Abstract: Implementations described and claimed herein provide systems and methods for developing a reservoir. In one implementation, a reservoir model is received. The reservoir model includes a static model and a dynamic model. The static model includes one or more clusters of a three-dimensional volume of the reservoir and an uncertainty quantification generated using a neural network. The dynamic model includes pressure values and fluid saturation values propagated across the three-dimensional volume through a nodal connectivity of neighboring clusters. A set of input features is generated from the static model and the dynamic model. The set of input features is related to a drilling attractiveness of a target region of the reservoir using a set of rules executed by a fuzzy inference engine. A quantification of the drilling attractiveness is generated. A recommendation for drilling in the reservoir is output based on the quantification of the drilling attractiveness.

Abstract: An architecture for predicting and modeling geological characteristics of a reservoir includes one or more neural networks, a static modeling module, a dynamic modeling module, and a fuzzy inference engine to provide recommendations for drilling a wellbore. The neural networks receive log data for coordinates along a well trajectory, and determine a geophysical relationship for a property of a subterranean formation as a function of distance vectors between the coordinates along the well trajectory and one or more sets of randomly generated coordinates. The static modeling module generates three-dimensional static models of a volume of interest based on predicted properties of formations residing therein from the neural networks.

Abstract: Implementations described and claimed herein provide systems and methods for developing a reservoir. In one implementation, a reservoir model is received. The reservoir model includes a static model and a dynamic model. The static model includes one or more clusters of a three-dimensional volume of the reservoir and an uncertainty quantification generated using a neural network. The dynamic model includes pressure values and fluid saturation values propagated across the three-dimensional volume through a nodal connectivity of neighboring clusters. A set of input features is generated from the static model and the dynamic model. The set of input features is related to a drilling attractiveness of a target region of the reservoir using a set of rules executed by a fuzzy inference engine. A quantification of the drilling attractiveness is generated. A recommendation for drilling in the reservoir is output based on the quantification of the drilling attractiveness.

Abstract: An architecture for predicting and modeling geological characteristics of a reservoir includes one or more neural networks, a static modeling module, a dynamic modeling module, and a fuzzy inference engine to provide recommendations for drilling a wellbore. The neural networks receive log data for coordinates along a well trajectory, and determine a geophysical relationship for a property of a subterranean formation as a function of distance vectors between the coordinates along the well trajectory and one or more sets of randomly generated coordinates. The static modeling module generates three-dimensional static models of a volume of interest based on predicted properties of formations residing therein from the neural networks.

Abstract: An architecture for predicting and modeling geological characteristics of a reservoir includes one or more neural networks, a static modeling module, a dynamic modeling module, and a fuzzy inference engine to provide recommendations for drilling a wellbore. The neural networks receive log data for coordinates along a well trajectory, and determine a geophysical relationship for a property of a subterranean formation as a function of distance vectors between the coordinates along the well trajectory and one or more sets of randomly generated coordinates. The static modeling module generates three-dimensional static models of a volume of interest based on predicted properties of formations residing therein from the neural networks.

Abstract: Implementations described and claimed herein provide systems and methods for developing a reservoir. In one implementation, a reservoir model is received. The reservoir model includes a static model and a dynamic model. The static model includes one or more clusters of a three-dimensional volume of the reservoir and an uncertainty quantification generated using a neural network. The dynamic model includes pressure values and fluid saturation values propagated across the three-dimensional volume through a nodal connectivity of neighboring clusters. A set of input features is generated from the static model and the dynamic model. The set of input features is related to a drilling attractiveness of a target region of the reservoir using a set of rules executed by a fuzzy inference engine. A quantification of the drilling attractiveness is generated. A recommendation for drilling in the reservoir is output based on the quantification of the drilling attractiveness.