This page documents the data sources, transformation logic, unit conventions, and known limitations underlying the dashboard. The scenario outputs shown on Tab 2 of the Power BI dashboard are scenario-based estimates derived from explicit assumptions and should not be interpreted as forecasts. All source data is publicly available. This is an independent project with no affiliation to any government or defence organisation.
Five datasets are loaded into a Microsoft Fabric Warehouse and transformed through a dbt pipeline before reaching the Power BI semantic model. All five reside in the schema USA_Global_Military_Presence_WH.dbt_dev_landing.
| Dataset | Description | Coverage |
|---|---|---|
| Base Structure Report FY25 | U.S. base inventory with plant replacement values and geo-coordinates. Site-level granularity. | FY2025 snapshot |
| DMDC Personnel Report | Defense Manpower Data Center personnel counts by location. Country/state level granularity. | Snapshot 2025-12-31 |
| GEM Main (field-level) | Global Oil and Gas Extraction Tracker - field identifiers, names, coordinates, and country attribution. March 2026 edition. | Global · ~190 countries |
| GEM Production (field-level) | Production volumes at field level in source units. Requires unit standardisation before aggregation. | Global · March 2026 |
| GEM Reserves (field-level) | Reserve volumes at field level across multiple classification schemes. Dashboard defaults to 2P (Proved + Probable). | Global · March 2026 |
Raw data is loaded into Fabric Warehouse via Get Data. A dbt pipeline then runs through three layers before the mart: staging (raw extraction and text standardisation), intermediate (canonical mapping, unit enrichment, region assignment), and mart (dimensions, facts, and the map locations model). The Power BI semantic model connects to the mart via an Import mode connection to the Fabric Warehouse SQL analytics endpoint.
| Layer | Materialisation | Schema |
|---|---|---|
| Staging | Views | dbt_dev_staging |
| Intermediate | Views | dbt_dev_intermediate |
| Mart | Tables | dbt_dev_mart |
The mart contains eleven models: five military (dim_geography, dim_site, dim_branch, fact_base_inventory, fact_personnel_assignment), five energy (dim_energy_field, dim_fuel_type, dim_reserves_classification, fact_energy_production, fact_energy_reserves), and one map model (map_locations). Military and energy schemas share no SQL join; cross-schema calculations are performed in DAX only.
Source production volumes arrive in mixed units and are standardised to two canonical forms before reaching the mart layer.
| Fuel | Production unit | Reserves unit | Conversion applied |
|---|---|---|---|
| Oil | bbl/day | bbl | Million bbl/year × 1,000,000 ÷ 365 |
| Gas | MWh/day | MWh | Million m³/year × 1,000,000 × 0.01056 ÷ 365 |
BOE records are retained in the pipeline for traceability but excluded from all value calculations, as they represent mixed hydrocarbon streams that cannot be priced with a single unambiguous market price. Gas and condensate, oil and gas, and unknown fuel types are similarly excluded. Their combined omission is estimated to affect approximately 5 to 10 percent of total gross value.
Reference prices are set to closing market levels on 27 February 2026, the day before the conflict began: $70/bbl for oil (Brent) and $45/MWh for European TTF gas. These are pre-war baselines. The scenario calculator allows wartime prices to be applied separately.
Gross loss is computed as two distinct components valued on different price bases by design.
| Component | Formula | Price basis |
|---|---|---|
| Lost Revenue | Production × disruption % × duration (days) × TWAP | Time-weighted average disrupted price - barrels sold into the wartime market |
| Reserve Impairment | Reserves × impairment % | Pre-war price - permanent asset loss measured against the pre-conflict baseline |
Oil and gas markets are characterised by inelastic short-run supply and demand, meaning supply losses produce disproportionate price responses. A short-run price multiplier of approximately 6-7× per 1% of supply lost is applied for oil; approximately 10× for gas, reflecting the additional constraints of pipeline dependency and limited short-run switching. Duration multipliers moderate the average disruption price over time as strategic reserves, alternative supply, and demand adjustment take effect.
On 28 February 2026, the United States and Israel launched coordinated strikes against Iranian military and leadership targets, initiating the 2026 Iran war. Iran responded with missile and drone attacks targeting U.S. military installations, Gulf energy infrastructure, and commercial shipping in the Strait of Hormuz. As of the date of this report, the conflict is in its third week with no ceasefire in effect.
This report estimates the gross economic loss and exposure associated with damage to U.S. military infrastructure and physical destruction of regional energy supply across the Middle East. The analysis evaluates a baseline scenario and presents scenario comparisons illustrating how gross economic loss scales under alternative conflict assumptions. It does not attempt to forecast the duration, escalation, or outcome of the conflict.
| Finding | Headline Result |
|---|---|
| Price Redistribution | The same price shock that costs China $6.22bn net earns the USA $72.44bn and Russia $28.64bn - with no additional physical damage to either country. |
| Symmetric Escalation | Under matched escalation severity, the GCC absorbs $111.35bn in gross energy losses vs $69.84bn for Iran - approximately 1.6× - due to greater production scale. |
| Dual Chokepoint Closure | Simultaneous Hormuz and Red Sea closure multiplies gross exposure 4.4× over a Hormuz-only baseline, from $22.29bn to $97.20bn. |
| Defence Alignment Gap | Saudi Arabia ranks first for energy exposure and last for U.S. military presence - a rank delta of −7. Oman has the highest exposure-to-military ratio at 103:1. |
| Country Concentration | Saudi Arabia ($6.06bn) and Iran ($6.05bn) are the two largest single-country energy exposures. Kuwait is the primary co-location risk. |
| Duration vs Intensity | A 180-day moderate conflict (Scenario B, $95bn) generates ~30% higher total exposure than a 45-day high-intensity conflict (Scenario A, $73bn). |
| Binding Escalation Lever | Duration is the dominant single lever: extending conflict to 180 days produces $63.51bn vs $42.90bn for intensity escalation and $38.94bn for price escalation. |
| $1 Trillion Stress Case | Trillion-scale exposure ($1.04T) requires simultaneous extreme assumptions across all parameters. Reserve impairment alone cannot reach this threshold. |
All physical damage parameters are held constant. Only energy prices change, reflecting today's market prices versus pre-conflict levels - Brent crude $102/bbl, TTF gas ~$57/MWh.
| Metric | USA | Russia | China |
|---|---|---|---|
| Oil Production (bbl/day) | 12,614,992 | 5,690,833 | 2,875,767 |
| Oil Windfall (60 days) | $24.22bn | $10.93bn | $5.52bn |
| Gas Windfall (60 days) | $48.22bn | $17.71bn | $7.34bn |
| Import Cost (60 days) | N/A | N/A | −$19.08bn |
| Net Position (60 days) | +$72.44bn | +$28.64bn | −$6.22bn |
Parameters are held symmetric across both sides. The asymmetry in outcomes is structural, not parametric - the GCC has a larger production base and more energy assets at risk.
| Metric | Baseline Iran | Baseline GCC | Escalation Iran | Escalation GCC |
|---|---|---|---|---|
| Total Energy Exposure | $6.05bn | $11.60bn | $69.84bn | $111.35bn |
| Oil Destroyed Value | $3.38bn | $8.14bn | $32.61bn | $78.45bn |
| Gas Destroyed Value | $2.67bn | $1.94bn | $37.23bn | $27.02bn |
| U.S. Base Damage (Gross) | N/A | $11.01bn | N/A | $27.52bn |
The Houthi Red Sea closure is treated as a direct extension of the Iran war scenario, not an independent event.
| Metric | Baseline (Hormuz) | Dual Chokepoint | Incremental |
|---|---|---|---|
| Total Energy Exposure | $22.29bn | $97.20bn | +$74.91bn |
| Oil Destroyed Value | $15.59bn | $66.81bn | +$51.22bn |
| Gas Destroyed Value | $5.02bn | $27.97bn | +$22.95bn |
| Oil Reserve Exposure | $1.65bn | $2.36bn | +$0.71bn |
A negative rank delta indicates military presence ranks lower than energy exposure - a structural defence gap.
| Country | Total Energy Exp. | Energy Rank | Base Damage Cost | Military Rank | Rank Delta |
|---|---|---|---|---|---|
| Saudi Arabia | $6.06bn | 1 | $0 | 8 | −7 |
| Qatar | $2.20bn | 2 | $1.31bn | 2 | 0 |
| UAE | $2.19bn | 3 | $228.81M | 4 | −1 |
| Oman | $1.13bn | 4 | $10.94M | 7 | −3 |
| Kuwait | $1.02bn | 5 | $8.84bn | 1 | +4 |
| Bahrain | $122.83M | 7 | $626.82M | 3 | +4 |
| Metric | Reference Case | Scenario A (Short, Hard) | Scenario B (Long, Moderate) |
|---|---|---|---|
| Energy Output Destroyed | 20% | 50% | 20% |
| Days of Destruction | 60 | 45 | 180 |
| Oil Price ($/bbl) | $70 | $120 | $100 |
| Total Energy Exposure | $22.29bn | $73.37bn | $95.04bn |
| Oil Destroyed Value | $15.59bn | $50.11bn | $66.81bn |
| Gas Destroyed Value | $5.02bn | $17.48bn | $25.82bn |
Single-variable design: all but one parameter held at baseline, isolating each lever and ruling out interaction effects.
| Metric | Baseline | Duration ×3 | Price Escalation | Intensity ×2 |
|---|---|---|---|---|
| Days / Price / Output | 60d / $70 / 20% | 180d / $70 / 20% | 60d / $120 / 20% | 60d / $70 / 40% |
| Total Energy Exposure | $22.29bn | $63.51bn | $38.94bn | $42.90bn |
| Oil Destroyed Value | $15.59bn | $46.77bn | $26.72bn | $31.18bn |
| Gas Destroyed Value | $5.02bn | $15.06bn | $9.32bn | $10.04bn |
| Parameter | Test A | Test B | Test C | Test D | Test E |
|---|---|---|---|---|---|
| Output Destroyed | 20% | 20% | 20% | 20% | 50% |
| Days | 60 | 60 | 60 | 60 | 365 |
| Reserve Impairment | 0.1% | 0.5% | 1% | 2% | 2.9% |
| Oil Price ($/bbl) | $70 | $70 | $70 | $70 | $200 |
| Total Energy Exposure | $22.29bn | $29.03bn | $37.46bn | $54.31bn | $1.04T |
| Exceeds $1 Trillion? | N | N | N | N | Y |
The scenario calculator is linear by construction. Every variable acts independently, scales linearly, and has no thresholds, feedbacks, or state dependence. Single-parameter sensitivity tests are therefore tautological - they confirm that more of X produces proportionally more Y, and are retained for coefficient transparency only. The analytical value in this report derives from findings that test combinations, reveal structural asymmetry, and generate conclusions that would not be obvious without the model: the simultaneity questions, the threshold analysis, and the geographic concentration findings. The model is a scenario accumulator, not a dynamic system, and it is used accordingly.
The baseline parameter set uses 20% base damage, 20% energy output destroyed, 60 days of disruption, 0.10% reserve impairment, $70/bbl oil, and $35/MWh gas. The 60-day baseline reflects the midpoint of the Defence Intelligence Agency's assessed Strait of Hormuz closure range of one to six months, consistent with Dallas Fed base case modelling. Oil and gas prices are pre-war references set to closing market levels on 27 February 2026, the day before the conflict began.
The geographic scope covers the Middle East: Bahrain, Iran, Iraq, Israel, Kuwait, Oman, Qatar, Saudi Arabia, UAE, and Yemen for energy assets; Bahrain, Israel, Jordan, Kuwait, Oman, Qatar, Saudi Arabia, and UAE for U.S. military presence. Egypt and Turkey are excluded from the military scope; Iran, Iraq, and Yemen carry no U.S. base replacement value by design. Iran reserve impairment is modelled at zero by design; total Iranian losses may be understated as a result.
The scope of oil production in this analysis includes "oil" and "crude oil" fields only, as classified in the Global Oil and Gas Extraction Tracker (GEM). Fields categorised as oil and gas or other mixed hydrocarbon streams are excluded by design, as these categories cannot be priced consistently against a single oil benchmark without additional allocation assumptions. Widely cited global oil production figures of around 100 million barrels per day typically refer to aggregated “all liquids” measures, which include a range of mixed hydrocarbon streams. As a result, reported oil volumes in this analysis represent a conservative, price‑consistent subset of commonly reported headline figures and should not be interpreted as “all liquids” production.
This scenario calculator treats all fields within a selected country as uniformly affected by the damage percentage. In practice, damage would be geographically concentrated and affect fields differently based on proximity to conflict zones, hardening, and redundancy. A more granular framework would assign damage probabilities at field level.
Reserve impairment represents permanent destruction or inaccessibility of reserves through wellhead destruction, reservoir pressure loss, or long-term field abandonment. The 0.10% baseline reflects the upper realistic bound based on historical precedent - Kuwait 1991, the most destructive documented conflict, produced less than 0.10% permanent reserve loss. Analysts should carefully distinguish between temporary production loss and permanent reserve impairment when interpreting outputs.
All dollar figures represent gross economic loss or gross economic exposure valued at reference prices, prior to adjustment for costs, substitution, market response, or recovery. They do not represent net, realised, discounted, or compensable economic losses. Production loss and reserve exposure represent different time horizons and should not be summed as a single realised loss figure.