Dynamic Calculation Method of Interwell Connectivity Based on the Attention Mechanism

Abstract

The analysis of interwell connectivity plays a pivotal role in the optimization of the injection-production relationship, the formulation of an oilfield development plan and the description of the remaining oil distribution. Nevertheless, conventional techniques for determining interwell connectivity, such as tracer analysis, interference well testing, and numerical simulation, are characterized by high costs and low efficiency. To address this challenge, an increasing number of artificial intelligence methods have been proposed. However, these methods often necessitate the availability of a substantial quantity of labeled data to facilitate model training, and sensitivity analysis is required to ascertain the interwell connectivity of the model training results. Furthermore, the majority of existing methods are unable to perform dynamic analysis of interwell connectivity. This paper proposes a dynamic calculation method of interwell connectivity based on an attention mechanism to address the aforementioned issues. The interwell connectivity is taken as the intermediate parameter of the neural network, with the interwell connectivity between each injection well and the current production well in the well group inverted by predicting the daily fluid production of each production well in the well group. The experimental results demonstrate that the interwell connectivity calculations presented in this paper are in accordance with the actual operational conditions observed in the oil field and the reservoir expertize of industry professionals. This indicates that our approch can effectively support dynamic decision-making in the production process of oil reservoirs.