ALBERT

Description: The connectivity of complex carbonate reservoirs has an essential impact on the exploration and development of these reservoirs. From geologic genesis, the connectivity of complex carbonate reservoirs is mainly controlled by faults and dissolution. Therefore, accurate identification of faults and karst caves is the key to studying reservoir connectivity. The Ordovician carbonate reservoir in the Hudson Oilfield of the Tarim Basin is used for our reservoir connectivity analysis study. First, we calculate the coherence and curvature attributes, respectively, and then merge the two attributes using a neural network algorithm. Finally, we use the ant-tracking method to track the faults for the merged data. The results show that the approach substantially enhances deterministic faults that can be seen directly on the seismic data, and subtle faults can also be identified. For reservoir identification, we use the diffraction imaging method to describe the karst reservoir in this study area. The results show that diffraction imaging can identify small-scale caves that cannot be well recognized on the seismic reflection data. Furthermore, the caves connected on the diffraction seismic data are isolated from each other on the seismic reflection data, making the connection between caves clearer. Based on the results of the fault and cave identification, we analyze the reservoir connectivity of the study area using the oil pressure and daily production data, which indicates that the north–northwest and near-north–south faults probably play a role in the connection of the reservoirs, whereas the northeast–east faults tend to block the connection of the reservoirs.