Reservoir Characterization and Geomodeling Study Notes

Reservoir Characterization and Geomodeling

Instructor: Dr. AKM Eahsanul Haque
Outlines

  • 1. Objectives

  • 2. Geological models versus Reservoir models

  • 3. Cellular models

  • 4. Data integration & Geological characterization workflow

  • 5. Reservoir uncertainties

  • 6. Overview on seismic interpretation

  • 7. Qualitative well-log interpretation

  • 8. Reservoir characterization

    • - Facies analysis ‐ Clastic environments

  • 9. Rock typing

  1. Principles of petrophysics

  1. Reservoir heterogeneities

  1. Reservoir characterization

  1. Reservoir modeling

  1. Conclusion

5. Reservoir Uncertainties

About Uncertainties

  • Introduction to Uncertainties: Uncertainty is inherent in reservoir characterization and can impact decision-making and project outcomes.

Uncertainties: Two Approaches

  • Deterministic: Involves fixed values and leads to a single outcome.

  • Probabilistic: Uses probability distributions to capture uncertainties leading to multiple possible outcomes.

Managing Uncertainty

  • Reservoir Model and Uncertainties: Two categories of uncertainties in reservoir modeling are:

    • Static Uncertainties: Include uncertainties related to structural, stratigraphy, and petrophysical aspects.

    • Dynamic Uncertainties: Evolve during production and often relate to the flow of fluids within the reservoir.

Uncertainties in Reservoir Characterization

  • Uncertainties are Ubiquitous: Complex physics, lack of data, and various factors can compound to create uncertainties.

  • Consequences: Ignoring uncertainties can jeopardize projects due to unexpected events.

Assessing Uncertainties

  • Reservoir Outcomes:

    • Undersizing: May lead to missing potential resource recovery.

    • Oversizing: Can result in non-economical development.

  • One primary goal of reservoir monitoring is to reduce dynamic uncertainties through improved understanding and description of reservoirs.

Complexity in Reservoir Understanding

  • Describing and understanding reservoirs is complicated due to factors like:

    • Complex geology,

    • Data heterogeneity, etc.

Murphy's Laws

  • Deterministic Version: "If anything can go wrong, it will."

  • Probabilistic Version: "If there is a 50-50 chance that something goes wrong, then 9 times out of 10, it will!"

Types of Uncertainty

  1. Data Uncertainties: Relate to measurement precision and accuracy.

  2. Interpretation Uncertainties: Stem from how data is understood and utilized.

  3. Characterization Uncertainties: Arise during synthesis and integration of information.

  4. Modeling Uncertainties: Involve conceptual frameworks and numerical values applied in models.

  • Uncertainties are Additive: The total uncertainty increases as each step of the workflow progresses.

Managing Uncertainties at Workflow Steps

  • Uncertainties present during structural and geological modeling include:

    • Structural (Seismic): Migration, velocity model, picking faults & horizons, time-to-depth conversion.

    • Geology: Sedimentological concepts, porosity, permeability, fluid contacts.

    • Dynamics: Fault transmissivity, permeability barriers, fluid properties, and saturation functions.

Geological Characterization Uncertainties

  • The main uncertainties in geological data relate to:

    • Geological and sedimentological conceptual models.

    • Petrophysical parameters such as porosity, N/G ratio (Net to Gross), and fluid saturations.

Uncertainty Scales

  • Scale Changes: Different scales of uncertainties include:

    • Pore scale: 1-100 μm

    • Core scale: 10 cm-10 m

    • Well scale: 50-500 m

    • Field scale: 1-100 km

Uncertainties in Petrophysical Data

  • Petrophysical measurements may vary, creating uncertainty in:

    • Core data readings and interpretation of rock types.

Rock Typing: Integrating Facies & Managing Uncertainties

  • Rock types are identified through the calibration of electrofacies with petrofacies. Each Rock Type is instrumental in populating 3D geomodels.

Dynamic Modeling Uncertainties

  • Relative Permeability: Properties need to be carefully modeled and understood.

  • Key variables in fluid dynamics are oil and water saturation, rock compressibility, and fluid rheology.

Key Points in Reservoir Characterization

  • Crucial uncertainties pertain to geophysics, geology, and reservoir engineering, including:

    • Velocity models, fault picking, fluid properties, and hydraulic dynamics.

6. Overview on Seismic Interpretation

Seismic Summary

  • Goals of seismic interpretation include gathering:

    • Structural and facies information between wells,

    • Seismic attributes to inform about the subsurface.

Seismic Interpretation Objectives and Deliverables

  • Structural Information: Define trap geometry and assess conventional seismic interpretation for time-to-depth conversion.

  • Deliverables: Maps of structural features, attributes, and fault networks.

Seismic Data for Reservoir Architecture

  • Utilization: Use seismic data to extract structural, lithological, and fluid characteristic data.

Seismic Data Integration

  1. Structural model

  2. Stratigraphic model

  3. Rock-type model

  4. Facies model

Time-to-Depth Conversion

  • Converts time maps (TWT - Two-Way Time) into depth maps (expressed in meters) using the formula:
    Depth map=(Velocity map1000)×(TWT map2)\text{Depth map} = \left(\frac{\text{Velocity map}}{1000}\right) \times \left(\frac{\text{TWT map}}{2}\right)

Extracting Information about the Reservoir

  • Amplitude Anomalies: Detection of high amplitudes can indicate sedimentary bodies, reservoirs, and fluid presence.

  • Porosity and Lithology Assessment: Inversion can reveal porosity distribution and fluid characterization in the subsurface.

Qualitative Well Log Interpretation

  • The "Quick-Look" Method: A practical approach to interpreting wireline logs to evaluate physical phenomena related to reservoir characteristics.

Types of Well Logs

  1. Geological Logs: Focus on formation and fluid evaluation and quantification.

  2. Driller Logs: Capture technical drilling information.

  3. Production Engineers Logs: Analyze fluid behavior in relation to their displacement within reservoirs.

Well Logging Setup

  • Involves multiple roles whereby mud plays a crucial part in keeping the well clear and in control.

Well Logging Tools and Applications

  • Various tools measure parameters such as

    • Borehole diameter (Caliper Tool)

    • Natural radioactivity (Gamma Ray Tool)

    • Formation resistivity (Resistivity Tool)

    • Fluid properties (Neutron, Density, Sonic Tools)

Quick-Look Log Interpretation Objectives

  • It aims to address:

    • Reservoir delineation,

    • Non-reservoir zone identification,

    • Fluids detection and estimation of saturations.

Typical Log Responses in Geological Formations

  • Patterns and average quick-look values for different lithologies are used to identify common characteristics for correlation and interpretation during field studies.

Formation Factor (Archie's Law)

  • The relationship governing the resistivity of formations according to:
    R<em>o=F×R</em>wR<em>o = F \times R</em>w where $Ro$ is the global resistivity and $Rw$ is the resistivity of water, with $F$ being the Formation Resistivity Factor.