Effect of fracture characteristics on history matching in the Qamchuqa reservoir: a case study from Iraq

Carbonate reservoirs in the Middle East hold a significant proportion of the remaining conventional oil reserves, which most of them were classified as naturally fractured. These reservoirs are well known for their low recoveries compared with their counterpart clastic unfractured reservoirs. Therefore, boosting their recoveries can assure to sustain the energy supply to the global oil markets. In this work, an example from the Middle East has been used to evaluate the impact of the fracture characteristics on the reservoir performance using four decades of production history. The characteristics of natural fractures have a remarkable impact on fluid flow behaviour in fractured reservoirs. Besides, these characteristics, such as fracture intensity, determines the matrix block dimensions, which in turn, influence the matrix recovery. However, it is challenging to estimate the fracture characteristics accurately due to the scarcity of the fracture data from subsurface measurements. Therefore, outcrop analogue data can be utilised to enhance the fracture modelling in fractured reservoirs and to fill the data gap between the seismic scale and well scale. In this work, we have integrated the outcrop fracture data with the core observations to improve the fracture modelling in the studied reservoir. Furthermore, a sensitivity study was carried out to investigate the impact of flow curves, gravity drainage mechanism, and its parameters, as well as the fracture storativity on the history matching in fractured reservoirs. Excellent matching results were obtained for the majority of the wells, as indicated by the low mismatch values. The obtained results highlighted the importance of fractures on the fluid flow behaviour, and hence the reservoir performance. Therefore, fracture properties should not be abusively modified using multipliers and local adjustment to avoid nullifying the reservoir characterisation efforts and degrade the simulation model capability for a future prediction. © 2020, Springer-Verlag GmbH Germany, part of Springer Nature.