Industry: Oil and Gas Exploration Product: ModelRisk Application: Reservoir Management — STOOIP, Recoverable Reserves, and EOR Decisions
The SPE Petroleum Resources Management System asks for reserves to be booked as P10/P50/P90 — the low, best, and high estimates. The framework exists for a single reason: subsurface uncertainty is irreducible, and a single deterministic STOOIP number is therefore a fiction. Yet many mature-field reservoir-management decisions — infill drilling, enhanced oil recovery (EOR), and abandonment timing — are still made against that fictional single number.
A multinational operator with a mature onshore basin asset rebuilt its reserves and EOR-decision workflow in ModelRisk. The volumetric distribution above tells the story: a P50 STOOIP of 100 MMstb is bracketed by a P10 of 57 and a P90 of 163 — close to a factor-of-three spread that the deterministic mid-case (101 MMstb) had collapsed to a single number. With the full distribution in hand, the team could finally test a WAG (water-alternating-gas) EOR project against the range of subsurface outcomes rather than the point estimate — and stage the capital commitment accordingly.
The volumetric STOOIP equation is
\[ \text{STOOIP} = \frac{7758 \cdot A \cdot h \cdot \phi \cdot (1 - S_w) \cdot (N/G)}{B_o} \;\;\text{(stb)} \]
with every term except the conversion constant 7758 carrying real uncertainty:
Crucially, porosity \(\phi\) and permeability \(k\) are jointly correlated (ρ ≈ 0.55, calibrated to core data) using a Gaussian copula. Permeability is itself LogNormal because of its multi-order-of-magnitude range. Independent sampling of \(\phi\) and \(k\) — the textbook default — generated impossible combinations (high \(\phi\) with negligible \(k\)) that distorted the recovery-factor distribution downstream.
Reading the distribution that opened this study, the simulation produces a P10 of about 57 MMstb, P50 of 100 MMstb, P90 of 163 MMstb, and mean near 106 MMstb. The deterministic "mid-case" computed term-by-term (the spreadsheet legacy) lands at 101 MMstb — close to the P50 — but the spread around it is the number that drives reserves-booking and capital decisions. The right-skew comes principally from porosity, area, and the porosity-permeability correlation flowing through to recovery factor.
Porosity is the single largest driver of STOOIP spread, followed by net thickness and area. Net-to-gross, water saturation, and \(B_o\) are second-tier. The ranking pointed the next subsurface-data dollar at core analysis to tighten porosity — a multi-well coring program with an expected porosity sigma reduction from 0.04 to 0.02. The model showed this would compress the P10-P90 STOOIP spread by roughly 25%, with a corresponding tightening of the booked-reserves range — a value-of-information case that justified the \$8M coring spend on its own.
The modeled correlation matrix imposes physically realistic structure: porosity and permeability move together (a realized Pearson correlation of about 0.39 on the matrix, from a rank-correlation target of 0.55 in the copula), and recovery factor co-varies strongly with permeability (≈ 0.73) because high-perm rock drains more completely. Setting these off-diagonals to zero — the legacy assumption — produces a recoverable-reserves distribution with the right mean but the wrong tails: the joint events "high \(\phi\), low \(k\), high RF" and "low \(\phi\), high \(k\), low RF" appear in the independent sample but are physically improbable, and they fatten both tails. The EOR-decision NPV is itself sensitive to the tail shape, so the correlation structure feeds directly into the next chart.
A WAG EOR project, capex \$250M, was projected to lift the recovery factor by Triangular(4%, 8%, 16%) — roughly 9% of STOOIP in additional recoverable reserves. The economic comparison:
The mean uplift is about \$2.4B, and crucially the P10 lifts too — by roughly \$0.9B — because the incremental EOR barrels are worth more than the modest \$250M capex even in the low-side reservoir cases. The two CDFs barely cross: EOR beats primary-only in 99.8% of futures. This is not a marginal coin-flip; on expected-value grounds the EOR project is clearly accretive, and the deterministic P50 comparison ("EOR adds NPV") understated rather than overstated the case.
What the distribution does sharpen is the timing question. The EOR uplift correlates with the same uncertain inputs that drive primary recovery — when the reservoir disappoints, the EOR uplift disappoints in step — so the spread of the EOR case is wider than the primary case even though its floor is higher. The decision the team took was therefore conditional on information, not on sign: approve the EOR engineering and procurement spend (\$45M) now, defer the major capex commitment (\$205M) by 18 months until the first phase of infill-well data tightens the porosity distribution. Re-running the simulation with a tighter porosity sigma showed the P10 NPV improvement from waiting was roughly \$300M — more than enough to justify the time-value loss.
Subsurface uncertainty does not collapse to a single number — and the field decisions that ride on it (EOR, infill, abandonment) are richer if the uncertainty is preserved. ModelRisk is what keeps the P10 and the P90 in the conversation alongside the P50, and what turns a "go" or "no-go" EOR decision into "go now for \$45M, decide the rest in 18 months" — a richer answer that the deterministic comparison cannot produce.