United States dependence on Middle Eastern oil imports over time

Model: Kimi K2.6 Checked on: 21/09/2026

Based on OECD Balanced International Merchandise Trade Statistics (BIMTS)¹, I analysed mirror-trade data—Middle Eastern exports to the United States—to track US dependence on mineral-fuel supplies from the region over time. The product category used is HS 27 (mineral fuels, mineral oils and products of their distillation), which captures crude oil, refined products and related hydrocarbons²³.

Key findings

Peak dependence was around 2012–2014. Middle Eastern exports to the US accounted for roughly 21 % of all world mineral-fuel exports to the US in 2012, valued at about $87 billion out of a $415 billion total².
Dependence has fallen sharply since then. By 2024 the share dropped to about 9 % ($22 billion out of $242 billion)².
Saudi Arabia has consistently been the largest supplier, followed by Iraq as a significant secondary source. Iranian shipments are effectively absent from the data in recent decades because of sanctions².
The decline mirrors the broader trend of the US reducing its reliance on imported oil, partly driven by the growth of domestic shale production.

Trends over time

Year	World exports to US ($ bn)	Middle East exports to US ($ bn)	Middle East share (%)
1995	61.7	9.6	15.6
2005	292.0	44.3	15.2
2008	480.7	84.2	17.5
2012	415.1	87.2	21.0
2016	156.7	27.0	17.2
2020	123.9	12.3	9.9
2024	242.2	21.8	9.0

Visual summary

The first chart shows total US mineral-fuel imports from the Middle East versus total world exports to the US (the mirror of US imports), illustrating the post-2012 decline³.

The second chart plots the Middle Eastern share, highlighting the peak around 2012–2014 and the subsequent drop to roughly 9–11 % in recent years³.

The third chart breaks the Middle East total down by country, confirming Saudi Arabia’s dominance and Iraq’s importance, while Iranian volumes have disappeared from the trade record³.

Caveats

The data are reported by exporting (Middle Eastern) countries rather than by the US directly, so they reflect partner-country records of shipments to the US.
The values are in current US dollars, meaning the shares can be affected by oil-price swings as well as by physical-volume changes.
HS 27 is broader than crude oil alone; it also includes refined products, coal-derived fuels and certain gases. For Middle Eastern suppliers, however, petroleum dominates this category.

OECD dataset: Balanced international merchandise trade statistics (BIMTS) - HS2017-2D

Code Run 2

Dataset: DSD_BIMTS@DF_BIMTS_HS2017_2D Dataset: DSD_BIMTS@DF_BIMTS_HS2017_2D

Dataset filters for `DSD_BIMTS@DF_BIMTS_HS2017_2D`

Filter	Value(s)
REF_AREA	W
COUNTERPART_AREA	USA
TRADE_FLOW	X
FREQ	A
PRODUCT_HS	HS17_27

Dataset filters for `DSD_BIMTS@DF_BIMTS_HS2017_2D`

Filter	Value(s)
REF_AREA	SAU, IRN, IRQ, KWT, ARE, QAT, OMN, BHR
COUNTERPART_AREA	USA
TRADE_FLOW	X
FREQ	A
PRODUCT_HS	HS17_27

Program output

Full history:
 year   world_total  middle_east_total  dependence_pct
 1995  61747.160506        9602.412284       15.551180
 1996  78850.151047       11120.475969       14.103303
 1997  79605.812026       11252.712618       14.135542
 1998  58915.572359        8445.342336       14.334652
 1999  79528.036717       14245.829807       17.912966
 2000 136572.621023       22122.106793       16.198054
 2001 120233.174103       19808.454628       16.475033
 2002 118897.233113       19497.015702       16.398208
 2003 156165.435893       26512.056646       16.976904
 2004 207821.425660       32597.411557       15.685299
 2005 292018.996401       44251.625772       15.153681
 2006 333064.533157       49231.560730       14.781388
 2007 363055.824292       54131.894463       14.910075
 2008 480735.641137       84243.538925       17.523880
 2009 268074.240669       37382.272401       13.944746
 2010 345374.905608       49539.848846       14.343789
 2011 450438.063589       74856.699893       16.618644
 2012 415111.363733       87244.576900       21.017150
 2013 376215.915578       76483.825305       20.329769
 2014 343514.661449       70447.658550       20.507904
 2015 193070.895427       31856.007397       16.499642
 2016 156713.339330       27017.249596       17.239917
 2017 196740.404555       32922.520913       16.733991
 2018 232872.844301       38589.832974       16.571204
 2019 200337.040371       22222.491390       11.092553
 2020 123947.262319       12287.152990        9.913210
 2021 215798.292737       19273.758283        8.931377
 2022 314335.363815       34554.559729       10.992896
 2023 259777.052565       27111.553127       10.436470
 2024 242230.570457       21815.130407        9.005936


Breakdown for key years (billion USD):

1995:
country
ARE    0.03
BHR    0.01
IRN    0.00
IRQ    0.00
KWT    1.40
OMN    0.15
QAT    0.01
SAU    7.99

2000:
country
ARE     0.14
BHR     0.02
IRN     0.04
IRQ     5.70
KWT     2.47
OMN     0.03
QAT     0.15
SAU    13.58

2005:
country
ARE     0.23
BHR     0.15
IRN     0.00
IRQ    10.26
KWT     4.41
OMN     0.41
QAT     0.14
SAU    28.64

2008:
country
ARE     0.21
BHR     0.05
IRN     0.00
IRQ    23.07
KWT     6.54
OMN     0.70
QAT     0.07
SAU    53.62

2012:
country
ARE     0.13
BHR     0.18
IRN     0.00
IRQ    19.31
KWT    12.02
OMN     0.36
QAT     0.45
SAU    54.80

2016:
country
ARE     0.24
BHR     0.11
IRN     0.00
IRQ     5.92
KWT     3.29
OMN     0.47
QAT     0.35
SAU    16.64

2020:
country
ARE    0.74
BHR    0.06
IRN    0.01
IRQ    3.09
KWT    0.47
OMN    0.01
QAT    0.37
SAU    7.53

2024:
country
ARE     1.36
BHR     0.20
IRN     0.00
IRQ     7.07
KWT     1.90
OMN     0.09
QAT     0.82
SAU    10.38

Code

import pandas as pd

world = pd.read_csv(DATASET_FILE0, low_memory=False)
countries = pd.read_csv(DATASET_FILE1, low_memory=False)

world = world[world['ADJUSTMENT'] == 'B_ADJ_RX']
countries = countries[countries['ADJUSTMENT'] == 'B_ADJ_RX']

world = world[['TIME_PERIOD', 'value']].copy()
world.columns = ['year', 'world_total']
world = world.sort_values('year').reset_index(drop=True)

me = countries.groupby('TIME_PERIOD')['value'].sum().reset_index()
me.columns = ['year', 'middle_east_total']

merged = pd.merge(world, me, on='year', how='outer')
merged['dependence_pct'] = 100 * merged['middle_east_total'] / merged['world_total']

print("Full history:")
print(merged.to_string(index=False))

# Also show full country breakdown for a few key years
print("\n\nBreakdown for key years (billion USD):")
countries_clean = countries[['REF_AREA', 'TIME_PERIOD', 'value']].copy()
countries_clean.columns = ['country', 'year', 'value']
country_pivot = countries_clean.pivot(index='year', columns='country', values='value').fillna(0)
for yr in [1995, 2000, 2005, 2008, 2012, 2016, 2020, 2024]:
    if yr in country_pivot.index:
        row = (country_pivot.loc[yr] / 1000).round(2)
        print(f"\n{yr}:")
        print(row.to_string())

Code Run 1

Dataset: DSD_BIMTS@DF_BIMTS_HS2017_2D Dataset: DSD_BIMTS@DF_BIMTS_HS2017_2D

Images

us_imports_origin.svg

us_dependence_share.svg

middle_east_by_country.svg

Dataset filters for `DSD_BIMTS@DF_BIMTS_HS2017_2D`

Filter	Value(s)
REF_AREA	W
COUNTERPART_AREA	USA
TRADE_FLOW	X
FREQ	A
PRODUCT_HS	HS17_27

Dataset filters for `DSD_BIMTS@DF_BIMTS_HS2017_2D`

Filter	Value(s)
REF_AREA	SAU, IRN, IRQ, KWT, ARE, QAT, OMN, BHR
COUNTERPART_AREA	USA
TRADE_FLOW	X
FREQ	A
PRODUCT_HS	HS17_27

Program output

Merged data (last 15 years):
    year    world_total  middle_east_total  dependence_pct
15  2010  345374.905608       49539.848846       14.343789
16  2011  450438.063589       74856.699893       16.618644
17  2012  415111.363733       87244.576900       21.017150
18  2013  376215.915578       76483.825305       20.329769
19  2014  343514.661449       70447.658550       20.507904
20  2015  193070.895427       31856.007397       16.499642
21  2016  156713.339330       27017.249596       17.239917
22  2017  196740.404555       32922.520913       16.733991
23  2018  232872.844301       38589.832974       16.571204
24  2019  200337.040371       22222.491390       11.092553
25  2020  123947.262319       12287.152990        9.913210
26  2021  215798.292737       19273.758283        8.931377
27  2022  314335.363815       34554.559729       10.992896
28  2023  259777.052565       27111.553127       10.436470
29  2024  242230.570457       21815.130407        9.005936

Country contributions (last 10 years, million USD):
country  ARE  BHR  IRN   IRQ  KWT  OMN  QAT   SAU
year                                             
2015     0.1  0.1  0.0   4.3  4.2  0.0  0.4  22.8
2016     0.2  0.1  0.0   5.9  3.3  0.5  0.4  16.6
2017     0.7  0.2  0.0  10.4  2.7  0.3  0.3  18.4
2018     1.5  0.1  0.0  11.7  1.8  0.0  0.7  22.7
2019     0.8  0.1  0.0   7.1  1.3  0.0  0.6  12.3
2020     0.7  0.1  0.0   3.1  0.5  0.0  0.4   7.5
2021     1.7  0.1  0.0   3.7  1.2  0.2  0.8  11.6
2022     2.0  0.2  0.0   7.5  2.2  0.3  1.2  21.1
2023     1.7  0.1  0.0   8.2  1.9  0.3  1.0  13.7
2024     1.4  0.2  0.0   7.1  1.9  0.1  0.8  10.4

Plots saved.

Code

import pandas as pd
import matplotlib.pyplot as plt

# Load data
world = pd.read_csv(DATASET_FILE0, low_memory=False)
countries = pd.read_csv(DATASET_FILE1, low_memory=False)

# Use balanced adjusted for re-exports consistently
world = world[world['ADJUSTMENT'] == 'B_ADJ_RX']
countries = countries[countries['ADJUSTMENT'] == 'B_ADJ_RX']

# Rename columns for clarity
world = world[['TIME_PERIOD', 'value']].copy()
world.columns = ['year', 'world_total']
world = world.sort_values('year').reset_index(drop=True)

# Aggregate middle east countries by year
me = countries.groupby('TIME_PERIOD')['value'].sum().reset_index()
me.columns = ['year', 'middle_east_total']

# Merge
merged = pd.merge(world, me, on='year', how='outer')
merged['dependence_pct'] = 100 * merged['middle_east_total'] / merged['world_total']

# Also compute country-level contributions for recent years
countries_clean = countries[['REF_AREA', 'TIME_PERIOD', 'value']].copy()
countries_clean.columns = ['country', 'year', 'value']
country_pivot = countries_clean.pivot(index='year', columns='country', values='value').fillna(0)

print("Merged data (last 15 years):")
print(merged.tail(15))

print("\nCountry contributions (last 10 years, million USD):")
print((country_pivot.tail(10) / 1000).round(1))

# Plot 1: Total US mineral fuel imports from Middle East vs World (in billion USD)
fig, ax1 = plt.subplots(figsize=(10, 6))
ax1.fill_between(merged['year'], merged['middle_east_total'] / 1000, alpha=0.3, label='Middle East exports to US')
ax1.plot(merged['year'], merged['world_total'] / 1000, label='World exports to US', color='black', linewidth=2)
ax1.plot(merged['year'], merged['middle_east_total'] / 1000, label='Middle East exports to US', color='crimson', linewidth=2)
ax1.set_xlabel('Year')
ax1.set_ylabel('Billion USD')
ax1.set_title('US Mineral Fuel Imports by Origin (Mirror Data)\nHS 27 - Mineral Fuels, Oils and Distillation Products')
ax1.legend()
ax1.grid(True, alpha=0.3)
plt.tight_layout()
plt.savefig('us_imports_origin.svg')
plt.close()

# Plot 2: Dependence share over time
fig, ax2 = plt.subplots(figsize=(10, 6))
ax2.plot(merged['year'], merged['dependence_pct'], color='darkblue', linewidth=2.5)
ax2.fill_between(merged['year'], merged['dependence_pct'], alpha=0.2, color='darkblue')
ax2.set_xlabel('Year')
ax2.set_ylabel('Share of US mineral fuel imports (%)')
ax2.set_title('US Dependence on Middle Eastern Mineral Fuel Imports\n(Mirror data: Middle East exports to US as % of world exports to US)')
ax2.set_ylim(0, max(merged['dependence_pct']) * 1.1)
ax2.grid(True, alpha=0.3)
plt.tight_layout()
plt.savefig('us_dependence_share.svg')
plt.close()

# Plot 3: Stacked area by country
fig, ax3 = plt.subplots(figsize=(10, 6))
countries_ordered = ['SAU', 'IRQ', 'IRN', 'KWT', 'ARE', 'QAT', 'OMN', 'BHR']
# Ensure order
plot_data = country_pivot.reindex(columns=[c for c in countries_ordered if c in country_pivot.columns], fill_value=0)
ax3.stackplot(plot_data.index, *(plot_data[c] / 1000 for c in plot_data.columns), 
              labels=[{'SAU':'Saudi Arabia','IRQ':'Iraq','IRN':'Iran','KWT':'Kuwait','ARE':'UAE','QAT':'Qatar','OMN':'Oman','BHR':'Bahrain'}[c] for c in plot_data.columns],
              alpha=0.8)
ax3.set_xlabel('Year')
ax3.set_ylabel('Billion USD')
ax3.set_title('Middle East Mineral Fuel Exports to US by Country')
ax3.legend(loc='upper left', reverse=True)
ax3.grid(True, alpha=0.3)
plt.tight_layout()
plt.savefig('middle_east_by_country.svg')
plt.close()

print("\nPlots saved.")