IPTC-18306-MS 3D and 4D Seismic Data Integration for ... · In the field considered for this study 4D seismic data has proven to be a key piece of information to understand the dynamic

IPTC-18306-MS

3D and 4D Seismic Data Integration for Geomodel Infilling: A Deep Offshore Turbiditic Field Case Study.

V. Silva, T. Cadoret, L.Bergamo, and R.Brahmantio, TOTAL E&P France

Copyright 2015, International Petroleum Technology Conference This paper was prepared for presentation at the International Petroleum Technology Conference held in Doha, Qatar, 7-9 December 2015. This paper was selected for presentation by an IPTC Programme Committee following review of information contained in an abstract submitted by the author(s). Contents of the paper, as presented, have not been reviewed by the International Petroleum Technology Conference and are subject to correction by the author(s). The material, as presented, does not necessarily reflect any position of the International Petroleum Technology Conference, its officers, or members. Papers presented at IPTC are subject to publication review by Sponsor Society Committees of IPTC. Electronic reproduction, distribution, or storage of any part of this paper for commercial purposes without the written consent of the International Petroleum Technology Conference is prohibited. Permission to reproduce in print is restricted to an abstract of not more than 300 words; illustrations may not be copied. The abstract must contain conspicuous acknowledgment of where and by whom the paper was presented. Write Librarian, IPTC, P.O. Box 833836, Richardson, TX 75083-3836, U.S.A., fax +1-972-952-9435

Abstract

When seismic data is of good quality and that it can be related to useful geological properties it can become a

useful driver to distribute spatial heterogeneity within the geomodel. This paper describes a workflow to

incorporate efficiently seismic data during the geomodel infilling process. We propose to combine the seismic

facies probability attribute (obtained after 3D pre-stack inversion) with 4D attributes. This is done by increasing

the seismic facies probability where 4D information highlights the presence of permeable reservoir facies.

Afterward the obtained attribute is combined with well information using Total in-house workflow to generate

the final facies proportion cubes. Finally, Associated Facies (AF) simulation is performed using SIS (Sequence

Indicator Simulation) algorithms. Comparison is performed between the AF obtained using 3D seismic data

only and AF obtained using combined 3D and 4D seismic data.

The integration of 4D information early in the geomodeling workflow (i.e. in facies modelling stage) improves

the efficiency of the dynamic back-loop by allowing an early combination of Static and dynamic information.

Thanks to this, history matching of dynamic reservoir simulation is facilitated.

Introduction

While defining a geological model, different pieces of information must be integrated to obtain a reliable spatial

organization of geological facies and petrophysical properties. In the field case presented below, a new reservoir

model is designed to improve the representation of geological facies heterogeneities for a turbiditic deep

offshore field.

Total in-house Seismic Reservoir Characterization tool (CARESS) enables conversion of inverted seismic

attributes (such as acoustic impedance and poisson’s ratio) into attributes describing the probability of

occurrence of selected geological facies. It must be pointed out that these attributes have the same resolution as

seismic and can possibly be influenced by pore fluid content.

In the field considered for this study 4D seismic data has proven to be a key piece of information to understand

the dynamic behaviour of the reservoirs. The main 4D attribute used in this study is the fractional P-wave

velocity changes between two different seismic monitors (DV/V). If no subsidence effect occurs in the reservoir

interval, it is assumed that strong DV/V response corresponds to some production or injection related effect

which can only occur in the presence of permeable sand.

IPTC-18306-MS 2

Until now, 4D seismic data was integrated in geomodel workflow following facies simulation to assign

deterministically reservoir facies in zones where strong 4D response is visible.

In this work we propose to combine the seismic facies probability attributes and 4D attribute (DV/V) earlier on

in the infilling process to better constrain the facies property infilling in the model.

Seismic data QC and seismic quality map definition

Before the use of the seismic data to constraint the geomodel infilling, the quality of the different seismic data

were assessed to estimate the relative confidence we can have on it. The computations are performed at

reservoir interval and the final product of this analysis is the seismic quality map.

The seismic stacks used for seismic inversion derives from the Broadband survey. Computation of several

attributes was performed for these stacks as RMS amplitudes, signal to noise analysis, standard AVO QC maps

between near and far stacks after common bandwidth filtering. The stretch between near and far stacks was

performed on raw data, without common bandwidth filtering.

Seismic inversion products are the input for the Caress study, so its qualities have a direct impact on the

potential seismic constraint. Over the reservoir interval the residuals amplitude are as expected weak and

without lateral or vertical organization. In order to analyze in more detail its distribution the residuals are

normalized with respect to the amplitude of the input sub-stacks. This is performed by dividing the RMS of the

residuals calculated in the interval of interest by the RMS amplitudes of the reference seismic.

The Caress quality assessment is performed at well scale by comparing the facies found at well with the caress

prediction, section view by analyzing the continuity of the events and at base map view by analysing the

Geological coherence.

Jointly with the Caress cubes, DV/V is another source of seismic constraint for model infilling. The assessment

of its quality is performed by analyzing the residual time shift between base and monitor survey below reservoir

after warping operation using such attribute. We then look for zones showing higher time shift which would be

an indication of less reliable DV/V.

4 different attributes have been selected to analyze the seismic quality. Figure 1 presents their spatial variation

within a layer defined around the reservoir interval. To highlight zones of relatively poorer AVO behaviour the

correlation and stretch of the seismic signal between Near and Far traces have been measured. Such attributes

allows getting some hint about lateral variation in the quality of NMO correction. The correlation map exhibits

higher correlation values between sub-stacks within the channels. The stretch attribute shows globally

reasonable values except in some small localized zones. Signal to Noise ratio is another obvious criteria to

understand the seismic quality. It has been computed using the near stack. It indicates that the seismic is noisier

in the central panel area because of a highly faulted zone. It highlights also areas of poor seismic signal in the

west flank of the survey where a salt diaper induces very steep seismic reflectors. A last seismic QC taken into

account to define a quality indicator is a normalized RMS inversion residual map computed for the near sub-

stack. It allows to assess, at least partially, if the pre-stack inversion process has been successful to find an

impedance solution explaining the seismic response. This map shows higher residuals, and therefore lower

impedance reliability, in the faulted zone within the sand channels. We interpret these areas as being less

reliable in term of inversion products and therefore less able to have an important weight in the reservoir model

infilling.

In order to define a qualitative indicator reflecting the seismic quality over our interval of interest a map is

defined using some of attributes described in the previous paragraph.

For each single attribute, a quality map was defined, as can be seen in Figure 1. To obtain a single attributes

quality map a superposition of each maps is achieved to delineate zones where low reliability seismic can be

defined.

Figure 2 displays the final seismic quality map. In overall the seismic quality is high to medium, except in the

salt zone and in a small zone to the west, possibly affected by shallower faulted zone. This quality map is

afterward integrated to weight the seismic information put in the Geomodel.

IPTC-18306-MS 3

3D and 4D Seismic attributes time alignment

Prior to the combination of 3D and 4D seismic attributes, a time alignment had to be performed between dataset

to compensate for time shifts introduced by 4D effects and different seismic acquisitions and processing.

While the Caress has been computed using a broadband seismic survey acquired while the field was in

production, the 4D attribute DV/V is obtained from 3D high resolution survey referenced to the baseline time. In

order to be used together the Caress and 4D DV/V attributes have therefore to be put in the same time and

geographical referential as they have been acquired and migrated differently. In order to do so the time shift has

been computed by measuring the amount of shift necessary to maximize the correlation between the Full stack

volume coming from both dataset.

3D and 4D attributes combination and geomodel infilling

The dataset for seismic constraint definition is composed of four CARESS lithoseismic probability cubes (non

reservoir, laminated sand, massive soft sand and massive hard sand) related to each lithology occurrence. Three

4D seismic monitors (M1, M2 and M3) are also available derived from different vintages of high resolution

seismic surveys. Figure 3 illustrates a schematic workflow for 3D and 4D seismic data combination.

Most probable facies cube was computed using as input the facies probability cubes by assigning in the output

cube the facies with highest probability value. Moreover, cumulative 4D attribute is calculated by adding to M3

the response of M1 and M2 above one percent cut-off of DV/V in zones where M3 does not presents a response

above the cut-off. This step was performed to account for possible attenuation/compensation in 4D response in

M3 monitor.

Afterward, a cross validation of the most probable facies and cumulative DV/V is performed by analyzing the

zones where the 4D signal presents a clear response (DV/V above 1%) and the CARESS facies probabilities

does not correspond to a reservoir facies. This allows the definition of a Facies Analysis Cross-Validation

attribute (FAC) highlighting the incoherent zones which require some modification of the facies probabilities.

Such update is performed by increasing the initial reservoir facies probability as shown in Figure 4. This step

was performed at seismic scale and afterword upscalled into the grid to be used during the model infilling

workflow.

Final facies proportion cubes for the model were then built using an in-house workflow (Figure 5) allowing to

combine two proportion cubes per facies: a quantitative cube (or reference cube) providing the target facies

proportions (mainly guided by facies proportions at wells), and a qualitative cube guiding spatial distribution of

the facies (derived from lithoseismic probability cubes providing 3D trends per facies). A seismic quality map

derived from 3D seismic attributes QC has also been used to vary spatially the weight given to the seismic

constraint introduced as qualitative cube.

One final proportion cube is defined for each facies (5 cubes, one per AF). These cubes were then used to

distribute the facies in the model using Sequence Indicator Simulation (SIS).

AF facies were modelled stochastically (Figure 5) in the reservoir grid using the following inputs:

Well AF upscaled (1D) at wells

Facies proportion cubes

Local Varying Azimuth (LVA) property computed in channelized Architectural Elements (AE4, AE5

& AE6)

Vertical and Horizontal Variography

The result of facies modelling was used as basis for Petrophysical modelling in the reservoir grid. The

petrophysical simulations were performed using the Sequential Gaussian Simulation (SGS) algorithm. First of

all, the Effective Porosity (e) was simulated. Given the good relationship of e with Net Effective Porosity

net, Net To Gross (NTG) and Permeability (K), these parameters were co-simulated with e.

IPTC-18306-MS 4

Two model infilling using seismic constraint were performed : one using only Caress lithoseismic probability

cubes and a second one using combined Caress lithoseismic probability cubes and 4D DV/V attribute.

Results Discussion and Conclusions

Assessment of the quality of the input seismic data is a key step for an optimum integration of the seismic data

in the geomodel infilling workflow. Even though the analysis based on various seismic attributes remains

qualitative, this assessment has allowed defining a 2D seismic confidence map which was later integrated in the

compromise workflow to balance the seismic weight in the output facies proportion cubes.

The spatial distribution of the geological facies within the reservoir grid obtained with both 3D only or

integrated 3D and 4D attributes exhibits a good coherency with the geologic context. Nonetheless, the

combination of 3D attributes (CARESS) and 4D attributes (DV/V) has increased the sand content of the

geomodel in comparison to the model infilling using the CARESS attribute alone. Statistics in AF for both

models show that reservoir facies proportion within the main channel area is higher when 4D seismic data is

integrated in the infilling workflow. More importantly, this integration is improving the reservoir facies

continuity as illustrated in Figure 6.

QC was also performed on the NTG per AE. It was observed a maximum NTG error (in comparison to well

input) of 3%,. This difference was considered as acceptable having to take into account the multiple constraints

used by the simulation (e.g. Seismic, Variograms with local variation, well constraint).

3D petrophysical properties coherence were also validated by a seismic back-loop study aiming to check the

compatibility between actual inverted seismic and synthetic elastic properties (IP, PR). These synthetic

attributes are obtained thanks to a petroelastic model using as input the geological model petrophysical

properties. This exercise has allowed demonstrating that modelled P-impedance and Poisson’s Ratio are

globally less than 6% different from the inverted values. This has been considered has a sufficient level of

coherency to validate the model infilling from this point of view.

To conclude it is important to stress the benefit of this integration of 4D information early on in the geomodel

infilling workflow. Indeed, it makes the dynamic modelling more reliable by allowing an early combination of

static and dynamic information. This has improved the dynamic loop by reducing the time needed to history

match the reservoir model with production data.

Acknowledgments

The authors would like to thank TOTAL for permission to publish this paper. The authors would like also to

thank Hildebrando Vicente-Pedro for assistance in the Facies modelling workflow.

References

Sengupta S., Cadoret T., Pivot F., (2014). Semi-Automatic Facies Up-scaling Techinique for Litho-Seismic

Classification – Application to a field located in Western Offshore Africa. IPTC Paper 17444 presented at

International Petroleum Technology Conference in Doha 2014.

Hubans C., Cauquil E.C., Brechet E. (2014). 4D (time lapse) seismic: an emerging tool for underwater

monitoring. OTC paper 25225 presented at Offshore Technology Conference held in Houston 2014.

IPTC-18306-MS 5

Fig. 1: Seismic QC attributes and confidence contours. Top left - correlation between near and far sub-stack, top right - stretch between near and far sub-stack. Bottom left – mean relative inversion residual for near sub-stack,

bottom right – mean signal to noise ratio for near sub-stack.

Seismic Confidence

Low

Medium

High

MeanSignal/Noise

Near Substack

Mean Relative

Inversion Residual

Near Substack

Correlation

Near/Far

Near/Far

Stretch

IPTC-18306-MS 6

Fig. 2: Seismic Quality Map highlighting zones with low, medium and high seismic confidence.

Fig. 3: Schematic workflow for 3D and 4D seismic data combination.

Confidence

Low

Medium

High

Time Alignment to Baseline reference

Compromise workflow

Seismic Quality Map

UPDATED CARESS

CARESS (Facies Probabilities)

4D DV/V (M1, M2, M3)

3D + 4D seismic data Combination

IPTC-18306-MS 7

Fig. 4: Caress lithoseismic probability update using 4D data. Red ellipse highlights a zone where 4D anomaly is visible but no reservoir facies exists.

Fig. 5: Compromise and Facies modeling workflow. Reference proportions from well data and auxiliary proportions from seismic data. Seismic quality map used to weight the seismic information (alpha parameter).

Context / applicability of the method (2)

Objective of the proposed method: given proportion targets (the

reference cube), add to it the shapes, the spatial organization contained

in the auxiliary cube or, in other words, modify the global proportions of

the auxiliary cube in order to honor those of the reference cube

The workflow uses the second option:

10 - Références, date, lieu

Ref. cube Aux. cube

Step 1: proportions of the auxiliary

cube are modified to honor the

proportions of the reference cubeRef. cube

unchanged

Aux. Cube

modified

Step 2: Combination. In Case of VPC

or global proportions, only the

auxiliary cube is used (a=0, b=1)

Ref. cube

unchanged

Aux. Cube

modifieda x +b x

With a + b =1To keep the proportions of the reference cube

x

x

Confidence

Low

Medium

High








Ref. cube Aux. cube




unchanged

Aux. Cube

modified




Ref. cube

unchanged

Aux. Cube

modifieda x +b x


IPTC-18306-MS 8

Fig. 6: Comparison between AF property generated using only 3D as seismic constraint and AF generated using combined 3D and 4D seismic.

AF

CARESS No 4D

AF

CARESS + 4D

AE6

AF No 4D

23.2%

16.2%17.7%

15.8%

27.1%

AE6

AF_4D30.3%

17%17.6%

12.9%

22.2%

Statistics AF without 4D

Statistics AF with 4D

Paper No. 18306

3D and 4D Seismic Data Integration for

Geomodel Infilling: A Deep Offshore Turbiditic

Field Case Study

V.A. DA SILVA*, T. CADORET, L. BERGAMO, R. BRAHMANTIO

6

1

OUTLINE Slide 2

IPTC 18306 • 3D and 4D Seismic Data Integration for Geomodel Infilling• Victoriano A. Da Silva

2

3

4

5

3D AND 4D SEISMIC DATA COMBINATION

How to integrate 3D and 4D attributes in the geomodel infilling workflow?

SEISMIC DATA QC AND CONFIDENCE MAP DEFINITION

Assessing the spatial variability of the seismic quality

CONTEXT

Motivations to perform the study

GEOMODEL INFILLING

Impact of the 4D integration in the outcome?

CONCLUSIONS

Key messages

SEISMIC BACKLOOP

Validating the petrophysical infilling

CONTEXT Slide 3


• This study was performed in a deep offshore turbiditic field

• Study motivated by the need of an updated model integrating new data

• 4D attribute (dVp/Vp) has proven to be useful as an additional lithological indicator

• This presentation will show a workflow to integrate efficiently the seismic information in the geomodel infilling workflow

• Seismic has also been used to quality control the geomodel infilling

6

1

OUTLINE Slide 4


2

3

4

5





CONTEXT


GEOMODEL INFILLING


CONCLUSIONS

Key messages

SEISMIC BACKLOOP


SEISMIC DATA QC AND CONFIDENCE MAP DEFINITION Slide 5


Seismic Quality Map

Seismic Attributes QC

Boundary compilation

Bad

Good

Bad

Good

Bad

Good

Bad

Good

6

1

OUTLINE Slide 6


2

3

4

5





CONTEXT


GEOMODEL INFILLING


CONCLUSIONS

Key messages

SEISMIC BACKLOOP


3D AND 4D SEISMIC DATA COMBINATION Slide 7


Initial Most Probable

Facies 4D attribute Cumulative DV/V

Zones to be updated

Abs(DV/V)>1% & MPF=0

-3%

+3%

+1%

-1%

Most Probable

Facies Post Update

Check the Consistency

Update the inconsistent zones

LithoSeismic Facies

3D AND 4D SEISMIC DATA COMBINATION: Spatial Continuity QC Slide 8


Most Probable Facies

before 4D Update: Most Representative in Layer

4D Signed Absolute

Maximum in Layer Most Probable Facies

after 4D Update: Most Representative in Layer

Attributes Combination

LithoSeismic Facies

3D AND 4D SEISMIC DATA COMBINATION: QC at well Slide 9


GR AF Sand Probability

before update

Sand Probability

After update 4D DV/V B00M2

Cut off used for facies update Increased Water Sand probability

Associated

Facies (AF)

WOC

LithoSeismic Facies

6

1

OUTLINE Slide 10


2

3

4

5





CONTEXT


GEOMODEL INFILLING


CONCLUSIONS

Key messages

SEISMIC BACKLOOP


FACIES MODELLING WORKFLOW Slide 11


From seismic

Litho-Cubes + 4D

Architectural Elements

AF/AE on wells

Cubes Aux

Cubes Ref

AF Target Proportions

Proportion cube from

CARESS, one cube per AF

« spatial

organisation »

Constant per AE, one

cube per GAF « target proportion »

Cubes PCProportion cube

with spatial

organisation

respecting the

target proportion,

one cube per AF

C

O

M

P

RO

M

I

S

E

Proportion of AF per AE (CARESS)

x

Confidence

Low

Medium

High








Ref. cube Aux. cube




unchanged

Aux. Cube

modified




Ref. cube

unchanged

Aux. Cube

modifieda x +b x


x

+








Ref. cube Aux. cube




unchanged

Aux. Cube

modified




Ref. cube

unchanged

Aux. Cube

modifieda x +b x









Ref. cube Aux. cube




unchanged

Aux. Cube

modified




Ref. cube

unchanged

Aux. Cube

modifieda x +b x


x

x

Confidence

Low

Medium

High








Ref. cube Aux. cube




unchanged

Aux. Cube

modified




Ref. cube

unchanged

Aux. Cube

modifieda x +b x


SISMODELLING

Final

Facies Proportion

Cubes

Geomodelling Results Slide 12


AF

Lithocubes No 4D

AF

Lithocubes + 4D

23.2%

16.2% 17.7%

15.8%

27.1%

30.3%

17% 17.6%

12.9%

22.2%

Statistics AF without 4D In AE6

Statistics AF with 4D In AE6

Observation:

Sand proportion more in line with wells results

AF2 AF3 AF4 AF5 AF6

AF2 AF3 AF4 AF5 AF6

6

1

OUTLINE Slide 13


2

3

4

5





CONTEXT


GEOMODEL INFILLING


CONCLUSIONS

Key messages

SEISMIC BACKLOOP


SEISMIC BACKLOOP (SBL) WORKFLOW Slide 14


3D Geomodel 3D Inverted cubes

Update Petrophysics

Comparison is done in

the Geomodel “space”

SEISMIC BACKLOOP AT WHOLE GRID Slide 15


Initial mismatch analysis on the whole 3D grid

Strong Mismatch

Moderate Mismatch

Small Mismatch

Rock Physics Model Display

• Evaluate the impact of petrophysical properties (VCL, PHI, SW) on the Elastic IP-PR) attributes by color code to obtain the Summary X-plot

IP

PR

Non-Reservoirs

Vcl

0

0.6

No

n-R

eserv

oir

s

Reserv

oir

s +DIP -DIP

-DPR

+DPR DIP = RPM - INVERSION DPR= RPM - INVERSION

SUMMARY X-PLOT AND STATISTICAL ANALYSIS OF ANOMALY BODY Slide 16


I

II

IV I

II

IV

+DIP -DIP

IP Overestimation Possible interpretations:

1. Overestimation

of SW

III

2. Underestimation of PHI

-DPR

+DPR

3. Combination of

options 1 and 2

0.5 0 PHIE

But…

1. Bodies located below WOC

(options 1 and 3 are unlikely

to happen )

2. Most likely a porosity effect

DIP = RPM - INVERSION DPR= RPM - INVERSION

0,0%2,0%4,0%6,0%8,0%

10,0%12,0%

IP Percentage Difference

SLAP_Initial

SLAP_Final

Anomaly

Threshold

High Stakes FACIES

Properties Update

• SLAP_Inial: Initial infilling

• SLAP_Final: porosity increased by 3pu

6

1

OUTLINE Slide 17


2

3

4

5





CONTEXT


GEOMODEL INFILLING


CONCLUSIONS

Key messages

SEISMIC BACKLOOP


CONCLUSIONS Slide 18


• Seismic constraint allows to distribute realistic lateral heterogeneities and increases the reservoir facies continuity

• Seismic quality map permit weighting spatially the seismic constraint during model infilling

• The integration of 4D information has reduced the time needed to history match the reservoir model with production data

• Seismic Backloop shows that model in-filling allow to globally honor the inverted 3D seismic

We thanks Total and its partners for the authorization

to present this work

Thank You / Questions

Slide 19

Documents

IPTC-18306-MS 3D and 4D Seismic Data Integration for ... · In the field considered for this study 4D seismic data has proven to be a key piece of information to understand the dynamic