Development of artificial neural networks and multiple regression analysis for estimating of formation permeability

Formulating a prediction tool that can estimate the formation permeability in uncored wells is of particular importance for many applications related to reservoir simulation and production management. Although formation permeability can be obtained from a laboratory or from a reservoir, core analysis and well-test data are limited due to cost and time-saving purposes. A major challenge of previous methods is that they are required other parameters to be previously computed such as porosity and water saturation. In addition, they are affected by the uncertainty that introduced by the cementation factor and saturation exponent. This study presents two prediction methods, multiple regression analysis (MRA) and artificial neural networks (ANNs), to estimate formation permeability using conventional well log data. The prediction methods were demonstrated by means of a field case in SE Iraq. The study uses core/ well log data from Mishrif reservoir which is mainly composed of carbonate (limestone) formations. Two traditional methods were reviewed and presented for permeability determination. These methods are the classical method and the flow zone indicator (FZI) method. At the same porosity, the results showed a wide range of formation permeability prediction. This result gives a special attention to the assumption that the relationship between permeability and porosity is generally unique in carbonate environments. The deep lateral log resistivity appears to be more conservative in the permeability function rather than other parameters, followed in decreasing order by bulk density, sonic travel time, micro and shallow resistivities, and shale volume. Although the presented models based on RA and ANNs resemble to be closely in determining the formation permeability, the correlation coefficient of ANNs was found to be higher than that obtained from RA, which indicated that the ANNs is more precise than RA. The comparison among previous methods shows the superiority of the FZI method rather than the classical method. However, core porosity and permeability should be previously determined to apply FZI method. This study presents efficient and cost-effective models for a prediction of permeability in uncored wells by incorporating conventional well logs.