Yamama Formation (Valanginian-Early Hauterivian) is one of the most important oil production reservoirs in southern Mesopotamian Zone. The Yamama Formation in south Iraq comprises outer shelf argillaceous limestones and oolitic, pelloidal, pelletal and pseudo-oolitic shoal limestones. The best oil prospects are within the oolite shoals. Yamama Formation is divided into seven zones: Upper Yamama, Reservoir Units YR-A & YR-B separated by YB-1, and YR-B Lower & two Tight zones: low (porosity, permeability and oil saturation) with variable amounts of bitumen. These reservoir units are thought to be at least partially isolated from each other.
Over the years, the prediction of penetration rate (ROP) has played a key rule for drilling engineers due it is effect on the optimization of various parameters that related to substantial cost saving. Many researchers have continually worked to optimize penetration rate. A major issue with most published studies is that there is no simple model currently available to guarantee the ROP prediction.
The main objective of this study is to further improve ROP prediction using two predictive methods, multiple regression analysis (MRA) and artificial neural networks (ANNs). A field case in SE Iraq was conducted to predict the ROP from a large number of parame