Estimation of Reservoir Porosity From Drilling Parameters Using Artificial Neural Networks

Porosity is one of the most important properties to be determined for evaluating hydrocarbon reservoirs. It represents the voids and empty volume inside the rock. This property is mostly obtained from well logs and/or laboratory experiments on core plugs or drilled cuttings. Despite the accuracy in the porosity values provided by these techniques, these methods are costly and time consuming. There is a need to relate the rock porosity to the drilling parameters since drilling process provides the initial insight to the formation. The use of artificial intelligence (AI) in drilling applications is a game changer since most of the unknown parameters are accounted during the modeling process. The objective of this paper is to implement an artificial neural network (ANN) technique to predict the porosity in the reservoir section from the drilling parameters. The data used to build the ANN model are based on real field data (2,800 data points) that were obtained from two horizontal wells (i.e. Well A and Well B). The data from Well A were used to train and test the ANN model with a training/testing ratio of 70:30. More than 30 sensitivity analyses were performed to select the optimum ANN model's design parameters. Well B data were used to validate the developed ANN model. The obtained results showed that ANNs can be used effectively to predict the porosity from the drilling parameters in the reservoir section with an average correlation coefficient of appproximately 0.96 and a root mean square error (RMSE) of almost 0.018. The best ANN parameter combination was with two layers, 30 neurons per layer with Levenberg-Marquardt training function and tan-sigmoid as the transfer function. The validation process confirmed that the ANN porosity model was able to predict the porosity of Well B with a correlation coefficient of 0.907 and an RMSE of 0.035.