Wireline NMR Combined With Azimuthal Logs

Define Flow Capacity and Homogeneity within

High Angle Geothermal Well

Chiara Cavalleri and Erik Wielemaker, Schlumberger

SPWLA France – Technical Session

Paris, SGF, 27 November 2018

▪ Introduction

▪ Project Objectives

▪ Evaluation Workflow

▪ Data Analysis and Results

▪ Summary

Outline

▪ Geothermal as a viable means to provide an alternative energy source.

▪ Intelligent workflows and data gathering are needed to manage those resources optimally.

▪ Highlight importance of logging in decision-making to confirm producibility potential and define

development strategies

▪ We will highlight an example from a near Horizontal well in the Cachan project (Paris , France)

▪ Example show how NMR data together with sonic and azimuthal image data was used to

determine mobility variations in target layer

▪ In particular, NMR data was critical to determine the strategy for completion of the well.

Introduction

▪ Evaluation of Sub-horizontal Geothermal Well –

injection unit

▪ Open hole drain, 8″1/2 in diameter and 1 005 m

long in the Dogger/Bathonian, Mid Jurassic,

oolithic limestone, target reservoir.

▪ Thin, metric size, (up) dip varying, bed structure.

▪ Wireline logging objectives: Determine Mobility

along the Oolite layer and assess homogeneity

to aid completion decisions

Cachan: A World First in Geothermal Development

Data analysis → Inputs to development

Rock quality and productive segments characterization Porosity and permeability; rock heterogeneity

Wireline Logging tractor conveyed

Dipole Sonic NMR Magnetic Resonance

Drilling & Geosteering

Cuttings analysis Logging while drilling

▪ The different logs combined to structural model

provide multiple important information.

▪ We look at layers’ heterogeneity and permeability

distribution.

Facies Analysis

Pore Size

Distribution

T2, msec

Sig

na

l Dis

trib

ution

Continuous

Permeability

T2, msec

Sig

na

l Dis

trib

ution

T2, msec

Sig

na

l Dis

trib

ution

Mineralogy-

Independent

Porosity

T2, msec

Sig

na

l Dis

trib

ution

Irreducible Water

and Free-Fluid

Volumes

T2, msec

Sig

na

l Dis

trib

ution

Bound-

fluid

Free-

fluid

∫T2=

T2

cutoff33ms

Proton Relaxation T2 Dist Porosity, Permeability

Porosity, Pore Structure & Connectivity

Formation Evaluation Approach

Data analysis → Inputs to development

Rock quality and productive segments characterization Porosity and permeability; rock heterogeneity

Wireline Logging tractor conveyed

Dipole Sonic NMR Magnetic Resonance

Drilling & Geosteering

Cuttings analysis Logging while drilling

▪ We also show some preliminary observation from

advanced sonic.

Formation Evaluation Approach

Dipole at any azimuth

azimuthal receivers

Homogeneous anisotropic

Frequency

Slo

wn

ess

VS

(q )Intrinsic:

Shales ,

fractures

Inhomogeneous anisotropic

Slo

wn

ess

Frequency

Stress

induced

VS

(r, q )

Homogeneous anisotropic

Frequency

Slo

wn

ess

VS

(q )Intrinsic:

Shales ,

fractures

Homogeneous anisotropic

Frequency

Slo

wn

ess

VS

(q )

Intrinsic:

Shales

,

fractures

Inhomogeneous anisotropic

Slo

wn

ess

Frequency

Stress

induced

VS

(r, q )

Slo

wn

ess

Frequency

Stress

induced

VS

(r, q )

Inhomogeneous anisotropic

3ms 200ms

• The formation is heterogeneous.

• NMR Carbonate Porosity Partitioning characterizes the pore system

and ability for fluid to move layer-by-layer

NMR Porosity and Permeability Analysis

Main pore system classes and

permeability from porosity spectrum

analysis

Permeability controlled by porosity

and macro-pore volumes

High Porosity zone

High Porosity zone

Layers with best productive performance

assessed and identified by Wireline

logging together with density images

Linking Logs to Structural UnderstandingSWI/FFI

Permeability

AZD Image

Porosities

RHO Bot/top

Pore size

NMR T2

Caliper/ GR

Measurements Orientation and Layering Effect

All measurements are sensitive to the

proximity of the other layer

Log analysis and

measurements

comparison define

target zones

properties and

contribution

NM

R

So

nic

Res

isti

vity

Depth of investigation [in]

Ver

t. r

eso

luti

on

[in

]

0

10

20

30

Den

sity

Adjacent (above) layer effect on deeper

azimuthal sensor

11

TOP

BOT

NMR measures the properties of the

target layer in most interval

NMR remain focused on oolite

layer and its properties variations

Higher permeability zones

We look at layers’

heterogeneity and

permeability

distribution.

Change in pores ability to conduit fluid, in

same porosity, is well defined by NMR

Different measurements response to tight

later approaching from below

Heterogeneity and mixed layering

Variable flow

capacity and

layering effects

clearly described

by logs.

Near monopole

single azimuth logsDipole

Fast Shear

Dipole

Slow Shear

▪ Layer distance and homogeneity even

more defined when looking at the full set

of sonic azimuthal sensors.

▪ Despite the proximity of dolomite faster

formation, the shear that is polarized.

horizontally read the properties of the

oolite in target zones.

▪ Adding confidence to the characterization

of oolite properties and variations

provided by high-res NMR.

A Deeper Look at Sonic

Wireline conveyed logging in high angle well proved being a viable approach.

Efficient Logging Conveyance

TVDSS [m]

Well

▪ The collaborative use of multiple methodologies (drilling, logging,…) improves producibility estimation and

effectively.

▪ The refined and integrated workflow can be applied to different cases and complex field descriptions with

flexibility for adaption to specific field challenges as needed.

Conclusions

While logging

• Porosity and permeability

• Layers homogeneity

After logging

• Target injection/producing layers selection

For the future

• Rock typing

• Far events’ mapping; structural model update

• Geomechanics

Thanks for your attention!

CCavalleri@slb.com

Special thanks to:

Pierre Ungemach, Miklos Antics, Melanie Davaux at GPC-IP

http://www.geoproduction.fr/

for their technical contribution and continuous collaboration

Dalkia for the input data

https://www.dalkia.fr/

Acknowledgement