Abstract: Machine learning based methane emissions monitoring includes collecting sensor data from sensors and applying an augmentation model to the sensor data to form a regression training set. A classification training set for a classification model is created by replacing regression output values from the regression training set with classification output values. The classification output values include binary values. Machine learning based methane emissions monitoring further includes training the regression model with the regression training set to generate a regression prediction and training the classification model with the classification training set to generate a classification prediction.

Abstract: Systems and methods presented herein generally relate to greenhouse gas emission management and, more particularly, to a greenhouse gas emission management workflow to perform greenhouse gas detection sensor placement or greenhouse gas leak detection. For example, the system and method enable improved gas sensor arrangements within an oil and gas worksite. In another example, the system and method enable the prediction of a location and a leak rate of a gas leak within an oil and gas production facility based on measurements collected by gas leak sensors disposed within the facility and prevailing wind information.

Abstract: Methods and systems are provided for optimizing well-logging using an optimized wait time determined by analysis nuclear magnetic resonance data to achieve faster and better quality borehole evaluation. The method comprises performing a nuclear magnetic resonance pre-log testing; identifying a wait time for a portion of a signal from the pre-log testing with a long T1 and T2, value at each depth of the pre-log testing, wherein T1 is defined as a longitudinal relaxation time and T2 is a transverse relaxation time ascertained from the nuclear magnetic resonance prelog testing; and constructing a logging program with a logging program wait time being consistent with the wait time identified.

Abstract: Systems and methods are described for calculating an emission rate of a fugitive gas based on a gas density image of the fugitive gas. In an example, a computing device receives a gas density image of a fugitive gas from a camera. The computing device determines how to optimize the fugitive gas in the camera's field of view and instructions the camera to adjust its bearing and zoom accordingly. The camera captures one or more additional images of the fugitive gas, and the computing device stitches the images together where appropriate. The computing device then calculates the emission rate by delineating the fugitive gas in the image and determining a flux of the gas using one of various calculation methods.

Abstract: Embodiments presented provide for a method for detecting emissions. The method establishes a map that is used with prevailing wind conditions to establish a point source location for methane gas emissions.

Abstract: Example embodiments provide a method for improved commissioning, interpretation, and automated or remote operation of a methane density camera designed for monitoring of gas emissions. In some embodiments, the method consists of three related but independent steps including commissioning, remote operation, and data interpretation.

Abstract: Systems and methods are described for calibrating an imaging or LIDAR based gas monitoring system for efficiently scanning for gas plumes. In an example, a calibration workflow that improves the accuracy of transformations from observed points in a particular camera frame to a coordinate system that is fixed with respect to the ground, such as a set of latitude, longitude, and height values; or a spherical polar coordinate system centered at the camera where the zenith is perpendicular to the ground.

Abstract: A method includes receiving an indication of an emission plume traveling along a first direction. The method also includes determining a cross-section of the emission plume, wherein the cross-section is substantially perpendicular to the first direction. Further, the method includes determining a travel path for an optical detector to obtain optical measurements along the cross-section, wherein the travel path extends in a second direction along the cross-section, and the optical detector is configured to obtain the optical measurements in a third direction crosswise to the travel path.

Abstract: Process for locating emission detecting camera(s) at a worksite. The process can include creating a site model, completing a camera coverage calculation loop that can include choosing a first camera location from the site model, and completing a source calculation loop to provide a plurality of coverage values of the first camera location for a plurality of potential emission sources in the site model. The process can also include calculating a coverage ratio from the plurality of coverage values to provide a first coverage ratio. The process can also include repeating the camera coverage calculation loop for an additional potential camera location from the site model to provide a plurality of coverage ratios. The process can also include creating an ordered list of the potential camera locations based on the coverage ratios. The process can also include choosing a camera position at the worksite from the ordered list.

Abstract: Systems and methods are described for an automatic and adaptive scanning method to efficiently scan for gas plumes using an imaging or LiDAR based gas monitoring system. In an example, the gas monitoring system can be coupled to a laser absorption spectroscopy with LiDAR. In an example, systems and methods for optimizing the utilization of the imaging or LiDAR based gas monitoring system includes planning, commissioning, acquiring data automatically, interpreting the data, or extracting gas emission events from the data, or a combination thereof, to provide a complete lifecycle of a gas leak and a comprehensive understanding of the gas emissions. In another example, systems and methods for detecting the presence of a plume of gas includes using supervised machine learning to train a model to recognize which images contain plumes of gas and estimate corresponding rates of gas leakage based on the images.

Abstract: Systems and methods are described for calculating an emission rate of a fugitive gas based on a gas density image of the fugitive gas. In an example, a computing device receives a gas density image of a fugitive gas from a camera. The computing device determines how to optimize the fugitive gas in the camera's field of view and instructions the camera to adjust its bearing and zoom accordingly. The camera captures one or more additional images of the fugitive gas, and the computing device stitches the images together where appropriate. The computing device then calculates the emission rate by delineating the fugitive gas in the image and determining a flux of the gas using one of various calculation methods.

Abstract: Systems and methods presented herein generally relate to greenhouse gas emission management and, more particularly, to a greenhouse gas emission management workflow to perform greenhouse gas detection sensor placement or greenhouse gas leak detection. For example, the system and method enable improved gas sensor arrangements within an oil and gas worksite. In another example, the system and method enable the prediction of a location and a leak rate of a gas leak within an oil and gas production facility based on measurements collected by gas leak sensors disposed within the facility and prevailing wind information.

Abstract: A method is provided for evaluating logging while drilling (LWD) nuclear magnetic resonance (NMR) measurement quality. The method may include rotating a bottom hole assembly (BHA) in a wellbore to drill. The BHA may include an NMR tool deployed at a first location and at least one motion sensor deployed at a second location, wherein the first and second locations are axially spaced apart in the BHA. The method may also include making NMR measurements and corresponding motion sensor measurements while drilling in the wellbore. The method may further include processing the motion sensor measurements to determine the NMR measurement quality of the corresponding NMR measurements.

Abstract: A method implements machine learning based methane emissions monitoring. The method includes collecting sensor data from a plurality of sensors. The method further includes applying an augmentation model to the sensor data to form a regression training set. The method further includes creating a classification training set for a classification model by replacing regression output values from the regression training set with classification output values. The classification output values include binary values. The method further includes training the regression model with the regression training set to generate a regression prediction. The method further includes training the classification model with the classification training set to generate a classification prediction.

Abstract: Methods and systems are provided for optimizing well-logging using an optimized wait time determined by analysis nuclear magnetic resonance data to achieve faster and better quality borehole evaluation. The method comprises performing a nuclear magnetic resonance pre-log testing; identifying a wait time for a portion of a signal from the pre-log testing with a long T1 and T2, value at each depth of the pre-log testing, wherein T1 is defined as a longitudinal relaxation time and T2 is a transverse relaxation time ascertained from the nuclear magnetic resonance prelog testing; and constructing a logging program with a logging program wait time being consistent with the wait time identified.

Abstract: Aqueous liquid which has been in contact with a subterranean geological formation, especially a hydrocarbon reservoir, is examined in order to detect or measure viscosifying polymer therein, by flowing a sample of the liquid through a constriction thereby causing extensional flow and alignment of any polymer molecules with the flow, and examining the solution for birefringence of aligned polymer molecules. The amount of birefringence is determined from intensity of light which has passed through the solution relative to intensity of the light source.

Abstract: Methods for improved interpretation of NMR data acquired from industrial samples by simultaneously detecting more than one resonant nucleus without removing the sample from the sensitive volume of the NMR magnet or radio frequency probe are disclosed. In other aspects, the present disclosure provides methods for robust imaging/analysis of spatial distribution of different fluids (e.g., 1H, 23Na, 19F) within a core or reservoir rock. NMR data may be interpreted in real-time during dynamic processes to enable rapid screening, e.g. of enhanced oil recovery techniques and products and/or to provide improved interpretation of well-logs. Measurements of resonant nuclei other than 1H may be performed in the laboratory or downhole with a NMR logging tool. In other aspects, the present disclosure describes a novel kernel function to extract values for underlying parameters that define relaxation time behavior of a quadrupolar nucleus.

Abstract: A method for making NMR measurements includes, in one embodiment, using an NMR tool to acquire NMR measurements that are effected by relative motion of the NMR tool and/or the specimen under investigation. The NMR tool may include a plurality of permanent magnets and a plurality of radio frequency (RF) coils. The relative motion is estimated and used to modify an NMR inversion kernel which is in turn used to transform the NMR measurements into motion-corrected NMR measurements. Corresponding systems, devices, and apparatuses are also disclosed herein.

Abstract: A method for correcting motion-effects from a downhole measurement includes, in one embodiment, determining relative motion of a downhole logging tool for a given logging operation in a borehole formed in an earth formation, determining a motion induced signal decay (MID) based upon the determined relative motion, determining a motion-effect inversion kernel (MEK) based upon the determined MID, using the downhole logging tool to acquire measurements that are affected by motion of the downhole logging tool during the logging operation, and using the MEK to process the acquired motion-affected measurements to obtain motion-corrected data. Corresponding systems, devices, and apparatuses are also disclosed herein.

Abstract: Systems and methods are provided for investigating a downhole formation using a nuclear magnetic resonance (NMR) tool having two or more radio frequency receiving coils. While the tool is moving through the borehole, the formation is magnetized and resulting signals are obtained. In accordance with the present approach, the acquired signals can be resolved azimuthally and can be reconstructed to obtain an indication of a parameter of the formation at multiple locations along the length of the borehole.