Key takeaways
– Estimated Ultimate Recovery (EUR) is an estimate of the total quantity of hydrocarbons that will be produced from a well, reservoir, or field over its productive life (including what’s already produced).
– EUR underpins commercial decisions: reserve classification, project valuation (NPV), investment go/no-go, and fiscal reporting.
– EUR can be derived several ways — volumetric calculation, decline‑curve analysis, reservoir simulation, and probabilistic aggregation — and each method has different data needs and uncertainty characteristics.
– EUR and reserve classifications can change with technology, production performance, and oil/gas prices; operators must update estimates as new data arrive.
What is Estimated Ultimate Recovery (EUR)?
EUR is an engineering estimate of the total recoverable volume of hydrocarbons from a specified asset over its lifetime. It combines what has already been produced with the forecast of future production. In practice EUR is used interchangeably with “recoverable reserves” when referring to the volume expected to be produced using current technology and under assumed economic conditions. Reserve categories (proved, probable, possible) reflect the degree of confidence that the EUR will be recovered. (See SPE definitions for standard classification practice.)
Why EUR matters
– Economic valuation: EUR is a core input to discounted cash‑flow (DCF) models and NPV calculations; without an EUR, credible valuation is impossible.
– Investment decisions: operators and investors use EUR to judge whether development costs can be recovered and what return will be achieved.
– Portfolio and reserve reporting: companies disclose reserves and EUR estimates to investors and regulators; changes affect market valuation.
– Field management: EUR informs development strategy (well spacing, enhanced recovery), timing, and capital allocation.
Main methods to estimate EUR
1. Volumetric (static) method
– Use geological and petrophysical data to estimate hydrocarbon volume in place (OOIP/OGIP), apply an estimated recovery factor to get recoverable volume.
– Best for pre‑production or early appraisal where production history is limited.
– Key inputs: reservoir area and thickness, porosity, net:gross, water saturation, formation volume factor, recovery factor.
2. Decline‑curve analysis (DCA)
– Fit a decline model to production history and extrapolate future production to recoverable limit.
– Common decline types: exponential (b = 0), hyperbolic (0 < b < 1), harmonic (b = 1) — Arps’ equations are widely used.
– Strengths: directly uses production data; practical for producing wells/fields.
– Key requirement: sufficient, representative production history; results sensitive to choice of decline model and changes in operating conditions.
3. Reservoir simulation (numerical flow simulation)
– Build dynamic 3D models using geological framework, rock and fluid properties, well locations, and production/injection schemes; simulate future production under scenarios.
– Can model complex physics (pressure interference, water/gas breakthrough, EOR).
– Data intensive and computationally costly but often most realistic for complex reservoirs or for optimization.
4. Probabilistic methods and Monte Carlo
– Treat key inputs (e.g., OOIP, recovery factor, decline parameters) as distributions and simulate many possible outcomes to produce P10/P50/P90 EUR estimates.
– Provides uncertainty ranges and confidence levels useful for reserves classification (for example, P90 ~ proved).
Basic formulas (conceptual)
– Exponential decline (Arps, b = 0): q(t) = qi * e^(−D*t). Total recoverable from start to infinite time: EUR = qi / D.
– General Arps hyperbolic: q(t) = qi / (1 + b*Di*t)^(1/b). Cumulative production expressions exist; for b < 1 the ultimate theoretical UR = qi^(1−b) / ((1 − b) * Di). (Use with care — model assumptions must fit data.)
Note: these are idealized expressions. Real EUR uses cumulative produced to date + forecasted remaining cumulative production from the model.
Practical, step‑by‑step procedure to estimate EUR
1. Define scope and objective
– What asset (well, interval, field)? Which fluids (oil, gas, NGL)? What confidence level (P50, P90)?
– Specify economic assumptions (price case, costs) if reserves classification required.
2. Gather and QC data
– Production history (rates, cumulative volumes), well tests, pressures.
– Geological maps, logs, core data, porosity, saturation, PVT data.
– Facilities/constraints, injection schemes, past workovers.
3. Select the primary estimation method(s)
– Early stage / no history → volumetric + probabilistic.
– Producing wells with stable history → decline‑curve analysis (exponential/hyperbolic).
– Complex reservoirs, enhanced recovery, or interaction effects → reservoir simulation.
– Often combine methods to cross‑check results.
4. Build the model and calibrate
– For volumetric: compute hydrocarbon in place and apply recovery factor ranges (informed by analogs or pilot data).
– For DCA: fit decline model(s) to historical production. Test multiple decline types and determine best statistical fit and engineering plausibility.
– For simulation: history‑match the model to production and pressure data; adjust uncertain parameters within realistic ranges.
5. Quantify uncertainty
– Run scenarios (low/expected/high) and use probabilistic techniques (Monte Carlo) to produce P10/P50/P90 EUR distributions.
– Sensitivity analysis on key inputs: recovery factor, decline rates, reserves cutoffs, price.
6. Apply economic screening
– For reserves classification, apply economic limits (e.g., when production becomes uneconomic at assumed price and costs) and calculate commercially recoverable portion of EUR.
– Compute NPV under chosen discount rate and price scenarios.
7. Document, report and classify
– Record assumptions, input data, model structure, fit statistics and uncertainties.
– Classify reserves per industry guidelines (e.g., SPE/WPC/SEC frameworks) — convert probabilistic outputs to proved/probable/possible as required.
– Include rationale for recovery factors, decline model choices, and any adjustments.
8. Monitor and update
– Reconcile forecasts with actual production; update model and EUR as new data arrive.
– Reclassify reserves when warranted by performance, technology changes, or price shifts.
Common pitfalls and caveats
– Short or unrepresentative production history can give misleading DCA extrapolations (transient behavior, rate changes, well interference).
– Ignoring operational changes (e.g., choke changes, compression, workovers) can bias decline model fits.
– Overly optimistic recovery factors or ignoring economic limits leads to inflated EUR and reserves.
– Using a single deterministic number hides uncertainty; always present ranges and confidence levels.
– Decline models assume continuity of reservoir behavior; abrupt changes (water breakthrough, gas coning) invalidate simple extrapolations.
How EUR links to reserve classification and economics
– EUR is the technical estimate; reserves classification (proved, probable, possible) adds confidence levels and economic criteria.
– Proven reserves (1P) generally correspond to a high‑confidence portion (e.g., P90) of the EUR that is commercially recoverable under current conditions.
– As prices rise, or new recovery methods become available, probable/possible volumes can convert into proved reserves because the economic breakeven and technological feasibility change.
Example (simple DCA sketch)
– Suppose a well has qi = 1,000 barrels/day and an exponential decline D = 0.2 yr−1. The theoretical total recoverable from start = qi / D = 1,000 / 0.2 = 5,000 barrel‑years, but to get volume units convert: for exponential the integrated volume = qi / D = 5,000 barrels. If the well has already produced 500 barrels, remaining EUR = 4,500 barrels. (This is illustrative; ensure consistent time units and data scaling in real cases.)
Reporting and standards
– Use accepted industry standards and definitions (e.g., Society of Petroleum Engineers’ Petroleum Reserves Definitions) when classifying and reporting reserves and EUR.
– Keep transparent documentation that allows auditors, partners, and regulators to understand assumptions and uncertainties.
Further reading and sources
– Investopedia: “Estimated Ultimate Recovery (EUR)” — overview and industry context. (Source provided)
– Society of Petroleum Engineers: Petroleum Reserves Definitions (archived 1997) — authoritative reserve classification guidance.
– Arps, J.J., 1945. “Analysis of Decline Curves.” Transactions of the AIME — foundational work on decline curve analysis.
If you want, I can:
– Walk through an actual EUR calculation for your well or field using your production and petrophysical data;
– Build a simple DCA forecast (exponential/hyperbolic) from a production history you provide;
– Show a Monte Carlo example to produce P10/P50/P90 EUR ranges.