Abstract: Processes can be employed to select cartographic reference system (CRS) recommendations from a CRS model where the CRS recommendations are matched to received seismic data. A learning mode can be used to build the CRS model where seismic data is matched to CRS. The learning mode can be automated using natural language processing system to parse the meta data for the seismic data, such as the name, area, or code, or label. The CRS model can be updated using an output from a user system, such as when a user manually matches a CRS to seismic data. The matched seismic data to CRS can be used as input to a user system or a computing system, such as a borehole operation system.

Abstract: Methods and apparatus for generating one or more reservoir 3D models are provided. In one or more embodiments, a method can include training a first machine learning model to generate one or more integrated enhanced logs based, at least in part, on an integrated data set, wherein the integrated data set includes seismic data and well log data; generating one or more integrated enhanced logs from the first machine learning model; grouping the one or more integrated enhanced logs into an ensemble of integrated enhanced logs to form a static reservoir 3D model of a subterranean reservoir; inputting additional data to the first machine learning model to produce one or more updated integrated enhanced logs; and grouping the one or more updated integrated enhanced logs into an ensemble of updated integrated enhanced logs to form an updated 3D model.

Abstract: A wellbore drilling system can generate a machine-learning model trained using historic drilling operation data for monitoring for a lost circulation event. Real-time data for a drilling operation can be received and the machine-learning model can be applied to the real-time data to identify a lost circulation event that is occurring. An alarm can then be outputted to indicate a lost circulation event is occurring for the drilling operation.

Abstract: A system can determine a location for future wells using machine-learning techniques. The system can receive seismic data about a subterranean formation and may determine a set of seismic attributes from the seismic data. The system can block the set of seismic attributes into a set of blocked seismic attributes by distributing the set of seismic attributes onto a geo-cellular grid representative of the subterranean formation. The system can apply a trained machine-learning model to the set of blocked seismic attributes to generate a composite seismic parameter. The system can distribute the composite seismic parameter in the subterranean formation to characterize formation locations based on a predicted presence of hydrocarbons.

Abstract: The disclosure presents processes to select cartographic reference system (CRS) recommendations from a CRS model where the CRS recommendations are matched to received seismic data. A learning mode can be used to build the CRS model where seismic data is matched to CRS. The learning mode can be automated using natural language processing system to parse the meta data for the seismic data, such as the name, area, or code, or label. The CRS model can be updated using an output from a user system, such as when a user manually matches a CRS to seismic data. The matched seismic data to CRS, e.g., seismic data-CRS match, can be used as input to a user system or a computing system, such as a borehole operation system.

Abstract: A system is described for determining a likelihood of a type of fluid in a subterranean reservoir. The system may include a processor and a non-transitory computer-readable medium that includes instructions executable by the processor to cause the processor to perform various operations. The processor may receive pre-stack seismic data having seismically-acquired data elements for geometric locations in a subterranean reservoir. The processor may determine, using the pre-stack seismic data, input features for each geometric location and may execute a trained model on the input features for determining a likelihood of a type of fluid in the subterranean reservoir and for determining a list of features affecting the likelihood. The processor may subsequently output the likelihood and the list of features.

Abstract: Optimizing seismic to depth conversion to enhance subsurface operations including measuring seismic data in a subsurface formation, dividing the subsurface formation into a training area and a study area, dividing the seismic data into training seismic data and study seismic data, wherein the training seismic data corresponds to the training area, and wherein the study seismic data corresponds to the study area, calculating target depth data corresponding to the training area, training a machine learning model using training inputs and training targets, wherein the training inputs comprise the training seismic data, and wherein the training targets comprise the target depth data, computing, by the machine learning model, output depth data corresponding to the study area based at least in part on the study seismic data; and modifying one or more subsurface operations corresponding to the study area based at least in part on the output depth data.

Abstract: An ensemble of machine learning models is trained to evaluate seismic and risk-related data in order to evaluate, value, or otherwise rank various prospective hydrocarbon reservoir (“prospects”) of a field. A classification machine learning model is trained to classify a prospect or region of a prospect based on the exploration risk level. From the seismic data, a frequency-filtered volume (FFV) for each prospect is calculated, where the FFV is a measure of reservoir volume which takes into account seismic resolution limits. Based on the risk classification and FFV, prospects of the field are ranked based on their economic value which is a combination of the risk associated with drilling and their potential reservoir volume.

Abstract: A system can determine a location for future wells using machine-learning techniques. The system can receive seismic data about a subterranean formation and may determine a set of seismic attributes from the seismic data. The system can block the set of seismic attributes into a set of blocked seismic attributes by distributing the set of seismic attributes onto a geo-cellular grid representative of the subterranean formation. The system can apply a trained machine-learning model to the set of blocked seismic attributes to generate a composite seismic parameter. The system can distribute the composite seismic parameter in the subterranean formation to characterize formation locations based on a predicted presence of hydrocarbons.

Abstract: A system is described for determining a likelihood of a type of fluid in a subterranean reservoir. The system may include a processor and a non-transitory computer-readable medium that includes instructions executable by the processor to cause the processor to perform various operations. The processor may receive pre-stack seismic data having seismically-acquired data elements for geometric locations in a subterranean reservoir. The processor may determine, using the pre-stack seismic data, input features for each geometric location and may execute a trained model on the input features for determining a likelihood of a type of fluid in the subterranean reservoir and for determining a list of features affecting the likelihood. The processor may subsequently output the likelihood and the list of features.

Abstract: Methods and apparatus for generating one or more reservoir 3D models are provided. In one or more embodiments, a method can include training a first machine learning model to generate one or more integrated enhanced logs based, at least in part, on an integrated data set, wherein the integrated data set includes seismic data and well log data; generating one or more integrated enhanced logs from the first machine learning model; grouping the one or more integrated enhanced logs into an ensemble of integrated enhanced logs to form a static reservoir 3D model of a subterranean reservoir; inputting additional data to the first machine learning model to produce one or more updated integrated enhanced logs; and grouping the one or more updated integrated enhanced logs into an ensemble of updated integrated enhanced logs to form an updated 3D model.

Abstract: Optimizing seismic to depth conversion to enhance subsurface operations including measuring seismic data in a subsurface formation, dividing the subsurface formation into a training area and a study area, dividing the seismic data into training seismic data and study seismic data, wherein the training seismic data corresponds to the training area, and wherein the study seismic data corresponds to the study area, calculating target depth data corresponding to the training area, training a machine learning model using training inputs and training targets, wherein the training inputs comprise the training seismic data, and wherein the training targets comprise the target depth data, computing, by the machine learning model, output depth data corresponding to the study area based at least in part on the study seismic data; and modifying one or more subsurface operations corresponding to the study area based at least in part on the output depth data.

Abstract: A wellbore drilling system can generate a machine-learning model trained using historic drilling operation data for monitoring for a lost circulation event. Real-time data for a drilling operation can be received and the machine-learning model can be applied to the real-time data to identify a lost circulation event that is occurring. An alarm can then be outputted to indicate a lost circulation event is occurring for the drilling operation.