Anthony Jimenez
18 August 2021
http://www.fa-jimenez.com/
This data is sourced from the Society of Petroleum Engineers Data Repository as part of the SPE Bleeding Edge of RTA Group (SPE-BERG).
SPE Data Repository: Data Set: dataset_1, Well Number: all_wells. From URL: https://www.spe.org/datasets/dataset_1/csv_files/dataset_1_all_wells/production_data
import pandas as pd
import numpy as np
import plotly.express as px
from plotly.subplots import make_subplots
import plotly.graph_objects as go
from scipy.optimize import differential_evolution
Steps accomplished in this section:
Analysis will be conducted on the LORIKEET well since this has 4031 days of production (DOP)
# Load in data
prod_data_url = r'https://raw.githubusercontent.com/ajmz1/DCA_optimizer/master/20210818_spe_rta_prod_data.csv'
df = pd.read_csv(prod_data_url)
# First 5 rows of data for inspection
df.head()
| Lease | Time (Days) | Choke Size | Gas Volume (MMscf) | Oil Volume (stb) | Water Volume (stb) | Gas Lift Inj Volume (MMscf) | Casing Pressure (psi(a)) | Tubing Pressure (psi(a)) | Active Pressure (psi(a)) | Line Pressure (psi(a)) | Pressure Source | Calculated Sandface Pressure (psi(a)) | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | OSPREY | 1.0 | NaN | 0.145 | 504.39 | 718.0 | NaN | 2064.695943 | 14.695943 | 2064.695943 | 14.695943 | Casing Pressure | 5050.159793 |
| 1 | OSPREY | 2.0 | NaN | 0.186 | 564.76 | 922.0 | NaN | 1989.695943 | 14.695943 | 1989.695943 | 14.695943 | Casing Pressure | 5009.599839 |
| 2 | OSPREY | 3.0 | NaN | 0.231 | 653.51 | 753.0 | NaN | 1864.695943 | 14.695943 | 1864.695943 | 14.695943 | Casing Pressure | 4795.991972 |
| 3 | OSPREY | 4.0 | NaN | 0.268 | 740.71 | 700.0 | NaN | 1814.695943 | 14.695943 | 1814.695943 | 14.695943 | Casing Pressure | 4696.626023 |
| 4 | OSPREY | 5.0 | NaN | 0.261 | 678.06 | 530.0 | NaN | 1714.695943 | 14.695943 | 1714.695943 | 14.695943 | Casing Pressure | 4546.990059 |
# "Summary statistics" of full dataframe
df.describe()
| Time (Days) | Choke Size | Gas Volume (MMscf) | Oil Volume (stb) | Water Volume (stb) | Gas Lift Inj Volume (MMscf) | Casing Pressure (psi(a)) | Tubing Pressure (psi(a)) | Active Pressure (psi(a)) | Line Pressure (psi(a)) | Calculated Sandface Pressure (psi(a)) | |
|---|---|---|---|---|---|---|---|---|---|---|---|
| count | 60967.000000 | 2288.000000 | 60748.000000 | 52537.000000 | 60958.000000 | 7651.000000 | 60967.000000 | 60967.000000 | 60967.000000 | 60967.000000 | 60137.000000 |
| mean | 852.282382 | 27.877185 | 5.982365 | 36.450496 | 77.349569 | 0.219279 | 1283.679384 | 759.501393 | 1390.495107 | 696.823718 | 2299.493810 |
| std | 824.906531 | 9.385130 | 7.607748 | 105.729334 | 149.923524 | 0.158324 | 1514.521635 | 899.819732 | 1465.415127 | 412.611400 | 1833.520500 |
| min | 0.000000 | 8.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | -14.650000 | -14.650000 | -14.650000 | 0.000000 | 6.780602 |
| 25% | 255.000000 | 22.000000 | 0.235618 | 0.000000 | 1.800000 | 0.000000 | 254.178000 | 0.000000 | 671.849500 | 405.369500 | 1161.582338 |
| 50% | 574.000000 | 26.000000 | 3.878150 | 0.000000 | 9.625000 | 0.294000 | 891.922000 | 701.079000 | 972.141000 | 823.305000 | 1572.866109 |
| 75% | 1178.000000 | 32.000000 | 8.115673 | 0.000000 | 78.000000 | 0.350000 | 1327.225500 | 1095.000000 | 1350.579000 | 1048.623500 | 2640.255039 |
| max | 4031.000000 | 48.000000 | 61.548720 | 1249.000000 | 2287.000000 | 0.532000 | 10239.180000 | 8499.389000 | 8700.180000 | 1310.234000 | 11093.735290 |
# Determine which well lease has the largest amount of days on production for analysis
max_DOP = df.groupby('Lease')['Time (Days)'].max().sort_values(ascending=False)
max_DOP[:5]
Lease LORIKEET 4031.0 EGRET 3692.0 OSTRICH 3158.0 GOOSE 2846.0 CASSOWARY 2393.0 Name: Time (Days), dtype: float64
# Filter main dataframe to keep only the well with the greatest production history data
df1 = df[df['Lease'] == max_DOP.index[0]]
df1.head()
| Lease | Time (Days) | Choke Size | Gas Volume (MMscf) | Oil Volume (stb) | Water Volume (stb) | Gas Lift Inj Volume (MMscf) | Casing Pressure (psi(a)) | Tubing Pressure (psi(a)) | Active Pressure (psi(a)) | Line Pressure (psi(a)) | Pressure Source | Calculated Sandface Pressure (psi(a)) | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 12239 | LORIKEET | 1.0 | NaN | 4.45783 | 0.0 | 332.0 | NaN | 7275.902 | 0.0 | 7275.902 | 1176.263 | Casing Pressure | 9243.492426 |
| 12240 | LORIKEET | 2.0 | NaN | 10.23198 | 0.0 | 482.0 | NaN | 6158.324 | 0.0 | 6158.324 | 1234.895 | Casing Pressure | 7786.533870 |
| 12241 | LORIKEET | 3.0 | NaN | 11.90695 | 0.0 | 541.0 | NaN | 5665.502 | 0.0 | 5665.502 | 1224.994 | Casing Pressure | 7222.107405 |
| 12242 | LORIKEET | 4.0 | NaN | 11.73224 | 0.0 | 556.0 | NaN | 5339.542 | 0.0 | 5339.542 | 1218.640 | Casing Pressure | 6869.085268 |
| 12243 | LORIKEET | 5.0 | NaN | 10.88005 | 0.0 | 538.0 | NaN | 5044.625 | 0.0 | 5044.625 | 1204.742 | Casing Pressure | 6550.734048 |
# Filter the dataframe to keep only the most important columns for analysis
df1_cols = df1.columns
cols = [df1_cols[1], df1_cols[3], df1_cols[5], df1_cols[7], df1_cols[8], df1_cols[12]]
pressure_calc_method = df1_cols[11]
df2 = df1[cols].reset_index(drop=True)
df2['Gas Volume Cumulative (MMscf)'] = df2['Gas Volume (MMscf)'].cumsum()
df2
| Time (Days) | Gas Volume (MMscf) | Water Volume (stb) | Casing Pressure (psi(a)) | Tubing Pressure (psi(a)) | Calculated Sandface Pressure (psi(a)) | Gas Volume Cumulative (MMscf) | |
|---|---|---|---|---|---|---|---|
| 0 | 1.0 | 4.45783 | 332.00 | 7275.902 | 0.000 | 9243.492426 | 4.45783 |
| 1 | 2.0 | 10.23198 | 482.00 | 6158.324 | 0.000 | 7786.533870 | 14.68981 |
| 2 | 3.0 | 11.90695 | 541.00 | 5665.502 | 0.000 | 7222.107405 | 26.59676 |
| 3 | 4.0 | 11.73224 | 556.00 | 5339.542 | 0.000 | 6869.085268 | 38.32900 |
| 4 | 5.0 | 10.88005 | 538.00 | 5044.625 | 0.000 | 6550.734048 | 49.20905 |
| ... | ... | ... | ... | ... | ... | ... | ... |
| 4026 | 4027.0 | 0.14167 | 0.00 | 1380.025 | 1224.612 | 1512.040190 | 2123.90747 |
| 4027 | 4028.0 | 0.06459 | 0.14 | 1404.532 | 1250.867 | 2666.041664 | 2123.97206 |
| 4028 | 4029.0 | 0.14653 | 1.01 | 1411.368 | 1245.581 | 2403.007754 | 2124.11859 |
| 4029 | 4030.0 | 0.06990 | 0.00 | 1372.138 | 1221.453 | 1507.705822 | 2124.18849 |
| 4030 | 4031.0 | 0.14285 | 0.00 | 1405.675 | 1248.167 | 1541.461046 | 2124.33134 |
4031 rows × 7 columns
# Make total plot
def production_y_pressure_plot():
fig = make_subplots(specs=[[{'secondary_y': True}]])
secondary_y = [False, False, False, True, True, True, True, False]
for idx, col_name in enumerate(df2.columns[1:]):
if idx == 0:
fig.add_trace(go.Scatter(x=df2['Time (Days)'], y=df2[col_name]*1000, name=col_name), secondary_y=secondary_y[idx])
else:
fig.add_trace(go.Scatter(x=df2['Time (Days)'], y=df2[col_name], name=col_name), secondary_y=secondary_y[idx])
fig.update_layout(title='Production and Pressure Plot',
legend={
'orientation': 'h',
'yanchor': 'top',
'xanchor': 'right',
'x': 0.75,
'y': -0.15,
})
fig.update_yaxes(title_text='Gas Volume (Mscf) <br> Water Volume (stb) <br> Cumulative Gas Volume (MMscf)', secondary_y=False,
range=[0, 10000])
fig.update_yaxes(title_text='Casing Pressure (psi) <br> Tubing Pressure (psi) <br> Calculated Sandface Pressure (psi)',
secondary_y=True, range=[0, 5000])
return fig.show()
# Make the specific production plot that is a subplot visual
def production_plot():
fig = make_subplots(rows=2, cols=2, subplot_titles=('Cartesian Production Volume', 'Semilog-y Production Volume',
'Cumulative Production Volume', 'Log-Log Production Volume'))
fig.add_trace(go.Scatter(x=df2['Time (Days)'], y=df2['Gas Volume (MMscf)'], name='Gas Volume (MMscf)'), row=1, col=1)
fig.update_layout(
legend={
'orientation': 'h',
'yanchor': 'top',
'xanchor': 'right',
'x': 0.75,
'y': -0.15,
})
fig.add_trace(go.Scatter(x=df2['Time (Days)'], y=df2['Gas Volume (MMscf)'], name='Gas Volume (MMscf)'), row=1, col=2)
fig.update_yaxes(type='log', row=1, col=2)
fig.add_trace(go.Scatter(x=df2['Time (Days)'], y=df2['Gas Volume Cumulative (MMscf)'], name='Cumulative Gas Volume (MMscf)'), row=2, col=1)
fig.add_trace(go.Scatter(x=df2['Time (Days)'], y=df2['Gas Volume (MMscf)'], name='Cumulative Gas Volume (MMscf)'), row=2, col=2)
fig.update_yaxes(type='log', row=2, col=2)
fig.update_xaxes(type='log', row=2, col=2)
return fig.show()
production_y_pressure_plot()