Reservoir oil-bearing evaluation based on machine learning

For reservoir oil-bearing evaluation, the traditional method relies on rich expert experience, which is subjective and inefficient. With the steady accumulation of logging data, one of the most important problems for oil exploration and development is that how to fully mining and analysis geological data to reduce human workload. Machine learning provides an effective way. In this paper, we use machine learning method to analyze logging curves, and then get the evaluation results of reservoir oil-bearing property. We establish serval prediction models and give the corresponding parameter optimization mechanism. The experiment results show that the machine learning algorithm and optimization mechanism proposed in this paper can well evaluate reservoir oil-bearing property based on logging data. This work provides a reference for improving the automation and efficiency of logging interpretation.