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: Techniques for predicting a landing zone of a wellbore include identifying a first subsurface geological model of a first subterranean layer located under a terranean surface that includes an upper boundary depth of the first subterranean layer and a lower boundary depth of the first subterranean layer; identifying a second subsurface geological model of a second subterranean layer deeper than the first subterranean layer that is independent of the first subsurface geological model and includes an upper boundary depth of the second subterranean layer; correlating a predicted landing zone for a plurality of wellbores using the first and second subsurface geological models that is based on a location of a horizontal portion of each wellbore; and generating data that comprises a representation of the correlated plurality of wellbores for presentation on a graphical user interface (GUI).

Abstract: Techniques for predicting a landing zone of a wellbore include identifying a first subsurface geological model of a first subterranean layer located under a terranean surface that includes an upper boundary depth of the first subterranean layer and a lower boundary depth of the first subterranean layer; identifying a second subsurface geological model of a second subterranean layer deeper than the first subterranean layer that is independent of the first subsurface geological model and includes an upper boundary depth of the second subterranean layer; correlating a predicted landing zone for a plurality of wellbores using the first and second subsurface geological models that is based on a location of a horizontal portion of each wellbore; and generating data that comprises a representation of the correlated plurality of wellbores for presentation on a graphical user interface (GUI).

Abstract: A computing system receives an indication of an energy project. The computing system monitors satellite image data of the energy project. The computing system determines that a first activity has been initiated for the energy project based on a change in satellite image data. The computing system monitors telemetry data associated with a location corresponding to the energy project and the satellite image data of the energy project. The computing system converts the telemetry data associated with the location corresponding to the energy project to a telemetry activity index. The computing system converts the satellite image data associated with the energy project to a satellite activity index. The computing system determines that the first activity has ended for the energy project based on changes in at least one of the telemetry activity index and the pad activity index.

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.