Description Usage Arguments Details Value Note Author(s) References Examples
Performs USGS assessment summarized in Charpentier (2010) as a vectorized Monte Carlo.
1 2 3 4  | 
auMC | 
 number of MC iterations to perform  | 
auType | 
 type of assessment, string specifier of 'oil' or 'gas'  | 
auProbability | 
 Probability: as a decimal percentage  | 
auAreaProductive | 
 Productive area of accumulation:   | 
auAreaDrainage | 
 Uncertainty about average drainage area of wells:   | 
auPercAreaUntested | 
 Percentage of total assessment-unit area that is untested:   | 
auPercAreaSweet | 
 Percentage of untested assessment-unit area in sweet spots:   | 
auPercFutureSS | 
 Future success ratio for sweet spots:   | 
auEURss | 
 Uncertainty about sweet spot average EUR (MMBO for oil; BCFG for gas):   | 
auPercFutureNS | 
 Future success ratio for non-sweet spots:   | 
auEURns | 
 Uncertainty about non-sweet spot average EUR (MMBO for oil; BCFG for gas):   | 
auGOR | 
 Gas/oil ratio (CFG/BO):   | 
auNGLGR | 
 NGL/gas ratio (BNGL/MMCFG):   | 
auLGR | 
 Liquids/gas ratio (BLIQ/MMCFG):   | 
year | 
 Year [XXXX] of factsheet publication of assessment numbers.  | 
If the AU is to be assessed as entirely sweet spot, simply omit the non-sweet spot variables. Due to the nature of random sampling, the simulation is vectorized and limited only by the available storage.
A list where each row represents an MC iteration variable combination while the columns contain the sample distributions. The column names are as follows
DrainageArea 
EURsweet 
EURnonSweet 
ProductiveArea 
PercAreaUntested
PercAreaSweet 
PercFutureSweet 
PercFutureNonSweet 
untestedArea 
SweetArea
NonSweetArea 
NumSweetWells 
NumNonSweetWells 
SweetAccumulation 
NonSweetAccumulation
totalAccumulation
Depending on the type of assessment, also returns (for 'Gas')
LGR 
SweetNGLinGas 
NonSweetNGLinGas 
totalNGLinGas
or returns (for 'Oil')
GOR 
NGLGR 
SweetGasinOil 
NonSweetGasinOil
totalGasInOil 
SweetNGLinOil 
NonSweetNGLinOil 
totalNGLinOil
Edited by CDMartinez 24 Nov 15.
As of July 2016, the standard 'z-score' for the EUR distribution is implemented as 2.326 (99%) in
enforceRankCorrelation. Assigning year <= 1 will assume zscore of 3.09 (99.9%)
as used in conventionalAssessment EUR distributions since approximately 2013.
Created by CDMartinez 10 Nov 15
Charpentier, Ronald R, and Troy Cook, 2010, Improved USGS Methodology for Assessing Continuous Petroleum Resources. U.S. Geological Survey Data Series 547, 2: 22.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15  | OGasmt <- continuousAssessment(auMC = 50000,
auType = 'Oil',
auProbability = 1,
auAreaProductive = c(2800000,3100000,3400000),
auAreaDrainage = c(320,400,600),
auPercAreaUntested = c(80,87,91),
auPercAreaSweet = c(24,29,70),
auPercFutureSS = c(98,99,100),
auEURss = c(0.225,0.25,0.325),
auPercFutureNS = c(80,90,95),
auEURns = c(0.075,0.15,0.25),
auGOR = c(500,1000,1500),
auNGLGR = c(35,85,115),
year = 2013)
round(continuousAssessmentSummary(OGasmt$risked))
 | 
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