each region approximates a primary depositional facies belt and has a general defined relative behaviour of initial wells potential, water-cut development, initial and remaining oil saturation and, most importantly, infill wells potential. The analysis has aided in narrowing the subsurface uncertainties and has given a promising explanation for the large variations in wells behaviour. Infill wells opportunities have been identified, selected and ranked relatively in each of the regions. The value of data analytics on large volumes of acquired information normally not used was demonstrated. Visualization of different data sources in one platform is a challenging task that usually stops engineers from experimenting. The team has found fit for purpose solutions to visualize different data sources through time. The shift of mind-set from uncertain complex models and evaluations into finding simple genetic performance regions of the reservoir was vital in unravelling infill potential.