The main objective of this project is to apply tools used in data science to carry out a basic petrophysical well log analysis. This involves transforming the well log measurements into reservoir properties like porosity, clay content etc. The uptmost goal is to have as much information about the subsurface as possible.
The format of the data utilized for this project is the Log ASCII Standard (LAS) is a standard file-format common in the oil-and-gas and water well industries to store well log information. This file type is formatted as shown below where the ~CURVE INFORMATION is the column header.
~VERSION INFORMATION
VERS. 2.0 :CWLS LOG ASCII STANDARD--VERSION 2.0
WRAP. NO :ONE LINE PER DEPTH STEP
~WELL INFORMATION
#MNEM.UNIT DATA DESCRIPTION
#================================================
STRT.FT 101.00000 :START DEPTH
STOP.FT 3666.00000 :STOP DEPTH
STEP.FT 0.50000 :STEP
NULL. -999.00000 :NULL VALUE
COMP. Data output from TerraStation II : COMPANY
WELL. Walakpa 1 : WELL NAME
UWI. 50-023-20013 : WELL UWI
API. 50-023-20013 : WELL API
LOC. 9 20N 19W : WELL LOCATION
DATE. NorthSlope.W08 : WELL DATE
FLD. No Project Selected : Project NAME
~CURVE INFORMATION
#================================================
M__DEPTH.FT :M__DEPTH
SP .MV :SP
GR .GAPI :GR
CALI .IN :CALI
BitSize .IN :BitSize
LL8 .OHMM :LL8
ILM .OHMM :ILM
ILD .OHMM :ILD
RHOB .G/CC :RHOB
NPHI .% :NPHI
DT .US/F :DT
MudWgt .LBS/GAL :MudWgt
~PARAMETER INFORMATION
#================================================
~OTHER INFORMATION
#================================================
~A
101.00000 -999.00000 -999.00000 -999.00000 12.25000 -999.00000 -999.00000 -999.00000 -999.00000 -0.07240 -999.00000 9.00000
101.50000 -999.00000 -999.00000 -999.00000 12.25000 -999.00000 -999.00000 -999.00000 -999.00000 -0.07360 -999.00000 9.00000
102.00000 -999.00000 -999.00000 -999.00000 12.25000 -999.00000 -999.00000 -999.00000 -999.00000 -0.07480 -999.00000 9.00000
Data of this nature is in abundance due to the high volume of oil and gas exploration. In this project, data from the Department of the Interior U.S. Geological Survey repository of Wildcat Wells in the National Petroleum Reserve in Alaska will be used (https://certmapper.cr.usgs.gov/data/PubArchives/OF00-200/WELLS/WELLIDX.HTM). However, this project is not exclusive to this data, therefore, the workflow and codes could be applied to data from other database.
The analysis of the well logs will be carried out using R and Python. However, bash and wget will be utilized to download and manipulate the data into the desired format.
Software: GitBash, R and Python
Packages: wget, bash, tidyverse, ggplot2, lasio and reticulate
To download the data, run bash data_download.sh in the command line.
To convert the data format from Las to csv which will be used as the input in R, a python script is utilized. The reticulate package is used to run the python script from R. If you don't have Python, skip this step and use the data in the data_csv folder to run the rest of the R script.
The various manipulations and operations on the well logs in done by running the R script called data_manipulation.R. This script creates three functions:
log_plotting(file), vsh_phi_calc(file), and vsh_plotting(file). The input of this function "file" is the name of the well data in csv format. e.g AW1.csv
The Output folder contains all the expected products from this project. To test this code, do not download the Output folder from this repo. Again, the data_csv folder is also an output which is used by the R script. download this folder iof you do not have Python on your system.
Log images A folder that contains all the well log images named after the name of the respective wells.
Vsh images A folder that contains the plot of calculated volume of shale for each wells.
Interpreted logs A folder that contains the final csv for each wells with the calculated properties.