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inst/doc/BlackOil.R
In Rmbal: Estimate Original Hydrocarbon in Place and Reservoir Performance
## ---- include = FALSE---------------------------------------------------------
knitr::opts_chunk$set(
collapse = TRUE,
comment = "#>"
)
## ---- fig.width = 6, fig.height= 4, fig.align = "center", warning = FALSE-----
library(Rmbal)
library(Rrelperm)
library(pracma)
library(minpack.lm)
library(ggplot2)
library(dplyr)
library(magrittr)
pvt_table_oil <- as.data.frame(Rpvt::pvt_oil("Field", "Field", "black_oil",
"Standing", "Beggs_Robinson", 200,
7150, 43, 0.7, c(0,0,0), pb = 4500,
warning = "no"))
colnames(pvt_table_oil) <- c("t", "p", "Rs", "Bo", "dens_o", "co", "muo", "Z",
"Bg", "dens_g", "cg", "mug", "m_p")
pvt_table_water <- as.data.frame(Rpvt::pvt_water("Field", "Field", "water",
"Spivey", "Spivey", 200, 7150,
0, "no", "no"))
colnames(pvt_table_water) <- c("t", "p", "Rsw", "Bw", "dens_w", "cw", "muw")
pvt_table <- dplyr::left_join(pvt_table_oil, pvt_table_water, by = c("p", "t"))
pvt_table$Rv <- rep(0, length.out = nrow(pvt_table)) # zero for black oil
pvt_table <- pvt_table %>% dplyr::select(p, Bo, Rs, Rv, Bg, Bw, muo, mug, muw)
p <- c(7150,6600,5800,4950,4500)
We <- rep(0, length.out = length(p))
Np <- c(0, 8.072, 22.549, 36.369, 43.473) * 1e6
Rp <- c(0, rep(pvt_table$Rs[nrow(pvt_table)], 4))
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(p))
mbal_optim_oil_lst <- mbal_optim_param_oil(input_unit = "Field", output_unit = "Field",
unknown_param = "N", aquifer_model = NULL,
m = 0, phi = 0.2, swi = 0.43, Np = Np,
Rp = Rp, Wp = Wp, Gi = Gi, Wi = Wi, We = We,
pb = 4500, p = p, pvt = pvt_table, cf = 4e-6,
wf = wf, sorg = 0, sorw = 0)
time_lst <- mbal_time(1:5, "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
## ----echo=TRUE, fig.align="center", fig.height=4, fig.width=6, warning=FALSE----
# gas saturation above the bubble point is zero, however the mbal_forecast_param_oil()
# requires a table of relative permeabilities as an input for the gas-oil system.
# Therefore, an arbitrary table is generated using the 'Rrelperm' package.
# The generated table does not impact the predictions above the bubble point.
rel_perm <- as.data.frame(Rrelperm::kr2p_gl(SWCON = 0.43, SOIRG = 0.15, SORG = 0.15,
SGCON = 0.05, SGCRIT = 0.05, KRGCL = 1,
KROGCG = 1, NG = 1.5, NOG = 1.0, NP = 101))
colnames(rel_perm) <- c("Sg", "Sl", "Krg", "Krog")
forecast_lst <- mbal_forecast_param_oil(input_unit = "Field", output_unit = "Field",
N = 6.35e8, m = 0, phi = 0.1, swi = 0.43,
Gi = Gi, pb = 4500, p = p, pvt = pvt_table,
cf = 4e-6, wf = wf, sorg = 0, rel_perm = rel_perm)
glimpse(forecast_lst)
forecast_results <- mbal_forecast_oil(forecast_lst, time_lst)
forecast_results
p1 <- 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()
p1
p2 <- 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()
p2
## ----echo=TRUE, fig.align="center", fig.height=4, fig.width=6, warning=FALSE----
library(Rmbal)
library(Rrelperm)
library(pracma)
library(ggplot2)
library(dplyr)
library(magrittr)
pvt_table_oil <- as.data.frame(Rpvt::pvt_oil("Field", "Field", "black_oil",
"Standing", "Beggs_Robinson", 200,
7150, 43, 0.7, c(0,0,0), pb = 4500,
warning = "no"))
colnames(pvt_table_oil) <- c("t", "p", "Rs", "Bo", "dens_o", "co", "muo", "Z",
"Bg", "dens_g", "cg", "mug", "m_p")
pvt_table_water <- as.data.frame(Rpvt::pvt_water("Field", "Field", "water",
"Spivey", "Spivey", 200, 7150,
3e-6, "no", "no"))
colnames(pvt_table_water) <- c("t", "p", "Rsw", "Bw", "dens_w", "cw", "muw")
pvt_table <- dplyr::left_join(pvt_table_oil, pvt_table_water, by = c("p", "t"))
pvt_table$Rv <- 0.0 # zero for black oil
pvt_table <- pvt_table %>% dplyr::select(p, Bo, Rs, Rv, Bg, Bw, muo, mug, muw)
p <- c(7150, 6600, 5800, 4950, 4500, 4350, 4060, 3840, 3600, 3480, 3260, 3100, 2940, 2800)
We <- rep(0, length.out = length(p))
Np <- c(0, 8.072, 22.549, 36.369, 43.473, 49.182, 58.383, 64.812, 69.562, 74.572,
78.4, 81.275, 83.879, 86.401) * 1e6
Rp <- c(0, rep(pvt_table$Rs[nrow(pvt_table)], 4), 1576, 1788, 1992, 2158, 2383, 2596, 2785, 2953, 3103)
Wp <- rep(0, length.out = length(p))
Wi <- rep(0, length.out = length(p))
Gi <- rep(0, length.out = length(p))
wf <- rep(1,length(p))
mbal_optim_oil_lst <- mbal_optim_param_oil(input_unit = "Field", output_unit = "Field",
unknown_param = "N", aquifer_model = NULL,
m = 0, phi = 0.2, swi = 0.43, Np = Np,
Rp = Rp, Wp = Wp, Gi = Gi, Wi = Wi, We = We,
pb = 4500, p = p, pvt = pvt_table, cf = 4e-6,
wf = wf, sorg = 0, sorw = 0)
glimpse(mbal_optim_oil_lst)
time_lst <- mbal_time(1:14, "year")
# a number of plots will be automatically generated for quality check
optim_results <- mbal_optim_oil(mbal_optim_oil_lst, time_lst)
glimpse(optim_results)
## ---- fig.width = 6, fig.height= 4, fig.align = "center", warning = FALSE-----
mbal_results <- mbal_perform_oil(optim_results, time_lst)
mbal_results
## ----echo=TRUE, fig.align="center", fig.height=4, fig.width=6, warning=FALSE----
# Step I: generating a set of pseudo relative permeability curves by minimizing
# the difference between field and theoretical 'fg' values
# 'swcrit' and 'sgcrit' values are estimated from the 'mbal_results' data frame
fun_fg <- function(x, swcrit, sgcrit, muo, mug, sg, fg, krg_kro) {
Kr_table <- Rrelperm::kr2p_gl(SWCON = swcrit, SOIRG = x[1], SORG = x[1],
SGCON = sgcrit, SGCRIT = sgcrit, KRGCL = x[2],
KROGCG = x[3], NG = x[4], NOG = x[5], NP = 101)
l <- length(fg)
krg_est_sub <- vector(length = l)
kro_est_sub <- vector(length = l)
krg_est_sub <- approx(x = Kr_table[,1], y = Kr_table[,3], xout = sg, rule = 2)$y
kro_est_sub <- approx(x = Kr_table[,1], y = Kr_table[,4], xout = sg, rule = 2)$y
fg_est <- (krg_est_sub / mug) / (krg_est_sub / mug + kro_est_sub / muo)
krg_kro_est <- krg_est_sub / kro_est_sub
error <- (fg - fg_est) ^ 2 + (krg_kro - krg_kro_est) ^ 2
return(error)
}
swcrit <- 0.44
sgcrit <- 0.015
p <- mbal_results$`P (psia)`
sg <- mbal_results$SGo # gas saturation in the oil leg
fg <- mbal_results$fg # in-situ gas fractional flow
krg_kro <- mbal_results$`krg/kro`
muo <- approx(x = pvt_table$p, y = pvt_table$muo, xout = p, rule = 2)$y
mug <- approx(x = pvt_table$p, y = pvt_table$mug, xout = p, rule = 2)$y
par <- c(0.1, 1, 1, 2, 2)
lower = c(0, 0.1, 0.1, 0.1, 0.1)
upper = c(1 - swcrit - sgcrit, 1.0, 1.0, 10.0, 10.0)
opt_results <- minpack.lm::nls.lm(par = par, fn = fun_fg, swcrit = swcrit, sgcrit = sgcrit,
muo = muo, mug = mug, sg = sg, fg = fg, krg_kro = krg_kro,
lower = lower, upper = upper)
opt_results
sol <- opt_results$par
sol
rel_perm <- as.data.frame(Rrelperm::kr2p_gl(SWCON = swcrit, SOIRG = sol[1], SORG = sol[1],
SGCON = sgcrit, SGCRIT = sgcrit, KRGCL = sol[2],
KROGCG = sol[3], NG = sol[4], NOG = sol[5],
NP = 101))
colnames(rel_perm) <- c("Sg", "Sl", "Krg", "Krog")
krg_est <- approx(x = rel_perm[,1], y = rel_perm[,3], xout = sg, rule = 2)$y
kro_est <- approx(x = rel_perm[,1], y = rel_perm[,4], xout = sg, rule = 2)$y
fg_est <- (krg_est/ mug) / (krg_est / mug + kro_est / muo)
fg_df <- data.frame(Sg = sg)
fg_df$fg <- fg_est
p_fg <- fg_df %>% ggplot(aes(sg, fg, color = "Model")) +
geom_point(size = 3) +
geom_point(data = mbal_results, aes(SGo, fg, color = "Field"), size = 3)+
xlim(c(0,1)) +
ylim(c(0,1)) +
scale_color_manual(name="Data", values=c("Model" = "red", "Field" = "black")) +
ggtitle("Gas Frational Flow Plot") +
theme_bw()
p_fg
p_forecast <- c(p, 2600, 2400, 2200, 2000, 1800, 1600, 1400, 1200)
Gi_forecast <- c(Gi, 0, 0, 0, 0, 0, 0, 0, 0)
wf_forecast <- c(wf, 1, 1, 1, 1, 1, 1, 1, 1)
time_lst_forecast <- mbal_time(1:22, "year")
forecast_lst <- mbal_forecast_param_oil(input_unit = "Field", output_unit = "Field",
N = 6.35e8, m = 0, phi = 0.1, swi = 0.43,
Gi = Gi_forecast, pb = 4500, p = p_forecast, pvt = pvt_table,
cf = 4e-6, wf = wf_forecast, sorg = 0, 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
p4 <- forecast_results %>% ggplot(aes(`P (psia)`, `Liq_volume`, color = "Forecast")) +
geom_point(size = 3) +
scale_color_manual(name="Data", values=c("Forecast" = "red")) +
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
## ----echo=TRUE, 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(3330, 3150, 3000, 2850, 2700, 2550, 2400)
Bo <- c(1.2511, 1.2353, 1.2222, 1.2122, 1.2022, 1.1922, 1.1822)
Rs <- c(510, 477, 450, 425, 401, 375, 352)
Bg <- c(0.00087, 0.00092, 0.00096, 0.00101, 0.00107, 0.00113, 0.00120)
cw <- 2e-6
Bwi <- 1.0
Bw <- Bwi * exp(cw * (p_pvt[1] - p_pvt))
Rv <- rep(0, length(p_pvt))
muo <- rep(0.5, length(p_pvt))
muw <- rep(0.25, length(p_pvt))
mug <- rep(0.02, 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(3330, 3150, 3000, 2850, 2700, 2550, 2400)
We <- rep(0, length.out = length(p))
Np <- c(0, 3.295, 5.903, 8.852, 11.503, 14.513, 17.730) * 1e6
Rp <- c(0, 1050, 1060, 1160, 1235, 1265, 1300)
Wp <- rep(0, length.out = length(p))
Wi <- rep(0, length.out = length(p))
Gi <- rep(0, length.out = length(p))
wf <- c(1, 1, 1, 0, 1, 0, 1)
mbal_optim_oil_lst <- mbal_optim_param_oil(input_unit = "Field", output_unit = "Field",
unknown_param = "N_m", aquifer_model = NULL,
phi = 0.2, swi = 0.2, Np = Np,
Rp = Rp, Wp = Wp, Gi = Gi, Wi = Wi, We = We,
pb = 3330, p = p, pvt = pvt_table, cf = 0,
wf = wf, sorg = 0.2, sorw = 0)
time_lst <- mbal_time(c(0, 365, 730, 1095, 1460, 1825, 2190), "day")
# 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)
## ----echo=TRUE, fig.align="center", fig.height=4, fig.width=6, warning=FALSE----
mbal_results <- mbal_perform_oil(optim_results, time_lst)
mbal_results
## ----echo=TRUE, fig.align="center", fig.height=4, fig.width=6, warning=FALSE----
# Step I: generating a set of pseudo relative permeability curves by minimizing
# the difference between field and theoretical 'fg' values
# 'swcrit' and 'sgcrit' values are estimated from the 'mbal_results' data frame
fun_fg <- function(x, swcrit, sgcrit, muo, mug, sg, fg, krg_kro) {
Kr_table <- Rrelperm::kr2p_gl(SWCON = swcrit, SOIRG = x[1], SORG = x[1],
SGCON = sgcrit, SGCRIT = sgcrit, KRGCL = x[2],
KROGCG = x[3], NG = x[4], NOG = x[5], NP = 101)
l <- length(fg)
krg_est_sub <- vector(length = l)
kro_est_sub <- vector(length = l)
krg_est_sub <- approx(x = Kr_table[,1], y = Kr_table[,3], xout = sg, rule = 2)$y
kro_est_sub <- approx(x = Kr_table[,1], y = Kr_table[,4], xout = sg, rule = 2)$y
fg_est <- (krg_est_sub / mug) / (krg_est_sub / mug + kro_est_sub / muo)
krg_kro_est <- krg_est_sub / kro_est_sub
error <- (fg - fg_est) ^ 2 + (krg_kro - krg_kro_est) ^ 2
return(error)
}
swcrit <- 0.2
sgcrit <- 0.00
p <- mbal_results$`P (psia)`
sg <- mbal_results$SGo # gas saturation in the oil leg
fg <- mbal_results$fg # in-situ gas fractional flow
krg_kro <- mbal_results$`krg/kro`
muo <- approx(x = pvt_table$p, y = pvt_table$muo, xout = p, rule = 2)$y
mug <- approx(x = pvt_table$p, y = pvt_table$mug, xout = p, rule = 2)$y
par <- c(0.1, 0.3, 1.0, 3, 3)
lower = c(0, 0.3, 1.0, 0.1, 0.1)
upper = c(1 - swcrit - sgcrit, 0.3, 1.0, 10.0, 10.0)
opt_results <- minpack.lm::nls.lm(par = par, fn = fun_fg, swcrit = swcrit, sgcrit = sgcrit,
muo = muo, mug = mug, sg = sg, fg = fg, krg_kro = krg_kro,
lower = lower, upper = upper)
opt_results
sol <- opt_results$par
sol
rel_perm <- as.data.frame(Rrelperm::kr2p_gl(SWCON = swcrit, SOIRG = sol[1], SORG = sol[1],
SGCON = sgcrit, SGCRIT = sgcrit, KRGCL = sol[2],
KROGCG = sol[3], NG = sol[4], NOG = sol[5],
NP = 101))
colnames(rel_perm) <- c("Sg", "Sl", "Krg", "Krog")
krg_est <- approx(x = rel_perm[,1], y = rel_perm[,3], xout = sg, rule = 2)$y
kro_est <- approx(x = rel_perm[,1], y = rel_perm[,4], xout = sg, rule = 2)$y
fg_est <- (krg_est/ mug) / (krg_est / mug + kro_est / muo)
fg_df <- data.frame(Sg = sg)
fg_df$fg <- fg_est
p_fg <- fg_df %>% ggplot(aes(sg, fg, color = "Model")) +
geom_point(size = 3) +
geom_point(data = mbal_results, aes(SGo, fg, color = "Field"), size = 3)+
xlim(c(0,1)) +
ylim(c(0,1)) +
scale_color_manual(name="Data", values=c("Model" = "red", "Field" = "black")) +
ggtitle("Gas Frational Flow Plot") +
theme_bw()
p_fg
p_forecast <- p_pvt
Gi_forecast <- c(Gi)
wf_forecast <- c(wf)
time_lst_forecast <- mbal_time(c(0, 365, 730, 1095, 1460, 1825, 2190), "day")
forecast_lst <- mbal_forecast_param_oil(input_unit = "Field", output_unit = "Field",
N = 1.37e8, m = 0.377, phi = 0.2, swi = 0.2,
Gi = Gi_forecast, pb = 3330, p = p_forecast, pvt = pvt_table,
cf = 0, wf = wf_forecast, sorg = 0.2, 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
p4 <- forecast_results %>% ggplot(aes(`P (psia)`, `Liq_volume`, color = "Forecast")) +
geom_point(size = 3) +
scale_color_manual(name="Data", values=c("Forecast" = "red")) +
ggtitle("CCE Liquid Volume Plot") +
theme_bw()
p4
p5 <- forecast_results %>%
tidyr::pivot_longer(cols = c("Igd", "Isd"), 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
## ----echo=TRUE, 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(2740, 2500, 2290, 2109, 1949, 1818, 1702, 1608, 1535, 1480, 1440)
Bo <- c(1.404, 1.374, 1.349, 1.329, 1.316, 1.303, 1.294, 1.287, 1.280, 1.276, 1.273)
Rs <- c(650, 592, 545, 507, 471, 442, 418, 398, 383, 371, 364)
Bg <- c(0.00093, 0.00098, 0.00107, 0.00117, 0.00128, 0.00139, 0.00150, 0.00160,
0.00170, 0.00176, 0.00182)
cw <- 3e-6
Bwi <- 1.0
Bw <- Bwi * exp(cw * (p_pvt[1] - p_pvt))
Rv <- rep(0, length(p_pvt))
muo <- rep(0.5, length(p_pvt))
muw <- rep(0.55, length(p_pvt))
mug <- rep(0.02, 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(2740, 2500, 2290, 2109, 1949, 1818, 1702, 1608, 1535, 1480, 1440)
We <- rep(0, length.out = length(p))
Np <- c(0, 7.88, 18.42, 29.15, 40.69, 50.14, 58.42, 65.39, 70.74, 74.54, 77.43) * 1e6
Rp <- c(0, 760, 845, 920, 975, 1025, 1065, 1095, 1120, 1145, 1160)
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 = "We",
aquifer_model = "pss_rad_edge", N = 312e6,
m = 0, phi = 0.25, swi = 0.25, Np = Np, Rp = Rp,
Wp = Wp, Gi = Gi, Wi = Wi, We = NULL, pb = 2740,
p = p, pvt = pvt_table, cf = 4e-6,
phi_a = 0.25, perm_h_a = 200, h_a = 100,
r_a = 5 * 9200, r_R = 9200, tetha = 140,
muw_a = 0.55, cw_a = 3e-6, cf_a = 4e-6,
wf = wf, sorw = 0.2, sorg = 0,
mult_len = c(1,1), lower = c(-Inf, 1),
upper = c(Inf, 1),
control = list(maxiter = 100), )
time_lst <- mbal_time(0:10, "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)
## ----echo=TRUE, fig.align="center", fig.height=4, fig.width=6, warning=FALSE----
mbal_results <- mbal_perform_oil(optim_results, time_lst)
mbal_results
p1 <- mbal_results %>% ggplot(aes(`P (psia)`, SOo, color = "Field")) +
geom_point(size = 3) +
scale_color_manual(name="Data", values=c("Field" = "black")) +
ggtitle("Oil Saturation Plot") +
theme_bw()
p1
p2 <- mbal_results %>% ggplot(aes(`P (psia)`, `GOR (SCF/STB)`, color = "Field")) +
geom_point(size = 3) +
scale_color_manual(name="Data", values = c("Field" = "black")) +
ggtitle("GOR Plot") +
theme_bw()
p2
p3 <- mbal_results %>% ggplot(aes(`P (psia)`, `RF_oil`, color = "Field")) +
geom_point(size = 3) +
scale_color_manual(name="Data", values=c("Field" = "black")) +
ggtitle("Oil Recovery Plot") +
theme_bw()
p3
p4 <- mbal_results %>%
tidyr::pivot_longer(cols = c("Isd", "Inwd", "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
## ----echo=TRUE, 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(2855, 2779, 2627, 2457, 2402, 2223, 2080, 1833, 1665, 1460) # psia
Bo <- c(1.2665, 1.2677, 1.2681, 1.2554, 1.2512, 1.2383, 1.2278, 1.2074, 1.1949,
1.1802) # RB/STB
Rs <- c(0.501, 0.501, 0.4973, 0.4671, 0.4574, 0.4269, 0.4024, 0.3579, 0.3277,
0.2908) * 1000 #SCF/STB
Bg <- c(0.9201, 0.9637, 1.0502, 1.0977, 1.1146, 1.201, 1.2825, 1.4584, 1.6112,
1.8526) / 1000 # RB/SCF
Bw <- c(1.0222, 1.0224, 1.0228, 1.0232, 1.0233, 1.0237, 1.024, 1.0246, 1.025,
1.0254) # RB/STB
Rv <- rep(0, length(p_pvt))
muo <- rep(0.5, length(p_pvt))
muw <- rep(0.55, length(p_pvt))
mug <- rep(0.02, 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(2855, 2779, 2627, 2457, 2402, 2223, 2080, 1833, 1665, 1460) # psia
Np <- c(0, 192821, 633942, 1314880, 1524400, 2152960, 2572000, 3200560,
3584680, 4003720) # STB
Rp <- c(0, 501, 492.2595442, 540.4827817, 558.2091315, 629.7005054,
708.8841369, 853.8693229, 947.8502963, 1053.050663) # SCF/STB
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 = "We",
aquifer_model = "pot", N = 20e6,
m = 0, phi = 0.28, swi = 0.208, Np = Np,
Rp = Rp, Wp = Wp, Gi = Gi, Wi = Wi,
We = NULL, pb = 2648, p = p, pvt = pvt_table,
cf = 26e-6, phi_a = 0.28, h_a = 200, r_a = 1000,
r_R = 300, tetha = 360, cw_a = 2.28e-6,
cf_a = 26e-6, mult_len = 3, wf = wf,
sorw = 0.0, sorg = 0)
time_lst <- mbal_time(c(0,305, 700, 1285, 1465, 2005, 2365, 2905, 3235, 3595), "day")
# 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)
## ----echo=TRUE, fig.align="center", fig.height=4, fig.width=6, warning=FALSE----
mbal_results <- mbal_perform_oil(optim_results, time_lst)
mbal_results
p1 <- mbal_results %>% ggplot(aes(`P (psia)`, SOo, color = "Field")) +
geom_point(size = 3) +
scale_color_manual(name="Data", values=c("Field" = "black")) +
ggtitle("Oil Saturation Plot") +
theme_bw()
p1
p2 <- mbal_results %>% ggplot(aes(`P (psia)`, `GOR (SCF/STB)`, color = "Field")) +
geom_point(size = 3) +
scale_color_manual(name="Data", values = c("Field" = "black")) +
ggtitle("GOR Plot") +
theme_bw()
p2
p3 <- mbal_results %>% ggplot(aes(`P (psia)`, `RF_oil`, color = "Field")) +
geom_point(size = 3) +
scale_color_manual(name="Data", values=c("Field" = "black")) +
ggtitle("Oil Recovery Plot") +
theme_bw()
p3
p4 <- mbal_results %>%
tidyr::pivot_longer(cols = c("Isd", "Inwd", "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
## ----echo=TRUE, 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(2627, 2457, 2402, 2223, 2080, 1833, 1665, 1460, 1400)
Bo <- c(1.2681, 1.2554, 1.2512, 1.2383, 1.2278, 1.2074, 1.1949, 1.1802, 1.17)
Rs <- c(0.501, 0.4973, 0.4671, 0.4574, 0.4269, 0.4024, 0.3579, 0.3277, 0.2908) * 1e3
Bg <- c(1.0502, 1.0977, 1.1146, 1.2010, 1.2825, 1.4584, 1.6112, 1.8526, 2.1) / 1e3
Bw <- c(1.0228, 1.0232, 1.0233, 1.0237, 1.0240, 1.0246, 1.0250, 1.0254, 1.0258)
Rv <- rep(0, length(p_pvt))
muo <- rep(0.5, length(p_pvt))
muw <- rep(0.55, length(p_pvt))
mug <- rep(0.02, 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(2627, 2457, 2402, 2223, 2080, 1833, 1665, 1460)
We <- rep(0, length.out = length(p))
Np <- c(0, 192821, 633942, 1314880, 1524400, 2152960, 2572000, 3200560)
Rp <- c(0, 490.1593, 492.2595, 540.4828, 558.2091, 629.7005, 708.8841, 853.8693)
Wp <- Wp <- c(0, 0, 0, 4, 7, 26, 60, 822)
Wi <- rep(0, length.out = length(p))
Gi <- rep(0, length.out = length(p))
wf <- c(1, 0, 1, 1, 1, 1, 1, 1)
mbal_optim_oil_lst <- mbal_optim_param_oil(input_unit = "Field", output_unit = "Field",
unknown_param = "We",
aquifer_model = "pot", N = 198e5,
m = 0.05, phi = 0.28, swi = 0.25, Np = Np, Rp = Rp,
Wp = Wp, Gi = Gi, Wi = Wi, We = NULL, pb = 2627,
p = p, pvt = pvt_table, cf = 26e-6,
phi_a = 0.28, perm_h_a = 100, h_a = 100,
r_a = 2592.013, r_R = 1269.619, tetha = 360,
muw_a = 0.55, cw_a = 2.28e-6, cf_a = 26e-6,
wf = wf, sorw = 0.2, sorg = 0,
mult_len = c(2))
time_lst <- mbal_time(c(0,305, 700, 1285, 1465, 2005, 2365, 2905), "day")
# 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)
## ----echo=TRUE, fig.align="center", fig.height=4, fig.width=6, warning=FALSE----
mbal_results <- mbal_perform_oil(optim_results, time_lst)
mbal_results
p1 <- mbal_results %>% ggplot(aes(`P (psia)`, SOo, color = "Field")) +
geom_point(size = 3) +
scale_color_manual(name="Data", values=c("Field" = "black")) +
ggtitle("Oil Saturation Plot") +
theme_bw()
p1
p2 <- mbal_results %>% ggplot(aes(`P (psia)`, `GOR (SCF/STB)`, color = "Field")) +
geom_point(size = 3) +
scale_color_manual(name="Data", values = c("Field" = "black")) +
ggtitle("GOR Plot") +
theme_bw()
p2
p3 <- mbal_results %>% ggplot(aes(`P (psia)`, `RF_oil`, color = "Field")) +
geom_point(size = 3) +
scale_color_manual(name="Data", values=c("Field" = "black")) +
ggtitle("Oil Recovery Plot") +
theme_bw()
p3
p4 <- mbal_results %>%
tidyr::pivot_longer(cols = c("Igd","Isd", "Inwd", "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
## ----echo=TRUE, 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(2000, 1800, 1700, 1640, 1600, 1400, 1200, 1000, 800, 600, 400, 200) # psia
Bo <- c(1.467, 1.472, 1.475, 1.463, 1.453, 1.408, 1.359, 1.322, 1.278, 1.237,
1.194, 1.141) # RB/STB
Rs <- c(838.5, 838.5, 838.5, 816.1, 798.4, 713.4, 621, 548, 464, 383.9, 297.4,
190.9) #SCF/STB
Bg <- c(1.749, 1.755, 1.758, 1.921, 1.977, 2.308, 2.73, 3.328, 4.163, 5.471,
7.786, 13.331) / 1000 # RB/SCF
Rv <- c(1192.6, 1192.6, 1192.6, 0.2, 0.2, 0, 0, 0, 0, 0, 0, 0) / 1e6 # STB/SCF
cw <- 2e-6
Bwi <- 1.0
Bw <- Bwi * exp(cw * (p_pvt[1] - p_pvt))
muo <- c(0.3201, 0.3114, 0.3071, 0.3123, 0.316, 0.34, 0.371, 0.397, 0.432, 0.471,
0.518, 0.589) # cp
muw <- rep(0.25, length(p_pvt))
mug <- c(0.3201, 0.3114, 0.3071, 0.0157, 0.0155, 0.014, 0.0138, 0.0132, 0.0126, 0.0121,
0.0116, 0.0108) # cp
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(2000, 1800, 1700, 1640, 1600, 1400, 1200, 1000, 800, 600, 400, 200) # psia
Gi <- rep(0, length.out = length(p))
wf <- rep(1, length.out = length(p))
# 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 = 1e6, m = 0.0, phi = 0.1, swi = 0.2,
Gi = Gi_forecast, pb = 1688, p = p_forecast, pvt = pvt_table,
cf = 2e-6, wf = wf_forecast, sorg = 0.15, rel_perm = rel_perm)
forecast_results <- mbal_forecast_oil(forecast_lst, time_lst_forecast)
forecast_results
reservoir_performance_table <- data.frame(p = p)
reservoir_performance_table$`RF_oil` <- c(0, 0.4, 0.5, 2.7, 4.4, 11.3, 16.1,
19.3, 22.2, 24.3, 26.2, 27.9) / 100
reservoir_performance_table$`Sg` <- c(0, 0, 0, 2.9, 5.3, 14.8, 22.3, 27.3, 32.2,
36.2, 39.9, 43.9) * 0.8 / 100
reservoir_performance_table$`GOR` <- c(0.84, 0.84, 0.84, 0.82, 0.8, 1.41, 2.17,
2.7, 3.52, 4.58, 5.56, 6.79) * 1000 # SCF/STB
p1 <- forecast_results %>% ggplot(aes(`P (psia)`, SGo, color = "Forecast")) +
geom_point(size = 3) +
geom_point(data = reservoir_performance_table, aes(`p`, Sg, color = "Field"))+
scale_color_manual(name="Data", values=c("Forecast" = "red", "Field" = "black")) +
ggtitle("Gas Saturation Plot") +
theme_bw()
p1
p2 <- forecast_results %>% ggplot(aes(`P (psia)`, `GOR (SCF/STB)`, color = "Forecast")) +
geom_point(size = 3) +
geom_point(data = reservoir_performance_table, aes(`p`, GOR, color = "Field"))+
scale_color_manual(name="Data", values=c("Forecast" = "red", "Field" = "black")) +
ggtitle("GOR Plot") +
theme_bw()
p2
p3 <- forecast_results %>% ggplot(aes(`P (psia)`, `RF_oil`, color = "Forecast")) +
geom_point(size = 3) +
geom_point(data = reservoir_performance_table, aes(`p`, RF_oil, color = "Field"))+
scale_color_manual(name="Data", values=c("Forecast" = "red", "Field" = "black")) +
ggtitle("Oil Recovery Plot") +
theme_bw()
p3
p4 <- forecast_results %>% ggplot(aes(`P (psia)`, `Liq_volume`, color = "Forecast")) +
geom_point(size = 3) +
scale_color_manual(name="Data", values=c("Forecast" = "red")) +
ggtitle("CCE Liquid Volume Plot") +
theme_bw()
p4
p5 <- forecast_results %>%
tidyr::pivot_longer(cols = c("Isd", "Ifwd"), names_to = "Drive Mechanism",
values_to = "Fraction", values_drop_na = TRUE) %>%
ggplot(aes(`P (psia)`, Fraction, fill = `Drive Mechanism`)) +
geom_area() +
ggtitle("Energy Plot") +
theme_bw()
p5
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## ---- include = FALSE---------------------------------------------------------
knitr::opts_chunk$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)
pvt_table_oil <- as.data.frame(Rpvt::pvt_oil("Field", "Field", "black_oil",
"Standing", "Beggs_Robinson", 200,
7150, 43, 0.7, c(0,0,0), pb = 4500,
warning = "no"))
colnames(pvt_table_oil) <- c("t", "p", "Rs", "Bo", "dens_o", "co", "muo", "Z",
"Bg", "dens_g", "cg", "mug", "m_p")
pvt_table_water <- as.data.frame(Rpvt::pvt_water("Field", "Field", "water",
"Spivey", "Spivey", 200, 7150,
0, "no", "no"))
colnames(pvt_table_water) <- c("t", "p", "Rsw", "Bw", "dens_w", "cw", "muw")
pvt_table <- dplyr::left_join(pvt_table_oil, pvt_table_water, by = c("p", "t"))
pvt_table$Rv <- rep(0, length.out = nrow(pvt_table)) # zero for black oil
pvt_table <- pvt_table %>% dplyr::select(p, Bo, Rs, Rv, Bg, Bw, muo, mug, muw)
p <- c(7150,6600,5800,4950,4500)
We <- rep(0, length.out = length(p))
Np <- c(0, 8.072, 22.549, 36.369, 43.473) * 1e6
Rp <- c(0, rep(pvt_table$Rs[nrow(pvt_table)], 4))
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(p))
mbal_optim_oil_lst <- mbal_optim_param_oil(input_unit = "Field", output_unit = "Field",
unknown_param = "N", aquifer_model = NULL,
m = 0, phi = 0.2, swi = 0.43, Np = Np,
Rp = Rp, Wp = Wp, Gi = Gi, Wi = Wi, We = We,
pb = 4500, p = p, pvt = pvt_table, cf = 4e-6,
wf = wf, sorg = 0, sorw = 0)
time_lst <- mbal_time(1:5, "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
## ----echo=TRUE, fig.align="center", fig.height=4, fig.width=6, warning=FALSE----
# gas saturation above the bubble point is zero, however the mbal_forecast_param_oil()
# requires a table of relative permeabilities as an input for the gas-oil system.
# Therefore, an arbitrary table is generated using the 'Rrelperm' package.
# The generated table does not impact the predictions above the bubble point.
rel_perm <- as.data.frame(Rrelperm::kr2p_gl(SWCON = 0.43, SOIRG = 0.15, SORG = 0.15,
SGCON = 0.05, SGCRIT = 0.05, KRGCL = 1,
KROGCG = 1, NG = 1.5, NOG = 1.0, NP = 101))
colnames(rel_perm) <- c("Sg", "Sl", "Krg", "Krog")
forecast_lst <- mbal_forecast_param_oil(input_unit = "Field", output_unit = "Field",
N = 6.35e8, m = 0, phi = 0.1, swi = 0.43,
Gi = Gi, pb = 4500, p = p, pvt = pvt_table,
cf = 4e-6, wf = wf, sorg = 0, rel_perm = rel_perm)
glimpse(forecast_lst)
forecast_results <- mbal_forecast_oil(forecast_lst, time_lst)
forecast_results
p1 <- 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()
p1
p2 <- 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()
p2
## ----echo=TRUE, fig.align="center", fig.height=4, fig.width=6, warning=FALSE----
library(Rmbal)
library(Rrelperm)
library(pracma)
library(ggplot2)
library(dplyr)
library(magrittr)
pvt_table_oil <- as.data.frame(Rpvt::pvt_oil("Field", "Field", "black_oil",
"Standing", "Beggs_Robinson", 200,
7150, 43, 0.7, c(0,0,0), pb = 4500,
warning = "no"))
colnames(pvt_table_oil) <- c("t", "p", "Rs", "Bo", "dens_o", "co", "muo", "Z",
"Bg", "dens_g", "cg", "mug", "m_p")
pvt_table_water <- as.data.frame(Rpvt::pvt_water("Field", "Field", "water",
"Spivey", "Spivey", 200, 7150,
3e-6, "no", "no"))
colnames(pvt_table_water) <- c("t", "p", "Rsw", "Bw", "dens_w", "cw", "muw")
pvt_table <- dplyr::left_join(pvt_table_oil, pvt_table_water, by = c("p", "t"))
pvt_table$Rv <- 0.0 # zero for black oil
pvt_table <- pvt_table %>% dplyr::select(p, Bo, Rs, Rv, Bg, Bw, muo, mug, muw)
p <- c(7150, 6600, 5800, 4950, 4500, 4350, 4060, 3840, 3600, 3480, 3260, 3100, 2940, 2800)
We <- rep(0, length.out = length(p))
Np <- c(0, 8.072, 22.549, 36.369, 43.473, 49.182, 58.383, 64.812, 69.562, 74.572,
78.4, 81.275, 83.879, 86.401) * 1e6
Rp <- c(0, rep(pvt_table$Rs[nrow(pvt_table)], 4), 1576, 1788, 1992, 2158, 2383, 2596, 2785, 2953, 3103)
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(p))
mbal_optim_oil_lst <- mbal_optim_param_oil(input_unit = "Field", output_unit = "Field",
unknown_param = "N", aquifer_model = NULL,
m = 0, phi = 0.2, swi = 0.43, Np = Np,
Rp = Rp, Wp = Wp, Gi = Gi, Wi = Wi, We = We,
pb = 4500, p = p, pvt = pvt_table, cf = 4e-6,
wf = wf, sorg = 0, sorw = 0)
glimpse(mbal_optim_oil_lst)
time_lst <- mbal_time(1:14, "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
## ----echo=TRUE, fig.align="center", fig.height=4, fig.width=6, warning=FALSE----
# Step I: generating a set of pseudo relative permeability curves by minimizing
# the difference between field and theoretical 'fg' values
# 'swcrit' and 'sgcrit' values are estimated from the 'mbal_results' data frame
fun_fg <- function(x, swcrit, sgcrit, muo, mug, sg, fg, krg_kro) {
Kr_table <- Rrelperm::kr2p_gl(SWCON = swcrit, SOIRG = x[1], SORG = x[1],
SGCON = sgcrit, SGCRIT = sgcrit, KRGCL = x[2],
KROGCG = x[3], NG = x[4], NOG = x[5], NP = 101)
l <- length(fg)
krg_est_sub <- vector(length = l)
kro_est_sub <- vector(length = l)
krg_est_sub <- approx(x = Kr_table[,1], y = Kr_table[,3], xout = sg, rule = 2)$y
kro_est_sub <- approx(x = Kr_table[,1], y = Kr_table[,4], xout = sg, rule = 2)$y
fg_est <- (krg_est_sub / mug) / (krg_est_sub / mug + kro_est_sub / muo)
krg_kro_est <- krg_est_sub / kro_est_sub
error <- (fg - fg_est) ^ 2 + (krg_kro - krg_kro_est) ^ 2
return(error)
}
swcrit <- 0.44
sgcrit <- 0.015
p <- mbal_results$`P (psia)`
sg <- mbal_results$SGo # gas saturation in the oil leg
fg <- mbal_results$fg # in-situ gas fractional flow
krg_kro <- mbal_results$`krg/kro`
muo <- approx(x = pvt_table$p, y = pvt_table$muo, xout = p, rule = 2)$y
mug <- approx(x = pvt_table$p, y = pvt_table$mug, xout = p, rule = 2)$y
par <- c(0.1, 1, 1, 2, 2)
lower = c(0, 0.1, 0.1, 0.1, 0.1)
upper = c(1 - swcrit - sgcrit, 1.0, 1.0, 10.0, 10.0)
opt_results <- minpack.lm::nls.lm(par = par, fn = fun_fg, swcrit = swcrit, sgcrit = sgcrit,
muo = muo, mug = mug, sg = sg, fg = fg, krg_kro = krg_kro,
lower = lower, upper = upper)
opt_results
sol <- opt_results$par
sol
rel_perm <- as.data.frame(Rrelperm::kr2p_gl(SWCON = swcrit, SOIRG = sol[1], SORG = sol[1],
SGCON = sgcrit, SGCRIT = sgcrit, KRGCL = sol[2],
KROGCG = sol[3], NG = sol[4], NOG = sol[5],
NP = 101))
colnames(rel_perm) <- c("Sg", "Sl", "Krg", "Krog")
krg_est <- approx(x = rel_perm[,1], y = rel_perm[,3], xout = sg, rule = 2)$y
kro_est <- approx(x = rel_perm[,1], y = rel_perm[,4], xout = sg, rule = 2)$y
fg_est <- (krg_est/ mug) / (krg_est / mug + kro_est / muo)
fg_df <- data.frame(Sg = sg)
fg_df$fg <- fg_est
p_fg <- fg_df %>% ggplot(aes(sg, fg, color = "Model")) +
geom_point(size = 3) +
geom_point(data = mbal_results, aes(SGo, fg, color = "Field"), size = 3)+
xlim(c(0,1)) +
ylim(c(0,1)) +
scale_color_manual(name="Data", values=c("Model" = "red", "Field" = "black")) +
ggtitle("Gas Frational Flow Plot") +
theme_bw()
p_fg
p_forecast <- c(p, 2600, 2400, 2200, 2000, 1800, 1600, 1400, 1200)
Gi_forecast <- c(Gi, 0, 0, 0, 0, 0, 0, 0, 0)
wf_forecast <- c(wf, 1, 1, 1, 1, 1, 1, 1, 1)
time_lst_forecast <- mbal_time(1:22, "year")
forecast_lst <- mbal_forecast_param_oil(input_unit = "Field", output_unit = "Field",
N = 6.35e8, m = 0, phi = 0.1, swi = 0.43,
Gi = Gi_forecast, pb = 4500, p = p_forecast, pvt = pvt_table,
cf = 4e-6, wf = wf_forecast, sorg = 0, 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
p4 <- forecast_results %>% ggplot(aes(`P (psia)`, `Liq_volume`, color = "Forecast")) +
geom_point(size = 3) +
scale_color_manual(name="Data", values=c("Forecast" = "red")) +
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
## ----echo=TRUE, 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(3330, 3150, 3000, 2850, 2700, 2550, 2400)
Bo <- c(1.2511, 1.2353, 1.2222, 1.2122, 1.2022, 1.1922, 1.1822)
Rs <- c(510, 477, 450, 425, 401, 375, 352)
Bg <- c(0.00087, 0.00092, 0.00096, 0.00101, 0.00107, 0.00113, 0.00120)
cw <- 2e-6
Bwi <- 1.0
Bw <- Bwi * exp(cw * (p_pvt[1] - p_pvt))
Rv <- rep(0, length(p_pvt))
muo <- rep(0.5, length(p_pvt))
muw <- rep(0.25, length(p_pvt))
mug <- rep(0.02, 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(3330, 3150, 3000, 2850, 2700, 2550, 2400)
We <- rep(0, length.out = length(p))
Np <- c(0, 3.295, 5.903, 8.852, 11.503, 14.513, 17.730) * 1e6
Rp <- c(0, 1050, 1060, 1160, 1235, 1265, 1300)
Wp <- rep(0, length.out = length(p))
Wi <- rep(0, length.out = length(p))
Gi <- rep(0, length.out = length(p))
wf <- c(1, 1, 1, 0, 1, 0, 1)
mbal_optim_oil_lst <- mbal_optim_param_oil(input_unit = "Field", output_unit = "Field",
unknown_param = "N_m", aquifer_model = NULL,
phi = 0.2, swi = 0.2, Np = Np,
Rp = Rp, Wp = Wp, Gi = Gi, Wi = Wi, We = We,
pb = 3330, p = p, pvt = pvt_table, cf = 0,
wf = wf, sorg = 0.2, sorw = 0)
time_lst <- mbal_time(c(0, 365, 730, 1095, 1460, 1825, 2190), "day")
# 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)
## ----echo=TRUE, fig.align="center", fig.height=4, fig.width=6, warning=FALSE----
mbal_results <- mbal_perform_oil(optim_results, time_lst)
mbal_results
## ----echo=TRUE, fig.align="center", fig.height=4, fig.width=6, warning=FALSE----
# Step I: generating a set of pseudo relative permeability curves by minimizing
# the difference between field and theoretical 'fg' values
# 'swcrit' and 'sgcrit' values are estimated from the 'mbal_results' data frame
fun_fg <- function(x, swcrit, sgcrit, muo, mug, sg, fg, krg_kro) {
Kr_table <- Rrelperm::kr2p_gl(SWCON = swcrit, SOIRG = x[1], SORG = x[1],
SGCON = sgcrit, SGCRIT = sgcrit, KRGCL = x[2],
KROGCG = x[3], NG = x[4], NOG = x[5], NP = 101)
l <- length(fg)
krg_est_sub <- vector(length = l)
kro_est_sub <- vector(length = l)
krg_est_sub <- approx(x = Kr_table[,1], y = Kr_table[,3], xout = sg, rule = 2)$y
kro_est_sub <- approx(x = Kr_table[,1], y = Kr_table[,4], xout = sg, rule = 2)$y
fg_est <- (krg_est_sub / mug) / (krg_est_sub / mug + kro_est_sub / muo)
krg_kro_est <- krg_est_sub / kro_est_sub
error <- (fg - fg_est) ^ 2 + (krg_kro - krg_kro_est) ^ 2
return(error)
}
swcrit <- 0.2
sgcrit <- 0.00
p <- mbal_results$`P (psia)`
sg <- mbal_results$SGo # gas saturation in the oil leg
fg <- mbal_results$fg # in-situ gas fractional flow
krg_kro <- mbal_results$`krg/kro`
muo <- approx(x = pvt_table$p, y = pvt_table$muo, xout = p, rule = 2)$y
mug <- approx(x = pvt_table$p, y = pvt_table$mug, xout = p, rule = 2)$y
par <- c(0.1, 0.3, 1.0, 3, 3)
lower = c(0, 0.3, 1.0, 0.1, 0.1)
upper = c(1 - swcrit - sgcrit, 0.3, 1.0, 10.0, 10.0)
opt_results <- minpack.lm::nls.lm(par = par, fn = fun_fg, swcrit = swcrit, sgcrit = sgcrit,
muo = muo, mug = mug, sg = sg, fg = fg, krg_kro = krg_kro,
lower = lower, upper = upper)
opt_results
sol <- opt_results$par
sol
rel_perm <- as.data.frame(Rrelperm::kr2p_gl(SWCON = swcrit, SOIRG = sol[1], SORG = sol[1],
SGCON = sgcrit, SGCRIT = sgcrit, KRGCL = sol[2],
KROGCG = sol[3], NG = sol[4], NOG = sol[5],
NP = 101))
colnames(rel_perm) <- c("Sg", "Sl", "Krg", "Krog")
krg_est <- approx(x = rel_perm[,1], y = rel_perm[,3], xout = sg, rule = 2)$y
kro_est <- approx(x = rel_perm[,1], y = rel_perm[,4], xout = sg, rule = 2)$y
fg_est <- (krg_est/ mug) / (krg_est / mug + kro_est / muo)
fg_df <- data.frame(Sg = sg)
fg_df$fg <- fg_est
p_fg <- fg_df %>% ggplot(aes(sg, fg, color = "Model")) +
geom_point(size = 3) +
geom_point(data = mbal_results, aes(SGo, fg, color = "Field"), size = 3)+
xlim(c(0,1)) +
ylim(c(0,1)) +
scale_color_manual(name="Data", values=c("Model" = "red", "Field" = "black")) +
ggtitle("Gas Frational Flow Plot") +
theme_bw()
p_fg
p_forecast <- p_pvt
Gi_forecast <- c(Gi)
wf_forecast <- c(wf)
time_lst_forecast <- mbal_time(c(0, 365, 730, 1095, 1460, 1825, 2190), "day")
forecast_lst <- mbal_forecast_param_oil(input_unit = "Field", output_unit = "Field",
N = 1.37e8, m = 0.377, phi = 0.2, swi = 0.2,
Gi = Gi_forecast, pb = 3330, p = p_forecast, pvt = pvt_table,
cf = 0, wf = wf_forecast, sorg = 0.2, 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
p4 <- forecast_results %>% ggplot(aes(`P (psia)`, `Liq_volume`, color = "Forecast")) +
geom_point(size = 3) +
scale_color_manual(name="Data", values=c("Forecast" = "red")) +
ggtitle("CCE Liquid Volume Plot") +
theme_bw()
p4
p5 <- forecast_results %>%
tidyr::pivot_longer(cols = c("Igd", "Isd"), 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
## ----echo=TRUE, 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(2740, 2500, 2290, 2109, 1949, 1818, 1702, 1608, 1535, 1480, 1440)
Bo <- c(1.404, 1.374, 1.349, 1.329, 1.316, 1.303, 1.294, 1.287, 1.280, 1.276, 1.273)
Rs <- c(650, 592, 545, 507, 471, 442, 418, 398, 383, 371, 364)
Bg <- c(0.00093, 0.00098, 0.00107, 0.00117, 0.00128, 0.00139, 0.00150, 0.00160,
0.00170, 0.00176, 0.00182)
cw <- 3e-6
Bwi <- 1.0
Bw <- Bwi * exp(cw * (p_pvt[1] - p_pvt))
Rv <- rep(0, length(p_pvt))
muo <- rep(0.5, length(p_pvt))
muw <- rep(0.55, length(p_pvt))
mug <- rep(0.02, 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(2740, 2500, 2290, 2109, 1949, 1818, 1702, 1608, 1535, 1480, 1440)
We <- rep(0, length.out = length(p))
Np <- c(0, 7.88, 18.42, 29.15, 40.69, 50.14, 58.42, 65.39, 70.74, 74.54, 77.43) * 1e6
Rp <- c(0, 760, 845, 920, 975, 1025, 1065, 1095, 1120, 1145, 1160)
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 = "We",
aquifer_model = "pss_rad_edge", N = 312e6,
m = 0, phi = 0.25, swi = 0.25, Np = Np, Rp = Rp,
Wp = Wp, Gi = Gi, Wi = Wi, We = NULL, pb = 2740,
p = p, pvt = pvt_table, cf = 4e-6,
phi_a = 0.25, perm_h_a = 200, h_a = 100,
r_a = 5 * 9200, r_R = 9200, tetha = 140,
muw_a = 0.55, cw_a = 3e-6, cf_a = 4e-6,
wf = wf, sorw = 0.2, sorg = 0,
mult_len = c(1,1), lower = c(-Inf, 1),
upper = c(Inf, 1),
control = list(maxiter = 100), )
time_lst <- mbal_time(0:10, "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)
## ----echo=TRUE, fig.align="center", fig.height=4, fig.width=6, warning=FALSE----
mbal_results <- mbal_perform_oil(optim_results, time_lst)
mbal_results
p1 <- mbal_results %>% ggplot(aes(`P (psia)`, SOo, color = "Field")) +
geom_point(size = 3) +
scale_color_manual(name="Data", values=c("Field" = "black")) +
ggtitle("Oil Saturation Plot") +
theme_bw()
p1
p2 <- mbal_results %>% ggplot(aes(`P (psia)`, `GOR (SCF/STB)`, color = "Field")) +
geom_point(size = 3) +
scale_color_manual(name="Data", values = c("Field" = "black")) +
ggtitle("GOR Plot") +
theme_bw()
p2
p3 <- mbal_results %>% ggplot(aes(`P (psia)`, `RF_oil`, color = "Field")) +
geom_point(size = 3) +
scale_color_manual(name="Data", values=c("Field" = "black")) +
ggtitle("Oil Recovery Plot") +
theme_bw()
p3
p4 <- mbal_results %>%
tidyr::pivot_longer(cols = c("Isd", "Inwd", "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
## ----echo=TRUE, 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(2855, 2779, 2627, 2457, 2402, 2223, 2080, 1833, 1665, 1460) # psia
Bo <- c(1.2665, 1.2677, 1.2681, 1.2554, 1.2512, 1.2383, 1.2278, 1.2074, 1.1949,
1.1802) # RB/STB
Rs <- c(0.501, 0.501, 0.4973, 0.4671, 0.4574, 0.4269, 0.4024, 0.3579, 0.3277,
0.2908) * 1000 #SCF/STB
Bg <- c(0.9201, 0.9637, 1.0502, 1.0977, 1.1146, 1.201, 1.2825, 1.4584, 1.6112,
1.8526) / 1000 # RB/SCF
Bw <- c(1.0222, 1.0224, 1.0228, 1.0232, 1.0233, 1.0237, 1.024, 1.0246, 1.025,
1.0254) # RB/STB
Rv <- rep(0, length(p_pvt))
muo <- rep(0.5, length(p_pvt))
muw <- rep(0.55, length(p_pvt))
mug <- rep(0.02, 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(2855, 2779, 2627, 2457, 2402, 2223, 2080, 1833, 1665, 1460) # psia
Np <- c(0, 192821, 633942, 1314880, 1524400, 2152960, 2572000, 3200560,
3584680, 4003720) # STB
Rp <- c(0, 501, 492.2595442, 540.4827817, 558.2091315, 629.7005054,
708.8841369, 853.8693229, 947.8502963, 1053.050663) # SCF/STB
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 = "We",
aquifer_model = "pot", N = 20e6,
m = 0, phi = 0.28, swi = 0.208, Np = Np,
Rp = Rp, Wp = Wp, Gi = Gi, Wi = Wi,
We = NULL, pb = 2648, p = p, pvt = pvt_table,
cf = 26e-6, phi_a = 0.28, h_a = 200, r_a = 1000,
r_R = 300, tetha = 360, cw_a = 2.28e-6,
cf_a = 26e-6, mult_len = 3, wf = wf,
sorw = 0.0, sorg = 0)
time_lst <- mbal_time(c(0,305, 700, 1285, 1465, 2005, 2365, 2905, 3235, 3595), "day")
# 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)
## ----echo=TRUE, fig.align="center", fig.height=4, fig.width=6, warning=FALSE----
mbal_results <- mbal_perform_oil(optim_results, time_lst)
mbal_results
p1 <- mbal_results %>% ggplot(aes(`P (psia)`, SOo, color = "Field")) +
geom_point(size = 3) +
scale_color_manual(name="Data", values=c("Field" = "black")) +
ggtitle("Oil Saturation Plot") +
theme_bw()
p1
p2 <- mbal_results %>% ggplot(aes(`P (psia)`, `GOR (SCF/STB)`, color = "Field")) +
geom_point(size = 3) +
scale_color_manual(name="Data", values = c("Field" = "black")) +
ggtitle("GOR Plot") +
theme_bw()
p2
p3 <- mbal_results %>% ggplot(aes(`P (psia)`, `RF_oil`, color = "Field")) +
geom_point(size = 3) +
scale_color_manual(name="Data", values=c("Field" = "black")) +
ggtitle("Oil Recovery Plot") +
theme_bw()
p3
p4 <- mbal_results %>%
tidyr::pivot_longer(cols = c("Isd", "Inwd", "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
## ----echo=TRUE, 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(2627, 2457, 2402, 2223, 2080, 1833, 1665, 1460, 1400)
Bo <- c(1.2681, 1.2554, 1.2512, 1.2383, 1.2278, 1.2074, 1.1949, 1.1802, 1.17)
Rs <- c(0.501, 0.4973, 0.4671, 0.4574, 0.4269, 0.4024, 0.3579, 0.3277, 0.2908) * 1e3
Bg <- c(1.0502, 1.0977, 1.1146, 1.2010, 1.2825, 1.4584, 1.6112, 1.8526, 2.1) / 1e3
Bw <- c(1.0228, 1.0232, 1.0233, 1.0237, 1.0240, 1.0246, 1.0250, 1.0254, 1.0258)
Rv <- rep(0, length(p_pvt))
muo <- rep(0.5, length(p_pvt))
muw <- rep(0.55, length(p_pvt))
mug <- rep(0.02, 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(2627, 2457, 2402, 2223, 2080, 1833, 1665, 1460)
We <- rep(0, length.out = length(p))
Np <- c(0, 192821, 633942, 1314880, 1524400, 2152960, 2572000, 3200560)
Rp <- c(0, 490.1593, 492.2595, 540.4828, 558.2091, 629.7005, 708.8841, 853.8693)
Wp <- Wp <- c(0, 0, 0, 4, 7, 26, 60, 822)
Wi <- rep(0, length.out = length(p))
Gi <- rep(0, length.out = length(p))
wf <- c(1, 0, 1, 1, 1, 1, 1, 1)
mbal_optim_oil_lst <- mbal_optim_param_oil(input_unit = "Field", output_unit = "Field",
unknown_param = "We",
aquifer_model = "pot", N = 198e5,
m = 0.05, phi = 0.28, swi = 0.25, Np = Np, Rp = Rp,
Wp = Wp, Gi = Gi, Wi = Wi, We = NULL, pb = 2627,
p = p, pvt = pvt_table, cf = 26e-6,
phi_a = 0.28, perm_h_a = 100, h_a = 100,
r_a = 2592.013, r_R = 1269.619, tetha = 360,
muw_a = 0.55, cw_a = 2.28e-6, cf_a = 26e-6,
wf = wf, sorw = 0.2, sorg = 0,
mult_len = c(2))
time_lst <- mbal_time(c(0,305, 700, 1285, 1465, 2005, 2365, 2905), "day")
# 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)
## ----echo=TRUE, fig.align="center", fig.height=4, fig.width=6, warning=FALSE----
mbal_results <- mbal_perform_oil(optim_results, time_lst)
mbal_results
p1 <- mbal_results %>% ggplot(aes(`P (psia)`, SOo, color = "Field")) +
geom_point(size = 3) +
scale_color_manual(name="Data", values=c("Field" = "black")) +
ggtitle("Oil Saturation Plot") +
theme_bw()
p1
p2 <- mbal_results %>% ggplot(aes(`P (psia)`, `GOR (SCF/STB)`, color = "Field")) +
geom_point(size = 3) +
scale_color_manual(name="Data", values = c("Field" = "black")) +
ggtitle("GOR Plot") +
theme_bw()
p2
p3 <- mbal_results %>% ggplot(aes(`P (psia)`, `RF_oil`, color = "Field")) +
geom_point(size = 3) +
scale_color_manual(name="Data", values=c("Field" = "black")) +
ggtitle("Oil Recovery Plot") +
theme_bw()
p3
p4 <- mbal_results %>%
tidyr::pivot_longer(cols = c("Igd","Isd", "Inwd", "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
## ----echo=TRUE, 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(2000, 1800, 1700, 1640, 1600, 1400, 1200, 1000, 800, 600, 400, 200) # psia
Bo <- c(1.467, 1.472, 1.475, 1.463, 1.453, 1.408, 1.359, 1.322, 1.278, 1.237,
1.194, 1.141) # RB/STB
Rs <- c(838.5, 838.5, 838.5, 816.1, 798.4, 713.4, 621, 548, 464, 383.9, 297.4,
190.9) #SCF/STB
Bg <- c(1.749, 1.755, 1.758, 1.921, 1.977, 2.308, 2.73, 3.328, 4.163, 5.471,
7.786, 13.331) / 1000 # RB/SCF
Rv <- c(1192.6, 1192.6, 1192.6, 0.2, 0.2, 0, 0, 0, 0, 0, 0, 0) / 1e6 # STB/SCF
cw <- 2e-6
Bwi <- 1.0
Bw <- Bwi * exp(cw * (p_pvt[1] - p_pvt))
muo <- c(0.3201, 0.3114, 0.3071, 0.3123, 0.316, 0.34, 0.371, 0.397, 0.432, 0.471,
0.518, 0.589) # cp
muw <- rep(0.25, length(p_pvt))
mug <- c(0.3201, 0.3114, 0.3071, 0.0157, 0.0155, 0.014, 0.0138, 0.0132, 0.0126, 0.0121,
0.0116, 0.0108) # cp
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(2000, 1800, 1700, 1640, 1600, 1400, 1200, 1000, 800, 600, 400, 200) # psia
Gi <- rep(0, length.out = length(p))
wf <- rep(1, length.out = length(p))
# 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$par
sol
rel_perm <- as.data.frame(Rrelperm::kr2p_gl(SWCON = swcrit_lab, SOIRG = sorgr_lab,
SORG = sorgr_lab, SGCON = sgcrit_lab,
SGCRIT = sgcrit_lab, KRGCL = 1,
KROGCG = 1, NG = sol[1], NOG = sol[2],
NP = 101))
colnames(rel_perm) <- c("Sg", "Sl", "Krg", "Krog")
p_forecast <- p
Gi_forecast <- Gi
wf_forecast <- wf
time_lst_forecast <- mbal_time(c(1:length(p_forecast)), "year")
forecast_lst <- mbal_forecast_param_oil(input_unit = "Field", output_unit = "Field",
N = 1e6, m = 0.0, phi = 0.1, swi = 0.2,
Gi = Gi_forecast, pb = 1688, p = p_forecast, pvt = pvt_table,
cf = 2e-6, wf = wf_forecast, sorg = 0.15, rel_perm = rel_perm)
forecast_results <- mbal_forecast_oil(forecast_lst, time_lst_forecast)
forecast_results
reservoir_performance_table <- data.frame(p = p)
reservoir_performance_table$`RF_oil` <- c(0, 0.4, 0.5, 2.7, 4.4, 11.3, 16.1,
19.3, 22.2, 24.3, 26.2, 27.9) / 100
reservoir_performance_table$`Sg` <- c(0, 0, 0, 2.9, 5.3, 14.8, 22.3, 27.3, 32.2,
36.2, 39.9, 43.9) * 0.8 / 100
reservoir_performance_table$`GOR` <- c(0.84, 0.84, 0.84, 0.82, 0.8, 1.41, 2.17,
2.7, 3.52, 4.58, 5.56, 6.79) * 1000 # SCF/STB
p1 <- forecast_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 <- forecast_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 <- forecast_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 <- forecast_results %>% ggplot(aes(`P (psia)`, `Liq_volume`, color = "Forecast")) +
geom_point(size = 3) +
scale_color_manual(name="Data", values=c("Forecast" = "red")) +
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
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