A short presentation indicating LR Senergy's proposed workflow for quality assurance and quality control of mercury injection capillary pressure data and its subsequent interpretation for input to static reservoir models. Core analysis data results are often an interpretation of the measured data, not a direct result of the measurements themselves. Interpretation is a subjective process and as such, data should never be merely accepted and implemented. It must always be considered and interpreted by the end user, to determine agreement with the original interpretation. This short slide show provides LR Senergy's suggested generic approach to QA/QC of MICP data.

1. LR Senergy
Mercury Injection Capillary Pressure
(MICP) Data Evaluation and
Interpretation

2. Overview
•Background
•Data Evaluation
•Data Interpretation

3. Background
• MICP is used extensively for Sw-height and rock typing
• LR-Senergy has processed, QC and interpreted over 3000
samples & datasets (no limitation in number of samples)
• High Pressure Mercury Injection (HPMI up to ~ 60,000 psi Hg-
Air):
• significant challenges due to small sample size and high
pressures potentially disrupting pore system
• Data is in general not quality controlled and difficult to handle
efficiently given the extensive data sets
• LR-Senergy has developed best practice workflows and QC
discrimination criteria to ensure only best quality data used for
Sw-Height / Rock Typing
Typical sample size
4 – 6 ml bulk volume

4. Data Evaluation

5. Data Evaluation
QC Considerations and Issues
1. Data inventory and loading (input routines customised for every data set
type (each lab has several formats))
2. Sample poroperm distributions check
3. Data availability: parent plug data, raw injection data, chip and parent
plug helium porosity, plug permeability, etc
4. Conformance correction verification
5. Injection sample pore volume
• must be greater than 0.5 ml
• impact of volume errors on Sw much greater
6. Injection sample versus parent plug porosity
7. Curve shape parameters checks
• capillary pressure curves
• Swir vs. controlling parameters, etc.
8. LR-Senergy has developed software to automate as many of the
processes as possible
9. Samples which do not meet the defined criteria are either rejected or
flagged as suspect
1 Use data with confidence
2 Sample Suspect - use data with care
3 Reject Sample - unacceptable data or uncertainty
Parent
plug
End trim (injection sample)

6. Data Evaluation
Workflow
Data Inventory
Lab Data Availability Check
(Data Availability Flags)
Independent Conformance
Correction estimation
Values Analysis (Value Flags)
Curve Shape/Trend analysis
(Shape Flags)
Overall Flags – Final QC Data
Base
MICP Interpretation*

7. Data Evaluation
Poroperm Distributions
• Ensure MICP samples are representative of net intervals and selection is
not biased

8. Data Evaluation
Conformance Correction
• Attempt to minimise subjectivity in conformance and entry pressure
selection and consequent injection volume correction
Lab Pc vs Sw
Cumulative
dV/dPc vs Shg
Incremental
Volume vs Pc
Cumulative
dV/dPc vs Pc

9. Data Evaluation
Porosity Comparison: Chip vs. Plug
• Reject if injection sample (Hg) porosity and parent plug (He) porosity
differ by > ±1.0 p.u.
• Should be ±0.5 p.u. (API RP40) or better, but allow for some heterogeneity
• Hg may not penetrate all pore throats penetrated by helium molecules
Hg
He

10. Data Evaluation
Pc vs. Sw curves and endpoints
“Swir” vs k
“Swir” vs 
Pc vs Sw

11. Data Evaluation
Overall QC Flags (example)
• Reject 10% of samples that failed QC
• Low rejection rate - generally much higher
• Remaining 90% treated with caution as full data lacking
• e.g. no He porosity on injection chip for direct comparison with Hg porosity

12. Data Interpretation

13. Data Interpretation
Pc vs. Sw
• Based on QC data
• Test Swanson (and Thomeer) permeability correlations
• Evaluate R35 for rock typing (or any possible R (R20, R25, R30, R40, etc.))
• Evaluate potential strategies for saturation-height modelling and rock typing

14. Data Interpretation
Field-Tuned Swanson Permeability
• (Sb/Pc)A at the apex is the maximum Sb/Pc ratio calculated from all points on the
Pc curve.
• Assumed to “represent the point where all of the major connected pore spaces
controlling permeability have been intruded by mercury”.
• Used to predict permeability in MICP samples where permeability measurement
not possible
64.1
.427
APc
Sb
Ka 






691.1
399
APc
Sb
Ka 






Swanson (1981)
(generic coefficients)
Tuned to Field Pc data

15. Data Interpretation
R (pore throat radius) Binning
• Traditional Winland R35: pore throat radius corresponding to pressure at which mercury saturates 35% of
pore volume. “Point where the pore network becomes interconnected”
• LR-Senergy can calculate R from field MICP data at any required mercury saturation, not sticking just to
the traditional Winland R35
• Multivariable regression can be used to determine which R produces the best fit for the actual MICP field
data
• MICP sample Hg porosity and Swanson permeability can be used in the regressions – not just parent plug
values
1.0 – 3.0 mm
3.0 – 8.0 mm
> 8 mm
< 0.1 mm
0.1 – 0.3 mm
0.3 – 1.0 mm
R35 Bins (based on field MICP data)
)(7796.0)(5783.05699.035 LogLogKRLog air 

16. Data Interpretation
Pore Throat Size Distribution (PSD) by R35 Bins
• Pore throat size modality and size distribution discriminated by R35 bins
(any R can be modelled)

17. Data Interpretation
Pc vs. Sw Modelling: J-function
• J function improved by
• using MICP sample porosity and Swansom permeability
• using normalised water saturation (Swn = 1-Swir)
• Swir = Sw at 640 ft HAFWL
• J function relationships effectively discriminated by R35 bins

18. Data Interpretation
Thomeer Curve Fitting: Rock Typing
• Thomeer model is extensively used to fit the Pc curves (hyperbolic function) for saturation
height modelling and rock typing
• Fitted curves are defined by the pore structure factor (G), threshold pressure (Pd) and the
total interconnected pore volume (Sb∞) (Bulk Volume saturation at infinite pressure ~
Porosity)
• LR-Senergy workflow can extract G, Pd and Sb∞ from each one of the QC MICP curves
and use the data to check for trends and potential clusters
Sb∞: Total interconnected PV at inf. Pc ( ~ )
Pd: Hg/A displacement pressure
G: pore geometrical factor
K: air permeability (mD)
Thomeer, J. H. (1983, April 1). Air Permeability as a Function of Three Pore-Network Parameters.
Society of Petroleum Engineers. doi:10.2118/10922-PA

19. Data Interpretation
Thomeer Curve Fitting: Rock Typing
•Thomeer Based Clusters (G, Pd and Sb∞ extracted from field MICP data)
G
Pd
Sb∞