Abstract: Systems and methods for generating an interference prediction for a target well are disclosed herein. A computing system generates a plurality of interference metrics for a plurality of interference events. For each well, the computing system generates a graph based representation of the well and its neighboring wells. The computing system generates a predictive model using a graph-based model by generating a training data set and learning, by the graph-based model, an interference value for each interference event based on the training data set. The computing system receives, from a client device, a request to generate an interference prediction for a target well. The computing system generates, via the predictive model, an interference metric based on the one or more metrics associated with the target well.

Abstract: Systems and methods for generating an interference prediction for a target well are disclosed herein. A computing system generates a plurality of interference metrics for a plurality of interference events. For each well, the computing system generates a graph based representation of the well and its neighboring wells. The computing system generates a predictive model using a graph-based model by generating a training data set and learning, by the graph-based model, an interference value for each interference event based on the training data set. The computing system receives, from a client device, a request to generate an interference prediction for a target well. The computing system generates, via the predictive model, an interference metric based on the one or more metrics associated with the target well.

Abstract: Systems and methods for generating an interference prediction for a target well are disclosed herein. A computing system generates a plurality of interference metrics for a plurality of interference events. For each well, the computing system generates a graph based representation of the well and its neighboring wells. The computing system generates a predictive model using a graph-based model by generating a training data set and learning, by the graph-based model, an interference value for each interference event based on the training data set. The computing system receives, from a client device, a request to generate an interference prediction for a target well. The computing system generates, via the predictive model, an interference metric based on the one or more metrics associated with the target well.