Prediction by reservoir porosity using micro-seismic attribute analysis by machine learning algorithms in an Iraqi Oil Field. (Turkish Journal of Computer and Mathematics Education (TURCOMAT), 2021)
The current work deals with an intelligent application that uses machine learning to analyze and attribute theresulting seismic data to improve and predict the locations of exploration, drilling, and production operations in petrophysicaloil. Statistical analyzes of exploratory data analysis (EDA) were used to extract seismic features. This follows the applicationof two intelligent approaches of Recurrent Neural Networks (RNN) and K-Nearest Neighbors (KNN) to predict porosity. Theparameters of seismic data with different seismic attributes were detected at the site by the seismic time-series method. Thelong-term memory (LSTM) algorithm is the most appropriate way to handle serial data. LSTM has a high capacity for datastructure manipulation, which is applied for porosity prediction. Which depends a lot on choosing the best attributes? The twoapproaches evaluate by absolute error (MAE) and root means square error (RMSE). The results for both models used showedthat using (LSTM) is more effective than using (KNN) in predicting porosity through seismic data, where the mean absoluteerror was obtained. (MAE) 0.017, while with KNN the mean absolute error (MAE) is 0.260 and the results showed that themodel used can predict porosity very effectively.