It is very challenging for any water saturation computation using the precise and actual cementation exponent m value of your reservoir. It is derived from a special core analysis based on the formation resistivity factor and porosity.
The impact of the cementation exponent on water saturation computation can change water saturation from 40% to 70% if your m value increases from 2 to 3.
It is pretty expensive to have cementation exponent in several wells in your field.

Proposed Solutions and Recommendations:

1. Predict mmm Using Machine Learning (ML):

• Data Requirements:

  • Input features: Wireline logs (e.g., porosity, resistivity, volume of shale) and interpreted logs.
  • Output: Known mmm values from cored wells.

• ML Workflow:

  1. Preprocess data (handle missing values, normalize features).
  2. Train ML models (e.g., Random Forest, Gradient Boosting, Neural Networks).
  3. Validate using a portion of the dataset with known mmm values.
  4. Apply the model to un-cored wells.

• Advantages:

  • Cost-effective and scalable.
  • Models can account for relationships between logs and mmm.

2. Cross-Plots of Porosity Types:

• Use published cross-plots linking isolated pores and total porosity:
  • Plot total porosity (ϕt\phi_tϕt​) vs. isolated porosity (ϕi\phi_iϕi​).
  • Intersection lines on these plots often represent mmm values for specific formations.
• This approach works well in the absence of core data but requires expertise in interpreting cross-plots.

3. Borehole Image Logs:

• Porosity Type Identification:

  • Use borehole images to distinguish intergranular, vuggy, and fracture porosities.
  • Correlate porosity types with mmm values derived from analog fields or sensitivity analysis.

• Advanced Analysis:

  • Advanced borehole image modules can quantify carbonate heterogeneity, which correlates with mmm.

• Sensitivity Analysis:

  • Perform water saturation computations across a range of mmm values.
  • Match SwS_wSw​ results with Dean-Stark water saturation (from core data, if available) to identify the most representative mmm.

4. Dielectric Logging Tools:

• Use dielectric tools to compute continuous m/nm/nm/n curves:
  • Based on bimodal carbonate interpretation models, these tools provide high-resolution estimates of mmm values.
  • Integrate these curves into Archie’s equation for more accurate SwS_wSw​ computations.

Practical Workflow:

1. Core Wells (SCAL Data):

   • Use SCAL-derived mmm values to train or validate ML models.
   • Correlate with wireline and borehole image logs for understanding trends.

2. Un-Cored Wells:

   • Use ML models to predict mmm based on wireline logs.
   • Supplement with cross-plots or borehole image interpretations for porosity types.

3. Dielectric Logs (if available):

   • Apply continuous m/nm/nm/n curves to refine water saturation estimates.

4. Validation and Sensitivity Analysis:

   • Validate predicted mmm values using independent datasets or analog fields.
   • Run sensitivity analysis with multiple mmm values to understand the range of water saturation and its impact on reservoir evaluation.

Key Takeaways:

• The cementation exponent mmm significantly influences water saturation calculations and reservoir characterization.
• Cost-effective alternatives to SCAL include ML-based predictions, borehole image interpretation, and dielectric tools.
• Sensitivity analysis with varying mmm values helps address uncertainties in water saturation estimates.