LR Senergy

Mercury Injection Capillary Pressure

(MICP) Data Evaluation and

Interpretation

Overview

•Background

•Data Evaluation

•Data Interpretation

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 size4 – 6 ml bulk volume

Data Evaluation

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)

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*

Data Evaluation

Poroperm Distributions

• Ensure MICP samples are representative of net intervals and selection is

not biased

Data Evaluation

Conformance Correction

• Attempt to minimise subjectivity in conformance and entry pressure

selection and consequent injection volume correction

Lab Pc vs Sw

CumulativedV/dPc vs Shg

IncrementalVolume vs Pc

CumulativedV/dPc vs Pc

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

Data Evaluation

Pc vs. Sw curves and endpoints

“Swir” vs k

“Swir” vs

Pc vs Sw

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

Data Interpretation

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

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

.427APc

SbKa

691.1

399APc

SbKa

Swanson (1981) (generic coefficients)

Tuned to Field Pc data

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 mm3.0 – 8.0 mm> 8 mm

< 0.1 mm0.1 – 0.3 mm0.3 – 1.0 mm

R35 Bins (based on field MICP data)

)( 7796.0)( 5783.05699.035 LogLogKRLog air

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)

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

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 factorK: 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

Data Interpretation

Thomeer Curve Fitting: Rock Typing

•Thomeer Based Clusters (G, Pd and Sb∞ extracted from field MICP data)

G

Pd

Sb∞