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.
Installation
You can install the released version of Rdca from CRAN with:
install.packages("Rdca")
Arps Exponential Examples
library(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: "2020-07-04" #> - 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 2020-07-04 1 998.5011 999.2504 0.0015 0 #> 2 2020-07-05 2 997.0045 1997.0030 0.0015 0 #> 3 2020-07-06 3 995.5101 2993.2601 0.0015 0 #> 4 2020-07-07 4 994.0180 3988.0240 0.0015 0 #> 5 2020-07-08 5 992.5281 4981.2968 0.0015 0 #> 6 2020-07-09 6 991.0404 5973.0808 0.0015 0 #> 7 2020-07-10 7 989.5549 6963.3783 0.0015 0 #> 8 2020-07-11 8 988.0717 7952.1914 0.0015 0 #> 9 2020-07-12 9 986.5907 8939.5225 0.0015 0 #> 10 2020-07-13 10 985.1119 9925.3736 0.0015 0
Arps Harmonic Examples
library(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: "2020-07-04" #> - 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 2020-07-04 1 930.2326 964.2755 0.06976744 1 #> 2 2020-08-03 2 869.5652 1863.4926 0.06521739 1 #> 3 2020-09-02 3 816.3265 2705.8779 0.06122449 1 #> 4 2020-10-03 4 769.2308 3498.1902 0.05769231 1 #> 5 2020-11-02 5 727.2727 4246.0497 0.05454545 1 #> 6 2020-12-03 6 689.6552 4954.1808 0.05172414 1 #> 7 2021-01-02 7 655.7377 5626.5921 0.04918033 1 #> 8 2021-02-01 8 625.0000 6266.7151 0.04687500 1 #> 9 2021-03-04 9 597.0149 6877.5089 0.04477612 1 #> 10 2021-04-03 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 39943.1
Arps Hyperbolic Examples
library(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