Abstract: A core analysis system having a trailer and an analysis assembly secured to the trailer. The analysis assembly includes an X-ray Fluorescence (XRF) detection subassembly defining a sample analysis area. The analysis assembly further includes a conveyor subassembly configured to selectively deliver one or more core samples to the sample analysis area of the XRF detection subassembly.

Abstract: Systems and methods for interpreting high-energy interactions on a sample are described in this application. In particular, this application describes an analysis method that comprises impinging radiation from a source on an analyte, detecting the energy interactions resulting from the impinging radiation using a radiation detector, adjusting the signal from the radiation detector using a machine learning module to emphasize specific parts of the detector signal, training the machine learning module in a supervised or unsupervised manner, producing quantitative and qualitative models using the machine leaning module, and then applying the machine learning module to additional energy interactions. The signal received by the detector can be preprocessed to emphasize specific parts of the detector signal, which is then mapped to a machine learning module for training in a supervised or unsupervised manner.

Abstract: A drill bit can be configured to form core segments during drilling. The drill bit can have having a central axis and a shank defining an inner bore. A crown can be coupled to the shank. The crown can define a cutting face and a core receiving slot that extends inwardly into the crown from the cutting face. The crown can define an inner operative circumference. A core break structure can be disposed within the shank. The core break structure can define a core break surface that extends inwardly toward the central axis and intersects an imaginary 3D projection of the inner circumference projected along the central axis.

Abstract: A core barrel head assembly having a longitudinal axis can comprise an elongate tube body having an outer surface, an interior cavity, a proximal end, and a distal end. The elongate tube body can define a helical groove that extends from the interior cavity to the outer surface of the elongate tube body. The helical groove can be configured to allow the elongate tube body to elastically extend and compress. The elongate tube body can define at least one aperture that extends between the interior cavity and the outer surface. The core barrel head assembly can further comprise a valve body that is attached to the elongate tube body and is movable with respect to the proximal end of the elongate tube along the longitudinal axis, as the elongate tube body compresses. When sufficiently compressed, the valve body restricts flow through the at least one aperture.

Abstract: A drilling system can comprise a drill string having a longitudinal axis and comprising at least one drill rod and a wireless sub coupled to the at least one drill rod. The wireless sub can comprise processing circuitry that is configured to detect mechanical impulses of the drill string. The processing circuitry can be configured to wirelessly transmit signals indicative of the mechanical impulses to a remote computing device.

Abstract: Systems and methods for interpreting high-energy interactions on a sample are described in this application. In particular, this application describes an analysis method that comprises impinging radiation from a source on an analyte, detecting the energy interactions resulting from the impinging radiation using a radiation detector, adjusting the signal from the radiation detector using a machine learning module to emphasize specific parts of the detector signal, training the machine learning module in a supervised or unsupervised manner, producing quantitative and qualitative models using the machine leaning module, and then applying the machine learning module to additional energy interactions. The signal received by the detector can be preprocessed to emphasize specific parts of the detector signal, which is then mapped to a machine learning module for training in a supervised or unsupervised manner.