Rdca is a Decline Curve Analysis (DCA) package for oil and gas reservoirs. It generates a table of rate, cumulative, nominal decline rate, and derivative of loss-ratio over time in a data frame format. It also provides an optimization tool to fit a DCA model on production data. The package currently supports Arps ‘exponential’, ‘harmonic’, ‘hyperbolic’, and ‘modified_hyperbolic’ models.
You can install the released version of Rdca from CRAN with:
Arps Exponential Exampleslibrary(Rdca)
library(magrittr)
library(ggplot2)
library(ggpubr)
dcl_param_exp <- decline_param(input_unit = "Field", output_unit = "Field", fluid = "oil",
model = "exponential", qi = 1000, Di = 0.0015, b = 0, q_abnd = NULL)
dcl_param_exp
#> $input_unit
#> [1] "Field"
#>
#> $output_unit
#> [1] "Field"
#>
#> $fluid
#> [1] "oil"
#>
#> $qi
#> [1] 1000
#>
#> $Di
#> [1] 0.0015
#>
#> $b
#> [1] 0
#>
#> attr(,"class")
#> [1] "exponential" "decline"
decline_time_exp <- decline_time(c(1:7300), unit = "day")
str(decline_time_exp)
#> List of 3
#> $ t : int [1:7300] 1 2 3 4 5 6 7 8 9 10 ...
#> $ unit : chr "day"
#> $ reference_date: Date[1:1], format: "2020-05-13"
#> - attr(*, "class")= chr [1:2] "day" "time"
decline_predict_exp <- decline_predict(dcl_param_exp, decline_time_exp)
head(decline_predict_exp, 10)
#> Date Time_(day) q_(bbl/day) Q_(bbl) D_(1/day) Beta
#> 1 2020-05-13 1 998.5011 999.2504 0.0015 0
#> 2 2020-05-14 2 997.0045 1997.0030 0.0015 0
#> 3 2020-05-15 3 995.5101 2993.2601 0.0015 0
#> 4 2020-05-16 4 994.0180 3988.0240 0.0015 0
#> 5 2020-05-17 5 992.5281 4981.2968 0.0015 0
#> 6 2020-05-18 6 991.0404 5973.0808 0.0015 0
#> 7 2020-05-19 7 989.5549 6963.3783 0.0015 0
#> 8 2020-05-20 8 988.0717 7952.1914 0.0015 0
#> 9 2020-05-21 9 986.5907 8939.5225 0.0015 0
#> 10 2020-05-22 10 985.1119 9925.3736 0.0015 0Arps Harmonic Exampleslibrary(Rdca)
library(ggplot2)
library(ggpubr)
dcl_param_harm <- decline_param(input_unit = "SI", output_unit = "SI", fluid = "oil",
model = "harmonic", qi = 1000, Di = 0.075, b = 1, q_abnd = 50)
dcl_param_harm
#> $input_unit
#> [1] "SI"
#>
#> $output_unit
#> [1] "SI"
#>
#> $fluid
#> [1] "oil"
#>
#> $qi
#> [1] 1000
#>
#> $Di
#> [1] 0.075
#>
#> $b
#> [1] 1
#>
#> $q_abnd
#> [1] 50
#>
#> attr(,"class")
#> [1] "harmonic" "decline"
decline_time_harm <- decline_time(c(1:360), unit = "month")
str(decline_time_harm)
#> List of 3
#> $ t : int [1:360] 1 2 3 4 5 6 7 8 9 10 ...
#> $ unit : chr "month"
#> $ reference_date: Date[1:1], format: "2020-05-13"
#> - attr(*, "class")= chr [1:2] "month" "time"
decline_predict_harm <- decline_predict(dcl_param_harm, decline_time_harm)
head(decline_predict_harm, 10)
#> Date Time_(month) q_(m3/month) Q_(m3) D_(1/month) Beta
#> 1 2020-05-13 1 930.2326 964.2755 0.06976744 1
#> 2 2020-06-12 2 869.5652 1863.4926 0.06521739 1
#> 3 2020-07-12 3 816.3265 2705.8779 0.06122449 1
#> 4 2020-08-12 4 769.2308 3498.1902 0.05769231 1
#> 5 2020-09-11 5 727.2727 4246.0497 0.05454545 1
#> 6 2020-10-12 6 689.6552 4954.1808 0.05172414 1
#> 7 2020-11-11 7 655.7377 5626.5921 0.04918033 1
#> 8 2020-12-11 8 625.0000 6266.7151 0.04687500 1
#> 9 2021-01-11 9 597.0149 6877.5089 0.04477612 1
#> 10 2021-02-10 10 571.4286 7461.5438 0.04285714 1
#> time_abnd_(months) EUR_(m3)
#> 1 253.3333 39943.1
#> 2 253.3333 39943.1
#> 3 253.3333 39943.1
#> 4 253.3333 39943.1
#> 5 253.3333 39943.1
#> 6 253.3333 39943.1
#> 7 253.3333 39943.1
#> 8 253.3333 39943.1
#> 9 253.3333 39943.1
#> 10 253.3333 39943.1Arps Hyperbolic Exampleslibrary(Rdca)
library(ggplot2)
library(ggpubr)
dcl_param_hyp <- decline_param(input_unit = "Field", output_unit = "Field", fluid = "gas",
model = "hyperbolic", qi = 100000, Di = 0.0055, b = 0.85,
q_abnd = 2000)
dcl_param_hyp
#> $input_unit
#> [1] "Field"
#>
#> $output_unit
#> [1] "Field"
#>
#> $fluid
#> [1] "gas"
#>
#> $qi
#> [1] 1e+05
#>
#> $Di
#> [1] 0.0055
#>
#> $b
#> [1] 0.85
#>
#> $q_abnd
#> [1] 2000
#>
#> attr(,"class")
#> [1] "hyperbolic" "decline"
decline_time_hyp <- decline_time(seq(as.Date("2000/1/1"), as.Date("2030/12/31"), "days"),
unit = "date")
str(decline_time_hyp)
#> List of 3
#> $ t : num [1:11323] 1 2 3 4 5 6 7 8 9 10 ...
#> $ unit : chr "date"
#> $ reference_date: Date[1:1], format: "2000-01-01"
#> - attr(*, "class")= chr [1:2] "day" "time"
decline_predict_hyp <- decline_predict(dcl_param_hyp, decline_time_hyp)
head(decline_predict_hyp, 10)
#> Date Time_(day) q_(MSCF/day) Q_(MMSCF) D_(1/day) Beta
#> 1 2000-01-01 1 99452.78 99.72593 0.005474407 0.85
#> 2 2000-01-02 2 98911.08 198.90741 0.005449051 0.85
#> 3 2000-01-03 3 98374.81 297.54991 0.005423929 0.85
#> 4 2000-01-04 4 97843.90 395.65882 0.005399038 0.85
#> 5 2000-01-05 5 97318.26 493.23947 0.005374374 0.85
#> 6 2000-01-06 6 96797.83 590.29708 0.005349934 0.85
#> 7 2000-01-07 7 96282.51 686.83683 0.005325716 0.85
#> 8 2000-01-08 8 95772.26 782.86379 0.005301716 0.85
#> 9 2000-01-09 9 95266.98 878.38300 0.005277931 0.85
#> 10 2000-01-10 10 94766.61 973.39938 0.005254359 0.85
#> time_abnd_(days) EUR_(MMSCF)
#> 1 5733.712 53805.81
#> 2 5733.712 53805.81
#> 3 5733.712 53805.81
#> 4 5733.712 53805.81
#> 5 5733.712 53805.81
#> 6 5733.712 53805.81
#> 7 5733.712 53805.81
#> 8 5733.712 53805.81
#> 9 5733.712 53805.81
#> 10 5733.712 53805.81