Quality Assuring the Petrophysical

Evaluation of Thin Beds

London Petrophysical Society

21 September 2017

Paul F. Worthington

Park Royd P&P (England) Ltd

Ascot, UK

Structure

Introduction

Scenario Approach

Volumetric Analysis

Field Examples

Conclusions

Introduction

Thin beds are one of the major causes of by-passed

pay in the world today

A thin bed is one that is not sufficiently thick to be

fully resolved by a logging tool

Tool Resolution

Introduction

Historically, thin bed evaluation has drawn upon

forward modelling or the signal enhancement of

standard logs

An alternative philosophy uses a coarse-resolution

three-dimensional induction tool plus an electrical

micro-imager to calculate the bed properties

– Simplifying assumptions

– Resistivity only

Porosity problem remains

Scenarios for Thin-bed Evaluation

Scenario Bed range

(cm)

Nature of Constituent Beds

Resolvable Detectable Pluggable Identifiable

A 10 - 60

B 3 - 10

C 1 - 3

D 0.1 - 1

E 0.1 - 60

Yes SomeNo

Scenario Approach

Separate the problem into parts

– Each scenario relates to a range of bed thickness

Bed thickness has to be considered relative to

– Tool resolution

– Tool availability

We focus on the porosity problem in cases where

– Core data exist in a key well

– Thin beds are not pluggable

– Layer boundaries cannot be discerned through

downhole measurement

Volumetric Analysis

Three references

Thomas & Stieber (1975)

Total porosity system

Ruhovets & Fertl (1982)

Effective porosity system

Juhasz (1986)

Total and effective

porosity systems

Volumetric Analysis

tsd = (t - (Vshl tsh)) / (1 - Vshl)

Vshl = (t - max + Vsh (1 - tsh)) / (1 - max)

Juhasz - total porosity system -

laminated sand/shale sequence

Volumetric Analysis

Avoid over-prediction of interstitial clay-mineral

volume fraction

– Correction factors

• from shale volume fraction (Vsh) to clay-mineral volume

fraction (Vcm)

• Any “shale” indicator

• Specifically for the estimation of porosity

Do not take max as the highest porosity seen in a

database

– It is a mathematically-derived quantity required

for closure

Bed thicknesses: 3.0 – 9.5 cm

All beds are assumed to be isotropic

Laminated shale fraction from electrical micro-

imaging log

BVH increases by 65% on application of thin-bed

analysis

A Scenario D approach (ignoring core and micro-

imaging data) does not produce matching results

– Disparity is attributed to requirement to estimate

maximum attainable porosity max

Example 1 – Scenario B D

Example 1 – Scenario B D

Input Parameter Output

Parameter

Scenario

B

Scenario

D

Bed thicknesses (cm) 3.0 – 9.5 t 0.200 0.200

c 0.231

Vcm / Vsh 0.714 Vshl 0.341 0.458

Rshl (m) 1.25 Vcm 0.571 0.571

Rv (m) 7.52 Vcmd 0.230 0.113

Rh (m) 3.00 Rtsd (m) 10.8

Rw (m) 0.08 tsd 0.231 0.251

m 1.83 Swsd 0.259

n 1.64 EHTsd (m) 0.113

No electrical micro-imager,

no multicomponent induction,

no whole core, est. max = 0.43

Bed thicknesses: 0.3 – 0.9 cm

All beds are assumed to be isotropic

Laminated shale fraction from multicomponent

induction log

BVH increases fourfold on application of thin-bed

analysis

An indirect approach (ignoring multicomponent

induction data) does not produce matching results

– Disparity is attributed to requirement to estimate

maximum attainable porosity max

Example 2 – Scenario D

Example 2 – Scenario D

Input Parameter Output

Parameter

Direct

method

Indirect

method

Bed thicknesses (cm) 0.3 – 0.9 t 0.189 0.189

b (g/cc) 2.35 Vsh 0.618

Vcm / Vsh 0.822 Vshl 0.436 0.579

Rshl (m) 1.05 Vcm 0.586

Rv (m) 3.33 Vcmd 0.150

Rh (m) 1.90 Rtsd (m) 5.09

Rw (m) 0.065 tsd 0.261 0.317

M 1.90 Swsd 0.369

N 1.81 EHTsd (m) 0.093

No electrical micro-imager,

no multicomponent induction,

no whole core , max = 0.40

Conclusions

We have assumed thin isotropic beds within a sand-

shale sequence

We have partitioned the thin-bed problem using

discrete scenarios

– based on specified ranges of bed thickness

– further distinguished by available data

For each scenario a workflow has evolved for

formation evaluation

The scenarios become more challenged as beds get

thinner

Conclusions

Where requisite data do not exist, a lower-level

scenario has to be adopted

– with greater uncertainty

The use of compositional equations in the

evaluation of porosity needs to be calibrated

– otherwise closure is not achievable

The overestimation of clay-mineral content by shale

indicators has been corrected

– but this requires some core porosity data from

the same depositional system

Conclusions

The approach has been set in terms of optimal data

acquisition

– sufficient to do the job

More complex layerings can be accommodated

– carbonate stringers

– anisotropic sand and shale

But these will require additional data

– e.g. nuclear magnetic resonance

Benefits are improved evaluations of hydrocarbons

in place and thence petroleum reserves

Epithet

80% of the subject matter of this presentation is

available in OnePetro as:

Majid, A.A. & Worthington, P.F. 2012.

Definitive petrophysical evaluation of thin

hydrocarbon reservoir sequences.

SPE Reservoir Evaluation & Engineering 15(5),

584-595. [October 2012]

This is the peer-reviewed version and not the 2011

OMC conference preprint, which is also in OnePetro