Multi-Scale Carbonate Reservoir Characterisation and Artificial Neural Networks Reveals Complexity in the Shuaiba Reservoir, Al Shaheen Field

The Shuaiba reservoir in Al Shaheen is a complex carbonate interval deposited in a platform to basin setting that has undergone significant diagenetic alteration during burial. Properly-constrained predictions of reservoir performance depend on understanding the processes that created (depositional) and altered (diagenesis) the carbonate rock, and therefore on characterizing rock properties at multiple scales. The Shuaiba reservoir in Al Shaheen has been appraised with vertical wells, and developed using extended reach horizontal wells, affording a unique opportunity for the multiscale investigation of lateral and vertical changes in the reservoir properties. This paper integrates a unique dataset of image logs (>100kms), conventional core analysis, thin sections, mercury injection capillary pressure (MICP), whole core computer tomography (micro-CT) scans, innovative 3D imaging of the pore network and mineral mapping with QEMSCAN. This integrated multi-scale work covering several orders of magnitude from μm to km has identified seven coherent petrophysical rock types (PRT’s). These PRT’s have been validated with blind tests and subsequently predicted using artificial neural network technology that enables characterization of the reservoir at well-to-well scale. Here we present the multidisciplinary workflow used to identify the seven rock types that has been used for prediction of Shuaiba reservoir properties.