mbal_forecast_param_gas.RdCreate an object of class 'forecast_gas'
mbal_forecast_param_gas( input_unit = "Field", output_unit = "Field", G = NULL, phi = NULL, swi = NULL, pd = NULL, p = NULL, pvt = NULL, cf = NULL, M = NULL, wf = NULL, rel_perm = NULL )
| input_unit | a unit system for parameters, only the character string 'Field' is accepted |
|---|---|
| output_unit | a unit system for properties, only the character string 'Field' is accepted |
| G | original gas in place, SCF. |
| phi | reservoir porosity, a numeric fraction |
| swi | initial water saturation in the reservoir, a numeric fraction |
| pd | dew point pressure, a numeric value, psi |
| p | reservoir pressure, a numeric vector, psi |
| pvt | a data frame of PVT properties including pressure 'p' in 'psi', oil formation volume factor 'Bo' in 'bbl/stb', solution gas-oil ratio 'Rs' in 'scf/stb', oil viscosity 'muo' in 'cp', volatilized oil-gas ratio 'Rv' in 'stb/scf', gas formation volume factor 'Bg' in 'bbl/scf', gas viscosity 'mug' in 'cp', water formation volume factor 'Bw' in 'bbl/stb', and water viscosity 'muw' in 'cp' |
| cf | formation compressibility, a numeric value or vector, 1/psi |
| M | ratio of non-net-pay pore volume to the reservoir (net-pay) volume, a numeric fraction. |
| wf | weight factor, a numeric vector of zeros and ones. A zero value excludes the entire row of reservoir history data at a particular time from the material balance analysis |
| rel_perm | a data frame with four columns: gas saturation 'Sg', liquid saturation 'Sl', gas relative permeability 'Krg', and oil relative permeability 'Krog' |
a list of class ’forecast_gas’ with all the required parameters for the mbal_forecast_gas() S3 methods
p_pvt <- c(3700, 3650, 3400, 3100, 2800, 2500, 2200, 1900, 1600, 1300, 1000, 700, 600, 400) Bo <- c(10.057, 2.417, 2.192, 1.916, 1.736, 1.617, 1.504, 1.416, 1.326, 1.268, 1.205, 1.149, 1.131, 1.093) Rv <- c(84.11765, 84.11765, 70.5, 56.2, 46.5, 39.5, 33.8, 29.9, 27.3, 25.5, 25.9, 28.3, 29.8, 33.5) / 1e6 Rs <- c(11566, 2378, 2010, 1569, 1272, 1067, 873, 719, 565, 461, 349, 249, 218, 141) Bg <- c(0.87, 0.88, 0.92, 0.99, 1.08, 1.20, 1.35, 1.56, 1.85, 2.28, 2.95, 4.09, 4.68, 6.53) / 1000 cw <- 3e-6 Bwi <- 10.05 Bw <- Bwi * exp(cw * (p_pvt[1] - p_pvt)) muo <- c(0.0612, 0.062, 0.1338, 0.1826, 0.2354, 0.3001, 0.3764, 0.4781, 0.6041, 0.7746, 1.0295, 1.358, 1.855, 2.500) mug <- c(0.0612, 0.062, 0.0554, 0.0436, 0.0368, 0.0308, 0.0261, 0.0222, 0.0191, 0.0166, 0.0148, 0.0135, 0.0125, 0.0115) muw <- rep(0.25, length(p_pvt)) pvt_table <- data.frame(p = p_pvt, Bo = Bo, Rs = Rs, Rv = Rv, Bg = Bg, Bw = Bw, muo = muo, mug = mug, muw = muw) rel_perm <- as.data.frame(Rrelperm::kr2p_gl(SWCON = 0.2, SOIRG = 0.15, SORG = 0.15, SGCON = 0.05, SGCRIT = 0.05, KRGCL = 1, KROGCG = 1, NG = 3.16, NOG = 2.74, NP = 101)) colnames(rel_perm) <- c("Sg", "Sl", "Krg", "Krog") p <- c(3700, 3650, 3400, 3100, 2800, 2500, 2200, 1900, 1600, 1300, 1000, 700, 600) wf <- rep(1, length.out = length(p)) forecast_lst <- mbal_forecast_param_gas(input_unit = "Field", output_unit = "Field", G = 2.41e10, phi = 0.1, swi = 0.2, pd = 3650, p = p, pvt = pvt_table, M = 0, cf = 2e-6, wf = wf, rel_perm = rel_perm) dplyr::glimpse(forecast_lst)#> List of 12 #> $ input_unit : chr "Field" #> $ output_unit: chr "Field" #> $ G : num 2.41e+10 #> $ phi : num 0.1 #> $ swi : num 0.2 #> $ pd : num 3650 #> $ p : num [1:13] 3700 3650 3400 3100 2800 2500 2200 1900 1600 1300 ... #> $ cf : num [1:13] 2e-06 2e-06 2e-06 2e-06 2e-06 2e-06 2e-06 2e-06 2e-06 2e-06 ... #> $ M : num 0 #> $ pvt :'data.frame': 14 obs. of 9 variables: #> ..$ p : num [1:14] 3700 3650 3400 3100 2800 2500 2200 1900 1600 1300 ... #> ..$ Bo : num [1:14] 10.06 2.42 2.19 1.92 1.74 ... #> ..$ Rs : num [1:14] 11566 2378 2010 1569 1272 ... #> ..$ Rv : num [1:14] 8.41e-05 8.41e-05 7.05e-05 5.62e-05 4.65e-05 ... #> ..$ Bg : num [1:14] 0.00087 0.00088 0.00092 0.00099 0.00108 0.0012 0.00135 0.00156 0.00185 0.00228 ... #> ..$ Bw : num [1:14] 10.1 10.1 10.1 10.1 10.1 ... #> ..$ muo: num [1:14] 0.0612 0.062 0.1338 0.1826 0.2354 ... #> ..$ mug: num [1:14] 0.0612 0.062 0.0554 0.0436 0.0368 0.0308 0.0261 0.0222 0.0191 0.0166 ... #> ..$ muw: num [1:14] 0.25 0.25 0.25 0.25 0.25 0.25 0.25 0.25 0.25 0.25 ... #> $ wf : num [1:13] 1 1 1 1 1 1 1 1 1 1 ... #> $ rel_perm :'data.frame': 101 obs. of 4 variables: #> ..$ Sg : num [1:101] 0 0.01 0.02 0.03 0.04 0.05 0.06 0.07 0.08 0.09 ... #> ..$ Sl : num [1:101] 1 0.99 0.98 0.97 0.96 0.95 0.94 0.93 0.92 0.91 ... #> ..$ Krg : num [1:101] 0 0 0 0 0 ... #> ..$ Krog: num [1:101] 1 1 1 1 1 ... #> - attr(*, "class")= chr [1:2] "volumetric_forecast_gas" "forecast_gas"