Implementations described and claimed herein provide systems and methods for developing a reservoir. In one implementation, observed data points in a volume along a well trajectory and well logs corresponding to observed data points are received at a neural network. Feature vectors are generated using the neural network. The feature vectors are defined based on a distance between each of the observed data points and randomly generated points in the volume. A 3D populated log is generated by propagating well log values of the feature vectors across the volume. Uncertainty is quantified by generating a plurality of realizations including the 3D populated log. Each of the realizations is different and equally probable. core values are generated from the realizations, and a static model of the reservoir is generated by clustering the volume into one or more clusters of rock types based on the core values.
G06N 3/04 - Architecture, e.g. interconnection topology
G06N 3/088 - Non-supervised learning, e.g. competitive learning
G06F 30/20 - Design optimisation, verification or simulation
G06N 3/043 - Architecture, e.g. interconnection topology based on fuzzy logic, fuzzy membership or fuzzy inference, e.g. adaptive neuro-fuzzy inference systems [ANFIS]
Implementations described and claimed herein provide systems and methods for developing a reservoir. In one implementation, a static model of the reservoir is received. The static model has one or more clusters of rock types. A reservoir graph is generated from the static model. The reservoir graph represents each of the one or more clusters as a vertex. A graph connectivity of the reservoir graph is defined through a nodal connectivity of neighboring vertices. Pressure values are propagated across three-dimensional space of the reservoir graph using the connectivity. A dynamic model of the reservoir is generated using the pressure values and fluid saturation values.
G06N 3/04 - Architecture, e.g. interconnection topology
G06N 3/088 - Non-supervised learning, e.g. competitive learning
G06F 30/20 - Design optimisation, verification or simulation
G06N 3/043 - Architecture, e.g. interconnection topology based on fuzzy logic, fuzzy membership or fuzzy inference, e.g. adaptive neuro-fuzzy inference systems [ANFIS]
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.
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. The dynamic modeling module determines connectivity values between clusters of formations based on nodal connectivity of neighboring clusters, assigns pressure values across the volume of interest, and generates a three-dimensional dynamic model for the volume of interest based on the pressure values.
G06F 30/20 - Design optimisation, verification or simulation
G06N 3/04 - Architecture, e.g. interconnection topology
G06N 3/043 - Architecture, e.g. interconnection topology based on fuzzy logic, fuzzy membership or fuzzy inference, e.g. adaptive neuro-fuzzy inference systems [ANFIS]
42 - Scientific, technological and industrial services, research and design
Goods & Services
Providing on-line non-downloadable software that uses artificial intelligence for analyzing and characterizing data for supporting decision making processes
6.
Static engine and neural network for a cognitive reservoir system
Implementations described and claimed herein provide systems and methods for developing a reservoir. In one implementation, observed data points in a volume along a well trajectory and well logs corresponding to observed data points are received at a neural network. Feature vectors are generated using the neural network. The feature vectors are defined based on a distance between each of the observed data points and randomly generated points in the volume. A 3D populated log is generated by propagating well log values of the feature vectors across the volume. Uncertainty is quantified by generating a plurality of realizations including the 3D populated log. Each of the realizations is different and equally probable. core values are generated from the realizations, and a static model of the reservoir is generated by clustering the volume into one or more clusters of rock types based on the core values.
G06N 3/04 - Architecture, e.g. interconnection topology
G06N 3/088 - Non-supervised learning, e.g. competitive learning
G06F 30/20 - Design optimisation, verification or simulation
G06N 3/043 - Architecture, e.g. interconnection topology based on fuzzy logic, fuzzy membership or fuzzy inference, e.g. adaptive neuro-fuzzy inference systems [ANFIS]
42 - Scientific, technological and industrial services, research and design
Goods & Services
Providing temporary use of on-line non-downloadable artificial intelligence software and software for analyzing and characterizing data and for supporting decision making processes
8.
SYSTEM FOR IMPROVED RESERVOIR EXPLORATION AND PRODUCTION
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. The dynamic modeling module determines connectivity values between clusters of formations based on nodal connectivity of neighboring clusters, assigns pressure values across the volume of interest, and generates a three-dimensional dynamic model for the volume of interest based on the pressure values.
Implementations described and claimed herein provide systems and methods for developing a reservoir. In one implementation, observed data points in a volume along a well trajectory and well logs corresponding to observed data points are received at a neural network. Feature vectors are generated using the neural network. The feature vectors are defined based on a distance between each of the observed data points and randomly generated points in the volume. A 3D populated log is generated by propagating well log values of the feature vectors across the volume. Uncertainty is quantified by generating a plurality of realizations including the 3D populated log. Each of the realizations is different and equally probable, core values are generated from the realizations, and a static model of the reservoir is generated by clustering the volume into one or more clusters of rock types based on the core values.
Implementations described and claimed herein provide systems and methods for developing a reservoir. In one implementation, a static model of the reservoir is received. The static model has one or more clusters of rock types. A reservoir graph is generated from the static model. The reservoir graph represents each of the one or more clusters as a vertex. A graph connectivity of the reservoir graph is defined through a nodal connectivity of neighboring vertices. Pressure values are propagated across three-dimensional space of the reservoir graph using the connectivity. A dynamic model of the reservoir is generated using the pressure values and fluid saturation values.
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.
Implementations described and claimed herein provide systems and methods for developing a reservoir. In one implementation, observed data points in a volume along a well trajectory and well logs corresponding to observed data points are received at a neural network. Feature vectors are generated using the neural network. The feature vectors are defined based on a distance between each of the observed data points and randomly generated points in the volume. A 3D populated log is generated by propagating well log values of the feature vectors across the volume. Uncertainty is quantified by generating a plurality of realizations including the 3D populated log. Each of the realizations is different and equally probable. core values are generated from the realizations, and a static model of the reservoir is generated by clustering the volume into one or more clusters of rock types based on the core values.
Implementations described and claimed herein provide systems and methods for developing a reservoir. In one implementation, a static model of the reservoir is received. The static model has one or more clusters of rock types. A reservoir graph is generated from the static model. The reservoir graph represents each of the one or more clusters as a vertex. A graph connectivity of the reservoir graph is defined through a nodal connectivity of neighboring vertices. Pressure values are propagated across three-dimensional space of the reservoir graph using the connectivity. A dynamic model of the reservoir is generated using the pressure values and fluid saturation values.
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. The dynamic modeling module determines connectivity values between clusters of formations based on nodal connectivity of neighboring clusters, assigns pressure values across the volume of interest, and generates a three-dimensional dynamic model for the volume of interest based on the pressure values.
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.
09 - Scientific and electric apparatus and instruments
Goods & Services
Computer operating systems using cognitive artificial intelligence for use in providing management, control, information, support and answers to queries for complex data characterization, analytics, and decision support
42 - Scientific, technological and industrial services, research and design
Goods & Services
Providing temporary use of on-line non-downloadable software using artificial intelligence for analyzing and characterizing data for supporting decision making processes
42 - Scientific, technological and industrial services, research and design
Goods & Services
Providing temporary use of on-line non-downloadable artificial intelligence software and software for analyzing and characterizing data and for supporting decision making processes
