Complex Network Method for Inferring Well Interconnectivity in Hydrocarbon Reservoirs

Reservoir management becomes increasingly critical as fields decline to a fully mature state. During this stage, engineers and managers must make decisions based on a limited set of field measurements (such as pressure and production rates). At the same time, up-to-date information concerning the reservoir’s geophysical characteristics and petrochemical properties may be unavailable. To aid in the expert’s appraisal of this production scenario, we present the results of applying a data-driven methodology based on visibility graph analysis (VGA) and multiplex visibility graphs (MVGs). It infers inter-well connectivities at the reservoir level and clarifies the degrees of mutual influence among wells. This parameter-free technique supersedes the limitations of traditional methods, such as the capacitance–resistance (CR) models and inter-well numerical simulation models (INSIMs) that rely heavily on geophysical data and are sensitive to porous datasets. We tested the method with actual data representing a field’s state over 62 years. The technique revealed short- and long-term dependencies between wells when applied to historical records of production rates (oil, water, and gas) and pressures (bottom and wellhead). The inferred connectivity aligned with documented operational trends and successfully identified stable connectivity structures. In addition, the interlayer mutual information (IMI) parameter exceeded 0.75 in most periods, confirming high temporal consistency. Moreover, validation by field experts confirmed that the inferred interconnectivity was consistent with the observed production.