reservoir geostatistics let’s use all the information!...–reservoir modelling o connectivity of...

Reservoir Geostatistics

Let’s Use All The Information!

Beth Rees

July 27, 2020

Outline

Geostatistical Inversion– Why and How

– Components of Information

Examples

– Reservoir Properties in Depth (West Africa)

– Understanding Pressure Depletion (Nile Delta)

– Predicting Production (Western Siberia)

– Predicting Porosity (Alberta Canada)

Summary

2

Rodina O. et al., Detailed Geological Model of Carbonate

Reservoir Based on Geostatistical AVA Inversion – A

Case Study, EAGE Ann. Mtg., 2008

Timan Pechora Carbonate

Why Geostatistical Inversion? Seismic vs Geomodel Grids

Technical Objectives– Well Planning

o Reservoir delineation

o Facies connectivity

o Efficiency – few wells, maximum production

– Reservoir Modelling

o Connectivity of the reservoir

o Flow simulation

o History Matching

– Uncertainty Assessment for Risk Management

o Multi-realization analysis: Facies, Properties

o Ranking (P10, P50, P90)

Business Objectives – Maximizing value

o Optimizing development plan

o Predicting reservoir performance

o Maximize Return On Investment (ROI)

Reservoir Geostatistics Deliverables– Details beyond seismic resolution

o Relax the restrictions of Deterministic Inversion

– Tightly integrated data for reservoir modeling

o Joint use of data with different scales

o 3D models of both facies and reservoir properties

– Uncertainty assessment

o Generate and rank multiple plausible realizations

Geostatistical Inversion – An Integration of Techniques

Geostatistical reservoir modeling

– Interpolates between wells

o Accurate near wells

o Multiple realizations with plausible details

o Not elsewhere

Deterministic inversion

– Estimates elastic properties to match the seismic

o Accurate within seismic bandwidth

o Unrealistically smooth

o Only one possibility

Geostatistical inversion

– Combines geostatistical modeling & deterministic

inversion, simultaneously in a statistically rigorous way

o Multiple plausible realizations at high detail (eg 1ms x 25m)

o Geological interpretations of the seismic away from wells

4

Modern Bayesian Geostatistics – How It Works

5

Plausible Reservoir Models

Well Logs

DATA (New Information)

Seismic Data & WaveletBAYESIAN INFERENCE

MCMC SAMPLING

Geology

PRIOR INFORMATION (Hypotheses)

Fluid Contacts

Stratigraphic

Grid

Rock Physics Heterogeneity

Seismic S/N

Facies Proportions

Multivariate PDFs

Facies Priors

Transfer

to

Reservoir

Model

(CPG)

Facies

Porosity

Vp/Vs

Saturation

Facies

Porosity

Vp/Vs

Saturation

Facies

Porosity

Vp/Vs

Saturation

Time /

Depth

1a 1b

2

3

4

6

Components of Information

Engineering

Geostatistical Inversion Components: Facies Type

Geophysics

Vo

lum

e C

lay

Porosity

Vp

/Vs

P Impedance

Co

re P

erm

eab

ilit

y (

mD

)

Core Porosity %

Geology

Petrophysics

Meaningful Facies in Multiple Domains

7

Facies

• Facies type

• Prior Probabilities

• Associations

Reservoir properties per facies

• Stratigraphic depth trends

• Multivariate PDFs

• Heterogeneity

• Saturation functions

• Rock physics models

Per Geological Layer

Inverted Ip + Well Facies

Inverted FaciesInverted Ip + Well Facies

Why Joint Inversion of Properties and Facies?

P-imp

Joint inversion

of P-Impedance

and Facies

Inversion of

P-Impedance

onlyP-imp

Less continuity of large-

scale features

Body edges are not as

sharp

Less values from the

extremes of the distributionSeismic

8

Geostatistical Inversion Components: Prior Probabilities

9

Facies

• Facies type


• Associations




• Heterogeneity




3D Prior ProbabilitiesFacies

LogsVertical

Trends

Constant

Proportions

Geostatistical Inversion Components: Hierarchical Relations

10

Level 1

Channel & Overbank always

separated by Levee

Level 2

Overbank is Shale and Muck

Level 2

Levee consists of Tight and

Loose

Level 2

Channel can be Oil, Gas or

Water. Water does not contact

gas

Level 3

Oil in Channel is Light or

Heavy

Facies

• Facies type


• Facies Associations




• Heterogeneity




Geostatistical Inversion Components: Depth Trends

11

Facies

• Facies type


• Associations




• Heterogeneity

• Saturation height functions



Engineering properties and elastic

properties do not behave alike

Geostatistical Inversion Components: Distributions

Effective Porosity

Volu

me o

f cla

y

Shale

Channel sand

Cemented sand

12

Facies

• Facies type


• Associations




• Heterogeneity




Probability

Data from…• Logs

• Rock Physics models

• Monte Carlo simulations

• Analogues

Geostatistical Inversion Components: Heterogeneity

Shale

Channel sand

Cemented sand

100 m

13

Facies

• Facies type


• Associations




• Heterogeneity




3D VariogramsVariogram Features

• Per layer

• Per facies

• Per property

• Azimuth-Aligned

o aka anisotropic

Geostatistical Inversion Components: Saturation & Fluid Contacts

0.1

103.14

38.21

log

shale

K

eff

sand

Sw

Sw

14

Porosity

Oil Saturation

Facies

• Facies type


• Associations




• Heterogeneity




Facies based model of Sw as a

function of porosity and permeability.

OWC

Geostatistical Inversion Components: Rock Physics Models

15

Facies

• Facies type


• Associations




• Heterogeneity

• Saturation height functions



Geostatistical Inversion Components: Seismic

Seismic Data

• Offset or angle stacks

• Time or depth migrated

• 4D, PP-PS, WAZ

• Known trends in wavelets

and noise

15-25 degrees

25-35 degrees

5-15 degrees

16

Facies

• Facies type


• Associations




• Heterogeneity




Well log data

Geostatistical Inversion Components: Logs

Porosity Vclay P Vel. Vp/VsFacies

De

pth

Log Constraints• Hard

• Soft

• Blind

Seismic Data

• Offset or angle stacks

• Time or depth migrated

• 4D, PP-PS, WAZ

• Known trends in wavelets

and noise

17

Facies

• Facies type


• Associations




• Heterogeneity




Well log data

18

Examples – Reservoir Properties in Depth (West Africa)

– Understanding Pressure Depletion (Nile Delta)

– Updating the Geologic Model(Western Siberia)

– Determining Probability of Porosity (Alberta Canada)

Example 1 – Inverting for Reservoir Properties in Depth

Offshore West Africa

– Passive shelf

– Main play is a Miocene/Oligocene turbidite

channel system

Objective: Evaluate potential around a

proposed well location

Five wells

– Good rock physics model

Four partial stacks

Brownfield, M.E., and Charpentier, R.R., 2006, U.S.G.S. Bulletin 2207-B, p. 52

Jimenez, J., Marquez, D., Saussus, D. and Bornard, R. [2013]

Incorporating Rock Physics into Geostatistical Seismic Inversion -

A Case Study. 75th EAGE Conference & Exhibition, Extended

Abstracts, We 07 01.

Geostatistical Inversion Workflow

Key challenge: High degree of

variation in quality of reservoir sands

Solution: Incorporating multi-level

facies modeling

Workflow

1. Stratigraphic modeling in depth

2. Facies definition

3. Geological trend modeling

4. Geostatistical inversion

5. Analysis of realizations

20

Stratigraphic Model

Five lithofacies identified

– Shale

– Brine Sand

– Oil Sand

o Low Quality

o Medium Quality

o High Quality

Reasonable separation between

shale, brine sands and oil sands

– Oil sand quality levels were not

separable

Facies Definition

21

Brine Sands

Petrophysical

Brine SandsElastic

Facies Definition: Associations, Ordering & Prior Probabilities

22

Shale Sand

25

75

Constant Proportions

MedLow High

Mic

rola

ye

r

1D Proportion TrendOil

SandBrine

Sand

3D Proportion Trend

Probability of Oil Sand

Probability of Brine Sand

Rock Physics Model

Rock physics is the link between seismic elastic and engineering properties

Different facies (shale, sandstone) will be modeled with different parameters

23

r, Vp, Vs

Elastic Properties

of Saturated Rock

Empirical relation

per faciesK

P

Capillary Pressure

Derived

Sw

Vclay

Hertz-Mindlin + modified

Hashin-Shtrikman

lower bound

Gassmann

Fluid Substitution

Dry Elastic Moduli

• Sandstone

• Shale

Simulated properties

Calculated properties

Inverted Facies Inverted Porosity

Inverted Vclay Inverted Sw

TV

D

OWC

OWC

0.5

0.3

0.4

0.6

0.2

0.1

0.0

0.8

0.4

0.6

1.0

0.2

0.20

0.10

0.15

0.25

0.05

Geostatistical Depth Inversion – Single Realization

24

TV

D

TV

DT

VD

OWC

OWC

Sand Shale

42%

44%

40%

High Scenario

Medium Scenario

Low Scenario

NTG Ranking for 3 Scenarios

P10

P50

P90

Uncertainty Analysis

P10

P50

P90

P10

P50

P90

P10

P50

P90

P10

P50

P90

Pay Volume

Nile Delta

– Abu Madi Formation

– Upper Miocene

– Fluviomarine

– Sandstone intercalated with

siltstone and shale

– Intraformational shale baffles

Data

– Six wells

– Six partial stacks

Sulistiono, et al., Integrating Seismic and Well data into

Highly Detailed Reservoir Model through AVA

Geostatistical Inversion, SPE 2015 (SPE-177963-MS)

Example 2 - Understanding Unexpected Pressure Depletion

Abdel-Fatah,

2015

Objective

– Understand the unexpected pressure depletion observed

in the field.

Original

Pressure

Depleted

Pressure

Key Question: What is the level of connectivity between north and south?

Solution: Flow simulation of multiple realizations

Geostatistical Inversion Workflow

27

Shale

Tim

e (

ms)

Facies from Deterministic Inversion Workflow

1. Stratigraphic modeling

2. Facies definition

3. Geostatistical inversion

4. Vclay and porosity cosimulation

5. Permeability and saturation modeling

6. Ranking of realizations

7. Upscaling P10, P50 and P90 realizations

8. Dynamic simulation

Sand

Lithotypes

28

Facies from Deterministic and Geostatistical Inversions

Comparison of Facies from Deterministic and Geostatistical Inversion

Tim

e (

ms)

Tim

e (

ms)

Deterministic Inversion

Geostatistical Inversion – Realization #1

Deterministic results

– Results at seismic BW

– Sands in lower reservoir

– No connection to south

Geostatistical results

– Modeled at 0.5 ms

– Thin sands throughout

– Connection possible

Shale

Sand

Lithotypes

29

Further Modeling, Ranking, Upscaling and Flow Simulation

P50 Properties Upscaled and Transferred to the Corner Point

Grid

Realization ranking based on net pore volume at proposed wells

Vertical dimension is Depth

Vclay Vclay

Lithotype Lithotype

Reservoir Grid CPG Grid

Realization upscaling to CPG

Co-Simulated reservoir properties of Vclay and Effective porosity

Permeability and saturation modelled from porosity

Flow simulation and history matching of realizations

Pressure simulation shows connectivity

between northern and southern area

30

Pressure from Dynamic Flow Simulation Using RockMod P50 Model

Pressure Changes: 2007-2012

Pressure

Sulistiono, et al., Integrating Seismic and Well data

into Highly Detailed Reservoir Model through AVA

Geostatistical Inversion, SPE 2015 (SPE-177963-MS)History matching done to observed well pressures

Feb. 2007

Apr. 2010

Dec. 2008 Dec. 2012

Sept. 2011

Example 3 – Updating the Geologic Model

Western Siberia

Clastic Reservoir

– Neokomian interval

– Net pay: 5 to 18 meters

– Porosity: 15% to 17%

Field under production for 10 years

– 10 exploration wells

– 30 production wells

– Variable OWC across the field

Objective: Update the geologic model to

incorporate the observed compartmentalization

Fillipova et al., Geostatistical Inversion as a Tool for the Accurate Updates

of the Hydrodynamic Models – Case Study, EAGE Ann. Mtg., 2013

Top Reservoir

Map

OWC -2523m

OWC -2485m

Elastic Properties

P Impedance

Vp

/Vs

Fre

quen

cyAddressing the Compartmentalization

Key challenge: Update the geologic model to incorporate the

observed compartmentalization

Solution: Use the seismic for mapping reservoir compartments

But: Poor separation between reservoir and non-reservoir

Solution 2: Analysis of the reservoir frequency volume

OWC -2523m

OWC -2485m

Reservoir Non-reservoir

P Impedance

Reservoir Frequency from Geostatistical Inversion

33

Tiled Stratigraphy

Analysis of reservoir frequency volume showed

four compartments, two of which have not been

drilled.

Comparison of Two Geological Models

Model1: No Seismic

Model 2: Geostatistical Inversion

Reservoir Non-Reservoir

Reservoir Non-Reservoir

34

Original geologic model

Built using only wells and

seismic horizons

More continuity

Updated geologic model

Built based on geostatistical

inversion results

Less continuity

History Match and Prediction at Newly Drilled Well

Net-to-Gross

• Predicted: 0.92

• Actual: 0.88

Production Rate

• Observed

• No Seismic

• Geostatistical

Inversion

2008 2009 2010 2011 2012 2013

35

Regional Platform

Platform Facies

LagoonReef Margin

EmbaymentLower

Foreslope

Reef Facies

FSL

Sand

ForeslopeReef Flat

Example 4 – Determining Porosity Probability

Swan Hills Reef– Devonian, Alberta, Canada

– Beaverhill Lake Group

Environment– 2810 m

– 65 wells – Producers & Injectors

– Up to 85 m Limestone

– Limestone Porosity to 18%

– Continuous pay up to 25 m

– Fluid: Oil

Van der Laan, J., Pendrel, J., Geostatistical Simulation of Porosity and Risk in a Swan Hills

Reef, CSEG Nat. Conv. Abs., 2001

Tim

e (

s)

1.72

1.74

1.76

1.78

1.80

1.82

1.84

Geostatistical Probability Analysis

Objective: Quantify and map areas with high

likelihood of porosity above the required threshold

Solution: Analyze the porosity probabilities from a

set of porosity realizations

Analysis Workflow

– Analyze porosity values of each cell across multiple

realizations to find porosity distributions for each cell.

– Select porosity threshold and create volume of

probabilities for that value from the porosity

distributions.

– Analyze volume of probabilities relative to that porosity

value.

Porosity

Pro

b. D

en

sit

y

0 5 10

Pro

b. D

en

sit

y

0 5 10

Prob. = 0.63Prob. = 0.15

Results of Geostatistical Probability Analysis

Geostatistical inversion is the best basis for

reservoir models

– Integrates a broad range of data

– Provides geologically realistic models

• Highly detailed from well information

• Laterally accurate from seismic information

• Distinct features from simultaneous property and

facies modeing

– Creates basis for range and uncertainty estimations

These reservoir models provide

– Rapid history matches to flow simulation

– Accurate predictions for future wells

Incorporating All of the Data

Reservoir properties

Shale

Sand

Tight

sand

Facies

Flow Simulator

Porosity,

Permeability,

Saturation and

others

Geostatistical inversion can help you meet technical

and business objectives

39

Thank You!Acknowledgements

• John Pendrel

• Leonardo Quevedo

• Rong Zeng

• Anton Ephanov

• Harry Debeye

• Irina Yakovleva

reservoir geostatistics let’s use all the information!...–reservoir modelling o connectivity of...

Documents