Abstract: A method and system for identifying a feature in seismic datasets using a machine learning (ML) network is provided. The method includes training the ML network by obtaining a seismic dataset and forming a plurality of seismic patches having a labeled feature. Training the ML network continues by predicting a candidate labeled feature patch for each seismic patch, forming a metric measuring a mismatch of the candidate labeled feature patch and the labeled feature and updating the ML network based on finding an extremum of the mismatch to form a trained ML network. The method further includes forming a plurality of production seismic patches having unlabeled features and inputting the patches into a trained ML network to predict a labeled feature patch having a labeled manifestation of the feature. A predicted labeled feature image may then be formed by merging the plurality of predicted labeled feature patches.

Abstract: Systems and methods are provided for seismic well tie domain conversion. In one embodiment, a process is provided to integrate well and seismic data for reservoir characterization. System configurations and processes described herein use neural networks to predict sonic well logs in the two way time (TWT) domain from measured well logs in depth, rather than predicting drift function. Embodiments are also directed to systems for reservoir characterization. Domain conversion of data includes receiving input data, preprocessing the data, and training a model to determine a length of an output sequence. The method also includes training the model for conversion of data based on at least one neural network. A sequence length prediction may be output as part of training and to perform modeling/prediction operations. The method also includes outputting the sequence of converted data in a TWT domain and a length of the sequence.

Abstract: A method is claimed for automatically transforming sonic well logs from a depth domain to a seismic two-way travel-time domain. The method includes obtaining a training well with a measured sonic well log in the depth domain and a borehole seismic dataset in the depth domain and obtaining an application well with only a measured sonic well log in the depth domain. The method further includes training a network to predict a transformed sonic well log for the training well based, at least in part, on the measured sonic well log and the borehole seismic dataset in the training well, and predicting with the network, the transformed sonic well log in the application well.

Abstract: A method is claimed for automatically transforming sonic well logs from a depth domain to a seismic two-way travel-time domain. The method includes obtaining a training well with a measured sonic well log in the depth domain and a borehole seismic dataset in the depth domain and obtaining an application well with only a measured sonic well log in the depth domain. The method further includes training a network to predict a transformed sonic well log for the training well based, at least in part, on the measured sonic well log and the borehole seismic dataset in the training well, and predicting with the network, the transformed sonic well log in the application well.

Abstract: Systems and methods are provided for seismic well tie domain conversion. In one embodiment, a process is provided to integrate well and seismic data for reservoir characterization. System configurations and processes described herein use neural networks to predict sonic well logs in the two way time (TWT) domain from measured well logs in depth, rather than predicting drift function. Embodiments are also directed to systems for reservoir characterization. Domain conversion of data includes receiving input data, preprocessing the data, and training a model to determine a length of an output sequence. The method also includes training the model for conversion of data based on at least one neural network. A sequence length prediction may be output as part of training and to perform modeling/prediction operations. The method also includes outputting sequence length in a TWT domain and output of transformed data.

Abstract: The present disclosure provides a method of processing gravity gradient data indicative of an output generated by an airborne gravity gradiometer that is moving along a flight path over a terrain. The method comprises the step of providing the gravity gradient data. The gravity gradient data comprising gravity gradient data elements that are associated with respective flight path segments of the airborne gravity gradiometer. Further, the method comprises providing terrain data indicative of a topography and a density or a density distribution of the terrain above a datum that is below the surface of the terrain over which the airborne gravity gradiometer is moved. The method also comprises providing information concerning the flight path of the airborne gravity gradiometer in three dimensions. In addition, the method comprises calculating the gravity gradient response of the terrain using the provided terrain data and the provided information concerning the flight path.

Abstract: The present disclosure provides a method of processing gravity gradient data indicative of an output generated by an airborne gravity gradiometer that is moving along a flight path over a terrain. The method comprises the step of providing the gravity gradient data. The gravity gradient data comprising gravity gradient data elements that are associated with respective flight path segments of the airborne gravity gradiometer. Further, the method comprises providing terrain data indicative of a topography and a density or a density distribution of the terrain above a datum that is below the surface of the terrain over which the airborne gravity gradiometer is moved. The method also comprises providing information concerning the flight path of the airborne gravity gradiometer in three dimensions. In addition, the method comprises calculating the gravity gradient response of the terrain using the provided terrain data and the provided information concerning the flight path.

Abstract: A container for sanitary storage of a toothbrush is disclosed having a base, a compartment, a lid base and a lid hingedly connected to the lid base. The container can be interconnected with other like containers for added stability. The compartment has two female receptacles on the right side and two female receptacles on the left side for receiving connector plugs. The container may be adapted to different sized toothbrushes by insertion of a filler block. The container may be optionally mounted on the wall.