| Popis: |
Drilling activities have progressed to deep and ultra deep seas in recent times and with it comes more challenges. Due to the difficulty of directly obtaining important parameters like in-situ stress and fracture gradient, simple models have been evolved. This study is a novel attempt to make up for the gap inherent in such models namely that they neglect chemical and thermal effects, settling for only effective stress and a time-dependent analysis. The study applied the Neural Network (NN) technology to predict geomechanical parameters. Neural Network (NN) as a branch of Artificial intelligence (AI) possesses the ability of training available parameters to replace data that cannot be immediately or easily acquired. Data of a well drilled in the Niger Delta Region of Nigeria was used as the case study. A training set of input data was used to train the network and a validation set ensured a completely independent measure of network accuracy. A Neural Network model was developed in Neuroph Studio, Java neural network platform and the Netbeans IDE. The model has the advantage of being easy to use, open source, cross-platform and generally designed to save the cost associated with wellbore instability. |
