# The Storage Security Calculator (SSC): Calculates CO2 geological storage security over 10,000 years. # Code associated with the manuscript: #"Assessing the capability of CO2 Storage Security to deliver on climate mitigation." #By Juan Alcalde, Stephanie Flude, #Mark Wilkinson, Gareth Johnson, Katriona Edlmann, Clare E. Bond, #Vivian Scott, Stuart M. V. Gilfillan, Xènia Ogaya and R. Stuart Haszeldine. # Instructions: # Run the entire code. (We recommend running in RStudio) # For the base case scenario: # Open BasicTable to review the results of the base case scenario # A graphical display of the base case scenario is automatically generated # View the key and change displayed parameters in Section 4.3 (Line 565) # For Monte Carlo uncertainty analysis: # run: temp <- SSCMC(n) - where n is the required number of realisations # run FigLoss() # Results table and CO2 loss curves over time will be displayed # For the sensitivity analysis results # If required, change the number of standard deviations being assessed in Section 4.2 (line 526) # Open MinMaxSA to view results #################################################################################### ########################## Section 1: General Set-up ############################### #################################################################################### set.seed(2100) ############################## 1.1 Define rtriangle ############################ ################## Code to define a triangle distribution courtesy of R. A. Godfrey ################## https://www.rdocumentation.org/packages/ExtDist/versions/0.6-3/topics/Triangular rtriangle <- function(n=1, a=0, b=1, c=(a+b)/2){ if(length(n)>1) n <- length(n) if(n<1 | is.na(n)) stop(paste("invalid argument: n =", n)) n <- floor(n) if(any(is.na(c(a,b,c)))) return(rep(NaN, times=n)) # to match behavior of runif if(a > c | b < c) return(rep(NaN, times=n)) # to match behavior of runif if(any(is.infinite(c(a,b,c)))) return(rep(NaN, times=n)) p <- runif(n) if(a != c){ # if a = c then i is always true i <- which((a + sqrt(p * (b - a)*(c - a))) <= c) j <- which((b - sqrt((1 - p) * (b - a) * (b - c))) > c) } else { i <- which((a + sqrt(p * (b - a)*(c - a))) < c) j <- which((b - sqrt((1 - p) * (b - a) * (b - c))) >= c) } if(length(i)!=0) p[i] <- a + sqrt(p[i] * (b - a) * (c - a)) if(length(j)!=0) p[j] <- b - sqrt((1 - p[j]) * (b - a) * (b - c)) return(p) } ########################################################################## ###################### 1.2 Define AbSetUp function ############################# ################This calculates Abandoned Well leakage rates AB1 and AB2 AbSetUp <- function( KnownWellDensity=0.44,# WELL DENSITY - wells / km2 Known abandoned well density wellUnderEst=1, # WELL DENSITY - Well under-estimation factor UnPlugWells=00, # WELL STATUS - Proportion of unplugged wells DegradWells=exp(-2.17), # PLUGGED WELL CONDITIONS - Proportion of Degraded wells IntactDegrad=0, # Pre-injection conversions: Known degraded wells remediated to intact. (Function not used here) IntactHighRate=0.054, # SLOW LEAKAGE - Plugged & intact wells with higher leak rate CO2largeBlowout=exp(13.4), # Mass of CO2 lost during abandoned well blowout CO2degraded=300, # Mass of CO2 lost per leaking well during continuous leakage through degraded wells CO2intactHigh=230, # Mass of CO2 lost per well for intact wells with the high leak rate CO2intactLow=0.004, # Mass CO2 lost per well for intact wells with the low leak rate InjectionPeriod=30, # Number of years of injection BlowoutWellYear=0.00002, # BLOWOUTS - Long-term blowout rate - events per well year over 10 kyr PlugBlowoutYear=exp(-8.6125), # BLOWOUTS - Proportion of plugged wells that blowout during injection period ActiveWellsAB1=0.05128205 # The number of active wells that convert to abandoned wells post injection ){ ## Initial Conditions TrueWellDensity <- KnownWellDensity * wellUnderEst UnKnownWellDensity <- TrueWellDensity - KnownWellDensity KnownUnPlugWells <- KnownWellDensity * UnPlugWells UnKnownUnPlugWells <- UnKnownWellDensity * UnPlugWells KnownPlugWells <- KnownWellDensity * (1-UnPlugWells) UnKnownPlugWells <- UnKnownWellDensity * (1-UnPlugWells) TotalUnPlugWells <- KnownUnPlugWells + UnKnownUnPlugWells TotalPlugWells <- KnownPlugWells + UnKnownPlugWells ############ Plugged well conditions KnownDegPlug <- KnownPlugWells * DegradWells UnKnownDegPlug <- UnKnownPlugWells * DegradWells KnownIntactPlug <- KnownPlugWells * (1-DegradWells) UnKnownIntactPlug <- UnKnownPlugWells * (1-DegradWells) ######### AB1 ################################ ## Plugged well status RemUnPlugWells <- KnownUnPlugWells KnownPlugWells <- RemUnPlugWells + KnownPlugWells #UnKnownPlugWells TotalPlugwells <- KnownPlugWells + UnKnownPlugWells #UnKnownUnPlugWells ###Known plugged well conditions RemDegPlug <- KnownDegPlug * IntactDegrad KnownIntactWells <- RemDegPlug + RemUnPlugWells + KnownIntactPlug KnownDegWells <- KnownDegPlug - RemDegPlug ####Unknown well conditions #UnKnownUnPlugWells UnKnownDegWells <- UnKnownPlugWells * DegradWells #UnKnownIntactPlug TotalUnKnownPlug <- UnKnownIntactPlug + UnKnownDegWells ################### BLOWOUTS ########## BlowoutUnPlugged <- UnKnownUnPlugWells / InjectionPeriod BlowoutPlugYear <- PlugBlowoutYear / InjectionPeriod BlowoutKnownKmYear <- BlowoutPlugYear * KnownWellDensity BlowoutUnKnownKmYear <- UnKnownPlugWells * BlowoutPlugYear BlowoutTotal <- BlowoutKnownKmYear + BlowoutUnKnownKmYear + BlowoutUnPlugged CO2blowout <- BlowoutTotal * CO2largeBlowout ################### SLOW LEAKAGE ###### TotalDegWells <- UnKnownDegWells + KnownDegWells TotalIntWells <- UnKnownIntactPlug + KnownIntactWells IntactWellsHighRate <- TotalIntWells * IntactHighRate IntactWellsLowRate <- TotalIntWells - IntactWellsHighRate CO2lostDegrad <- TotalDegWells * CO2degraded CO2lostIntactHigh <- IntactWellsHighRate * CO2intactHigh CO2lostIntactLow <- IntactWellsLowRate * CO2intactLow AB1 <- CO2blowout + CO2lostDegrad + CO2lostIntactHigh + CO2lostIntactLow ############################### AB2 ##################### ###Well status at start of AB2 (end AB1) #UnKnownUnPlugWells UnKnownBlewOut <- BlowoutUnKnownKmYear * InjectionPeriod #KnownDegWells UnKnownUnRemediated <- UnKnownDegWells - UnKnownBlewOut ###### New well conditions NewTotalWells <- TrueWellDensity + ActiveWellsAB1 ###### Known Wells KnownWellsAB2 <- KnownIntactWells + ActiveWellsAB1 + UnKnownUnPlugWells + UnKnownBlewOut + KnownDegWells ####### UnknownWells #UnKnownUnRemediated #UnKnownIntactPlug UnKnownIntactHighRate <- UnKnownIntactPlug * IntactHighRate UnKnownIntactLowRate <- UnKnownIntactPlug * (1 - IntactHighRate) ####### Total Wells by Status #UnKnownUnRemediated #UnKnownIntactHighRate TotalIntactLowRate <- KnownWellsAB2 + UnKnownIntactLowRate ########### BLOWOUTS BlowoutKm2Year <- BlowoutWellYear * NewTotalWells CO2lostBlowoutAB2 <- BlowoutKm2Year * CO2largeBlowout ########### SLOW LEAKAGE CO2lostDegrad <- UnKnownUnRemediated * CO2degraded CO2lostIntactHigh <- UnKnownIntactHighRate * CO2intactHigh CO2lostIntactlow <- TotalIntactLowRate * CO2intactLow AB2 <- CO2lostBlowoutAB2 + CO2lostDegrad + CO2lostIntactHigh + CO2lostIntactlow outputAB1 <- c(TrueWellDensity, UnKnownWellDensity, KnownUnPlugWells, UnKnownUnPlugWells, KnownPlugWells, UnKnownPlugWells, TotalUnPlugWells, TotalPlugWells, KnownDegPlug, UnKnownDegPlug, KnownIntactPlug, UnKnownIntactPlug, RemUnPlugWells, KnownPlugWells, UnKnownPlugWells, TotalPlugwells, UnKnownUnPlugWells, RemDegPlug, KnownIntactWells, KnownDegWells, UnKnownUnPlugWells, UnKnownDegWells, UnKnownIntactPlug, TotalUnKnownPlug, BlowoutUnPlugged, BlowoutPlugYear, BlowoutKnownKmYear, BlowoutUnKnownKmYear, BlowoutTotal, CO2blowout, TotalDegWells, TotalIntWells, IntactWellsHighRate, IntactWellsLowRate, CO2lostDegrad, CO2lostIntactHigh, CO2lostIntactLow, AB1) outputAB2 <- c(UnKnownUnPlugWells, UnKnownBlewOut, KnownDegWells, UnKnownUnRemediated, UnKnownUnRemediated, UnKnownIntactPlug, UnKnownIntactHighRate, UnKnownIntactLowRate, UnKnownUnRemediated, UnKnownIntactHighRate, TotalIntactLowRate, BlowoutKm2Year, CO2lostBlowoutAB2, CO2lostDegrad, CO2lostIntactHigh, CO2lostIntactlow, AB2) output <- c(AB1,AB2) } ############################################################################ #######################Section 2 - Base Case Function######################## ############################################################################ ##This code runs a base case scenario, based on the parameters as defined below. ## It calls AbSetUp and replaces the AbSetUp default parameters with the ones defined here. SSCBase <- function(CO2target = 1.2e10, # total injection target, tonnes injectperWell=0.75e6, ##### tpa InjectionPeriod=30, ##### years meanPlumeArea=exp(-0.7595), #### km2 per Mt #################### ACTIVE WELL LEAKAGE ActiveWellFreq = exp(-2.17), # Slow leak frequency, proportion active wells leaking (5) SlowLeakInjector = 158.5, # slow leak mass - t CO2 per annum per well MinorBlowFreq = 0.0693, # Minor blowout frequency, events / well / year MinorBlowout = exp(exp(1.27)), # Minor blowout (t CO2 / blowout) MajorBlowFreq = 1.48e-4, # Major blowout frequency (t / event / year) CO2MajorBlowout = exp(exp(2.57)), # Major blowout mass (t) (10) ################## Abandonned well LEAKAGE KnownWellDensity= 0.44,#### well density (wells / km2) wellUnderEst=1, #### well underestimation factor UnPlugWells=0, #### proportion unplugged wells DegradWells=exp(-2.17), #### proportion degraded wells IntactHighRate=0.054, #### proportion intact with high rate (15) IntactDegrad=0, ### Pre-remediate degraded? (Extra function not used in this scenario) PlugBlowoutYear=exp(-8.6125), #### Short term blowout risk (wells / 30 years) BlowoutWellYear=2e-5, #### long trm blowout risk (wells / yr) CO2largeBlowout=exp(13.4), #### Blowout Mass (t) CO2degraded=300, #### Degraded well leak mass (t) (20) CO2intactHigh=230, #### Intact well leak mass - high (t) CO2intactLow=0.004, #### Intact well leak mass - high (t) ######################### NatLeakRate = exp(0.693), #### Natural leakage rate (t / km2 / year) ####################### INPUTS for variable leakage decay curves A = 12, ## long-term leakage rate, central case B = 0.25715, ## exponential decay factor, central case (25) ################## res_sat = 0.58, # Res sat fraction migration = 1 # no of volumes of rock the CO2 'sees' ){ # has res trapping prop to FREE CO2 ( = injected - (min + sol trapped); not total injected CO2) ######################### input data time <- 10000 # total run time for model annualinject <- CO2target / InjectionPeriod # Mt wells <- annualinject/injectperWell #################### natural pathways leakAreaNP <- CO2target * meanPlumeArea / 1e6 # km2 (1e6 as leak/area in Mt but target in t) leakNatPaths <- leakAreaNP * NatLeakRate # t/year #################### Active wells ## tonnes / year leakActiveWells <- wells * (SlowLeakInjector * ActiveWellFreq + MinorBlowFreq * MinorBlowout + CO2MajorBlowout * MajorBlowFreq) ##################### Abandonned wells ## calc ActiveWellsAB1, i.e. the Active wells converted to abandoned at the end of injection ActiveWellsAB1 <- wells / CO2target / meanPlumeArea * 1e6 AB <- AbSetUp(KnownWellDensity,wellUnderEst,UnPlugWells, DegradWells, IntactDegrad, IntactHighRate, CO2largeBlowout,CO2degraded,CO2intactHigh,CO2intactLow, InjectionPeriod,BlowoutWellYear,PlugBlowoutYear, ActiveWellsAB1) leakAbWells1 <- CO2target * meanPlumeArea * AB[1] / 1e6 leakAbWells2 <- CO2target * meanPlumeArea * AB[2] / 1e6 ################# OUTPUT MATRIX leak <- matrix(0,ncol=10,nrow=time,byrow=F) dimnames(leak)[[2]] <- c("time","injCO2","leakCO2","cumleakCO2","A", # 1-5 "B","mintrap","soltrap" , "res_trap", "free CO2") leak[,1] <- seq(1,time) ############### CO2 injection leak[1:30,2] <- CO2target * leak[1:30,1]/30 leak[31:time,2] <- CO2target ################# LEAKAGE ################################################### # rows year <=30 leak[1:30,3] <- (leakNatPaths + leakActiveWells + leakAbWells1)*leak[1:30,2]/CO2target # rows year > starts at year 31 as 100 % of the max leakage rate leak[31:time,3] <- (A + (100- A)* exp(-B * (leak[31:time,1]-31))) * (leakNatPaths + leakAbWells2) / 100 ################## cummulative leakage leak[1,4] <- 0.5 * leak[1,3] for (i in 2:time){ leak[i,4] <- leak[(i-1),4] + 0.5 * (leak[i,3] + leak[i-1,3]) } ################## TRAPPING - mineral and sol first, rest goes to residual trapping # min trap proportional to total UNLEAKED CO2 # mineral trapping ADJUSTED to START at t=0 (was t=31) otherwise 0 (default in 'leak') leak[,7] <- (leak[,2] - leak[,4]) * (-1.669497e-13 * leak[,1]^3 + 2.903893e-9 * leak[,1]^2 + 1.403357e-5*leak[,1]) # # solubility trapping ADJUSTED to START at t=0 (was t=31) # sol trap proportional to total UNLEAKED CO2 leak[,8] <- (leak[,2]-leak[,4]) * 0.204 * (leak[,1])^0.0342144145 ## Xu et al. (2003) # residual trapping leak[,9] <- (leak[,2]- (leak[,7] + leak[,8])) * (1 - (1-res_sat)^migration) # leaked not removed!! #######free CO2 leak[,10] <- leak[,2]- (leak[,7] + leak[,8] + leak[,4] + leak[,9]) leak[,5] <- A # returns the variables from the leakage decay model leak[,6] <- B output <- leak[leak[,10]>0,] # trims the output if free CO2 < 0 #Run the Basic function (Section 4) for final leakage values. # Run the BasicTable code (Section 4) for a plot of CO2 partitioning over time. } ################################################################# ###################Section 3 - Monte Carlo Analysis########################### ################################################################# ##This code carries out a Monte Carlo simulation for n realisations. ## Random numbers are selected for each parameter from the distributions defined below. ## Minimum, P05, P50, P95, and maximum values are extracted for selected years. SSCMC <- function(n=5){ # Calls SSCBase for range of plume areas, does MC for n times # Outputs data at years time # output at 1000 year intervals (output 1 is 1000 years etc) output0001 <- matrix(rep(0,10),ncol=10,nrow=1) # 1 year output0003 <- matrix(rep(0,10),ncol=10,nrow=1) # 3 year output0010 <- matrix(rep(0,10),ncol=10,nrow=1) # 10 year output0030 <- matrix(rep(0,10),ncol=10,nrow=1) # 30 years output0100 <- matrix(rep(0,10),ncol=10,nrow=1) # 100 years output0500 <- matrix(rep(0,10),ncol=10,nrow=1) # 500 years output1 <- matrix(rep(0,10),ncol=10,nrow=1) # 1000 years output2 <- matrix(rep(0,10),ncol=10,nrow=1) # 2000 years output3 <- matrix(rep(0,10),ncol=10,nrow=1) # 3000 years output4 <- matrix(rep(0,10),ncol=10,nrow=1) # 4000 years output5 <- matrix(rep(0,10),ncol=10,nrow=1) # 5000 years output6 <- matrix(rep(0,10),ncol=10,nrow=1) # 6000 years output7 <- matrix(rep(0,10),ncol=10,nrow=1) # 7000 years output8 <- matrix(rep(0,10),ncol=10,nrow=1) # 8000 years output9 <- matrix(rep(0,10),ncol=10,nrow=1) # 9000 years output10 <- matrix(rep(0,10),ncol=10,nrow=1) # 10,000 years for (i in 1:n) { newLine <- SSCBase(CO2target = 1.2e10, # total injection target, t, FIXED injectperWell=rnorm(1,0.75e6,0.00415e6), ##### tonnes per annum InjectionPeriod=30, ##### years FIXED meanPlumeArea=rlnorm(1,-0.7595,0.1763), #### km2 per Mt from S North Sea gas fields #################### ACTIVE WELL LEAKAGE ActiveWellFreq = rlnorm(1,-2.17,0.6), # Slow leak frequency, proportion active wells leaking (5) SlowLeakInjector = rnorm(1,158.5,5.2), # slow leak mass - t CO2 per annum per well MinorBlowFreq = runif(1,0.062,0.0762), ##### 0.0693 +/- 10% Minor blowout frequency, events / well / year MinorBlowout = exp(rlnorm(1,1.27,0.0397)), # Minor blowout mass (t CO2 / blowout) MajorBlowFreq = rnorm(1,1.48e-4,3.33e-5), ##### value = 4.96e-4 +/- 10%, Major blowout frequency (t / event / year) CO2MajorBlowout = exp(rlnorm(1,2.57,0.011)), # Major blowout mass (t) (10) exp(6.6)=750 t as requested ################## Abandoned well LEAKAGE KnownWellDensity=runif(1,0.4,0.48), #####0.44 +/- 10%, well density (wells / km2) wellUnderEst=1, #### well underestimation factor UnPlugWells=0, #### proportion unplugged wells DegradWells=rlnorm(1,-2.17,0.6), # proportion degraded wells IntactHighRate=runif(1,0.049,0.0595), #####0.054 +/- 10%, proportion intact with high rate (15) IntactDegrad=0, ##### Pre-remediate degraded? PlugBlowoutYear=rlnorm(1,-8.6125,0.23),# 1/2000 to 1/9000, Short term blowout risk (wells / 30 years) BlowoutWellYear=runif(1,1e-5,1e-4), #####1e-4 +/-10% long trm blowout risk (wells / yr) CO2largeBlowout=rlnorm(1,13.4,0.35), # Large Blowout Mass (t) CO2degraded=runif(1,270,330), ####300 +/- 10% Degraded well leak mass (t) (20) CO2intactHigh=runif(1,207,253), ####230 +/- 10% Intact well leak mass - high (t) CO2intactLow=runif(1,0.0036,0.0044), ####0.004 +/- 10%, Intact well leak mass - high (t) ######################### NatLeakRate = rlnorm(1,0.693,0.37), #### Natural leakage rate (t / km2 / year) ####################### INPUTS for variable leakage decay curves A = rtriangle(1,3,53,12), # long-term leakage rate, central case B = runif(1,0.0143,0.5), # exponential decay factor, central case (25) ################## res_sat = rnorm(1,0.58,0.0286), # Res sat fraction migration = 1) # no of volumes of rock the CO2 'sees' NOT VARIED #### take data at year X , take last data point if ran out of leakable CO2 before time if (dim(newLine)[[1]]>1) {output0001 <- rbind(output0001,newLine[1,])} else {output0001 <- rbind(output0001,newLine[dim(newLine)[[1]],])} if (dim(newLine)[[1]]>3) {output0003 <- rbind(output0003,newLine[3,])} else {output0003 <- rbind(output0003,newLine[dim(newLine)[[1]],])} if (dim(newLine)[[1]]>10) {output0010 <- rbind(output0010,newLine[10,])} else {output0010 <- rbind(output0010,newLine[dim(newLine)[[1]],])} if (dim(newLine)[[1]]>30) {output0030 <- rbind(output0030,newLine[30,])} else {output0030 <- rbind(output0030,newLine[dim(newLine)[[1]],])} if (dim(newLine)[[1]]>100) {output0100 <- rbind(output0100,newLine[100,])} else {output0100 <- rbind(output0100,newLine[dim(newLine)[[1]],])} if (dim(newLine)[[1]]>500) {output0500 <- rbind(output0500,newLine[500,])} else {output0500 <- rbind(output0500,newLine[dim(newLine)[[1]],])} if (dim(newLine)[[1]]>1000) {output1 <- rbind(output1,newLine[1000,])} else {output1 <- rbind(output1,newLine[dim(newLine)[[1]],])} if (dim(newLine)[[1]]>2000) {output2 <- rbind(output2,newLine[2000,])} else {output2 <- rbind(output2,newLine[dim(newLine)[[1]],])} if (dim(newLine)[[1]]>3000) {output3 <- rbind(output3,newLine[3000,])} else {output3 <- rbind(output3,newLine[dim(newLine)[[1]],])} if (dim(newLine)[[1]]>4000) {output4 <- rbind(output4,newLine[4000,])} else {output4 <- rbind(output4,newLine[dim(newLine)[[1]],])} if (dim(newLine)[[1]]>5000) {output5 <- rbind(output5,newLine[5000,])} else {output5 <- rbind(output5,newLine[dim(newLine)[[1]],])} if (dim(newLine)[[1]]>6000) {output6 <- rbind(output6,newLine[6000,])} else {output6 <- rbind(output6,newLine[dim(newLine)[[1]],])} if (dim(newLine)[[1]]>7000) {output7 <- rbind(output7,newLine[7000,])} else {output7 <- rbind(output7,newLine[dim(newLine)[[1]],])} if (dim(newLine)[[1]]>8000) {output8 <- rbind(output8,newLine[8000,])} else {output8 <- rbind(output8,newLine[dim(newLine)[[1]],])} if (dim(newLine)[[1]]>9000) {output9 <- rbind(output9,newLine[9000,])} else {output9 <- rbind(output9,newLine[dim(newLine)[[1]],])} if (dim(newLine)[[1]]>10000) {output10 <- rbind(output10,newLine[10000,])} else {output10 <- rbind(output10,newLine[dim(newLine)[[1]],])} } output0001 <- output0001[output0001[,1]!=0,] # removes the dummy row used to make the matrix 'output' output0003 <- output0003[output0003[,1]!=0,] output0010 <- output0010[output0010[,1]!=0,] output0030 <- output0030[output0030[,1]!=0,] output0100 <- output0100[output0100[,1]!=0,] output0500 <- output0500[output0500[,1]!=0,] output1 <- output1[output1[,1]!=0,] output2 <- output2[output2[,1]!=0,] output3 <- output3[output3[,1]!=0,] output4 <- output4[output4[,1]!=0,] output5 <- output5[output5[,1]!=0,] output6 <- output6[output6[,1]!=0,] output7 <- output7[output7[,1]!=0,] output8 <- output8[output8[,1]!=0,] output9 <- output9[output9[,1]!=0,] output10 <- output10[output10[,1]!=0,] output <- list(output0001,output0003,output0010,output0030,output0100,output0500,output1,output2,output3,output4,output5,output6,output7,output8,output9,output10) } ## Run the FigLoss function (FigLoss(),) to produce output table and plot ############################################################################# #########################Section 4 - Data Interrogation###################### ############################################################################ #Sections of code to be run as required ###### 4.1 Basic function returns the total leakage at yr = 10,000 as a % of injected.##### Basic <- function(...) { Basic <- SSCBase(...) Basic <- Basic[dim(Basic)[[1]],] # takes last line Basic <- Basic[4]/Basic[2]*100 # % leak at end of run return(Basic) } ########### 4.2 Code for sensitivity Analysis################# #This calculates the % leak at t=10,000 for the base case, but varying each parameter according to the min and max pairs below SDN <- 2 # Set the number of standard deviations to test the sensitivity on MinMaxSA <- as.data.frame(t(as.matrix(data.frame( BC = Basic(), # Base case scenario ###### Min and Max pairs below reset the min and max values for each parameter Min_injectperWell = Basic(injectperWell=0.75e6-(SDN*0.083e6)), # SDN Standard Deviations Max_injectperWell = Basic(injectperWell=0.75e6+(SDN*0.083e6)), Min_meanPlumeArea = Basic(meanPlumeArea=exp(-0.7595-(SDN*0.8815))), # SDN Standard Deviations Max_meanPlumeArea = Basic(meanPlumeArea=exp(-0.7595+(SDN*0.8815))), # Min_ActiveWellFreq = Basic(ActiveWellFreq=exp(-2.17-(SDN*0.6))), # SDN Standard Deviations Max_ActiveWellFreq = Basic(ActiveWellFreq=exp(-2.17+(SDN*0.6))), # Min_SlowLeakInjector = Basic(SlowLeakInjector=158.5-(SDN*18.83)), # SDN Standard Deviations Max_SlowLeakInjector = Basic(SlowLeakInjector=158.5+(SDN*18.83)), # Min_MinorBlowout = Basic(MinorBlowout=exp(exp(1.27-(SDN*0.21)))), # SDN Standard Deviations Max_MinorBlowout = Basic(MinorBlowout=exp(exp(1.27+(SDN*0.21)))), # Min_MajorBlowFreq = Basic(MajorBlowFreq=1.48e-4-(SDN*3.33e-5)), # SDN Standard Deviations Max_MajorBlowFreq = Basic(MajorBlowFreq=1.48e-4+(SDN*3.33e-5)), # Min_CO2MajorBlowout = Basic(CO2MajorBlowout=exp(exp(2.257-(SDN*0.045)))), # SDN Standard Deviations Max_CO2MajorBlowout = Basic(CO2MajorBlowout=exp(exp(2.257+(SDN*0.045)))), # Min_KnownWellDensity = Basic(KnownWellDensity=0) , Max_KnownWellDensity = Basic(KnownWellDensity=5) , Min_wellUnderEst = Basic(wellUnderEst=1), # Minimum value Max_wellUnderEst = Basic(wellUnderEst=1.1), # Maximum value Min_DegradWells = Basic(DegradWells=exp(-2.17-(SDN*0.6))), # SDN Standard Deviations Max_DegradWells = Basic(DegradWells=exp(-2.17+(SDN*0.6))), # Min_PlugBlowoutYear = Basic(PlugBlowoutYear=exp(-8.6125-(SDN*0.23))), # SDN Standard Deviations Max_PlugBlowoutYear = Basic(PlugBlowoutYear=exp(-8.6125+(SDN*0.23))), # Min_BlowoutWellYear = Basic(BlowoutWellYear=1e-5), # Minimum value Max_BlowoutWellYear = Basic(BlowoutWellYear=1e-4), # Maximum value Min_CO2largeBlowout = Basic(CO2largeBlowout=exp(13.4-(SDN*0.35))), # SDN Standard Deviations Max_CO2largeBlowout = Basic(CO2largeBlowout=exp(13.4+(SDN*0.35))), # Min_NatLeakRate = Basic(NatLeakRate=exp(0.693-(SDN*0.37))), # SDN Standard Deviations Max_NatLeakRate = Basic(NatLeakRate=exp(0.693+(SDN*0.37))), Min_A = Basic(A=3), # Minimum value Max_A = Basic(A=53), # Maximum value Min_B = Basic(B=0.0143), # Minimum value Max_B = Basic(B=0.5), #Maximum value Min_res_sat = Basic(res_sat=0.58-(SDN*0.1897)), # SDN Standard Deviations Max_res_sat = Basic(res_sat=0.58+(SDN*0.1897)) # )))) ## Change the SDN as required. Run Code. Call "MnMaxSA" for output. #################### 4.3 Plot CO2 partitioning over time ###################### ## Code creates a plot of CO2 partitioning over 10 kyrs BasicTable <- SSCBase() plot(BasicTable[,"time"],BasicTable[,"injCO2"], type="l", xlab = "time (years)", ylab = "CO2 (t)", log = "x") #, ylim = c(1e5, 2e10), lines(BasicTable[,"time"],BasicTable[,"cumleakCO2"], col="red") # Plots cumulatively leaked lines(BasicTable[,"time"],BasicTable[,("mintrap")]+BasicTable[,("soltrap")], col="green") # Plots chemically trapped lines(BasicTable[,"time"],BasicTable[,"res_trap"], col="blue") #Plots residually trapped lines(BasicTable[,"time"],BasicTable[,"free CO2"], col="purple", lty=2) lines(BasicTable[,"time"],BasicTable[,("mintrap")]+BasicTable[,("soltrap")]+BasicTable[,("res_trap")]+BasicTable[,("free CO2")], col="dark red", lty=2) # Plots CO2 remaining in reservoir lines(BasicTable[,"time"],BasicTable[,("mintrap")]+BasicTable[,("soltrap")]+BasicTable[,("res_trap")], col="orange") # Plots immobilised CO2 #Comment out lines that aren't required ############ 4.4 Plot the different probabilities of leakage over time############# ####Plots the Max, p95, P59, P5, and Min model results for 15 time steps ####To creat plot, run temp <- SSCMC(n) where n is the required iterations FigLoss <- function(){ # plots graph of % loss vs time at 1, 30, 100, 500, 1000, and then 1000 year increments # input file is temp DEFAULT output <- matrix(rep(0,16*6),ncol=6,nrow=16) # col1=time output[,1] <- c(1,3,10,30,100,500,seq(1000,10000,1000)) for (i in 1:16) { loss <- temp[[i]][,4]/temp[[i]][,2]*100 output[i,2:6] <- quantile(loss,c(0,0.05,0.5,0.95,1)) } plot(output[,1],output[,6], type="l", xlim=c(1,10000),ylim=c(0,output[16,6]), main="black = max, blue = P95, red = P50, green=P5, purple = min", xlab="Time years",ylab="% loss CO2",log="x" ) lines(output[,1],output[,2],col="purple") lines(output[,1],output[,3],col="blue") lines(output[,1],output[,4],col="red") lines(output[,1],output[,5],col="green") dimnames(output)[[2]] <- c("time","min","P95","P50","P05","max") output }