An NIR analyzer with the optical probes across a pipe, or in a bypass configuration, after a stabilizer in an oil or condensate production plant. Prior to use, liquid samples from the plant are analyzed in a chemical lab to obtain reference vapor pressure or compositional values. A chemometric model using known techniques is then built with the captured absorption spectra and the reference lab results. Preprocessing methodologies can be used to help mitigate interferences of the fluid, instrument drift, and contaminate build up on the lenses in contact with the fluid. The chemometric model is implemented through the NIR analyzer as the calibration curve to predict the vapor pressure or other values of the flowing fluid in real time.

A method and a system for predicting a molecular weight of a hydrocarbon fluid are provided. An exemplary method includes measuring a density of the hydrocarbon fluid, obtaining an alternative measurement of a physical property of the hydrocarbon fluid, calculating an index value for the hydrocarbon fluid from the alternative measurement, and calculating a predicted molecular weight using an equation that combines the density with the index value. The predicted molecular weight is provided as an output.

The present invention relates to methods and systems for predicting the likelihood of acute respiratory distress syndrome (ARDS) in adult subjects. The invention further relates to methods of treatment and identification of subjects with an increased likelihood of developing ARDS as determined by the disclosed methods.

The present invention relates to methods and systems for predicting the likelihood of acute respiratory distress syndrome (ARDS) in adult subjects. The invention further relates to methods of treatment and identification of subjects with an increased likelihood of developing ARDS as determined by the disclosed methods.

The present disclosure describes a computer implemented method, a system, and a computer program product of determining intrinsic viscosity and Huggins constant of an unknown sample. In an embodiment, the method, system, and computer program product include receiving concentration detector signal values over time from a concentration detector corresponding to a series of aliquots of an unknown sample injected into an instrument chain, receiving specific viscosity values over time from a viscometer corresponding to the series of aliquots, calculating a total mass of each of the aliquots, calculating a first intermediate viscosity value of each of the aliquots, calculating a second intermediate viscosity value of each of the aliquots, and fitting the total mass, the first intermediate viscosity value, and the second intermediate viscosity value to a fitting, resulting in a calculated intrinsic viscosity of the unknown sample and a calculated Huggins constant of the unknown sample.

The present disclosure describes a computer implemented method, a system, and a computer program product of determining intrinsic viscosity and Huggins constant of an unknown sample. In an embodiment, the method, system, and computer program product include receiving concentration detector signal values over time from a concentration detector corresponding to a series of aliquots of an unknown sample injected into an instrument chain, receiving specific viscosity values over time from a viscometer corresponding to the series of aliquots, calculating a total mass of each of the aliquots, calculating a first intermediate viscosity value of each of the aliquots, calculating a second intermediate viscosity value of each of the aliquots, and fitting the total mass, the first intermediate viscosity value, and the second intermediate viscosity value to a fitting, resulting in a calculated intrinsic viscosity of the unknown sample and a calculated Huggins constant of the unknown sample.

Fluid holdups within a downhole sampling tool are identified by comparing high- and low-resolution measurements of fluid samples.

A non-contact system for the sensing the concentration of a compound includes a hyperspectral imaging device configured to capture a hyperspectral image of a fluid, a flow cell configured to enable the capturing of a hyperspectral image of a fluid, a process, and a memory. The memory includes instructions stored thereon which, when executed by the processor, cause the system to generate a hyperspectral image of the fluid in the flow cell, generate several spectral signals based on the hyperspectral image, provide the spectral signal as an input to a machine learning network, and predict by the machine learning network the concentration of a compound in a fluid.

A non-contact system for the sensing the concentration of a compound includes a hyperspectral imaging device configured to capture a hyperspectral image of a fluid, a flow cell configured to enable the capturing of a hyperspectral image of a fluid, a process, and a memory. The memory includes instructions stored thereon which, when executed by the processor, cause the system to generate a hyperspectral image of the fluid in the flow cell, generate several spectral signals based on the hyperspectral image, provide the spectral signal as an input to a machine learning network, and predict by the machine learning network the concentration of a compound in a fluid.

Asphaltene onset pressure of a formation fluid is determined by subjecting the fluid to a plurality of tests where depressurization is conducted at a different depressurization rate for each test while optically monitoring the fluid for asphaltene flocculation. The pressures at which asphaltene flocculation are detected in each test are fit to a curve as a function of depressurization rate, and the curve is extrapolated to a pressure (e.g., 0 psi) to provide the asphaltene onset pressure.