Industry: Oil and Gas Exploration Product: ModelRisk Application: Pipeline Throughput Optimization Under Field and Capacity Uncertainty
A gathering system's nameplate is the headline number; the tariff agreement is the number that pays. A North American midstream operator running a 300,000 bbl/d crude pipeline serving five upstream fields had committed under a take-or-pay structure to deliver at least 85% of nameplate — 255,000 bbl/d — on a rolling monthly average. The deterministic forecast summed the five field mid-cases and got 280,000 bbl/d, comfortably above the floor. The probabilistic re-run in ModelRisk, shown above, told a different story: a mean daily throughput of only 259,000 bbl/d and a breach of the 85% floor on roughly a third of days, with a P10 of 237,000 bbl/d. A "comfortable" plan was demonstrably not.
The team rebuilt throughput as a stochastic sum of five field-level processes feeding a capacity-constrained pipeline, ran 10,000 simulated days, and used the output to size the next \$180M capacity expansion against the actual distribution of demand rather than the average.
Daily throughput is
\[ \text{Throughput} = \min\Bigl(\sum_{i=1}^{5} Q_i \cdot U_i \cdot (1 - C_i),\; \text{PipeCapacity}\Bigr) \]
where each input is uncertain:
The cap term \(\min(\cdot, \text{PipeCapacity})\) is what introduces a real non-linearity: on good days the pipeline is the binding constraint and gains from field outperformance are lost.
Returning to the distribution above: the deterministic plan said 280k bbl/d, but the simulation reports a mean of only 259k bbl/d, with a P10 of roughly 237k bbl/d and a P90 of 290k bbl/d. Most importantly, the probability that daily throughput falls below the 85% tariff floor (255k bbl/d) is roughly 33%. A plan written against the deterministic sum is a plan that breaches the take-or-pay one day in three.
The visible spike at zero days reflects the 2% planned-outage probability — separated from the operating distribution rather than averaged in, because the regulatory metric for the tariff is computed on operating days only.
Field A (mature, conventional) is the largest contributor at about 89k bbl/d mean with a P10-P90 of about 26k bbl/d. Field B (new wells, still ramping) has a smaller mean — about 67k bbl/d — but its P10-P90 spans roughly 35k bbl/d, the widest of any field. This concentration of variance in Field B is the throughput plan's structural exposure: when Field B has a bad week, the tariff floor is at risk regardless of how well the other four fields do.
The ranking confirms what the per-field chart hints at: Field B production variability is the single largest driver of throughput uncertainty (~±18,000 bbl/d half-spread), with pipeline uptime second and Field A decline-rate uncertainty third. Field E end-of-life timing is a fifth-place driver — not negligible, but not where the next dollar of de-risking spend belongs.
The headline question was whether to upgrade the pipeline to 360k bbl/d nameplate (a \$180M project) alongside Field B's planned 15-well drilling program. Re-running the full simulation with both changes:
That last finding was the project's most consequential output: the commercial team had been pricing the upgrade against the assumption that 85% utilization would hold by analogy with the current pipe. The simulation showed it would not — the upgraded mean of 291k bbl/d sits at only 81% of the new nameplate — which changed the take-or-pay negotiation with shippers and the project IRR by roughly two points.
The deterministic sum of five mid-case field forecasts is a number with no probability attached. The 85%-tariff-floor breach rate is a probability with consequences. ModelRisk is what gets the operator from the first number to the second — and from there to a capacity-upgrade decision sized to the demand that will actually show up.