Petrolib: Official Documentations

Overview

This is a python software package designed to help geoscientists perform quick formation evaluation workflow by estimating reservoir petrophysical parameters such as:

• Volume of Shale using various methods like Clavier, Stieber and Larionov methods

• Porosity - Effective and Total porosities using the Density and Wyllie’s sonic methods.

• Water Saturation - using both archie and simmandoux methods

• Permeability

In addition to estimating these parameters, log plots are automatically displayed for proper interpretation. Also a pay summary result is generated in XLSX to help quantify the over-all quality of reservoirs.

Installation

You can install the package into your local machine using the pip command:

pip install petrolib

To import the package, use:

import petrolib
print(petrolib.__version__)

>>> '1.2.5'

Contents

Overview

This is a python software package designed to help geoscientists perform quick formation evaluation workflow by estimating reservoir petrophysical parameters such as:

• Volume of Shale using various methods like Clavier, Stieber and Larionov methods

• Porosity - Effective and Total porosities using the Density and Wyllie’s sonic methods.

• Water Saturation - using both archie and simmandoux methods

• Permeability

In addition to estimating these parameters, log plots are automatically displayed for proper interpretation. Also a pay summary result/dataframe is produced to help quantify the over-all quality of the reservoirs. Cutoff such as the porosity, shale volume and water saturation are applied to flag pay regions. The pay summary include:

• net, gross and not net thicknesses

• % net-to-gross

• average volume of shale

• average porosity

• bulk volume of water

• water saturation.

Interestingly, the parameters are computed and displayed only for the zones of interest picked. Plots such as neutron-density and pickett are available for reservoir assessment. Geolocations of the wells can also be visualised.

Installation

You can install the package into your local machine using the pip command:

pip install petrolib

Functionalities

The package is designed to handle:

• Loading of well data

• Processing of well log data

• Statistical analysis such as log frequencies and correlation

• Well log visualization

• Plot well locations on an actual map

• Facilitates the loading of well tops.

• Plot log curves along with zonation tracks

• Neutron-density cross plot

• Pickett Plot

Quickstart

import necessaries

from pathlib import Path
from petrolib import procs
from petrolib import file_reader as fr
from petrolib.workflow import Quanti
from petrolib.plots import tripleCombo, Zonation, plotLog

Load data

well_path = Path(r"./15_9-19.las")
tops_path = Path(r'./well tops.csv')

df, las = fr.load_las(well_path, return_csv=True, curves=['GR', 'RT', 'NPHI', RHOB'])

Process data

df = procs.process_data(df, 'GR', 'RT', 'NPHI', 'RHOB')

Triple combo

%matplotlib inline
tripleCombo(df, 'DEPTH', 'GR', 'RT', 'NPHI', 'RHOB', ztop=3300,
            zbot=3450, res_thres=10, fill='right', palette_op='rainbow', limit='left')

Zone plot

zones = Zonation(df, path=tops_path)
zones.plotZone('DEPTH', ['GR', 'RT', 'RHOB', 'NPHI', 'CALI'], 3300, 3600, '15_9-19')
plotLog('DEPTH', ['GR', 'RT', 'RHOB', 'NPHI', 'CALI'], 3300, 3600, '15_9-19')

Calling the zonation object to extra info

ztop, zbot, zn, fm = zones()

Formation evaluation

pp = Quanti(df, zn, ztop, zbot, fm, 'DEPTH', 'GR', 'RT', 'NPHI', 'RHOB', use_mean=True)
vsh = pp.vshale(method='clavier', show_plot=True, palette_op='cubehelix', figsize=(9,12))
por = pp.porosity(method='density', show_plot=True, figsize=(10, 12))
sw = pp.water_saturation(method='archie', show_plot=True, figsize=(10, 12))
perm = pp.permeability(show_plot=True, figsize=(9, 10))
flags = pp.flags(por_cutoff=.12, vsh_cutoff=.5, sw_cutoff=0.8, show_plot=True, palette_op='cubehelix', figsize=(20, 15))

ps = pp.paySummary(name='15-9_F1A')

Save results to excel

pp.save(file_name='Pay Summary')

Modules

file_reader

A Python module for loading data into code environment.

Handle files with extension such LAS, TSV, CSV or TXT

petrolib.file_reader.load_las(file: Path | str, return_csv: bool = False, curves: list | tuple = None) → lasio.las.LASFile | pd.DataFrame

Function to read LAS file

Parameters:

• file (pathlib.Path or str) – Filename or filepath specifying the LAS file

• return_csv (bool default False) – If True, both dataframe and LAS object are returned. If False, returns only LAS object

• curves (list or tuple, optional) – If specified, returns only dataframe containing the log curves specified. If not, all available logs are imported

Return type:

Returns either LAS and/or dataframe object of the well data

Example

>>> #return both dataframe containing only ['GR','RT', 'RHOB'] curves and the lasio object
>>> df, las = load_las(well_path, return_csv=True, curves=['GR', 'RT', 'RHOB'])

>>> #return only LAS object
>>> las = load_las(well_path)

petrolib.file_reader.load_table(file: Path | str, curves: list[str] = None, delimiter: str = None, header: int | list[int] | str = 'infer', skiprows: list | int = None, sheet_name: int | str | list = None) → pd.DataFrame

Function to load a table data, either csv, tsv, or excel file

Parameters:

• file (pathlib.Path or str) – Filename or filepath specifying the file

• curves (list or tuple, optional) – If specified, returns only dataframe containing the log curves specified If not, all available logs are imported

• delimiter (str, default ',') – Delimiter to use

• header (int, list of int, default 'infer') – Row number(s) to use as the column names, and the start of the data. Default behavior is to infer the column names. See official pandas doc for more..

• skiprows (list, int , optional) – Line numbers to skip (0-indexed) or number of lines to skip (int) at the start of the file

• sheet_name (str, int, list, default None) –

  Strings are used for sheet names. Integers are used in zero-indexed sheet positions.

  Available cases:

  • 0 : 1st sheet as a DataFrame

  • 1: 2nd sheet as a DataFrame

  • ”Sheet1” : Load sheet with name “Sheet1”

  • [0, 1, “Sheet5”]: Load first, second and sheet named “Sheet5” as a dict of DataFrame

  • defaults to None: All sheets.

  See help(pd.read_excel) for more

Example

>>> well_path = Path(r"C:\Users\USER\Documents\petrolib\test\petrolib\petrolib\15_9-19.csv")

>>> #loads all logs
>>> df = load_table(well_path)

>>> #loads specific
>>> df = load_table(well_path, ['GR', 'RT'], skiprows=[1])
>>> df

interp

Python module for reservoir interpretation from plots

petrolib.interp.crossPlot(df: DataFrame, column_x: str, column_y: str, hue: str | None = None, color_code: str | None = None, figsize: tuple = (20, 7), rhob_fluid: float = 1.0, res_name: str | None = None, cmap='viridis')

Plots the cross plot relationship of density against porosity on compatible scales to facilitate in identification of reservoir type and its fluid type.

This code was initially written by Yohanes Nuwara but was modified to give the resulting plot a more classic and pretty view.

Parameters:

• df (pd.DataFrame) – Dataframe of well

• column_x (str) – Porosity column

• column_y (str) – Density column

• hue (str) – Column to color code the scatter plot by

• color_code (str default None) – Color code typing. If ‘num’, arg hue must be a continuous column. If ‘cat’, argument hue must be a categorical column

• figsize (tuple) – Size of plot

• rhob_fluid (float, default 1.0) – Fluid density

• res_name (str) – Reservoir name

• cmap (str) – color map

Return type:

A plot showing the neutron-density cross plot

Example

>>> from petrolib.interp import crossPlot
>>> crossPlot(df=df, column_x='NPHI', column_y='RHOB', res_name='RES_A', color_code='num', hue='GR')

petrolib.interp.picketPlot(df: DataFrame, rt: str = 'RT', por: str = 'NPHI', rwa: float = 0.018, a: float = 1.0, m: float = 1.8, n: float = 2.0, res_name: str | None = None, figsize: tuple = (20, 8), hue: str | None = None, color_code: str | None = None, cmap=None)

Plot Pickett plot based on a pattern recognition approach to solving Archie’s equation. The resistivity and porosity logs are plotted on a logarithmic scales to evaluate formation characteristics of conventional, granular reservoirs. Read more here: https://wiki.seg.org/wiki/Pickett_plot

Parameters:

• df (pd.DataFrame) – Dataframe

• rt (str) – Resistivity column

• rwa (float default 0.03) – Formation water resisitivity

• a (float default, 1.) – Turtuosity factor

• m (float default 2.) – Cementation factor

• n (float default 2.) – Saturation exponent

• res_name (str) – Reservoir/Zone name

• figsize (tuple) – Size of plot

• hue (str) – Column to color code the scatter plot by.

• color_code (str default None) – Color code typing. If ‘num’, arg hue must be a continuous column If ‘cat’, argument hue must be a categorical column if None, there is no color coding

• cmap (str) – Color map option

Example

>>> import petrolib.interp.picketPlot
>>> picketPlot(df, color_code='num', hue='GR', cmap='rainbow')

>>> from petrolib.interp import picketPlot
>>> picketPlot(df, rt='RT', por='NPHI')

plots

A Python module for displaying log, lithofacies and zonation plots

class petrolib.plots.Zonation(df: DataFrame, zones: List[Dict[str, List[float]]] | None = None, path: Path | None = None)

Bases: object

This is a zonation class that extract zone/reservoir information from the file. This information may include top, bottom and zone/reservoir name. This information can be accessed when an instance of Zonation object is called.

df

Dataframe of well data

Type:

pd.DataFrame

zones

A optional attribute containing list of dictionaries that holds zone information If None, path must be supplied

Type:

list of dictionaries, default None

path

A csv file containing reservoir info. To be able to use this, the information in the file should be entered in the following format

top, bottom, zonename 100, 200, RES_A 210, 220, RES_B

If None, zone must be passed

Type:

Path or str, default None

Example

>>> from petrolib.plots import Zonation
>>> zones = Zonation(df, path='./well tops.csv')
>>> zones = Zonation(df, zones = [{'RES_A':[3000, 3100]}, {'RES_B':[3300, 3400]}])

>>> #get reservoir information by calling the Zonation object
>>> #ztop = top ; zbot = base; zn = zonename ; fm = formation mids to place zone name in plot
>>> ztop, zbot, zn, fm = zones()

plotZone(depth: str, logs: List[str], top: float, bottom: float, title: str = 'Log Plot', figsize: tuple = (8, 12))

Plots log curves with zonation track.

Parameters:

• depth (str) – Depth column

• logs (list of str) – A list of logs curves to include in plot

• top (float) – Minimum depth to zoom on

• bottom (float) – Maximum depth to zoom on

• title (str) – Plot title

• figsize (tuple) – Size of plot

Example

>>> Zonation.plotZone('DEPTH', ['GR', 'RT', 'RHOB', 'NPHI', 'CALI'], 3300, 3600, 'Volve')

petrolib.plots.plotLoc(data: list[lasio.las.LASFile], shape_file: Path = None, area: str = None, figsize: tuple = (7, 7), label: list = None, withmap: bool = False, return_table: bool = False) → pd.DataFrame | None

Plots location of wells The longitude and latitude must be available in the LAS files

Parameters:

• data (list) – List of lasio.las.LASFile objects

• shape_file (str, default None) – A .shp, .shx or .dbf file containing the shape file of the area where the well is located If not supplied, area must be passed

• area (str default, None) – Well location. Must inlcude area from gpd.datasets.get_path(‘naturalearth_lowres’)

• figsize (tuple) – Size of plot

• label (list of str) – Well name(s). Must be a list of equal length with data

• withmap (bool default False) – Plots location of wells on a map if True Plots the location on a scatter plot if False

• return_table (bool default False) – Return well information such as the long, lat and name

Example

>>> plotLoc(data=[las], area='Norway', label=['15_9-F-1A'], withmap=True, figsize=(30, 10))

petrolib.plots.plotLog(df: DataFrame, depth: str, logs: List[str], top: float, bottom: float, title: str = 'Log Plot', figsize: tuple = (8, 12))

Plots log curves singly. To plot overlay plots use plots.plotLogs or plots.plotLogFacies

Parameters:

• df (pd.DataFrame) – Dataframe

• depth (str) – Depth column

• logs (list of str) – A list of logs curves to include in plot

• top (float) – Minimum depth to zoom on

• bottom (float) – Maximum depth to zoom on

• title (str) – Plot title

• figsize (tuple) – Size of plot

Example

>>> from petrolib.plots import plotLog
>>> plotLog('DEPTH', ['GR', 'RT', 'RHOB', 'NPHI', 'CALI'], 3300, 3600, 'Volve')
>>> plotLog(df,'DEPTH', ['NPHI'], 2751, 2834.58, 'Fre-1')

petrolib.plots.plotLogFacies(df: DataFrame, depth: str, logs: List[list], top: float, bottom: float, facies: str | None = None, title: str = 'Log Plot', figsize: tuple = (8, 12))

Plots overlayed/super-imposed log curves along with the facies.

Parameters:

• df (pd.DataFrame) – Dataframe

• depth (str) – Depth column

• logs (list of lists/str) – A list of logs curves to include in plot

• top (float) – Minimum depth to zoom on

• bottom (float) – Maximum depth to zoom on

• facies (str) –

  Facies columns. Supported facies include :

  [‘Sandstone’, ‘Sandstone/Shale’, ‘Shale’, ‘Marl’, ‘Dolomite’, ‘Limestone’, ‘Chalk’, ‘Halite’, ‘Anhydrite’, ‘Tuff’, ‘Coal’, ‘Basement’]

• title (str) – Plot title

• figsize (tuple) – Size of plot

Example

>>> from petrolib.plots import *
>>> plotLogFacies(well11, 'DEPTH', ['GR', 'RT', ['RHOB', 'NPHI']], facies='litho', top=well11.DEPTH.min(),
                        bottom=well11.DEPTH.max(), figsize=(9, 12), title='15-9-F1A')

petrolib.plots.plotLogs(df: DataFrame, depth: str, logs: List[List[str]], top: float, bottom: float, title: str = 'Log Plot', figsize: tuple = (8, 12))

Plots overlayed/super-imposed log curves.

Parameters:

• df (pd.DataFrame) – Dataframe

• depth (str) – Depth column

• logs (list of lists/str) – A list of logs curves to include in plot

• top (float) – Minimum depth to zoom on

• bottom (float) – Maximum depth to zoom on

• title (str) – Plot title

• figsize (tuple) – Size of plot

Example

>>> plotLogs(well11, 'DEPTH', ['GR', 'RT', ['RHOB', 'NPHI']], top=well11.DEPTH.min(),
>>>                        bottom=well11.DEPTH.max(), figsize=(9, 12), title='15-9-F1A')

petrolib.plots.plotZoneCombo(data: DataFrame, depth: str, gr: str, res: str, nphi: str, rhob: str, ztop: float, zbot: float, ztops: List[float], zbots: List[float], zonename: List[str], limit: str, res_thres: float = 10.0, palette_op: str | None = None, fill: str | None = None, title: str = 'Log plot', figsize: tuple = (10, 20)) → None

Function for plotting three combo logs alongside the zonation/reservoir track

Parameters:

• df (pd.DataFrame) – Dataframe of data

• depth (str) – Depth column

• gr (str) – Gamma ray column

• res (str) – Resistivity column

• nphi (str) – Neutron porosity column

• rhob (str) – Bulk density column

• ztop (float) – Top or minimum depth value

• zbot (float) – Bottom or maximum depth value

• ztops (list of float) – Tops (depth) of each reservoir/zones

• zbots (list of float) – Bottom (depth) of each reservoir/zones

• zonename (list of str) – Name of each zones

• limit (str default None) – Tells which side to fill the gamma ray track, [‘left’, ‘right’]. lf None, it’s filled in both sides delineating shale-sand region

• res_thres (float) – Resistivity threshold to use in the identification on hydrocarbon bearing zone

• palette_op (str optional) – Palette option to fill gamma ray log. If None, `fill must be provided

• fill (str default None) –

  To show either of the porous and/or non porous zones in the neutron-density crossover. Can either be [‘left’, ‘right’, ‘both’]

  • default None - show neither of the porous nor non-porous zones

  • ’left’ - shows only porous zones

  • ’right’ - shows only non-porous zones

  • ’both’ - shows both porous and non-porous zones

• figsize (tuple) – Size of plot

Example

>>> from petrolib.plots import plotZoneCombo
>>> plotZoneCombo(well11, 'DEPTH', 'GR', 'RT', 'NPHI', 'RHOB', min(ztop), max(zbot),
>>>          ztop, zbot, zn, fill='both', limit=None, figsize=(13, 30), title='ATAGA-11')

petrolib.plots.tripleCombo(data: DataFrame, depth: str, gr: str, res: str, nphi: str, rhob: str, ztop: float, zbot: float, res_thres: float = 10.0, fill: str | None = None, palette_op: str | None = None, limit: str | None = None, figsize: tuple = (9, 10), title: str = 'Three Combo Log Plot') → None

Plots a three combo log of well data

Parameters:

• data (pd.DataFrame) – Dataframe of data

• depth (str) – Depth column

• gr (str) – Gamma ray column

• res (str) – Resistivity column

• nphi (str) – Neutron porosity column

• rhob (str) – Bulk density column

• ztop (list) – Top or minimum depth to zoom on.

• zbot (list) – Bottom or maximum depth to zoom on.

• res_thres (float) – Resistivity threshold to use in the identification on hydrocarbon bearing zone

• fill (str default None) –

  To show either of the porous and/or non porous zones in the neutron-density crossover. Can either be [‘left’, ‘right’, ‘both’]

  • default None - show neither of the porous nor non-porous zones

  • ’left’ - shows only porous zones

  • ’right’ - shows only non-porous zones

  • ’both’ - shows both porous and non-porous zones

• palette_op (str optional) – Palette option to fill gamma ray log

• limit (str default None) – Tells which side to fill the gamma ray track, [‘left’, ‘right’] lf None, it’s filled in both sides delineating shale-sand region

• figsize (tuple) – Size of plot

• title (str) – Title of plot

Example

>>> import matplotlib inline
>>> from petrolib.plots import tripleCombo
>>> # %matplotlib inline
>>> tripleCombo(df, 'DEPTH', 'GR', 'RT', 'NPHI', 'RHOB', ztop=3300,
>>>                     zbot=3450, res_thres=10, fill='right', palette_op='rainbow', limit='left')

procs

Python module for data processing and lithofacies modelling

petrolib.procs.model_facies(df: DataFrame, gr: str, env: str = 'SS') → DataFrame

Models lithofacies from Gamma ray log specific to a particular environment

Parameters:

• df (pd.DataFrame) – dataframe

• gr (str) – Gamma ray log column

• env (str) – Environment type. Either siliciclastic or carbonate * ‘SS’ for Siliclastic (Shale and Sandstone) environment * ‘CO’ for carbonate (Anhydrite, Limestone, Dolomite, Sandstone, Shale) environment

Example

>>> from petrolib.interp import model_facies
>>> model_facies(df, gr='GR', env='SS')

petrolib.procs.process_data(df: DataFrame, gr: str, rt: str, nphi: str, rhob: str, dt: str | None = None, trim: str = 'both') → DataFrame

Function to preprocess data before beginning petrophysics workflow. This processing workflow uses conventional values for the log curves. To use user-defined preprocessing method , refer to the petrolib.data.procs.trim()

Parameters:

• df (pd.DataFrame) – dataframe object

• gr (str) – Gamma ray column

• rt (str) – Resistivity column

• nphi (str) – Neutron porosity column

• rhob (str) – Bulk density column

• sonic (str, default None) – Sonic column (optional)

• trim (str default 'both') – Conditions for trim arbitrary values * ‘max’ : to trim values higher than conventional maximum values * ‘min’ : to trim values lower than conventional lower values * default ‘both’ : to trim both lower and higher values to conventional high and lower values

Return type:

A new copy of dataframe containing processed data

Example

>>> df = process_data(df, 'GR', 'RT', 'NPHI', 'RHOB')
>>> df.describe()
            |DEPTH  |  GR   |   RT  |  NPHI  |  RHOB|
            +-------+-------+-------+--------+------+
      count | 35361 | 34671 | 34211 | 10524  |10551 |
     -------+-------+-------+-------+--------+------+
       mean | 1913.9| 56.97 |  1.95 |  0.17  | 2.48 |
     -------+-------+-------+-------+--------+------+
        min |  145.9| 0.15  |  0.2  |  0.03  | 1.98 |
      ------+-------+-------+-------+--------+------+
        max | 3681.9|  200  | 2000  |  0.45  |  2.93|
       ----------------------------------------------

petrolib.procs.set_alias(df: DataFrame, DEPTH: str, GR: str, RT: str, NPHI: str, RHOB: str, DT: str | None = None) → DataFrame

Function to rename the log curves in order to maintain petrophysics conventions

Parameters:

• df (pd.DataFrame) – dataframe object

• DEPTH (str) – Depth column

• GR (str) – Gamma ray column

• RT (str) – Resistivity column

• NPHI (str) – Neutron porosity column

• RHOB (str) – Bulk density column

• DT (str, default None) – Sonic column (optional)

Return type:

Returns data of renamed log curves

Example

>>> df = set_alias(df, 'DEPT', 'GR','RES', 'NPHI', 'RHOB')
>>> print(df.columns)
>>> ['DEPTH', 'GR', 'RT', 'NPHI', 'RHOB']

petrolib.procs.trim(df: pd.DataFrame, col: str, lower: int | float, upper: int | float)

Function to preprocess data by trimming arbitrary values

Parameters:

• df (pd.DataFrame) – Dataframe

• col (str) – Log curve to trim its values

• lower (int or float) – Lower limit or minimum value

• upper (int or float) – Upper limit or maximum value

Return type:

Dataframe with user defined log limits

Example

>>> trim(df, 'GR', lower=0, upper=200)

stats

Python module for handling data statistics

class petrolib.stats.Correlation(dataframe: DataFrame)

Bases: object

A correlation class for pearson and chatterjee method of statistical significance.

Parameters:

df (pd.DataFrame) – Takes in only the dataframe

corr(method: str = 'chatterjee')

Function to calculate the linear (Pearson’s) and non-linear (Chatterjee’s) relationships between log curves. Relationship between well logs are usually non-linear.

Parameters:

method (str, default 'chatterjee') –

Method of correlation. {‘chatterjee’, ‘pearsonr’, ‘linear’, ‘nonlinear’}

• ’linear’ is the same as ‘pearsonr’

• ’nonlinear’ is the same as ‘chatterjee’

Return type:

Correlation matrix of all possible log curves combination

Example

>>> corr = Correlation(df)
>>> v = corr.corr(method='chatterjee)

plot_heatmap(title: str = 'Correlation Heatmap', figsize: slice = (12, 7), annot: bool = True, cmap=None)

Plots the heat map of Correlation Matrix

Parameters:

• title (str) – Title of plot

• figsize (slice) – Size of plot

• annot (bool, default True) – To annotate the coefficient in the plot

• cmap (matplotlib colormap name or object, or list of colors, optional) – The mapping from data values to color space

Example

>>> corr = Correlation(df)
>>> v = corr.corr(method='chatterjee)
>>> corr.plot_heatmap(cmap='Reds')

petrolib.stats.displayFreq(df: pd.DataFrame, *cols: tuple[str], bins: int = 12, figsize: slice = (8, 8))

Function to plot the frequency distribution of well log curves

Parameters:

• df (pd.DataFrame) – Dataframe of data

• cols (tuple[str]) – log curves to show its distribution

• bins (int) – Number of bins to group the data

• figsize (slice) – Size of plot

Return type:

Shows a plot of the frequency distribution of well log curves

Example

>>> from petrolib.stats import displayFreq
>>> displayFreq(df, 'GR','CALI', 'COAL', 'DT', 'DT_LOG', bins=15, figsize=(20,10))

utils

exception petrolib.utils.MnemonicError(msg)

Bases: Exception

Exception raised if the log curve/menemonic passed is not found

workflow

Python module for petrophysics

class petrolib.workflow.Quanti(df: DataFrame, zonename: list, ztop: list, zbot: list, f_mids: list, depth: str, gr: str, rt: str, nphi: str, rhob: str, sonic: str | None = None, use_mean: bool | None = None, use_median: bool | None = None)

Bases: object

Class to petrophysics workflow to evaluate any number of reservoirs of interest. Computes IGR/VSH, Total and Effective Porosities, Water and Hydrocarbon Saturation, Permeability

df

Dataframe of data

Type:

pd.DataFrame

zonename

List of zonenames. Will be accessed from the zonation file passed into Zonation class

Type:

list

ztop

Tops of the reservoirs. Will be accessed from the zonation file passed into Zonation class

Type:

list

zbot

Bottoms of the reservoirs. Will be accessed from the zonation file passed into Zonation class

Type:

list

f_mids

Formation mids to help place the zonename in the plots. Will be accessed from the zonation file passed into Zonation class

Type:

list

depth

Depth column

Type:

str

gr

Gamma ray column

Type:

str

rt

Resistivity column

Type:

str

nphi

Neutron porosity column

Type:

str

rhob

Bulk density column

Type:

str

sonic

Sonic column (optional)

Type:

str default None

use_mean

For cutoff. Whether to use mean of GR in IGR/VSH computation or not. If None, uses either median or average value

Type:

bool default None

use_median; bool default None

For cutoff. Whether to use median of GR in IGR/VSH computation or not. If None, uses either mean or average value

Example

>>> #loading libraries/packages
>>> from petrolib.workflow import Quanti
>>> from petrolib.plot import Zonation
>>> from petrolib.file_reader import load_las
>>> from pathlib import Path

>>> #loading well file and zonation/tops file
>>> well_path = Path(r"./15_9-F-1A.LAS")
>>> contact_path = Path(r"./well tops.csv")

>>> las, df= load_las(well_path, curves=['GR', 'RT', 'NPHI', 'RHOB'], return_las=True)

>>> #creating zonation class to extra info
>>> zones = Zonation(df, path=contact_path)
>>> ztop, zbot, zn, fm = zones()

>>> #creating quanti class
>>> pp = Quanti(df, zn, ztop, zbot, fm, 'DEPTH', 'GR', 'RT', 'NPHI', 'RHOB', use_mean=True)

flags(vsh_cutoff: float, por_cutoff: float, sw_cutoff: float, ref_unit: str = 'm', show_plot: bool = False, palette_op: str | None = None, figsize: tuple | None = None)

Create the {ROCK, RES, PAY} flags

To use, must have called vshale, porosity, water_saturation and permeability methods

Parameters:

• vsh_method (float) – Volume of Shale cutoff. Applied only to [‘ROCK’] flag

• por_cutoff (float) – Porosity cutoff. Applied only to the [‘ROCK’, ‘RES’] flags

• sw_cutoff (float) – Water Saturation cutoff. Applied only to the [‘ROCK’, ‘RES’, ‘PAY] flags

• ref_unit (str default 'm') – Reference unit for measured depth. Defaults to metres

• show_plot (bool default False) – Display plot if True.. Plots GR, RT, VSH, SW, Perm, NPHI/RHOB, PHIE/PHIT, [‘ROCK’, ‘RES’, ‘PAY] flags and Zonation track

• palette_op (str default None) – palette option for VSH coloring. Check https://matplotlib.org/stable/tutorials/colors/colormaps.html for availabel palette options

• figsize (tuple default None) – Size of plot

Return type:

Either/Both Dataframe containing the flags and the plot if show_plot=True

Example

>>> # Create Quanti class
>>> pp = Quanti(df, zn, ztop, zbot, fm, 'DEPTH', 'GR', 'RT', 'NPHI', 'RHOB')

>>> # Display plot only
>>> pp.flags(por_cutoff=.12, vsh_cutoff=.5, sw_cutoff=0.8, show_plot=True, palette_op='cubehelix', figsize=(20, 15))

>>> # Display data only
>>> y = pp.flags(por_cutoff=.12, vsh_cutoff=.5, sw_cutoff=0.8)
>>> result = pd.concat(y)
>>> print(result)

paySummary(name: str)

Computes the

• net, grossand not net thicknesses

• net-to-gross

• average volume of shale

• average porosity value

• bulk volume of water

• water saturation

for each of the three flags {ROCK, RES, PAY}

Parameters:

name (str) – Name of the well

Return type:

Displays the Pay Summary Report table

Example

>>> pp.paySummary(name='15-9_F1A')

permeability(show_plot: bool = False, figsize: tuple | None = None)

Computes the permeability. To use, must have called vshale and porosity and water_saturation methods

Parameters:

• show_plot (bool default False) – Display plot if True.. Plots PHIE, Permeability and Zone track

• figsize (tuple default None) – Size of plot

Return type:

Either/Both Dataframe containing the Perm and the plot if show_plot=True

Example

>>> # create Quanti class
>>> pp = Quanti(df, zn, ztop, zbot, fm, 'DEPTH', 'GR', 'RT', 'NPHI', 'RHOB')

>>> # display plot only
>>> pp.vshale(method='clavier')
>>> pp.porosity(method='density')
>>> pp.water_saturation(method='archie')
>>> pp.permeability(show_plot=True, figsize=(9, 10))

>>> # display data only
>>> x = pp.vshale(method='clavier')
>>> y = pp.porosity(method='density')
>>> z = pp.water_saturation(method='archie')
>>> a = pp.permeability()
>>> result = pd.concat(a)
>>> print(result)

porosity(method: str = 'density', rhob_shale: float = 2.4, rhob_fluid: float = 1.0, rhob_matrix: float = 2.65, fzs: float | None = None, show_plot: bool = False, figsize: tuple | None = None)

Computes the effective and total porosities using the ‘density’ and Wyllie’s ‘sonic’ method. To use, must have called the vshale method

Parameters:

• method (str default 'density') – Porosity method. {‘density’, ‘sonic’}

• rhob_shale (float default 2.4) – Shale matrix

• rhob_fluid (float default 1.0) – Fluid density

• rhob_matrix (float default 2.65) – Matrix density

• fzs (float default None) – Flushed zone saturation for PHIE. If None, it is calculated from rhob_fluid, rhob_shale and rhob_matrix

• show_plot (bool default False) – Display plot if True.. Plots RHOB, VSH, PHIE/PHIT and Zone track

• figsize (tuple default None) – Size of plot

Return type:

Either/Both Dataframe containing the PHIE/PHIT and the plot if show_plot=True

Example

>>> # create Quanti class
>>> pp = Quanti(df, zn, ztop, zbot, fm, 'DEPTH', 'GR', 'RT', 'NPHI', 'RHOB')

>>> # display plot only
>>> pp.porosity(method='density', show_plot=True, figsize=(10, 12))

>>> # display data only
>>> y = pp.porosity(method='density')
>>> result = pd.concat(y)
>>> print(result)

report()

Displays the methods used in each parameter estimations and cutoff used for flagging

save(file_name: str)

A method to save the pay summary results into a excel file

Parameters:

file_name (str) – name of to save the pay summary report as

vshale(method: str = 'linear', show_plot: bool = False, palette_op: str | None = None, figsize: tuple | None = None)

Computes the Volume of Shale

Parameters:

• method (str default 'linear') – Volume of Shale method. {‘linear’, ‘clavier’, ‘larionov_ter’, ‘larionov_older’, ‘stieber_1’, ‘stieber_2, ‘stieber_m_pliocene’} * Linear = Gamma Ray Index (IGR) * Larionov Tertiary * Larionov Older Rocks * Stieber (Miocene/Pliocene)

• show_plot (bool default False) – Display plot if True.. Plots GR, VSH and Zone track

• palette_op (str default None) – Palette option for to color code vshale plot. Check https://matplotlib.org/stable/tutorials/colors/colormaps.html

• figsize (tuple default None) – Size of plot

Return type:

Either/Both Dataframe containing the VSH and the plot if show_plot=True

Example

>>> # create Quanti class
>>> pp = Quanti(df, zn, ztop, zbot, fm, 'DEPTH', 'GR', 'RT', 'NPHI', 'RHOB')

>>> # display plot only
>>> pp.vshale(method='clavier', show_plot=True, palette_op='cubehelix', figsize=(9,12))

>>> # display data only
>>> x = pp.vshale(method='clavier')
>>> x = pd.concat(x)
>>> print(x)

water_saturation(method: str = 'archie', rw: float = 0.03, a: float = 1.0, m: float = 2.0, n: float = 2.0, show_plot: bool = False, figsize: tuple | None = None)

Computes water and hydrocarbon saturation To use, must have called both vshale and porosity methods

Parameters:

• method (str default 'archie') – Water Saturation method. {‘archie’, ‘simmandoux’}

• rw (float default 0.03) – Formation water resisitivity

• a (float default, 1.) – Turtuosity factor

• m (float default 2.) – Cementation factor

• n (float default 2.) – Saturation exponent

• show_plot (bool default False) – Display plot if True.. Plots RT, SW, PHIE/PHIT and Zone track

• figsize (tuple default None) – Size of plot

Return type:

Either/Both Dataframe containing the SW, SH and the plot if show_plot=True

Example

>>> # create Quanti class
>>> pp = Quanti(df, zn, ztop, zbot, fm, 'DEPTH', 'GR', 'RT', 'NPHI', 'RHOB')

>>> # display plot only
>>> pp.water_saturation(method='archie', show_plot=True, figsize=(10, 12))

>>> # display data only
>>> z = pp.water_saturation(method='archie')
>>> result = pd.concat(z)
>>> print(result)

Tutorial

A notebook has been prepared to walk user through on how to use the package for petrophysical workflow. Find the link here:

https://github.com/joshua-atolagbe/tutorials

Indices and tables

• Index

• Module Index

• Search Page