Identification of Water Flooding Advantage Seepage Channels Based on Meta-Learning

Abstract

As the water injection oilfield enters into the high water cut stage, a large number of water flooding advantage seepage channels are formed in the local reservoir dynamically changing with the water injection process, which seriously affects the water injection development effect. In oilfield production, water injection and fluid production profile test data are direct evidence to identify advantage seepage channels. In recent years, some scholars have carried out research related to the identification of advantage seepage channels based on machine learning methods; however, the insufficient profile test data limit the quantity and quality of learning samples, leading to problems such as low prediction accuracy of learning models. Therefore, the author proposes a new method of advantage seepage channel identification based on meta-learning techniques, using the MAML algorithm to optimize the neural network model so that the model can still perform well in the face of training tasks with low data sample size and low data quality. Finally, the model was applied to the actual blocks in the field to identify the advantage seepage channels, and the identification results were basically consistent with the tracer monitoring results, which confirmed the feasibility of the method. It provides a new solution idea for the task of identifying advantage seepage channels and other tasks with low data quality, which has a certain guiding significance.