ST-3 Oil Production Data
Andrew Leach
August, 2026
The Alberta Energy Regulator (AER) provides monthly data for oil production in Alberta, including from the oil sands region in a report known as the ST-3. The data are available here.
#packages used
library(tidyverse) #basic set of data wrangling tools from the best part of the R universe
library(readxl) #it does what it says
library(scales) #makes graphs nicer
library(lubridate) #makes dates easier to handle
library(knitr) #using this to make the html document
library(prettydoc) #nice tables in the html doc
library(zoo) #time series data
library(viridis) #color-blind friendly palettes for graphs
library(patchwork) #allows you to combine plots
library(kableExtra) #nice tables in R Markdown
library(curl) #use this for checking internet connections
library(roll) #rolling means
library(ggsci)
library(janitor)
library(httr)
#unit conversions
m3_bbl<-function(x) x*6.2898
bbl_m3<-function(x) x/6.2898
#create tableau palettes
colors_tableau10 <- function()
{
return(c("#1F77B4", "#FF7F0E", "#2CA02C", "#D62728", "#9467BD", "#8C564B",
"#E377C2", "#7F7F7F", "#BCBD22", "#17BECF"))
}
colors_tableau10_light <- function()
{
return(c("#AEC7E8", "#FFBB78", "#98DF8A", "#FF9896", "#C5B0D5", "#C49C94",
"#F7B6D2", "#C7C7C7", "#DBDB8D", "#9EDAE5"))
}
colors_tableau10_medium <- function()
{
return(c("#729ECE", "#FF9E4A", "#67BF5C", "#ED665D", "#AD8BC9", "#A8786E",
"#ED97CA", "#A2A2A2", "#CDCC5D", "#6DCCDA"))
}
download_forbidden_file <- function(url, destfile,refer="https://www.aer.ca/") {
headers <- c(
"User-Agent" = "Mozilla/5.0 (Macintosh; Intel Mac OS X 10.14; rv:66.0) Gecko/20100101 Firefox/66.0",
"Referer" = refer
)
r <- GET(
url,
add_headers(.headers = headers)
)
stop_for_status(r)
writeBin(
content(r, "raw"),
destfile
)
invisible(destfile)
}#I'm going to make a function here so that I can specify whether I want to download all/none/new
st_3_online<-function(download="none"){
#every year is xlss file which makes it easy
#specify the rows we're going to want
#download="none"
#keep_rows<-c("Crude Oil Light", "Crude Oil Medium",
# "Crude Oil Heavy","Crude Oil Ultra Heavy","Total Conventional Oil Production","Crude Oil Ultra-Heavy",
# "Condensate Production","In Situ Production","Mined Production",
# "Non-Upgraded Total","Upgraded Production","Total Oil Sands Production",
# "Total Production","Nonupgraded Total")
#use a list to store each of the data sets we're going to download
data_store <- list()
years<-seq(2010,2016)
i<-1
filename<-paste("Oil_2010-2016.xlsx",sep="")
if(tolower(download)=="all")#if you chose to download the old data. correct case errors in case someone sends "ALL"
if(has_internet()) #if you're connected, go get the file
if(!file.exists(filename))
download.file(paste("https://www.aer.ca/documents/sts/st3/",filename,sep=""),filename,mode="wb")
#year<-2010 #testing
for(year in years){ #loop over 2010-2016, but the data are stored in different sheets
production_data <- read_excel(filename, sheet = as.character(year), skip = 4)
#the sheets are a bit of a mess, so we need to clean them up
names(production_data)[1]<-"product" #name column 1
# the %>% or pipeline is basically a "pass to" command
# start with production data, pass to select and remove cols 2 and 15, pass to filter and keep and rows with
# the names in the keep_rows object we created above
production_data<-production_data %>% select(-2,-15)%>% #%>% filter(product %in% keep_rows)
filter(!is.na(product),!is.na(Jan))%>%
mutate(across(-1, as.numeric))
#rename the column with the annual data s (grep is a search function that, in this case, is looking for
#whatever year we're processing and it will name the column annual
names(production_data)[grep(as.character(year),names(production_data))]<-"annual"
#assign the year to whatver year we're processing
production_data$year<-year
# create a long-form data set that will have product, annual level for that product, the year we're processing, and #the monthly data
production_data<-production_data %>% pivot_longer(cols=-c("product","annual","year"),
names_to="month",values_to = "production")
#store those data in a node in the list
data_store[[i]]<-production_data
#step your list counter one unit
i<-i+1
}
#let's get 2017-2020 data - same process, but they are in seperate files
#https://static.aer.ca/prd/documents/sts/st3/ST3_2021-12_Oil.xlsx
years<-seq(2017,2025)
#year<-2023
for(year in years){ #loop over 2017-2020
filename<-paste("Oil_",year,".xlsx",sep="") #use paste to put the year into the filename
if(year==2025)
filename<-"Oil_2025.xlsx"
if(year==2024)
filename<-"ST3_Oil_2024.xlsx"
if(year==2023)
filename<-"ST3_2023-12_Oil.xlsx"
if(year==2022)
filename<-"ST3_2022-12_Oil.xlsx"
if(year==2021)
filename<-"ST3_2021-12_Oil.xlsx"
if(tolower(download)=="all")#if you chose to download the old data. correct case errors in case someone sends "ALL"
if(has_internet()) #if you're connected, go get the file
if(!file.exists(filename))
if(year!=2024)
download_forbidden_file(paste("https://www.aer.ca/documents/sts/st3/",filename,sep=""),filename)
if(has_internet()) #if you're connected, go get the file
if(!file.exists(filename))
if(year==2024)
download_forbidden_file("https://www.aer.ca/2025-02/ST3_Oil_2024.xlsx",filename,mode="wb")
if(has_internet()) #if you're connected, go get the file
if(!file.exists(filename))
if(year==2025)
download_forbidden_file("https://www.aer.ca/prd/documents/sts/st3/Oil_2025.xlsx",filename)
production_data <- read_excel(filename, sheet = "Data", skip = 4)
names(production_data)[1]<-"product"
production_data<-production_data %>% select(-2,-15) %>% filter(!is.na(product),!is.na(Jan))%>%
mutate(across(-1, as.numeric))
names(production_data)[grep(as.character(year),names(production_data))]<-"annual"
production_data$year<-year
production_data<-production_data %>% pivot_longer(cols=-c("product","annual","year"),
names_to="month",values_to = "production")
data_store[[i]]<-production_data
i<-i+1
}
#Most recent data are stored as "current"
filename<-"Oil_current.xlsx"
#download.file("https://www.aer.ca/documents/sts/st3/Oil_current.xlsx",destfile=filename,mode="wb")
url<-"https://www.aer.ca/documents/sts/st3/Oil_current.xlsx"
needs_refresh <- !file.exists(filename) ||
difftime(Sys.time(), file.info(filename)$mtime, units = "hours") > 24
if(needs_refresh)
download_forbidden_file(url, filename,refer="https://www.aer.ca/")
# then manually trigger download or extract via browser context
#check for interne
# if(has_internet()) #if you're connected, go get the file
# download.file("https://www.aer.ca/documents/sts/st3/Oil_current.pdf",filename,mode="wb")
#download.file(paste("https://www.aer.ca/documents/sts/st3/",filename,sep=""),filename,mode="wb")
production_data <- read_excel(filename, sheet = "Data", skip = 4)
year<-2026
names(production_data)[1]<-"product"
production_data<-production_data %>% select(-2,-15) %>% filter(!is.na(product),!is.na(Jan))%>%
mutate(across(-1, as.numeric))
names(production_data)[grep(as.character(year),names(production_data))]<-"annual"
production_data$year<-year
production_data<-production_data %>% pivot_longer(cols=-c("product","annual","year"),
names_to="month",values_to = "production")
production_data<-production_data %>% filter(production!=0)#keep only non-zero production
data_store[[i]]<-production_data
#now, stack all the elements stored in your list of data into a data frame
all_production<-do.call(rbind,data_store)
#now, we'll manipulate the data. mutate is adding a column based on a calculation
#so, all_production is all_prodcution passed to mutate, where the production variable is set to numeric
#and we format the dates using ymd since that's the format in the spreadsheet
# (remember, the lubridate package makes dates easy)
all_production<-all_production %>% mutate(production=as.numeric(production),
date=ymd(paste(year,as.character(month),1,sep="-")))
save(all_production,file = "st3_oil.rdata")
#next part is working with factors. Factors basically store data as a numeric code or level (1,2,3,4) and a
#set of labels attached to the levels
#take all_production,and pass to mutate, change the column product to a factor
all_production <-all_production %>% mutate(product=as_factor(product)) %>%
#and now we're going to re-code some of the labels so that we make them common #across the data we loaded
mutate(
product=fct_recode(product,
"In Situ Bitumen"="In Situ Production",
"Mined Bitumen"="Mined Production",
"Non-Upgraded Bitumen"="Non-Upgraded Total",
"Non-Upgraded Bitumen"="Nonupgraded Total",
"Synthetic Crude"="Upgraded Production",
"Conventional Oil"="Total Crude Oil Production",
"Conventional Oil"="Total Conventional Oil Production",
"Total"="Total Production",
"Total Oil Sands"="Total Oil Sands Production",
"Condensate"="Condensate Production"
),
#and we're going to sort them so that mined bitumen is last
product=fct_relevel(product,"Mined Bitumen",after=5))
#last, we're going to collapse factors into on common level, so light and medium go into light, heavy and ultra-heavy
#are classified as heavy
all_production <-all_production %>% mutate(product=fct_collapse(product,
"Conventional Light Oil" = c("Crude Oil Light","Crude Oil Medium"),
"Conventional Heavy Oil"= c("Crude Oil Heavy","Crude Oil Ultra Heavy")
)) %>%
#now, because for some rows we'll now have 2 entries for production from conventional light oil, for example,
# in a month, we have to collapse them
#group your data by product, year, month, and date
group_by(product,year,month,date)%>%
#and combined the monthly and annual production from all prducts with the same label by summing them
summarize(production=sum(production),annual=sum(as.numeric(annual))) %>%
ungroup()
#last thing - we'll use zoo to create some time series descriptives
#add year and quarter
all_production <-all_production %>% mutate(year=year(date), quarter=quarter(date))%>%
#add rolling quarterly and annual data and lags
group_by(product)%>% arrange(date)%>% mutate(
roll_4m=roll_mean(production,4),
roll_12m=roll_sum(production,12),
growth_12m=(roll_12m-lag(roll_12m,12))/lag(roll_12m,12),
lag12_raw=(production-lag(production,12))/lag(production,12),
lag12_roll=(roll_4m-lag(roll_4m,12))/lag(roll_4m,12))
#and that's it - you've made a data set of Alberta oil production for the last decade
all_production # the last thing the function does is what it returns. We want it to return the data set
}
all_production<-st_3_online(download = "none") #call the function we just made to get the data
save(all_production,file = "st3_oil.rdata")
#create an annual data set using group_by and summarize
annual<-all_production %>%
mutate(days=days_in_month(date))%>%
group_by(year,product) %>% summarise(production=sum(production),
days=sum(days))%>%
arrange(year)%>% group_by(product)%>%
mutate(yoy_growth=round((production-lag(production,1))/lag(production,1)*100,2))
#create an annual data set using group_by and summarize
#same for a quarterly data_set
quarterly<-all_production %>%
mutate(days=days_in_month(date))%>%
group_by(year,quarter,product) %>% summarise(production=mean(production),days=sum(days))
quarterly <-quarterly %>%arrange(year,quarter)%>% group_by(product)%>%
mutate(q_lasty=round((production-lag(production,4))/lag(production,4)*100,2))With the data downloaded and compiled into a single file, with annual and quarterly subfiles, we can graph some data.
dispo<-
ggplot(filter(all_production,product%in%c("Total Alberta Use","Removals from Alberta")))+
geom_area(aes(date,m3_bbl(production)/days_in_month(date)/10^6,group=product,fill=product),color="black", size=0.5)+
scale_fill_manual("",values = pal_jco()(10),guide = "legend")+
#scale_fill_viridis("",discrete = T,option="F",direction = -1,end = .9)+
scale_x_date(date_breaks = "1 year",date_labels = "%Y",expand = c(0,0))+
scale_y_continuous(expand = c(0,0),breaks=pretty_breaks())+
#scale_colour_manual("",values=my_palette,guide = "legend")+
guides(fill=guide_legend(nrow=1))+
expand_limits(y=4.5)+
expand_limits(x=Sys.Date())+
theme_irpp(base_size = 16)+
rotate_x()+
labs(y="Oil and Bitumen Disposition (Millions of barrels per day)",x=NULL,
title="Alberta Conventional Oil and Bitumen Disposition",
#subtitle="For Operators with Production above 25k bbl/d",
caption=paste("Source: AER ST-3 data current to ",format.Date(max(all_production$date),format = "%B, %Y") ,", graph by @andrew_leach",sep=""))
#dispo
ggsave(plot=dispo,filename = "st3_dispo.png",width=11,height=6.5,dpi=300,bg="white")
graph_data<-c("Conventional Light Oil","Conventional Heavy Oil","Condensate",
"In Situ Bitumen","Mined Bitumen")
all_crude<-ggplot(filter(all_production,product%in%graph_data)%>%
mutate(product=fct_relevel(product,"Condensate")))+
geom_area(aes(date,m3_bbl(production)/days_in_month(date)/10^6,group=product,fill=product),color="black", size=0.5)+
scale_fill_manual("",values = pal_jco()(10),guide = "legend")+
#scale_fill_viridis("",discrete = T,option="F",direction = -1,end = .9)+
scale_x_date(date_breaks = "1 year",date_labels = "%Y",expand = c(0,0))+
scale_y_continuous(expand = c(0,0),breaks=pretty_breaks())+
#scale_colour_manual("",values=my_palette,guide = "legend")+
guides(fill=guide_legend(nrow=1))+
expand_limits(y=4.5)+
expand_limits(x=Sys.Date())+
theme_irpp(base_size = 16)+
labs(y="Oil and Bitumen Production (Millions of barrels per day)",x=NULL,
title="Alberta Conventional Oil and Bitumen Production",
#subtitle="For Operators with Production above 25k bbl/d",
caption=paste("Source: AER ST-3 data current to ",format.Date(max(all_production$date),format = "%B, %Y") ,", graph by @andrew_leach",sep=""))
#all_crude
#if you want to create a png in normal code, use this
#ggsave(all_crude,"oil_prod.png",width=12,height=6)
all_crude_short<-ggplot(filter(all_production,year>=2015,product%in%graph_data)%>%
mutate(product=fct_relevel(product,"Condensate")))+
geom_area(aes(date,m3_bbl(production)/days_in_month(date)/10^6,group=product,fill=product),color="black", size=0.5)+
scale_fill_manual("",values = pal_jco()(10),guide = "legend")+
#scale_fill_viridis("",discrete = T,option="F",direction = -1,end = .9)+
scale_x_date(date_breaks = "1 year",date_labels = "%Y",expand = c(0,0))+
scale_y_continuous(expand = c(0,0),breaks=pretty_breaks())+
#scale_colour_manual("",values=my_palette,guide = "legend")+
guides(fill=guide_legend(nrow=1))+
expand_limits(y=4.5)+
expand_limits(x=Sys.Date())+
theme_irpp(base_size = 16)+
labs(y="Oil and Bitumen Production (Millions of barrels per day)",x=NULL,
title="Alberta Conventional Oil and Bitumen Production",
#subtitle="For Operators with Production above 25k bbl/d",
caption=paste("Source: AER ST-3 data current to ",format.Date(max(all_production$date),format = "%B, %Y") ,", graph by @andrew_leach",sep=""))
#all_crude_short
all_crude_weekly<-ggplot(filter(all_production,product%in%graph_data))+
geom_area(aes(date,6.2898*(production)/days_in_month(date)/10^6,group=product,fill=product))+
scale_fill_manual("",values = colors_tableau10(),guide = "legend")+
scale_x_date(date_breaks = "6 months",date_labels = "%b\n%Y",expand = c(0,0))+
scale_y_continuous(expand = c(0,0),breaks=pretty_breaks())+
#scale_colour_manual("",values=my_palette,guide = "legend")+
guides(fill=guide_legend(nrow=2))+
scale_fill_manual(NULL,values=colors_ua10())+
scale_x_date(date_breaks = "1 year", date_labels = "%b\n%Y",expand=c(0,0))+
scale_y_continuous(expand = c(0, 0)) +
guides(fill=guide_legend(nrow=2))+
labs(y="Oil and Bitumen Production (Millions of barrels per day)",x=NULL,
title="Alberta Conventional Oil and Bitumen Production",
#subtitle="For Operators with Production above 25k bbl/d",
caption=paste("Source: AER ST-3 data current to ",format.Date(max(all_production$date),format = "%B, %Y") ,", graph by @andrew_leach",sep=""))+
theme_irpp(base_size = 16)
#if you want to create a png in normal code, use this
#save(all_crude_weekly,file="../weekly_charts/st3_prod.gph")
#ggsave(all_crude_weekly,file="../weekly_charts/st3_prod.gph")
oil_sands<-
ggplot(filter(all_production,product%in%graph_data,product!="Total Conventional Oil",product!="Conventional Heavy Oil",product!="Conventional Light Oil",product!="Condensate"))+
geom_area(aes(date,m3_bbl(production)/days_in_month(date)/10^6,group=product,fill=product),color="black",size=0.5)+
#scale_fill_viridis("",discrete = T,option="A",direction = -1,end = .9)+
scale_fill_manual("",values = pal_jco()(2),guide = "legend")+
scale_x_date(date_breaks = "1 year",date_labels = "%Y",,expand = c(0,0))+
scale_y_continuous(expand = c(0,0),breaks=pretty_breaks())+
expand_limits(y=4)+
#scale_colour_manual("",values=my_palette,guide = "legend")+
#guides(fill=FALSE,colour=FALSE)+
expand_limits(x=Sys.Date())+
guides(fill=guide_legend(nrow=1))+
theme_irpp(base_size = 16)+
labs(y="Oil Sands Bitumen Production (millions of barrels per day)",x=NULL,
title="Alberta Oil Sands Bitumen Production",
caption=paste("Source: AER ST-3 data current to ",format.Date(max(all_production$date),format = "%B %Y") ,", graph by @andrew_leach",sep=""))
#oil_sands
upgraded<-
all_production%>%filter(product%in%c("Non-Upgraded Bitumen","Conventional Heavy Oil","Conventional Light Oil","Synthetic Crude","Condensate"))%>%
mutate(product=factor(product,levels =c("Non-Upgraded Bitumen","Conventional Heavy Oil","Conventional Light Oil","Synthetic Crude","Condensate")),
product=fct_rev(product)
)%>%
ggplot()+
geom_area(aes(date,m3_bbl(production)/days_in_month(date)/10^6,group=product,fill=product),color="black",size=0.5)+
#scale_fill_viridis("",discrete = T,option="A",direction = -1,end = .9)+
scale_fill_manual("",values = pal_jco()(5),guide = "legend")+
scale_x_date(date_breaks = "1 year",date_labels = "%Y",,expand = c(0,0))+
scale_y_continuous(expand = c(0,0),breaks=pretty_breaks())+
expand_limits(y=4)+
#scale_colour_manual("",values=my_palette,guide = "legend")+
#guides(fill=FALSE,colour=FALSE)+
expand_limits(x=Sys.Date())+
guides(fill=guide_legend(nrow=1))+
theme_irpp(base_size = 16)+
labs(y="Oil Production (millions of barrels per day)",x=NULL,
title="Alberta Oil Production",
caption=paste("Source: AER ST-3 data current to ",format.Date(max(all_production$date),format = "%B %Y") ,", graph by @andrew_leach",sep=""))
#if you want to create a png in normal code, use this
ggsave(plot=upgraded,filename = "st3_oil_sands_upg.png",width=15,height=8,dpi=300,bg="white")
oil_sands_plain<-ggplot(filter(all_production,product%in%graph_data,product!="Total Conventional Oil",product!="Conventional Heavy Oil",product!="Conventional Light Oil",product!="Condensate"))+
geom_area(aes(date,m3_bbl(production)/days_in_month(date)/10^6,group=product,fill=product),color="black",size=0.5)+
scale_fill_manual("",values = grey.colors(n=3,end=0.85,start = 0.3),guide = "legend")+
scale_x_date(date_breaks = "1 year",date_labels = "%b\n%Y",,expand = c(0,0))+
scale_y_continuous(expand = c(0,0),breaks=pretty_breaks())+
expand_limits(y=4)+
expand_limits(x=Sys.Date(),y=4.4)+
guides(fill=guide_legend(nrow=1))+
theme_irpp(base_size = 16)+
labs(y="Oil Sands Bitumen Production (millions of barrels per day)",x=NULL)
#title="Alberta Oil Sands Bitumen Production",
#caption=paste("Source: AER ST-3 data current to ",format.Date(max(all_production$date),format = "%B %Y") ,", graph #by @andrew_leach",sep=""))
#if you want to create a png in normal code, use this
ggsave(plot=oil_sands,filename = "st3_oil_sands_prod.png",width=12,height=6,dpi=300,bg="white")All production, 2015-present
all_crude_short
ggsave(plot=all_crude_short,filename = "st3_oil_prod_short.png",width=16,height=7,dpi=300,bg="white")All production, 2010-present
all_crude
#save(all_crude,file="../ab_budget/st3_prod.gph")
ggsave(plot=all_crude,filename = "st3_oil_prod.png",width=16,height=7,dpi=300,bg="white")Annual production history from ST-98 (mmbbl/d)
#get longer history files from ST98
#download.file("https://www.aer.ca/documents/sts/st98/2025/st98-2025-executive-summary-data.xlsx",mode="wb",dest="st98_exec.xlsx")
energy_supply<-read_excel("st98_exec.xlsx",sheet="Figures",range="BR33:CD71")%>%slice(-1)%>%
mutate(
# First column to integer
across(1, as.integer),
# All other columns to numeric
across(-1, as.numeric)
)%>%
select(1,8:12) %>%
rename_with(~ str_remove(., "\\n.*"))%>%
rename_with(~ str_remove(., "\\r.*"))%>%
#clean_names()%>%
pivot_longer(-Year,values_to="production_mmbbld",names_to = "crude_stream")
st_98<-ggplot(energy_supply%>%
filter(Year<=2025)%>%
mutate(crude_stream=as_factor(crude_stream),
crude_stream=fct_recode(crude_stream,"Pentanes Plus and Condensate"="Pentanes plus and condensate"),
crude_stream=fct_recode(crude_stream,"Light and Medium Crude Oil"="Light and medium"),
crude_stream=fct_recode(crude_stream,"Heavy Crude Oil"="Heavy"),
crude_stream=fct_recode(crude_stream,"Upgraded Bitumen"="Upgraded bitumen"),
crude_stream=fct_recode(crude_stream,"Nonupgraded Bitumen"="Nonupgraded bitumen"),
crude_stream=fct_relevel(crude_stream,"Pentanes Plus and Condensate"),
)%>%
I()
)+
geom_area(aes(Year,production_mmbbld,group=crude_stream,fill=crude_stream),color="black", size=0.5)+
scale_fill_manual("",values = pal_jco()(10),guide = "legend")+
scale_y_continuous(expand = c(0,0),breaks=pretty_breaks(n=10))+
scale_x_continuous(expand = c(0,0),breaks=pretty_breaks(n=15))+
guides(fill=guide_legend(nrow=1))+
expand_limits(y=5)+
theme_irpp(base_size = 16)+
labs(y="Oil and Bitumen Production (millions of barrels per day)",x=NULL,
title="Alberta Condensate, Conventional Oil, and Bitumen Production",
#subtitle="For Operators with Production above 25k bbl/d",
caption=paste("Source: AER ST-98, graph by @andrew_leach",sep=""))
save(st_98,file="../ab_budget/st98_prod.gph")
st_98
Recent Production (mmbbl/d)
all_production%>%filter(product%in%c("Conventional Light Oil","Conventional Heavy Oil","Mined Bitumen","In Situ Bitumen","Total Oil Sands","Total"),date>=max(date)-years(1)) %>%
select(product,date,production)%>%
mutate(production=m3_bbl(production)/10^6/days_in_month(date),
product=gsub(" Production","",product))%>%
pivot_wider(names_from=product,values_from = production)%>%
mutate(date=format(date,"%b %Y")
)%>%rename(Date=date)%>%
kbl(escape = FALSE,table.attr = "style='width:100%;'") %>%
kable_styling(fixed_thead = T,bootstrap_options = c("hover", "condensed","responsive"),full_width = T)%>%
#scroll_box(width = "1000px", height = "600px")%>%
column_spec(1, extra_css = "white-space: nowrap;") %>%
I() | Date | Conventional Light Oil | Conventional Heavy Oil | Mined Bitumen | In Situ Bitumen | Total Oil Sands | Total |
|---|---|---|---|---|---|---|
| Jun 2025 | 0.388 | 0.035 | 1.77 | 1.84 | 3.42 | 4.06 |
| Jul 2025 | 0.392 | 0.035 | 1.89 | 1.98 | 3.67 | 4.31 |
| Aug 2025 | 0.392 | 0.035 | 1.84 | 1.96 | 3.58 | 4.23 |
| Sep 2025 | 0.386 | 0.035 | 1.86 | 1.93 | 3.57 | 4.21 |
| Oct 2025 | 0.396 | 0.036 | 1.73 | 1.97 | 3.48 | 4.14 |
| Nov 2025 | 0.410 | 0.036 | 1.91 | 2.02 | 3.72 | 4.40 |
| Dec 2025 | 0.397 | 0.035 | 1.96 | 1.99 | 3.75 | 4.40 |
| Jan 2026 | 0.389 | 0.036 | 1.77 | 2.01 | 3.55 | 4.21 |
| Feb 2026 | 0.397 | 0.035 | 1.67 | 2.02 | 3.49 | 4.15 |
| Mar 2026 | 0.408 | 0.034 | 1.70 | 2.02 | 3.50 | 4.17 |
| Apr 2026 | 0.412 | 0.034 | 1.73 | 1.97 | 3.47 | 4.15 |
| May 2026 | 0.403 | 0.032 | 1.67 | 1.86 | 3.33 | 3.99 |
| Jun 2026 | 0.397 | 0.032 | 1.68 | 1.98 | 3.46 | 4.12 |
Annual Production (mmbbl/d)
annual%>%filter(product%in%c("Conventional Light Oil","Conventional Heavy Oil","Mined Bitumen","In Situ Bitumen","Total Oil Sands","Total")) %>%
mutate(production=m3_bbl(production)/10^6/days,
product=gsub(" Production","",product))%>%select(-yoy_growth)%>%pivot_wider(names_from=product,values_from = production)%>%
rename(Year=year)%>%
select(-days)%>%
mutate(Year=ifelse(Year==last(Year),paste(Year,"(to date)"),Year))%>%
kbl(escape = FALSE,table.attr = "style='width:100%;'") %>%
kable_styling(fixed_thead = T,bootstrap_options = c("hover", "condensed","responsive"),full_width = T)%>%
#scroll_box(width = "1000px", height = "600px")%>%
column_spec(1, extra_css = "white-space: nowrap;") %>%
I() | Year | Conventional Light Oil | Conventional Heavy Oil | Mined Bitumen | In Situ Bitumen | Total Oil Sands | Total |
|---|---|---|---|---|---|---|
| 2010 | 0.316 | 0.144 | 0.857 | 0.763 | 1.49 | 1.97 |
| 2011 | 0.347 | 0.144 | 0.893 | 0.859 | 1.64 | 2.14 |
| 2012 | 0.407 | 0.149 | 0.930 | 1.009 | 1.82 | 2.39 |
| 2013 | 0.431 | 0.151 | 0.976 | 1.117 | 1.96 | 2.56 |
| 2014 | 0.439 | 0.151 | 1.038 | 1.272 | 2.17 | 2.79 |
| 2015 | 0.392 | 0.137 | 1.162 | 1.368 | 2.38 | 2.93 |
| 2016 | 0.326 | 0.118 | 1.147 | 1.393 | 2.41 | 2.91 |
| 2017 | 0.332 | 0.114 | 1.275 | 1.550 | 2.67 | 3.18 |
| 2018 | 0.372 | 0.117 | 1.472 | 1.583 | 2.91 | 3.49 |
| 2019 | 0.375 | 0.112 | 1.551 | 1.556 | 2.95 | 3.52 |
| 2020 | 0.323 | 0.033 | 1.483 | 1.504 | 2.83 | 3.33 |
| 2021 | 0.324 | 0.034 | 1.592 | 1.680 | 3.10 | 3.61 |
| 2022 | 0.359 | 0.035 | 1.617 | 1.715 | 3.16 | 3.73 |
| 2023 | 0.374 | 0.037 | 1.647 | 1.776 | 3.23 | 3.82 |
| 2024 | 0.382 | 0.035 | 1.714 | 1.848 | 3.36 | 3.98 |
| 2025 | 0.394 | 0.036 | 1.767 | 1.936 | 3.50 | 4.15 |
| 2026 (to date) | 0.401 | 0.034 | 1.705 | 1.976 | 3.47 | 4.13 |