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: A system and method of generating a wellbore layout are disclosed herein. A computing system receives, from a client device, one or more parameters associated with a target location for the target wellbore layout. The computing system receives, from the client device, one or more constraints for the target wellbore layout. The computing system generates a plurality target wellbore layout based on the parameters and constraints in accordance with a plurality of configurations as defined by one or more genetic algorithms. The computing system evaluates each target wellbore layout to generate an overall fitness score. The computing system selects a target wellbore layout with the highest scoring score of each generated overall fitness score, repeating the process until an optimal wellbore layout is selected.

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: A system and method of generating a wellbore layout are disclosed herein. A computing system receives, from a client device, one or more parameters associated with a target location for the target wellbore layout. The computing system receives, from the client device, one or more constraints for the target wellbore layout. The computing system generates a plurality target wellbore layout based on the parameters and constraints in accordance with a plurality of configurations as defined by one or more genetic algorithms. The computing system evaluates each target wellbore layout to generate an overall fitness score. The computing system selects a target wellbore layout with the highest scoring score of each generated overall fitness score, repeating the process until an optimal wellbore layout is selected.

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: Embodiments disclosed herein generally relate to a method and a system to generate well trajectories. A computing device receives one or more parameters associated with a target well in a target location. The computing device receives two or more data points for the target well in the target location. The computing device generates a modified wellbore path based on the one or more parameters associated with a target well and the two or more data points via a trained wellbore prediction model. The computing device compares the modified wellbore path for the target well to one or more wellbore paths of one or more wells co-located with the target well in the target location. The computing device updates the modified wellbore path for the target well by adjusting one or more coordinates of the modified wellbore path based on the comparison. The computing device generates a three-dimensional model of the target location.