A system and methods for petro-steering methodologies are provided. An exemplary method obtains rock fabric data, and integrate rock fabric data with dynamic productivity data to identify patterns between the rock fabric data and dynamic productivity data. Gas rates and steering values are predicted across UBCT wells based on the patterns.

A system and methods for petro-steering methodologies are provided. An exemplary method obtains rock fabric data, and integrate rock fabric data with dynamic productivity data to identify patterns between the rock fabric data and dynamic productivity data. Gas rates and steering values are predicted across UBCT wells based on the patterns.

Embodiments described herein utilize Non-Destructive Inspection (NDI) scan data obtained during a process performed on a surface of a structure to update a location of an NDI scanner on the surface. A subsurface feature within the structure is detected based on the NDI scan data, which are correlated with pre-defined position data for the subsurface feature. A measured location of the NDI scanner on the surface is corrected based on the pre-defined position data for the subsurface feature.

Methods are provided for determining a depositional environment of a sample of a subterranean environment. An example method includes measuring intensities for a crystallographic plane (CP) 100 peak and a CP 101 peak for quartz in a diffractogram, calculating a ratio of the intensities of the CP 100 peak to the CP 101 peak, and identifying a depositional environment for the sample from the ratio.

Methods are provided for determining a depositional environment of a sample of a subterranean environment. An example method includes measuring intensities for a crystallographic plane (CP) 100 peak and a CP 101 peak for quartz in a diffractogram, calculating a ratio of the intensities of the CP 100 peak to the CP 101 peak, and identifying a depositional environment for the sample from the ratio.

A method and system are presented for use in X-ray based measurements on patterned structures. The method comprises: processing data indicative of measured signals corresponding to detected radiation response of a patterned structure to incident X-ray radiation, and subtracting from said data an effective measured signals substantially free of background noise, said effective measured signals being formed of radiation components of reflected diffraction orders such that model based interpretation of the effective measured signals enables determination of one or more parameters of the patterned structure, wherein said processing comprises: analyzing the measured signals and extracting therefrom a background signal corresponding to the background noise; and applying a filtering procedure to the measured signals to subtract therefrom signal corresponding to the background signal, resulting in the effective measured signal.

A method and system are presented for use in X-ray based measurements on patterned structures. The method comprises: processing data indicative of measured signals corresponding to detected radiation response of a patterned structure to incident X-ray radiation, and subtracting from said data an effective measured signals substantially free of background noise, said effective measured signals being formed of radiation components of reflected diffraction orders such that model based interpretation of the effective measured signals enables determination of one or more parameters of the patterned structure, wherein said processing comprises: analyzing the measured signals and extracting therefrom a background signal corresponding to the background noise; and applying a filtering procedure to the measured signals to subtract therefrom signal corresponding to the background signal, resulting in the effective measured signal.

Method and system are disclosed for determining sample composition from spectral data acquired by a charged particle microscopy system. Chemical elements in a sample are identified by processing the spectral data with a trained neural network (NN). If the identified chemical elements not matching with a known elemental composition of the sample, the trained NN is retrained with the spectral data and the known elemental composition of the sample. The retrained NN can then be used to identify chemical elements within other samples.

An intelligent lithology identification system and method based on images and spectrum technology. The intelligent lithology identification system includes a rock shape analysis system, an image identification system, a sample processing system, a spectrum analysis system, and a central analysis and control system; wherein the central analysis and control system determines the final lithology of a sample according to the rock identification results from the image identification system and the analysis results from the spectrum analysis system. The technical solution further identifies the content and type of minerals by using spectrum technology, integrates and analyzes the results of spectrum analysis and image identification, and finally gives the lithology of the rock, which greatly improves the accuracy of lithology identification.

System for treatment positioning is provided. The system may include a treatment component, an imaging component, and a couch. The treatment component may include a radiation source that has a radiation isocenter. The couch may be movable between the treatment component and the imaging component, and include a positioning line that has a positioning feature. The system may acquire at least one first image relating to a subject and the positioning line using the radiation source at a set-up position. The system may also acquire at least one second image relating to the subject and the positioning line using the imaging component at an imaging position. The system may further determine a treatment isocenter of a target of the subject based on the at least one second image, and determine a treatment position of the subject based on the first image(s), the second image(s), and the positioning line.