Publications

Machine learning is an algorithm based on pattern recognition and the concept that computers can learn without being programmed to perform specific tasks. Machine learning applications that are commonly used in the oil and gas companies are petrophysical estimation and well log classification, seismic structural identification, production forecasting, and artificial intelligence tasks. The goal of this study is to integrate machine learning workflows to evaluate how reservoir hydrocarbon distribution can help prospecting, field development, and production optimization, especially 4D seismic studies. Also to observe the fluid flow and to detect bypassed oil pockets changes during the production. The workflow consists of three phases: planning, execution, and delivery. The first phase consists of collecting and preprocessing wells, seismic and interpretation data. Once the plan is considered satisfactory, it will be followed by the execution that is started with data cleaning, processing, classification, and data validation. Machine learning methods are then deployed to build an electrofacies and reservoir distribution model for the Hugin Formation using Multi-Resolution Graph-Based Clustering (MRGC). After these models reach a satisfactory level, seismic attribute analysis is performed using Principal Component Analysis (PCA) and Democratic Neural Network Association (DNNA) to create a facies probability volume. The last step in this phase is to detect geobodies of oil sand and propose an infill well or injection strategy to enable the enhancement of the oil recovery. Once the machine learning results are satisfying, tthe status of the workflow will change from execution to the delivery phase to create the final project presentation. In our study, DNNA has demonstrated excellent prediction and facies classification to image a large volume encompassing some wellbores, changes in the fluid flow during production between baseline, and monitoring seismic surveys with a good Matthews correlation coefficient of 0.849554. It allows the operator to observe the dynamic processes in and around the reservoir to help the placement of infill wells more effectively, increas development and production success, reduce risk when following proposed infill wells. The integration of machine learning can also improve the understanding of hydrocarbons in the field. It shapes E&P business strategies in a way that may increase profit revenues, such as enhanced oil recovery of an effective and efficient infill well and optimizing an injection strategy.