Successful Carbonate Well Log Facies Prediction Using an Artificial Neural Network Method: Wafra Maastrichtian Reservoir, Partitioned Neutral Zone (PNZ), Saudi Arabia and Kuwait
Authors: Tang Hong , Toomey Niall , Meddaugh Scott W. 
Published: October 2009

Abstract
The Maastrichtian (Upper Cretaceous) reservoir is one of five prolific oil reservoirs in the giant Wafra oil field. The Maastrichtian oil production is largely from subtidal dolomites at an average depth of 2500 feet. Carbonate deposition occurred on a very gently dipping, shallow, arid, and restricted ramp setting that transitioned between normal marine conditions to restricted lagoonal environments. The average porosity of the reservoir interval is about 15%, although productive zones have porosity values up to 30-40%. The average permeability of the reservoir interval is about 30 md. Individual core plugs have measured permeability up to 1200 mD.
The currently available core data for the Maastrichtian is limited to six wells, five of which are essentially along the axis of the reservoir. As a result, the use of this core data set for stratigraphic and depositional interpretation is somewhat limited. This study was undertaken to determine: (1) if facies could be successfully predicted (classified) from well log data and (2) if the predicted facies could be used to extend the stratigraphic and depositional interpretation to the flanks of the reservoir.
Efforts to predict facies from well logs in carbonate reservoirs is difficult due to the complex carbonate facies structures, strong diagenetic overprint, and challenging log analysis due in part to the presence of vugs and fractures. In the study, a workflow including (1) core description preprocessing (2) log and core data clean-up, and (3) probabilistic neural network (PNN) facies analysis was used to accurately predict facies from log data. First, the core descriptions were digitized and the number of facies reduced from twenty to six (mudstone, packstone, mud-rich packstone, grain-rich packstone, grainstone, and floatstone). Next, the log data for the cored wells were normalized and "cleaned" by removing intervals with incomplete data, evidence of significant vugs (or fractures in very limited cases), poor well bore conditions, or acoustic log or resistivity log spikes.
For validation purpose, training sets were randomly selected from two cored wells. Hold-off data were used to validate prediction ability. The log data used for facies prediction include seven curves – GR, SP, DT, NPHI, RHOB, PE, and RT. GR spectrum logs, which are generally better lithological logs than GR were available for only a few wells; therefore, GR spectrum logs were not used in this study.
After evaluation of a variety of statistical approaches, a probabilistic neural network PNN-based approach was used to predict facies from well log data. PNN was selected as a tool because it has the capability to delineate complex nonlinear relationships between facies and log data. PNN was shown to outperform multivariate statistic algorithms and in this study gave good prediction accuracy (above 70%). The prediction uncertainty was quantified by two probabilistic logs-discriminant ability and overall confidence. These probabilistic logs can be used to evaluate the prediction uncertainty during interpretation. Lithofacies were predicted for 15 key wells in the Wafra Maastrichtian reservoir and effectively used to extend the understanding of the Maastrichtian stratigraphy, depositional setting, and facies distribution.