Oilfield development performance prediction is a significant and complex problem in oilfield development. Reasonable prediction of oilfield development performance can guide the adjustment of the development plan. Moreover, the reservoir will change slowly during reservoir development because of flowing water however, previous networks that forecast production dynamics ignored it, which leads to inaccurate predictions. Routine well-wise injection and production measurements contain important subsurface structure and properties. So, for the dynamic prediction of oil/water two-phase waterflooded reservoirs, we built a deep learning framework named adaptive correction interwell connectivity model based on graph convolutional networks (GCN) and gated recurrent unit (GRU). It includes two parts: The first part is the adaptive correction model based on GCN, which uses dynamic production data to automatically correct the initial interwell connectivity computed by permeability, porosity, interwell distance, and so on. The second part is the adaptive learning model based on GRU, which predicts the production performance of oil wells according to the time characteristics of production performance data. This framework considers the influence that changes in reservoir conditions have on production over time to solve the problem of inaccurate production dynamic prediction. It can also predict interwell connectivity. For oilfields with too many wells, using the embedding idea classifies similar wells into one category, saving time for training and avoiding overfitting problems. Applying the model to five different reservoirs to predict interwell connectivity, well oil production rate, and well water cut compare the results with artificial neural networks (ANN), GRU, and long short-term memory (LSTM) models and compare the interwell connectivity with numerical simulation software ,tNavigator® (Rock Flow Dynamics Llc), too. When the model is applied in Block B of Bohai A reservoir, the mean absolute percentage error of “Adaptive Graph convolutional network and GRU” (AG-GRU) is 2.1150% while the LSTM is 9.8872%. The error reduces by 78.6%. The injected water has a direction from the water injection well to the production well; this paper only considers the interwell connectivity without considering the direction. Further research is needed to consider the water injection direction and form a weighted directed graph.

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