Title: Oil Initially in Place (OIIP): What It Is, Why It Matters, and Practical Steps to Estimate It
Overview
Oil initially in place (OIIP or OIP) is the estimated total volume of crude oil contained in a subsurface reservoir before production begins. OIIP is a volumetric, geological estimate of the oil physically present in the pore space of the reservoir rock — not the amount that can be produced. Estimating OIIP is the first crucial step in valuing a prospect and planning field development; subsequent analysis converts OIIP into recoverable reserves and commercial decisions.
Key terms
– OIIP / OIP: Oil Initially in Place (the total oil in the reservoir at discovery).
– STOIIP: Stock Tank Oil Initially in Place — OIIP expressed as stock-tank barrels at surface pressure and temperature.
– OGIP: Original Gas in Place (for gas reservoirs).
– HCIIP: Hydrocarbons Initially in Place (generic term including oil and gas).
– Recovery Factor (RF): The percentage of OIIP that can be recovered using available methods.
– Formation Volume Factor (Bo): Ratio of reservoir oil volume to surface oil volume (bbl reservoir per STB). Used to convert reservoir barrels to stock-tank barrels.
Why OIIP matters
– Signals the resource potential of a reservoir and guides further technical and economic evaluation.
– Provides the starting point for estimating recoverable volumes (reserves) and designing development plans.
– Influences lease valuation, investment decisions, and timing of production depending on economics (price, costs, technology).
– Regularly re-evaluated as more data arrive (wells, core, production) and as oil prices or recovery technology change.
What data and measurements are needed
– Areal extent (A) of the hydrocarbon-bearing zone (acres or square meters).
– Net pay or net reservoir thickness (h) — thickness of rock that contains producible hydrocarbons (ft or meters).
– Porosity (φ) — percentage/decimal fraction of pore space available to hold fluids (from core, logs).
– Water saturation (Sw) — fraction of pore space filled by water; thus hydrocarbon saturation = (1 − Sw).
– Formation volume factor (Bo) — from PVT lab analysis (reservoir bbl per stock-tank bbl).
– Reservoir mapping (seismic, well logs) to define geometry and heterogeneity.
– Calibration data (core measurements, well tests, production data) to reduce uncertainty.
Volumetric formula and step-by-step calculation (industry-standard)
The common industry volumetric formula for STOIIP (in stock-tank barrels) is:
STOIIP = 7758 × A × h × φ × (1 − Sw) / Bo
Where:
– STOIIP is stock-tank barrels (STB)
– 7758 is the conversion factor from acre-feet to barrels (1 acre-ft = 7758 bbl)
– A is area in acres
– h is net pay thickness in feet
– φ (phi) is porosity (fraction, e.g., 0.20)
– Sw is water saturation (fraction)
– Bo is formation volume factor (reservoir bbl per STB)
Example calculation
Assume: A = 640 acres (1 sq mile), h = 50 ft, φ = 0.20, Sw = 0.25, Bo = 1.20
1. Hydrocarbon pore volume (acre-ft): A × h × φ × (1 − Sw) = 640 × 50 × 0.20 × 0.75 = 4,800 acre-ft
2. Convert to reservoir barrels: 4,800 × 7,758 = 37,238,400 reservoir bbl
3. Convert to stock-tank barrels: 37,238,400 / 1.20 ≈ 31,031,999 STB
So STOIIP ≈ 31.0 million stock-tank barrels.
Converting OIIP to recoverable volumes
Use a recovery factor (RF) to estimate recoverable oil:
Recoverable oil (STB) = STOIIP × RF
Example: If RF = 30%, recoverable ≈ 31.0 million × 0.30 = 9.3 million STB.
Typical RF considerations (general guidance)
– RF depends on reservoir type, drive mechanism, rock and fluid properties, and applied recovery methods.
– Tight/shale reservoirs: RF often only a few percent unless stimulated (fracturing) and produced with special methods.
– Conventional reservoirs: primary + secondary (e.g., waterflood) recovery commonly ranges from low double digits up to ~40% in favorable cases; enhanced oil recovery (EOR) can raise RF further.
– RF is highly case-specific; treat ranges as indicative and justify estimates with engineering studies.
Practical step-by-step workflow for estimating OIIP and deciding on development
1. Define the objective and level of uncertainty acceptable (scoping vs. reserves booking).
2. Gather geological and petrophysical data: cores, well logs, seismic interpretation, structural maps.
3. Map the reservoir: define areal extent, identify net pay, and create thickness maps.
4. Measure/estimate porosity and water saturation:
– Core analysis (best), calibrated with well logs (density, neutron, resistivity).
– Use multiple methods and cross-checks to identify bias.
5. Determine fluid properties and Bo from PVT analyses or analogs if lab data unavailable.
6. Compute STOIIP using the volumetric formula; perform this deterministically for a base case (P50) and for alternative scenarios (low/high).
7. Quantify uncertainty:
– Perform sensitivity analysis on area, h, φ, Sw, Bo.
– Use probabilistic methods (Monte Carlo) to produce P90/P50/P10 estimates.
8. Estimate recovery factors:
– Base RF on analogous fields, drive mechanism, and planned recovery methods (primary, waterflood, EOR).
– Run reservoir simulations if data allow to estimate production profiles and RF.
9. Convert recoverable volumes to economic analysis:
– Forecast production rates, cash flows, capital and operating costs.
– Evaluate break-even prices, NPV, IRR under scenarios and sensitivity to oil price.
10. Make decisions:
– Prioritize drilling and development where expected returns are acceptable.
– Consider phasing development, pilot tests, or deferring prospects until technology or prices improve.
11. Update estimates as new data arrive (wells, production history, pressure data) and adjust RF and reserves.
Uncertainty management and best practices
– Use multiple independent data sources (core, logs, seismic) and reconcile discrepancies.
– Calibrate volumetric estimates with flow test and pressure data when available.
– Adopt probabilistic approaches for early-stage prospects; deterministic single-point estimates can mislead.
– Document assumptions and ranges for each input parameter.
– Re-evaluate OIIP and RF over the life of the asset as production and new data reduce uncertainty.
Common pitfalls to avoid
– Treating OIIP as synonymous with reserves; they are distinct concepts.
– Overreliance on single-point estimates (no sensitivity analysis).
– Using uncalibrated porosity or Sw values from only one well across a heterogeneous reservoir.
– Ignoring formation volume factor variability with pressure and temperature.
– Failing to incorporate economic constraints when converting recoverable volumes into development decisions.
When to use more advanced methods
– If the reservoir is heterogeneous or complex, use dynamic reservoir simulation calibrated to test or production data to estimate recoverable volumes and timing.
– Use seismic inversion and geostatistics to better quantify spatial uncertainty in porosity and saturation.
– Consider pilot EOR tests before committing full-field EOR projects.
Checklist for an OIIP evaluation (quick reference)
– Reservoir map and net pay thickness established
– Porosity and Sw estimates with data sources and uncertainty ranges
– PVT data or reliable analogs for Bo
– STOIIP calculated for base, low, and high cases
– Recovery factor range justified and documented
– Probabilistic analysis (if early-stage) or reservoir simulation (if mature)
– Economic sensitivity analysis (price, costs, timing)
– Plan to update with new wells/production tests
Sources and further reading
– Investopedia — “Oil Initially in Place (OIIP)” (source provided): https://www.investopedia.com/terms/o/oil-initially-in-place.asp
– U.S. Energy Information Administration (EIA) — glossary and resources on reserves and resources
– Society of Petroleum Engineers (SPE) — papers and recommended practices on volumetric estimation, reserves classification, and uncertainty analysis
Final note
OIIP is a vital geological starting point for oil-field evaluation but must be combined with realistic recovery factors, reservoir engineering, and economic analysis to determine whether development is commercial. Regular re-assessment and transparent handling of uncertainty are essential to good decision-making.