An Interpretable Interflow Simulated Graph Neural Network for Reservoir Connectivity Analysis

Abstract

Summary Reservoir connectivity analysis plays an essential role in controlling water cut in the middle and later stages of reservoir development. The traditional analysis methods, such as well test and tracer, may result in interruption and high reservoir development costs. Analyzing connectivity through history data is an advisable alternative method because the fluctuation of data reflects interwell interference. However, most of the former data-driven methods, such as capacitance and resistance model (CRM), estimate connectivity using formulas in relatively simple forms, leading to inadequate expression for underground interwell flow. In this paper, an interpretable recurrent graph neural network (GNN) is proposed to construct an interacting process imitating the real interwell flow regularity and overcoming the weakness in previous methods. In contrast, it is formed by a deep enough neural network structure with a relatively larger number of parameters when compared with the CRM model. In detail, this method makes the first use of both rate information and bottomhole pressure (BHP) to completely describe the hidden state of wells and the energy information exchanged among them, which are then continually updated in spatial and temporal ways. Meanwhile, a self-defined recurrent structure deals with the time lag and attenuation phenomenon as it records the residual energy from past timestamps. Finally, it calculates BHP for each production well with the manually specified production rate as extra input data. Detailed results are presented in two examples. Our proposed method shows significant advantages to other methods due to its reasonable structure and great ability to fit nonlinear mapping.