EOR Screening Definition / Meaning
EOR Screening (Enhanced Oil Recovery Screening) is the systematic, multi-stage process of evaluating and selecting the most technically and economically viable enhanced oil recovery (EOR) method for a specific reservoir. It is a critical early-phase activity within reservoir management and improved recovery workflows, performed after primary and secondary (waterflood) recovery potentials have been exhausted or are nearing their economic limit. The goal is to identify which EOR technique—such as gas injection (CO2, hydrocarbon, nitrogen), chemical flooding (polymer, surfactant, alkaline), thermal methods (steam injection, in-situ combustion), or microbial EOR—has the highest probability of success given the reservoir’s unique rock and fluid properties.
Purpose and Importance
EOR screening prevents costly missteps by filtering out incompatible technologies early. A reservoir that is too tight for polymer flooding, too deep for steam injection, or lacking sufficient residual oil saturation for any EOR process will be flagged. The screening process also provides a ranked list of candidate methods, allowing operators to focus detailed simulation and pilot studies on the most promising options. In practice, EOR screening is often the first step in a stage-gate decision process, where each successive phase (screening, laboratory testing, simulation, pilot, full-field implementation) requires a go/no-go decision based on technical and economic criteria.
Key Screening Criteria
EOR screening relies on a set of established technical criteria, often derived from industry databases (e.g., the U.S. Department of Energy’s EOR database) and published correlations. The most common parameters include:
| Parameter | Typical Range for Successful EOR | Why It Matters |
|---|---|---|
| Oil Viscosity (cP) | < 10 (miscible gas); < 100 (polymer); > 100 (thermal) | High viscosity reduces sweep efficiency; thermal methods reduce viscosity by heating. |
| Oil Gravity (°API) | > 25 (miscible gas); 15–25 (chemical); < 20 (thermal) | Lighter oils are more amenable to miscible displacement; heavy oils require heat. |
| Reservoir Depth (ft) | < 3,000 (thermal); 2,000–8,000 (gas); < 5,000 (chemical) | Depth affects pressure, temperature, and injection costs; shallow reservoirs lose heat quickly. |
| Permeability (mD) | > 10 (polymer); > 50 (thermal); > 1 (miscible gas) | Low permeability restricts injectivity and causes high pressure drops. |
| Residual Oil Saturation (Sor) | > 25% PV | Higher remaining oil ensures enough target for EOR to be economic. |
| Clay Content | < 10% (chemical); variable for others | Clays can adsorb chemicals (especially polymers and surfactants), reducing effectiveness. |
Screening Methodology
The process typically follows these steps:
- Data Collection: Gather all available reservoir data: PVT (pressure-volume-temperature) reports, core analysis, well logs, production history, and fluid samples.
- Quick Filtering: Apply binary (yes/no) criteria from published screening guides (e.g., Taber’s screening criteria) to eliminate clearly unsuitable methods. For example, if oil viscosity is 500 cP, thermal methods pass but miscible gas fails.
- Scoring and Ranking: Use a weighted scoring system (e.g., 0–10 scale) for each criterion, then sum scores for each EOR method. The highest-scoring methods are shortlisted.
- Economic Screening: Perform a preliminary economic analysis using estimated capital and operating costs, oil price, and recovery factor assumptions. This step often uses discounted cash flow (DCF) or net present value (NPV) calculations.
- Risk Assessment: Identify key uncertainties (e.g., injectivity, chemical stability, gas availability) and assign a risk level. Methods with high technical risk may be deprioritized.
Common EOR Screening Tools
- Spreadsheet-based screening matrices: Simple, customizable, and widely used in early-stage evaluations.
- Expert systems and decision trees: Rule-based software that mimics the logic of experienced reservoir engineers.
- Machine learning models: Emerging tools that use historical EOR project data to predict success probability based on reservoir attributes.
- Commercial software packages: Integrated platforms (e.g., CMG, Schlumberger EOR screening modules) that combine screening with simulation capabilities.
Limitations and Best Practices
EOR screening is a first-pass evaluation and cannot replace detailed laboratory experiments or reservoir simulation. It assumes generic correlations that may not hold for unconventional or highly heterogeneous reservoirs. Best practices include:
- Updating screening criteria with field-specific data whenever possible.
- Including a sensitivity analysis to account for uncertainty in key parameters.
- Using multiple screening methods (e.g., both rule-based and statistical) to cross-validate results.
- Documenting assumptions and data sources for auditability.
Usage Example
“After completing the EOR screening for the North Sea Alpha field, the team identified CO2 miscible flooding as the top candidate, with a score of 8.2 out of 10, followed by polymer flooding at 6.5. The screening also flagged high clay content as a risk for chemical methods, leading to a decision to proceed with CO2 coreflood tests.”
Conclusion
EOR screening is an indispensable tool in the reservoir engineer’s arsenal, enabling efficient allocation of resources toward the most promising improved recovery technologies. When performed rigorously, it reduces project cycle time, minimizes financial risk, and increases the likelihood of a successful EOR implementation.