MakeoverMonday: Global Oil Production - Who Still Moves the Map

Country-level oil output in terawatt-hours from Our World in Data: top producers, trajectories since 1990, and shifting shares of the world total

Overview

Oil output remains a core lens on geopolitics and the energy transition. This makeover uses Our World in Data’s country-level series (Energy Institute and Shift Data Portal, harmonised by OWID) in terawatt-hours (energy-equivalent) to compare how the largest producers evolved since 1990.

• Data Source: Our World in Data - Oil production
• Angle: Absolute trajectories for the current top producers, then each country’s share of annual world production

Dataset

import sys
from pathlib import Path

import pandas as pd
import numpy as np
import plotly.graph_objects as go

_p = Path.cwd()
if (_p / "_mm_layout.py").exists():
    _posts = _p
elif (_p.parent / "_mm_layout.py").exists():
    _posts = _p.parent
elif (_p / "posts" / "_mm_layout.py").exists():
    _posts = _p / "posts"
else:
    _posts = _p
sys.path.insert(0, str(_posts))
from _mm_layout import apply_mm_layout

url = (
    "https://ourworldindata.org/grapher/oil-production-by-country.csv"
    "?v=1&csvType=full&useColumnShortNames=false"
)
raw = pd.read_csv(url, storage_options={"User-Agent": "chokotto-makeover/1.0"})
raw.columns = raw.columns.str.replace(" ", "_")

oil = raw.rename(columns={"Oil": "oil_twh"}).copy()
oil = oil[oil["Year"] >= 1990]
oil = oil[oil["Entity"].notna()]
# Drop aggregates that would double-count
exclude = {"World", "Africa", "Asia", "Europe", "North America", "South America", "Oceania"}
oil = oil[~oil["Entity"].isin(exclude)]

latest_y = int(oil["Year"].max())
top_entities = (
    oil[oil["Year"] == latest_y]
    .nlargest(8, "oil_twh")["Entity"]
    .tolist()
)

by_year_total = oil.groupby("Year", as_index=False)["oil_twh"].sum()
by_year_total = by_year_total.rename(columns={"oil_twh": "world_total"})

top8 = oil[oil["Entity"].isin(top_entities)].merge(by_year_total, on="Year")
top8["share_pct"] = np.where(
    top8["world_total"] > 0, top8["oil_twh"] / top8["world_total"] * 100, 0
)

My Makeover

The same eight producers still dominate volume, but ranks drift

color_map = {e: PAL[i % len(PAL)] for i, e in enumerate(top_entities)}

fig = go.Figure()
for entity in top_entities:
    sub = oil[oil["Entity"] == entity].sort_values("Year")
    fig.add_trace(go.Scatter(
        x=sub["Year"],
        y=sub["oil_twh"],
        mode="lines",
        name=entity,
        line=dict(color=color_map[entity], width=3 if entity == top_entities[0] else 2),
        hovertemplate=f"{entity}<br>%{{x}}: %{{y:,.0f}} TWh<extra></extra>",
    ))

lead = top_entities[0]
lead_last = oil[(oil["Entity"] == lead) & (oil["Year"] == latest_y)]["oil_twh"].sum()

fig.update_layout(
    **THEME,
    xaxis=dict(title=""),
    yaxis=dict(title="Oil production (terawatt-hours)"),
)
apply_mm_layout(
    fig,
    f"Oil output (TWh): {lead} led with {lead_last:,.0f} TWh in {latest_y} among top 8",
    subtitle="Top 8 countries by output in the latest year; lines from 1990",
    legend_position="top",
    n_legend_items=len(top_entities),
)
add_source(fig)
assert_no_title_overlap(fig)
fig.show()

try:
    fig.write_image("chart-1.png", width=1200, height=520, scale=2)
except Exception:
    pass

Share of world production: concentration vs diversification

years = sorted(top8["Year"].unique())
wide = top8.pivot(index="Year", columns="Entity", values="share_pct").reindex(years)
wide = wide.fillna(0)

fills = [
    "rgba(180,83,9,0.35)", "rgba(15,118,110,0.32)", "rgba(29,78,216,0.28)",
    "rgba(124,58,237,0.26)", "rgba(190,18,60,0.24)", "rgba(100,116,139,0.22)",
    "rgba(202,138,4,0.2)", "rgba(3,105,161,0.2)",
]
fig2 = go.Figure()
for i, entity in enumerate(top_entities):
    if entity not in wide.columns:
        continue
    fig2.add_trace(go.Scatter(
        x=wide.index,
        y=wide[entity],
        name=entity,
        stackgroup="one",
        mode="lines",
        line=dict(width=0.5),
        fillcolor=fills[i % len(fills)],
        hovertemplate=f"{entity}: %{{y:.1f}}%<extra></extra>",
    ))

fig2.update_layout(
    **THEME,
    xaxis=dict(title=""),
    yaxis=dict(title="Percent of summed country totals", range=[0, 100]),
)
apply_mm_layout(
    fig2,
    "Share of estimated world oil output: eight largest producers (100% stacked)",
    subtitle=(
        "World total = sum of country-level series in dataset "
        "(excludes regional aggregates)"
    ),
    legend_position="top",
    n_legend_items=len(top_entities),
)
add_source(fig2)
assert_no_title_overlap(fig2)
fig2.show()

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This post is part of Makeover Monday.