A method for making natural gamma ray measurements of a subterranean formation includes causing a natural gamma ray sensor on an LWD tool to acquire a spectral gamma ray measurement while a neutron source emits neutrons. The measurements are evaluated to compute first and second drilling fluid activation corrections using corresponding first and second correction methodologies. The first and second corrections are processed to compute a third drilling fluid activation correction which is applied to the gamma ray measurements to compute a corrected total natural gamma ray measurement.

A method for making natural gamma ray measurements of a subterranean formation includes causing a natural gamma ray sensor on an LWD tool to acquire a spectral gamma ray measurement while a neutron source emits neutrons. The measurements are evaluated to compute first and second drilling fluid activation corrections using corresponding first and second correction methodologies. The first and second corrections are processed to compute a third drilling fluid activation correction which is applied to the gamma ray measurements to compute a corrected total natural gamma ray measurement.

The disclosure relates to methods and systems for identifying formations that are clay-free or with minimal amount of clays, which have high hydrocarbon potential, by using both low frequency permittivity measurements (e.g., low-frequency permittivity measurements) and natural gamma ray flux measurements.

The disclosure relates to methods and systems for identifying formations that are clay-free or with minimal amount of clays, which have high hydrocarbon potential, by using both low frequency permittivity measurements (e.g., low-frequency permittivity measurements) and natural gamma ray flux measurements.

Methods for logging a well utilizing natural radioactivity originating from clay based particulates are disclosed. The methods can include utilizing a gravel pack slurry containing a liquid and gravel pack particles to hydraulically place the particles into a gravel pack zone of a borehole penetrating a subterranean formation and obtaining a post gravel pack data set by lowering into the borehole traversing the subterranean formation a gamma ray detector and detecting gamma rays resulting from a native radioactivity of the gravel pack particles. The methods can further include using the post gravel pack data set to determine a location of the gravel pack particles and correlating the location of the gravel-pack particles to a depth measurement of the borehole to determine the location, height, and/or percent fill of gravel-pack particles placed in the gravel pack zone of the borehole.

Methods for logging a well utilizing natural radioactivity originating from clay based particulates are disclosed. The methods can include utilizing a gravel pack slurry containing a liquid and gravel pack particles to hydraulically place the particles into a gravel pack zone of a borehole penetrating a subterranean formation and obtaining a post gravel pack data set by lowering into the borehole traversing the subterranean formation a gamma ray detector and detecting gamma rays resulting from a native radioactivity of the gravel pack particles. The methods can further include using the post gravel pack data set to determine a location of the gravel pack particles and correlating the location of the gravel-pack particles to a depth measurement of the borehole to determine the location, height, and/or percent fill of gravel-pack particles placed in the gravel pack zone of the borehole.

The use of scintillator compositions having a cubic garnet structure for gamma detection in downhole oil and gas explorations is provided. Specifically, two primary compositions of interest are disclosed, Ca.sub.2LnHf.sub.2Al.sub.3O.sub.12 and NaLn.sub.2Hf.sub.2Al.sub.3O.sub.12, where Ln is Y, Gd, Tb, or La. Under gamma ray excitation, the electron-hole pairs produced in the garnet lattice structure are trapped by an activator ion to yield an efficient emission in the visible portion of the electromagnetic spectrum. The cubic garnet structure enables the use of these materials as ceramic scintillators with considerable advantages over related single crystals in various ways as disclosed herein, including reduction in cost and improvement in overall performance and durability.

The use of scintillator compositions having a cubic garnet structure for gamma detection in downhole oil and gas explorations is provided. Specifically, two primary compositions of interest are disclosed, Ca.sub.2LnHf.sub.2Al.sub.3O.sub.12 and NaLn.sub.2Hf.sub.2Al.sub.3O.sub.12, where Ln is Y, Gd, Tb, or La. Under gamma ray excitation, the electron-hole pairs produced in the garnet lattice structure are trapped by an activator ion to yield an efficient emission in the visible portion of the electromagnetic spectrum. The cubic garnet structure enables the use of these materials as ceramic scintillators with considerable advantages over related single crystals in various ways as disclosed herein, including reduction in cost and improvement in overall performance and durability.

Aspects of the subject technology relate to performing gamma spectral analysis based on machine learning. Gamma spectrum data, which can be associated with a gamma spectrum can be gathered. The gamma spectrum data can include an energy channel and a count rate for gamma rays detected by one or more gamma detectors. A spectral image can be constructed based on the gamma spectrum data. One or more machine learning models can be trained based on the spectral image. Additionally, one or more features of the gamma spectrum can be extracted from the spectral image through the one or more machine learning models.

Aspects of the subject technology relate to performing gamma spectral analysis based on machine learning. Gamma spectrum data, which can be associated with a gamma spectrum can be gathered. The gamma spectrum data can include an energy channel and a count rate for gamma rays detected by one or more gamma detectors. A spectral image can be constructed based on the gamma spectrum data. One or more machine learning models can be trained based on the spectral image. Additionally, one or more features of the gamma spectrum can be extracted from the spectral image through the one or more machine learning models.