Abstract: A method includes operating a neutron generator in a loading mode by ionizing ionizable gas within an ion source of the neutron generator to create a plurality of ions, and accelerating the plurality of ions by providing a first voltage to a target rod that supports the target to create a first ion beam that bombards a target of the neutron generator. The method also includes operating the neutron generator in a generating mode to generate a plurality of neutrons by accelerating the plurality of ions by providing a second voltage to the target rod to create a second ion beam that bombards the target. The second voltage is greater than the first voltage.

Abstract: A conveying device (100) and an inspection system (1000) are provided. The conveying device includes: a first support frame (1); a first conveying mechanism (2) and a second conveying mechanism (3) which are installed on the first support frame; and a switching mechanism (4) configured to selectively lift the first conveying mechanism or the second conveying mechanism in a vertical direction, such that the first conveying mechanism or the second conveying mechanism carries goods (401) and conveys the goods in a first horizontal direction or a second horizontal direction different from the first horizontal direction. The conveying device may achieve a positioning and a smooth continuous conveying of goods, and achieve a smooth conveying and a calibrated positioning of the goods in different postures.

Abstract: Aspects of the subject technology relate to determining a holdup measurement based on a gamma spectrum through machine learning. A spectral image based on a gamma spectrum generated downhole in a wellbore can be accessed. A component of a holdup measurement for the wellbore can be classified into a specific quantized level through application of a machine learning classification model to the spectral image. A continuous value for the component of the holdup measurement for the wellbore can be quantified by applying a machine learning quantization model associated with the quantized level.

Abstract: A neutron generator with an ion source within a housing may be used for generating neutrons for neutron logging downhole in a wellbore. The ion source within the housing of the neutron generator may include a hot cathode, an ion source cylinder, a first grid separated from the ion source cylinder, and an extractor separated from the ion source cylinder, the extractor having a second grid.

Abstract: A downhole neutron generator includes a housing, a gas reservoir positionable within the housing, a target rod positionable within the housing and having a longitudinal axis aligned with a central axis of the housing, an ion source positionable adjacent to the gas reservoir and between the target rod and the gas reservoir, and a target positionable on a surface of the target rod facing the ion source. The target includes a first metal layer on the surface of the target rod and a second metal layer positionable adjacent to the first metal layer facing the ion source. The second metal layer is a scandium layer.

Abstract: A method and system for identifying one or more petrophysical properties in a formation. The method and system may include disposing a pulsed-neutron logging tool into a borehole that is disposed in a formation, emitting a neutron from a neutron source on the pulsed-neutron logging tool into the formation, and capturing one or more gammas expelled from formation in response to the neutron from the neutron source to form a plurality of pulsed neutron logging (PNL) measurements in a log. The method and system may further include comparing the log to a database with a cost function to form a solution; and identifying a plurality of petrophysical properties based at least in part on the solution.

Abstract: Systems and methods may utilize information collected by a pulsed-neutron logging tool along with modeling a characterization of a borehole to form a 3-stage correction algorithm. This algorithm may be used to find an oil, water, and gas holdup in the borehole. During operations, a pulsed neutron logging tool which emits neutrons to interact with nuclei inducing gamma radiation. The gamma radiation is detected into a response which may be correlated to the location of a holdup in a borehole by using the entire spectrum or ratios of selected peaks. In examples, a borehole density index may be implemented to complement the response and improve accuracy and measurement confidence.

Abstract: Aspects of the subject technology relate to determining holdup compensated formation saturation while refraining from calculating holdup. Inelastic gamma spectrum data for an inelastic gamma spectrum generated downhole in a wellbore can be accessed. Further, capture gamma spectrum data for one or more capture gamma spectrums generated downhole in the wellbore can be accessed. A model that accounts for holdup measurement can be applied to both the inelastic gamma spectrum data and the capture gamma spectrum data to identify a compensated oil saturation for a formation surrounding at least a portion of the wellbore based on both the inelastic gamma spectrum and the one or more capture gamma spectrums.

Abstract: Systems and methods employed measure borehole density by neutron induced gammas using a pulsed neutron tool. Traditional nuclear density methods only measure a bulk average density of the surrounding material. As discussed below, methods to measure only the borehole density excluding the contamination from the formation are disclosed. Specifically, the proposed methods use unique signatures from each geometric region to directly measure the borehole density or compensate for the contamination from formation. This method may be achieved by a borehole density measurement using differential attenuation of capture gamma from casing iron, a borehole density measurement using differential attenuation of inelastic gamma from oxygen, a differential attenuation of any induced gamma from any element from borehole and formation, or any combination thereof.

Abstract: Systems and methods for determining holdup in a wellbore using a neutron-based downhole tool. In examples, the tool includes nuclear detectors that may measure gammas induced by highly energized pulsed-neutrons emitted by a generator. The characteristic energy and intensity of detected gammas indicate the elemental concentration for that interaction type. A detector response may be correlated to the borehole holdup by using the entire spectrum or the ratios of selected peaks. As a result, measurements taken by the neutron-based downhole tool may allow for a two component (oil and water) or a three component (oil, water, and gas) measurement. The two component or three component measurements may be further processed using machine learning (ML) and/or artificial intelligence (AI) with additional enhancements of semi-analytical physics algorithms performed at the employed network's nodes (or hidden layers).

Abstract: A neutron tube. At least some of the illustrative embodiments including: generating, from a neutron tube, a first neutron burst having a first characteristic energy spectra; and generating, from the neutron tube, a second neutron burst having a second characteristic energy spectra different than the first characteristic energy spectra, the generating the second neutron burst within one second of generating the first neutron burst.

Abstract: The disclosure provides a well integrity monitoring tool for a wellbore, a method, using a nuclear tool and an EM tool, for well integrity monitoring of a wellbore having a multi-pipe configuration, and a well integrity monitoring system. In one example, the method includes: operating a nuclear tool in the wellbore to make a nuclear measurement at a depth of the wellbore, operating an EM tool in the wellbore to make an EM measurement at the depth of the wellbore, determining a plurality of piping properties of the multi-pipe configuration at the depth employing the EM measurement, determining, employing the piping properties, a processed nuclear measurement from the nuclear measurement, and employing the processed nuclear measurement to determine an integrity of a well material at the depth and within an annulus defined by the multi-pipe configuration.

Abstract: Aspects of the subject technology relate to determining a holdup measurement based on a gamma spectrum through machine learning. A spectral image based on a gamma spectrum generated downhole in a wellbore can be accessed. A component of a holdup measurement for the wellbore can be classified into a specific quantized level through application of a machine learning classification model to the spectral image. A continuous value for the component of the holdup measurement for the wellbore can be quantified by applying a machine learning quantization model associated with the quantized level.

Abstract: 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.

Abstract: Systems and methods may utilize information collected by a pulsed-neutron logging tool along with modeling a characterization of a borehole to form a 3-stage correction algorithm. This algorithm may be used to find an oil, water, and gas holdup in the borehole. During operations, a pulsed neutron logging tool which emits neutrons to interact with nuclei inducing gamma radiation. The gamma radiation is detected into a response which may be correlated to the location of a holdup in a borehole by using the entire spectrum or ratios of selected peaks. In examples, a borehole density index may be implemented to complement the response and improve accuracy and measurement confidence.

Abstract: Systems and methods for determining holdup in a wellbore using a neutron-based downhole tool. In examples, the tool includes nuclear detectors that may measure gammas induced by highly energized pulsed-neutrons emitted by a generator. The characteristic energy and intensity of detected gammas indicate the elemental concentration for that interaction type. A detector response may be correlated to the borehole holdup by using the entire spectrum or the ratios of selected peaks. As a result, measurements taken by the neutron-based downhole tool may allow for a two component (oil and water) or a three component (oil, water, and gas) measurement. The two component or three component measurements may be further processed using machine learning (ML) and/or artificial intelligence (AI) with additional enhancements of semi-analytical physics algorithms performed at the employed network's nodes (or hidden layers).

Abstract: Systems and methods employed measure borehole density by neutron induced gammas using a pulsed neutron tool. Traditional nuclear density methods only measure a bulk average density of the surrounding material. As discussed below, methods to measure only the borehole density excluding the contamination from the formation are disclosed. Specifically, the proposed methods use unique signatures from each geometric region to directly measure the borehole density or compensate for the contamination from formation. This method may be achieved by a borehole density measurement using differential attenuation of capture gamma from casing iron, a borehole density measurement using differential attenuation of inelastic gamma from oxygen, a differential attenuation of any induced gamma from any element from borehole and formation, or any combination thereof.

Abstract: The inelastic and capture ratio is optimized for porosity measurements in downhole applications. Pulsed-neutron data is acquired using a pulsed-neutron downhole tool. At each sampling point or log depth, the inelastic count rates and capture rates are computed. The inelastic count rate is corrected for the capture count background to increase porosity sensitivity. The capture count rate is computed by summing a range of time windows in the decay curve. In this process, the inelastic and capture responses are matched for borehole sensitivity. The ratio of inelastic and capture counts is computed. This ratio is the input to the characterized transform algorithm to compute measured porosity.

Abstract: A system includes a neutron source positionable within a wellbore to emit one or more neutrons toward a formation surrounding the wellbore. The system also includes a gamma ray detector positionable within the wellbore to detect gamma rays. Further, the system includes a gamma ray analyzer that can perform operations. The operations can include receiving data indicating detected gamma rays from the gamma ray detector. Additionally, the operations include determining, from the data indicating the detected gamma rays, an amount of activated tracer material present within the wellbore originating from non-radioactive tracer material of drill-in fluid. The operations also include determining, from the amount of activated tracer material, an amount of filtercake buildup in a wellbore, a depth of fluid-loss filtrate into the formation surrounding the wellbore, or a combination thereof.

Abstract: The inelastic and capture ratio is optimized for porosity measurements in downhole applications. Pulsed-neutron data is acquired using a pulsed-neutron downhole tool. At each sampling point or log depth, the inelastic count rates and capture rates are computed. The inelastic count rate is corrected for the capture count background to increase porosity sensitivity. The capture count rate is computed by summing a range of time windows in the decay curve. In this process, the inelastic and capture responses are matched for borehole sensitivity. The ratio of inelastic and capture counts is computed. This ratio is the input to the characterized transform algorithm to compute measured porosity.