9/21/2020
Plot a basic drilling shock data with matplotlib | bertha jekyll blog
About
Plot a basic drilling shock data with
matplotlib
Jul 7, 2020
This is a very simple approach to plot your vibration/shock data vs. depth using python
panda and matplotlib. The shock is categorised according to 4 levels: low (green), medium
(yellow), high (red) and severe level (maroon). The plot is useful when you want to do a quick
analysis post-run or as an appendix for end of well report. It is also easy to customise when it
comes to the reference levels (normally in vector unit g) and colors.
Input: excel file
Output: plot from matplotlib
Instruction.
1. First, make sure your excel file has the shock channels, in this example i provided lateral
shock channel. Erase all the invalid or -999.25 values. Make sure the name of the channel
is simple an without any space.
2. Code. Check that you have all the required python library. Here I am using python 3.7
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9/21/2020
Plot a basic drilling shock data with matplotlib | bertha jekyll blog
import pandas as pd
import matplotlib.pyplot as plt
from matplotlib.collections import LineCollection
#define variables
y= df["your axial shock channel"]
x= df["your hole depth channel"]
#set up colors & customise your shock level condition. Here I used some random
c =['green' if a <=1.5 else 'yellow' if a<=2.5 else 'red' if a<=5 else 'maroon
#convert time series to line segments
lines =[((x0,y0), (x1,y1)) for x0, y0, x1, y1 in zip(x[:-1], y[:-1], x[1:], y[
colored_lines =LineCollection(lines, colors=c, linewidths=(2,))
#this is for plotting
plt.style.use('fivethirtyeight')
fig, ax = plt.subplots(1)
ax.add_collection(colored_lines)
ax.autoscale_view()
plt.ylabel("Axial Shock level(g)")
plt.xlabel("Hole Depth")
plt.show()
Remember you can always customise your plot to make it look nicer and informative by for
example displaying a legend or adjust the scale of the plot (using xticks or yticks).
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Author Bertha Amelia
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