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inst/doc/VolatileOil.R
In Rmbal: Estimate Original Hydrocarbon in Place and Reservoir Performance
## ---- include = FALSE---------------------------------------------------------
knitr::opts_chunk$set(
collapse = TRUE,
comment = "#>"
)
## ---- fig.width = 6, fig.height= 4, fig.align = "center", warning = FALSE-----
library(Rmbal)
library(Rrelperm)
library(pracma)
library(minpack.lm)
library(ggplot2)
library(dplyr)
library(magrittr)
p_pvt <- c(5070, 4998, 4798, 4698, 4658, 4598, 4398, 4198, 3998, 3798, 3598, 3398,
3198, 2998, 2798, 2598, 2398, 2198, 1998, 1798, 1598, 1398, 1198, 998, 798, 598)
Bo <- c(2.704, 2.713, 2.740, 2.754, 2.707, 2.631, 2.338, 2.204, 2.093, 1.991, 1.905,
1.828, 1.758, 1.686, 1.632, 1.580, 1.534, 1.49, 1.45, 1.413, 1.367, 1.333, 1.305,
1.272, 1.239, 1.205) # RB/STB
Rv <- c(343, 343, 343, 343, 116, 111, 106, 94, 84, 74, 66, 60, 54, 49, 44, 39, 36,
33, 30, 28, 26, 25, 24.1, 23.9, 24.4, 26.4) / 1e6 # STB/SCF
Rs <- c(2909, 2909, 2909, 2909, 2834, 2711, 2247, 2019, 1828, 1651, 1500, 1364, 1237,
1111, 1013, 918, 833, 752, 677, 608, 524, 461, 406, 344, 283, 212) # SCF/STB
Bg <- c(9.27472e-04, 9.30559e-04, 9.39820e-04, 9.44622e-04, 0.83, 0.835, 0.853,
0.874, 0.901, 0.933, 0.97, 1.015, 1.066, 1.125, 1.196, 1.281, 1.38, 1.498,
1.642, 1.819, 2.035, 2.315, 2.689, 3.19, 3.911, 5.034) / 1000 # RB/SCF
cw <- 3e-6
Bwi <- 1.05
Bw <- Bwi * exp(cw * (p_pvt[1] - p_pvt))
muo <- c(742, 735, 716, 706, 718, 739, 847, 906, 968, 1028, 1104, 1177, 1242, 1325,
1409, 1501, 1598, 1697, 1817, 1940, 2064, 2223, 2438, 2629, 2882, 3193) / 10000
mug <- c(742, 735, 716, 706, 375, 367, 350, 327, 306, 288, 271, 255, 240, 227, 214,
203, 193, 184, 175, 168, 161, 155, 150, 146, 142, 138) / 10000
muw <- rep(0.25, length(p_pvt))
liq_vol <- c(1000, 1000, 1000, 1000, 967, 847, 747, 683, 630, 584, 544, 508, 471, 433,
402, 368, 336, 305, 271, 239, 209, 177, 146, 117, 89, 63) / 1000
pvt_table <- data.frame(p = p_pvt, Bo = Bo, Rs = Rs, Rv = Rv, Bg = Bg,
Bw = Bw, muo = muo, mug = mug, muw = muw)
p <- c(5070, 4998, 4798, 4698, 4658, 4598, 4398, 4198, 3998, 3798, 3598, 3398,
3198, 2998, 2798, 2598, 2398, 2198, 1998, 1798, 1598, 1398, 1198, 998, 798)
We <- rep(0, length.out = length(p))
Np <- c(0, 36, 130, 184, 227, 302, 582, 808, 1022, 1227, 1388, 1528, 1646, 1764, 1861,
1947, 2022, 2097, 2151, 2215, 2269, 2323, 2366, 2420, 2463) * 1e6 / 1000
Rp <- c(0, 2909.000, 2909.000, 2909.000, 2894.273, 2887.417, 2847.079, 2928.218, 3072.407,
3286.064, 3578.530, 3918.848, 4290.401, 4661.565, 5053.735, 5470.981, 5889.713,
6294.230, 6760.576, 7172.009, 7640.811, 8059.836, 8499.155, 8900.413, 9329.679)
Wp <- rep(0, length.out = length(p))
Wi <- rep(0, length.out = length(p))
Gi <- rep(0, length.out = length(p))
wf <- rep(1, length.out = length(p))
mbal_optim_oil_lst <- mbal_optim_param_oil(input_unit = "Field", output_unit = "Field",
unknown_param = "N", aquifer_model = NULL,
m = 0, phi = 0.1, swi = 0.2, Np = Np,
Rp = Rp, Wp = Wp, Gi = Gi, Wi = Wi, We = We,
pb = 4698, p = p, pvt = pvt_table, cf = 2e-6,
wf = wf, sorg = 0.15, sorw = 0.0)
time_lst <- mbal_time(c(1:length(p)), "year")
# a number of plots will be automatically generated for quality check
optim_results <- mbal_optim_oil(mbal_optim_oil_lst, time_lst)
glimpse(optim_results)
## ---- fig.width = 6, fig.height= 4, fig.align = "center", warning = FALSE-----
mbal_results <- mbal_perform_oil(optim_results, time_lst)
mbal_results
## ----fig.align="center", fig.height=4, fig.width=6, warning=FALSE-------------
# Step I: generating a set of pseudo relative permeability curves using
# laboratory 'Kr' values
sg_lab <- c(0.05, 0.152, 0.248, 0.352, 0.448, 0.552, 0.65)
krg_lab <- c(0, 0.05, 0.09, 0.18, 0.3, 0.5, 1)
kro_lab <- c(1, 0.6, 0.35, 0.13, 0.04, 0.01, 0)
swcrit_lab <- 0.2
sgcrit_lab <- 0.05
sorgr_lab <- 0.15
fun_kr <- function(x, swcrit, sgcrit, sorg, sg, krg, kro) {
kr_est <- Rrelperm::kr2p_gl(SWCON = swcrit, SOIRG = sorg, SORG = sorg,
SGCON = sgcrit, SGCRIT = sgcrit, KRGCL = 1,
KROGCG = 1, NG = x[1], NOG = x[2], NP = 101)
krg_est_sub <- approx(x = kr_est[,1], y = kr_est[,3], xout = sg, rule = 2)$y
kro_est_sub <- approx(x = kr_est[,1], y = kr_est[,4], xout = sg, rule = 2)$y
error <- (krg - krg_est_sub) ^ 2 + (kro - kro_est_sub) ^ 2
return(error)
}
par <- c(2, 2)
opt_results <- minpack.lm::nls.lm(par = par, fn = fun_kr, swcrit = swcrit_lab,
sgcrit = sgcrit_lab, sorg = sorgr_lab, sg = sg_lab,
krg = krg_lab, kro = kro_lab,
lower = c(0.1,0.1), upper = c(10,10))
sol <- opt_results$par
sol
rel_perm <- as.data.frame(Rrelperm::kr2p_gl(SWCON = swcrit_lab, SOIRG = sorgr_lab,
SORG = sorgr_lab, SGCON = sgcrit_lab,
SGCRIT = sgcrit_lab, KRGCL = 1,
KROGCG = 1, NG = sol[1], NOG = sol[2],
NP = 101))
colnames(rel_perm) <- c("Sg", "Sl", "Krg", "Krog")
p_forecast <- p
Gi_forecast <- Gi
wf_forecast <- wf
time_lst_forecast <- mbal_time(c(1:length(p_forecast)), "year")
forecast_lst <- mbal_forecast_param_oil(input_unit = "Field", output_unit = "Field",
N = 10179044, m = 0.0, phi = 0.1, swi = 0.2,
Gi = Gi_forecast, pb = 4698, p = p_forecast,
pvt = pvt_table, cf = 2e-6, wf = wf_forecast,
sorg = 0.15, rel_perm = rel_perm)
glimpse(forecast_lst)
forecast_results <- mbal_forecast_oil(forecast_lst, time_lst_forecast)
forecast_results
p1 <- forecast_results %>% ggplot(aes(`P (psia)`, SOo, color = "Forecast")) +
geom_point(size = 3) +
geom_point(data = mbal_results, aes(`P (psia)`, SOo, color = "Field"))+
scale_color_manual(name="Data", values=c("Forecast" = "red", "Field" = "black")) +
ggtitle("Oil Saturation Plot") +
theme_bw()
p1
p2 <- forecast_results %>% ggplot(aes(`P (psia)`, `GOR (SCF/STB)`, color = "Forecast")) +
geom_point(size = 3) +
geom_point(data = mbal_results, aes(`P (psia)`, `GOR (SCF/STB)`, color = "Field")) +
scale_color_manual(name="Data", values=c("Forecast" = "red", "Field" = "black")) +
ggtitle("GOR Plot") +
theme_bw()
p2
p3 <- forecast_results %>% ggplot(aes(`P (psia)`, `RF_oil`, color = "Forecast")) +
geom_point(size = 3) +
geom_point(data = mbal_results, aes(`P (psia)`, `RF_oil`, color = "Field")) +
scale_color_manual(name="Data", values=c("Forecast" = "red", "Field" = "black")) +
ggtitle("Oil Recovery Plot") +
theme_bw()
p3
liq_vol_CCE <- data.frame(P = p_pvt, liq_vol = liq_vol)
p4 <- forecast_results %>% ggplot(aes(`P (psia)`, `Liq_volume`, color = "Forecast")) +
geom_point(size = 3) +
geom_point(data = liq_vol_CCE, aes(P, `liq_vol`, color = "Lab")) +
scale_color_manual(name="Data", values=c("Forecast" = "red", "Lab" = "black")) +
ggtitle("CCE Liquid Volume Plot") +
theme_bw()
p4
p5 <- forecast_results %>%
tidyr::pivot_longer(cols = c("Isd", "Ifwd"), names_to = "Drive Mechanism",
values_to = "Fraction", values_drop_na = TRUE) %>%
ggplot(aes(`P (psia)`, Fraction, fill = `Drive Mechanism`)) +
geom_area() +
ggtitle("Energy Plot") +
theme_bw()
p5
## ----fig.align="center", fig.height=4, fig.width=6, warning=FALSE-------------
library(Rmbal)
library(Rrelperm)
library(pracma)
library(ggplot2)
library(dplyr)
library(magrittr)
p_pvt <- c(4998, 4798, 4698, 4658, 4598, 4398, 4198, 3998, 3798, 3598, 3398, 3198,
2998, 2798, 2598, 2398, 2198, 1998, 1798, 1598, 1398, 1198, 998, 798,
598) # psia
Bo <- c(2.71261, 2.73953, 2.75371, 2.70727, 2.63143, 2.33771, 2.20391, 2.09309,
1.99116, 1.90524, 1.82832, 1.75726, 1.68592, 1.63232, 1.58028, 1.53414,
1.49008, 1.44996, 1.41304, 1.36658, 1.33283, 1.30465, 1.27171, 1.23937,
1.20516) # RB/STB
Rs <- c(2909, 2909, 2909, 2834, 2711, 2247, 2019, 1828, 1651, 1500, 1364, 1237,
1111, 1013, 918, 833, 752, 677, 608, 524, 461, 406, 344, 283,
212) #SCF/STB
Bg <- c(0.932, 0.942, 0.947, 0.83, 0.835, 0.853, 0.874, 0.901, 0.933, 0.97,
1.015, 1.066, 1.125, 1.196, 1.281, 1.38, 1.498, 1.642, 1.819, 2.035,
2.315, 2.689, 3.19, 3.911, 5.034) / 1000 # RB/SCF
Rv <- c(343, 343, 343, 116, 111, 106, 94, 84, 74, 66, 60, 54, 49, 44, 39,
36, 33, 30, 28, 26, 25, 24.1, 23.9, 24.4, 26.4) / 1e6 # STB/SCF
cw <- 3e-6
Bwi <- 1.05
Bw <- Bwi * exp(cw * (p_pvt[1] - p_pvt))
muo <- c(735, 716, 706, 718, 739, 847, 906, 968, 1028, 1104, 1177, 1242, 1325,
1409, 1501, 1598, 1697, 1817, 1940, 2064, 2223, 2438, 2629, 2882,
3193) / 10000
mug <- c(735, 716, 706, 375, 367, 350, 327, 306, 288, 271, 255, 240, 227, 214,
203, 193, 184, 175, 168, 161, 155, 150, 146, 142, 138) / 10000
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)
p <- c(4998, 4798, 4698, 4658, 4598, 4398, 4198, 3998, 3798, 3598, 3398, 3198,
2998, 2798, 2598, 2398, 2198, 1998, 1798, 1598, 1398, 1198, 998, 798,
598) # psia
Np <- c(0, 1.05, 1.63, 2.08, 2.88, 5.87, 8.36, 10.64, 12.63, 14.25, 15.65, 16.88,
17.98, 18.9, 19.71, 20.41, 21.03, 21.59, 22.09, 22.57, 22.98, 23.34,
23.69, 24.03, 24.38) / 100
Rp <- c(0, 2.909, 2.909, 2.909, 2.87, 2.86, 2.97, 3.18, 3.5, 3.87, 4.27, 4.68, 5.12,
5.56, 6.02, 6.47, 6.91, 7.36, 7.8, 8.26, 8.68, 9.08, 9.48, 9.86,
10.25) * 1000 # SCF/STB
Wp <- rep(0, length.out = length(p))
We <- c(0, 0.0016, 0.0037, 0.005, 0.0067, 0.0149, 0.0261, 0.03096, 0.0559, 0.0742,
0.0952, 0.1179, 0.1424, 0.1688, 0.1966, 0.2261, 0.257, 0.2892, 0.3224,
0.357, 0.3923, 0.4285, 0.4658, 0.5038, 0.5425) # RB
Gi <- rep(0, length.out = length(p))
Wi <- rep(0, length.out = length(p))
wf <- rep(1, length.out = length(p))
mbal_optim_oil_lst <- mbal_optim_param_oil(input_unit = "Field", output_unit = "Field",
unknown_param = "N", aquifer_model = NULL,
m = 0, phi = 0.1, swi = 0.2, Np = Np,
Rp = Rp, Wp = Wp, Gi = Gi, Wi = Wi,
We = We, pb = 4698, p = p, pvt = pvt_table,
cf = 2e-6, wf = wf, sorg = 0.15, sorw = 0.15)
time_lst <- mbal_time(c(1:length(p)), "year")
# a number of plots will be automatically generated for quality check
optim_results <- mbal_optim_oil(mbal_optim_oil_lst, time_lst)
glimpse(optim_results)
## ---- fig.width = 6, fig.height= 4, fig.align = "center", warning = FALSE-----
mbal_results <- mbal_perform_oil(optim_results, time_lst)
mbal_results
reservoir_performance_table <- data.frame(p = p)
reservoir_performance_table$`RF_oil` <- c(0, 1.05, 1.63, 2.08, 2.88, 5.87, 8.36,
10.64, 12.63, 14.25, 15.65, 16.88, 17.98,
18.9, 19.71, 20.41, 21.03, 21.59, 22.09,
22.57, 22.98, 23.34, 23.69, 24.03,
24.38) / 100
reservoir_performance_table$`Sg` <- c(0, 0, 0, 3.4, 8.6, 26.1, 32.8, 37.7, 41.7,
44.7, 47.4, 49.7, 52.1, 53.8, 55.4, 57.1, 58.6,
60.1, 61.6, 63.6, 65.1, 66.6, 68.4, 70.3,
72.5) * 0.8 / 100
reservoir_performance_table$`GOR` <- c(2.91, 2.91, 2.91, 2.83, 2.75, 2.98, 3.49, 4.48,
5.9, 7.62, 9.14, 10.87, 12.96, 15.23, 18.09,
20.54, 22.88, 25.63, 28.03, 31.09, 33.28,
35.48, 37.12, 37.3, 35.28) * 1000 # SCF/STB
p1 <- mbal_results %>% ggplot(aes(`P (psia)`, SGo, color = "Forecast")) +
geom_point(size = 3) +
geom_point(data = reservoir_performance_table, aes(`p`, Sg, color = "Field"))+
scale_color_manual(name="Data", values=c("Forecast" = "red", "Field" = "black")) +
ggtitle("Gas Saturation Plot") +
theme_bw()
p1
p2 <- mbal_results %>% ggplot(aes(`P (psia)`, `GOR (SCF/STB)`, color = "Forecast")) +
geom_point(size = 3) +
geom_point(data = reservoir_performance_table, aes(`p`, GOR, color = "Field"))+
scale_color_manual(name="Data", values=c("Forecast" = "red", "Field" = "black")) +
ggtitle("GOR Plot") +
theme_bw()
p2
p3 <- mbal_results %>% ggplot(aes(`P (psia)`, `RF_oil`, color = "Forecast")) +
geom_point(size = 3) +
geom_point(data = reservoir_performance_table, aes(`p`, RF_oil, color = "Field"))+
scale_color_manual(name="Data", values=c("Forecast" = "red", "Field" = "black")) +
ggtitle("Oil Recovery Plot") +
theme_bw()
p3
p4 <- mbal_results %>%
tidyr::pivot_longer(cols = c("Inwd", "Isd", "Ifwd"), names_to = "Drive Mechanism",
values_to = "Fraction", values_drop_na = TRUE) %>%
ggplot(aes(`P (psia)`, Fraction, fill = `Drive Mechanism`)) +
geom_area() +
ggtitle("Energy Plot") +
theme_bw()
p4
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## ---- include = FALSE---------------------------------------------------------
knitr::opts_chunk$set(
collapse = TRUE,
comment = "#>"
)
## ---- fig.width = 6, fig.height= 4, fig.align = "center", warning = FALSE-----
library(Rmbal)
library(Rrelperm)
library(pracma)
library(minpack.lm)
library(ggplot2)
library(dplyr)
library(magrittr)
p_pvt <- c(5070, 4998, 4798, 4698, 4658, 4598, 4398, 4198, 3998, 3798, 3598, 3398,
3198, 2998, 2798, 2598, 2398, 2198, 1998, 1798, 1598, 1398, 1198, 998, 798, 598)
Bo <- c(2.704, 2.713, 2.740, 2.754, 2.707, 2.631, 2.338, 2.204, 2.093, 1.991, 1.905,
1.828, 1.758, 1.686, 1.632, 1.580, 1.534, 1.49, 1.45, 1.413, 1.367, 1.333, 1.305,
1.272, 1.239, 1.205) # RB/STB
Rv <- c(343, 343, 343, 343, 116, 111, 106, 94, 84, 74, 66, 60, 54, 49, 44, 39, 36,
33, 30, 28, 26, 25, 24.1, 23.9, 24.4, 26.4) / 1e6 # STB/SCF
Rs <- c(2909, 2909, 2909, 2909, 2834, 2711, 2247, 2019, 1828, 1651, 1500, 1364, 1237,
1111, 1013, 918, 833, 752, 677, 608, 524, 461, 406, 344, 283, 212) # SCF/STB
Bg <- c(9.27472e-04, 9.30559e-04, 9.39820e-04, 9.44622e-04, 0.83, 0.835, 0.853,
0.874, 0.901, 0.933, 0.97, 1.015, 1.066, 1.125, 1.196, 1.281, 1.38, 1.498,
1.642, 1.819, 2.035, 2.315, 2.689, 3.19, 3.911, 5.034) / 1000 # RB/SCF
cw <- 3e-6
Bwi <- 1.05
Bw <- Bwi * exp(cw * (p_pvt[1] - p_pvt))
muo <- c(742, 735, 716, 706, 718, 739, 847, 906, 968, 1028, 1104, 1177, 1242, 1325,
1409, 1501, 1598, 1697, 1817, 1940, 2064, 2223, 2438, 2629, 2882, 3193) / 10000
mug <- c(742, 735, 716, 706, 375, 367, 350, 327, 306, 288, 271, 255, 240, 227, 214,
203, 193, 184, 175, 168, 161, 155, 150, 146, 142, 138) / 10000
muw <- rep(0.25, length(p_pvt))
liq_vol <- c(1000, 1000, 1000, 1000, 967, 847, 747, 683, 630, 584, 544, 508, 471, 433,
402, 368, 336, 305, 271, 239, 209, 177, 146, 117, 89, 63) / 1000
pvt_table <- data.frame(p = p_pvt, Bo = Bo, Rs = Rs, Rv = Rv, Bg = Bg,
Bw = Bw, muo = muo, mug = mug, muw = muw)
p <- c(5070, 4998, 4798, 4698, 4658, 4598, 4398, 4198, 3998, 3798, 3598, 3398,
3198, 2998, 2798, 2598, 2398, 2198, 1998, 1798, 1598, 1398, 1198, 998, 798)
We <- rep(0, length.out = length(p))
Np <- c(0, 36, 130, 184, 227, 302, 582, 808, 1022, 1227, 1388, 1528, 1646, 1764, 1861,
1947, 2022, 2097, 2151, 2215, 2269, 2323, 2366, 2420, 2463) * 1e6 / 1000
Rp <- c(0, 2909.000, 2909.000, 2909.000, 2894.273, 2887.417, 2847.079, 2928.218, 3072.407,
3286.064, 3578.530, 3918.848, 4290.401, 4661.565, 5053.735, 5470.981, 5889.713,
6294.230, 6760.576, 7172.009, 7640.811, 8059.836, 8499.155, 8900.413, 9329.679)
Wp <- rep(0, length.out = length(p))
Wi <- rep(0, length.out = length(p))
Gi <- rep(0, length.out = length(p))
wf <- rep(1, length.out = length(p))
mbal_optim_oil_lst <- mbal_optim_param_oil(input_unit = "Field", output_unit = "Field",
unknown_param = "N", aquifer_model = NULL,
m = 0, phi = 0.1, swi = 0.2, Np = Np,
Rp = Rp, Wp = Wp, Gi = Gi, Wi = Wi, We = We,
pb = 4698, p = p, pvt = pvt_table, cf = 2e-6,
wf = wf, sorg = 0.15, sorw = 0.0)
time_lst <- mbal_time(c(1:length(p)), "year")
# a number of plots will be automatically generated for quality check
optim_results <- mbal_optim_oil(mbal_optim_oil_lst, time_lst)
glimpse(optim_results)
## ---- fig.width = 6, fig.height= 4, fig.align = "center", warning = FALSE-----
mbal_results <- mbal_perform_oil(optim_results, time_lst)
mbal_results
## ----fig.align="center", fig.height=4, fig.width=6, warning=FALSE-------------
# Step I: generating a set of pseudo relative permeability curves using
# laboratory 'Kr' values
sg_lab <- c(0.05, 0.152, 0.248, 0.352, 0.448, 0.552, 0.65)
krg_lab <- c(0, 0.05, 0.09, 0.18, 0.3, 0.5, 1)
kro_lab <- c(1, 0.6, 0.35, 0.13, 0.04, 0.01, 0)
swcrit_lab <- 0.2
sgcrit_lab <- 0.05
sorgr_lab <- 0.15
fun_kr <- function(x, swcrit, sgcrit, sorg, sg, krg, kro) {
kr_est <- Rrelperm::kr2p_gl(SWCON = swcrit, SOIRG = sorg, SORG = sorg,
SGCON = sgcrit, SGCRIT = sgcrit, KRGCL = 1,
KROGCG = 1, NG = x[1], NOG = x[2], NP = 101)
krg_est_sub <- approx(x = kr_est[,1], y = kr_est[,3], xout = sg, rule = 2)$y
kro_est_sub <- approx(x = kr_est[,1], y = kr_est[,4], xout = sg, rule = 2)$y
error <- (krg - krg_est_sub) ^ 2 + (kro - kro_est_sub) ^ 2
return(error)
}
par <- c(2, 2)
opt_results <- minpack.lm::nls.lm(par = par, fn = fun_kr, swcrit = swcrit_lab,
sgcrit = sgcrit_lab, sorg = sorgr_lab, sg = sg_lab,
krg = krg_lab, kro = kro_lab,
lower = c(0.1,0.1), upper = c(10,10))
sol <- opt_results$par
sol
rel_perm <- as.data.frame(Rrelperm::kr2p_gl(SWCON = swcrit_lab, SOIRG = sorgr_lab,
SORG = sorgr_lab, SGCON = sgcrit_lab,
SGCRIT = sgcrit_lab, KRGCL = 1,
KROGCG = 1, NG = sol[1], NOG = sol[2],
NP = 101))
colnames(rel_perm) <- c("Sg", "Sl", "Krg", "Krog")
p_forecast <- p
Gi_forecast <- Gi
wf_forecast <- wf
time_lst_forecast <- mbal_time(c(1:length(p_forecast)), "year")
forecast_lst <- mbal_forecast_param_oil(input_unit = "Field", output_unit = "Field",
N = 10179044, m = 0.0, phi = 0.1, swi = 0.2,
Gi = Gi_forecast, pb = 4698, p = p_forecast,
pvt = pvt_table, cf = 2e-6, wf = wf_forecast,
sorg = 0.15, rel_perm = rel_perm)
glimpse(forecast_lst)
forecast_results <- mbal_forecast_oil(forecast_lst, time_lst_forecast)
forecast_results
p1 <- forecast_results %>% ggplot(aes(`P (psia)`, SOo, color = "Forecast")) +
geom_point(size = 3) +
geom_point(data = mbal_results, aes(`P (psia)`, SOo, color = "Field"))+
scale_color_manual(name="Data", values=c("Forecast" = "red", "Field" = "black")) +
ggtitle("Oil Saturation Plot") +
theme_bw()
p1
p2 <- forecast_results %>% ggplot(aes(`P (psia)`, `GOR (SCF/STB)`, color = "Forecast")) +
geom_point(size = 3) +
geom_point(data = mbal_results, aes(`P (psia)`, `GOR (SCF/STB)`, color = "Field")) +
scale_color_manual(name="Data", values=c("Forecast" = "red", "Field" = "black")) +
ggtitle("GOR Plot") +
theme_bw()
p2
p3 <- forecast_results %>% ggplot(aes(`P (psia)`, `RF_oil`, color = "Forecast")) +
geom_point(size = 3) +
geom_point(data = mbal_results, aes(`P (psia)`, `RF_oil`, color = "Field")) +
scale_color_manual(name="Data", values=c("Forecast" = "red", "Field" = "black")) +
ggtitle("Oil Recovery Plot") +
theme_bw()
p3
liq_vol_CCE <- data.frame(P = p_pvt, liq_vol = liq_vol)
p4 <- forecast_results %>% ggplot(aes(`P (psia)`, `Liq_volume`, color = "Forecast")) +
geom_point(size = 3) +
geom_point(data = liq_vol_CCE, aes(P, `liq_vol`, color = "Lab")) +
scale_color_manual(name="Data", values=c("Forecast" = "red", "Lab" = "black")) +
ggtitle("CCE Liquid Volume Plot") +
theme_bw()
p4
p5 <- forecast_results %>%
tidyr::pivot_longer(cols = c("Isd", "Ifwd"), names_to = "Drive Mechanism",
values_to = "Fraction", values_drop_na = TRUE) %>%
ggplot(aes(`P (psia)`, Fraction, fill = `Drive Mechanism`)) +
geom_area() +
ggtitle("Energy Plot") +
theme_bw()
p5
## ----fig.align="center", fig.height=4, fig.width=6, warning=FALSE-------------
library(Rmbal)
library(Rrelperm)
library(pracma)
library(ggplot2)
library(dplyr)
library(magrittr)
p_pvt <- c(4998, 4798, 4698, 4658, 4598, 4398, 4198, 3998, 3798, 3598, 3398, 3198,
2998, 2798, 2598, 2398, 2198, 1998, 1798, 1598, 1398, 1198, 998, 798,
598) # psia
Bo <- c(2.71261, 2.73953, 2.75371, 2.70727, 2.63143, 2.33771, 2.20391, 2.09309,
1.99116, 1.90524, 1.82832, 1.75726, 1.68592, 1.63232, 1.58028, 1.53414,
1.49008, 1.44996, 1.41304, 1.36658, 1.33283, 1.30465, 1.27171, 1.23937,
1.20516) # RB/STB
Rs <- c(2909, 2909, 2909, 2834, 2711, 2247, 2019, 1828, 1651, 1500, 1364, 1237,
1111, 1013, 918, 833, 752, 677, 608, 524, 461, 406, 344, 283,
212) #SCF/STB
Bg <- c(0.932, 0.942, 0.947, 0.83, 0.835, 0.853, 0.874, 0.901, 0.933, 0.97,
1.015, 1.066, 1.125, 1.196, 1.281, 1.38, 1.498, 1.642, 1.819, 2.035,
2.315, 2.689, 3.19, 3.911, 5.034) / 1000 # RB/SCF
Rv <- c(343, 343, 343, 116, 111, 106, 94, 84, 74, 66, 60, 54, 49, 44, 39,
36, 33, 30, 28, 26, 25, 24.1, 23.9, 24.4, 26.4) / 1e6 # STB/SCF
cw <- 3e-6
Bwi <- 1.05
Bw <- Bwi * exp(cw * (p_pvt[1] - p_pvt))
muo <- c(735, 716, 706, 718, 739, 847, 906, 968, 1028, 1104, 1177, 1242, 1325,
1409, 1501, 1598, 1697, 1817, 1940, 2064, 2223, 2438, 2629, 2882,
3193) / 10000
mug <- c(735, 716, 706, 375, 367, 350, 327, 306, 288, 271, 255, 240, 227, 214,
203, 193, 184, 175, 168, 161, 155, 150, 146, 142, 138) / 10000
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)
p <- c(4998, 4798, 4698, 4658, 4598, 4398, 4198, 3998, 3798, 3598, 3398, 3198,
2998, 2798, 2598, 2398, 2198, 1998, 1798, 1598, 1398, 1198, 998, 798,
598) # psia
Np <- c(0, 1.05, 1.63, 2.08, 2.88, 5.87, 8.36, 10.64, 12.63, 14.25, 15.65, 16.88,
17.98, 18.9, 19.71, 20.41, 21.03, 21.59, 22.09, 22.57, 22.98, 23.34,
23.69, 24.03, 24.38) / 100
Rp <- c(0, 2.909, 2.909, 2.909, 2.87, 2.86, 2.97, 3.18, 3.5, 3.87, 4.27, 4.68, 5.12,
5.56, 6.02, 6.47, 6.91, 7.36, 7.8, 8.26, 8.68, 9.08, 9.48, 9.86,
10.25) * 1000 # SCF/STB
Wp <- rep(0, length.out = length(p))
We <- c(0, 0.0016, 0.0037, 0.005, 0.0067, 0.0149, 0.0261, 0.03096, 0.0559, 0.0742,
0.0952, 0.1179, 0.1424, 0.1688, 0.1966, 0.2261, 0.257, 0.2892, 0.3224,
0.357, 0.3923, 0.4285, 0.4658, 0.5038, 0.5425) # RB
Gi <- rep(0, length.out = length(p))
Wi <- rep(0, length.out = length(p))
wf <- rep(1, length.out = length(p))
mbal_optim_oil_lst <- mbal_optim_param_oil(input_unit = "Field", output_unit = "Field",
unknown_param = "N", aquifer_model = NULL,
m = 0, phi = 0.1, swi = 0.2, Np = Np,
Rp = Rp, Wp = Wp, Gi = Gi, Wi = Wi,
We = We, pb = 4698, p = p, pvt = pvt_table,
cf = 2e-6, wf = wf, sorg = 0.15, sorw = 0.15)
time_lst <- mbal_time(c(1:length(p)), "year")
# a number of plots will be automatically generated for quality check
optim_results <- mbal_optim_oil(mbal_optim_oil_lst, time_lst)
glimpse(optim_results)
## ---- fig.width = 6, fig.height= 4, fig.align = "center", warning = FALSE-----
mbal_results <- mbal_perform_oil(optim_results, time_lst)
mbal_results
reservoir_performance_table <- data.frame(p = p)
reservoir_performance_table$`RF_oil` <- c(0, 1.05, 1.63, 2.08, 2.88, 5.87, 8.36,
10.64, 12.63, 14.25, 15.65, 16.88, 17.98,
18.9, 19.71, 20.41, 21.03, 21.59, 22.09,
22.57, 22.98, 23.34, 23.69, 24.03,
24.38) / 100
reservoir_performance_table$`Sg` <- c(0, 0, 0, 3.4, 8.6, 26.1, 32.8, 37.7, 41.7,
44.7, 47.4, 49.7, 52.1, 53.8, 55.4, 57.1, 58.6,
60.1, 61.6, 63.6, 65.1, 66.6, 68.4, 70.3,
72.5) * 0.8 / 100
reservoir_performance_table$`GOR` <- c(2.91, 2.91, 2.91, 2.83, 2.75, 2.98, 3.49, 4.48,
5.9, 7.62, 9.14, 10.87, 12.96, 15.23, 18.09,
20.54, 22.88, 25.63, 28.03, 31.09, 33.28,
35.48, 37.12, 37.3, 35.28) * 1000 # SCF/STB
p1 <- mbal_results %>% ggplot(aes(`P (psia)`, SGo, color = "Forecast")) +
geom_point(size = 3) +
geom_point(data = reservoir_performance_table, aes(`p`, Sg, color = "Field"))+
scale_color_manual(name="Data", values=c("Forecast" = "red", "Field" = "black")) +
ggtitle("Gas Saturation Plot") +
theme_bw()
p1
p2 <- mbal_results %>% ggplot(aes(`P (psia)`, `GOR (SCF/STB)`, color = "Forecast")) +
geom_point(size = 3) +
geom_point(data = reservoir_performance_table, aes(`p`, GOR, color = "Field"))+
scale_color_manual(name="Data", values=c("Forecast" = "red", "Field" = "black")) +
ggtitle("GOR Plot") +
theme_bw()
p2
p3 <- mbal_results %>% ggplot(aes(`P (psia)`, `RF_oil`, color = "Forecast")) +
geom_point(size = 3) +
geom_point(data = reservoir_performance_table, aes(`p`, RF_oil, color = "Field"))+
scale_color_manual(name="Data", values=c("Forecast" = "red", "Field" = "black")) +
ggtitle("Oil Recovery Plot") +
theme_bw()
p3
p4 <- mbal_results %>%
tidyr::pivot_longer(cols = c("Inwd", "Isd", "Ifwd"), names_to = "Drive Mechanism",
values_to = "Fraction", values_drop_na = TRUE) %>%
ggplot(aes(`P (psia)`, Fraction, fill = `Drive Mechanism`)) +
geom_area() +
ggtitle("Energy Plot") +
theme_bw()
p4
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