Abstract: A method and computing system is disclosed herein. The computing system receives, from a remote computing device, a set of production information for a plurality of plays from a region. The set of production information is directed to past production of the well. The computing system generates an input data set based on the set of production information. The input data set is generated by imputing missing production data to the production information and collapsing the data set into calendar days. The computing system fits one or more decline curves to the input data set. The computing system consolidates the one or more curves into a new data set. The computing system classifies the one or more curves by identifying a subset of wells in the new data set. The subset of wells includes a threshold amount of production information. The computing system delivers the classified results to the remote computing device.

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: Embodiments disclosed herein generally relate to a method and system for oil and gas predictive analytics. A computer system receives a set of production information for a well located in a region. The computing system generates a set of general reference groups comprising one or more reference wells for the region. The computing system determines whether the set of production information for the well includes the threshold amount of production information. The computing system selects a subset of reference wells from the general reference groups based on one or more traits of the well. The computing system generates a reference curve based on the set of production information associated with each reference well in the subset of reference wells. The computing system fits a decline curve to the reference curve, to determine an estimated ultimate recovery of the 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: Embodiments disclosed herein generally relate to a method and system for oil and gas predictive analytics. A computer system receives a set of production information for a well located in a region. The computing system generates a set of general reference groups comprising one or more reference wells for the region. The computing system determines whether the set of production information for the well includes the threshold amount of production information. The computing system selects a subset of reference wells from the general reference groups based on one or more traits of the well. The computing system generates a reference curve based on the set of production information associated with each reference well in the subset of reference wells. The computing system fits a decline curve to the reference curve, to determine an estimated ultimate recovery of the well.

Abstract: Embodiments can provide a method for allocating production for an oil or gas well, comprising: receiving input data comprising well test data, completion/recompletion data, and lease production data; detecting, through an outlier detection analysis, one or more outlying data points in the well test data; detecting, through a change point detection analysis of the well test data, one or more data points indicative of an intervention; generating a decline curve for each period in between the one or more data points indicative of an intervention and disregarding the one or more outlying data points; determining the production of a well for a predetermined period of time by integrating the decline curve over the predetermined period of time; and calculating the allocated production for the well by multiplying the ratio of the production of the well to the sum of the production for all wells in the lease by a production per lease value.