Abstract: Methods, apparatus, systems and articles of manufacture are disclosed to characterize households with media meter data. An example method includes identifying, with a processor, a power status and a first automatic gain control (AGC) value for an exposure minute from a panelist audience meter in a first household, the panelist audience meter comprising a power sensor, identifying a second AGC value and a daypart for a household tuning minute from a first media meter (MM) in the first household, the MM comprising microphones to collect audio data, and calculating model coefficients based on the exposure minute and the household tuning minute to be applied to data from a second MM in a second household, the model coefficients to facilitate a power status probability calculation in the second household devoid of the panelist audience meter having the power sensor.

Abstract: Methods, apparatus, systems and articles of manufacture are disclosed to characterize households with media meter data. An example method includes identifying, with a processor, a household minute credited to a station in a household by a first media meter (MM) device, the first MM device collecting only audio data, determining whether a panelist audience meter device is crediting the station at the same time in the household, when the panelist audience meter device is crediting the same station, associating the household minute with an ambient tuning status, identifying a first automatic gain control (AGC) value and a first code status of the household minute, calculating model coefficients based on the ambient tuning status, the first AGC value and the first code status, and calculating a probability of ambient tuning based on the model coefficients.

Abstract: Methods, apparatus, systems and articles of manufacture are disclosed to characterize households with media meter data.

Abstract: Methods, apparatus, systems and articles of manufacture are disclosed to characterize households with media meter data. An example method includes identifying, with a processor, a household minute credited to a station in a household by a first media meter (MM) device, the first MM device collecting only audio data, determining whether a panelist audience meter device is crediting the station at the same time in the household, when the panelist audience meter device is crediting the same station, associating the household minute with an ambient tuning status, identifying a first automatic gain control (AGC) value and a first code status of the household minute, calculating model coefficients based on the ambient tuning status, the first AGC value and the first code status, and calculating a probability of ambient tuning based on the model coefficients.

Abstract: Methods, apparatus, systems and articles of manufacture are disclosed to characterize households with media meter data.

Abstract: Methods, apparatus, systems and articles of manufacture are disclosed to characterize households with media meter data. An example method includes calculating, with a processor, an exposure proportion for a category of interest based on visitor exposure minutes and total exposure minutes, calculating a tuning proportion for the category of interest based on household tuning minutes and total tuning minutes, calculating expected exposure minutes for the category of interest based on a product of the total exposure minutes and the exposure proportion, calculating expected tuning minutes for the category of interest based on a product of the total tuning minutes and the viewing proportion, and determining an average visitor parameter (AVP) based on a ratio of the expected exposure minutes and the expected tuning minutes.

Abstract: Methods, apparatus, systems and articles of manufacture are disclosed to characterize households with media meter data. An example method includes identifying, with a processor, a power status and a first automatic gain control (AGC) value for an exposure minute from a panelist audience meter in a first household, the panelist audience meter comprising a power sensor, identifying a second AGC value and a daypart for a household tuning minute from a first media meter (MM) in the first household, the MM comprising microphones to collect audio data, and calculating model coefficients based on the exposure minute and the household tuning minute to be applied to data from a second MM in a second household, the model coefficients to facilitate a power status probability calculation in the second household devoid of the panelist audience meter having the power sensor.

Abstract: Methods, apparatus, systems and articles of manufacture are disclosed to calculate a probability of index consistency. An example method disclosed herein includes selecting a first benchmark audience rating and a first brand user rating, calculating a first ratio of the first benchmark audience rating and a first benchmark audience effective sample size, calculating a second ratio of the first brand user rating and a brand user effective sample size, calculating a standard error based on the first and second ratios and an index based on the first benchmark audience rating and the first brand user rating, and calculating a first consistency value based on the standard error.

Abstract: A method for estimating minimum miscibility enrichment (MME) for an injectant used in gas flooding of a reservoir at a given operating pressure comprising performing a plurality of slim tube simulations for the reservoir, determining minimum miscibility pressure (MMP) for a plurality of injected gases, creating a plot of recovery factor (RF) vs. 1?(MMP?P)/MMP wherein P is a dimensionless pressure, wherein the plot has having a y-intercept and slope, obtaining a recovery factor equation RF=i+s(1?(MMP?P)/MMP) wherein i is the y-intercept and s is the slope, determining a value for I, determining a value for s and calculating the recovery factor.

Abstract: A method for selecting a candidate reservoir for enhanced oil recovery from a plurality of reservoirs comprising selecting a reservoir, calculating a normalized raw score based on target oil for the reservoir (STarget Oil), calculating a normalized raw score based on recovery factor for the reservoir (SRecovery Factor), and evaluating the plurality of reservoirs based on STarget Oil and SRecovery Factor.