598819 80 Geological Geomechanical Modeling Marchesi

8/10/2019 598819 80 Geological Geomechanical Modeling Marchesi

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Mecnica das Rochas para Recursos Naturais e Infraestrutura

SBMR 2014Conferncia Especializada ISRM 09-13 Setembro 2014

CBMR/ABMS e ISRM, 2014

SBMR 2014

Geological-geomechanical modeling as a support for the design

and monitoring of oil wells

Vivian Rodrigues MarchesiPUC-Rio, Rio de Janeiro, Brazil,[email protected]

Dbora Lopes Pilotto Domingues

PUC-Rio, Rio de Janeiro, Brazil,[email protected]

Alvaro Gustavo Talavera Lopez,


Sergio Augusto Barreto da Fontoura


Clemente Jos Gonalves

Petrobras, Rio de Janeiro, Brazil,[email protected] Fonseca Alcure

Petrobras, Rio de Janeiro, Brazil,[email protected]

SUMMARY: Well design and drilling strategy planning are critical steps during exploration and

development of oil and gas fields, but the workflow for well design usually follows a simplified

methodology that generally focuses on only one or on a few correlation wells. 3D models are only

available for, and focused on, reservoir volume prediction and fluid flow simulation. Lately, it is

possible to see some efforts to enhance the full comprehension of the whole field and to develop a

more robust well design by using 3D characterization techniques. This paper shows the steps

involved in the development of a 3D geological-geomechanical model and how these models can beused as a robust tool to support decision makers. The methodology consists of preparing a

geological model which comprises both overburden and reservoir zones, studying and distributing

representative geomechanical facies, distributing properties/data of interest, applying correlations

between initial data and rock mechanics properties, and calculating in situ stresses. Results of a case

study show that an integrated analysis between geologists and geomechanical engineers is

instrumental for an efficient 3D geomechanical characterization. Some direct benefits of these

models are a global view of field behavior and integrated data, facilitating communication between

expert teams, anticipating and preparing for possible drilling hazards and instantly extracting data

for each desired well path, and increasing the reliability of well design.

KEYWORDS: 3D geological-geomechanical modeling, well design, stability analysis.

1 INTRODUCTION

Increasing geology complexity featured in new

oil and gas reserves has forced petroleum

industry to change its method of creating well

design. The classical workflow method includes

defining a few offset, already drilled, wells as a

guide for well design. This methodology is wellestablished and sufficiently accurate for non-

complex fields.

However, the challenge of new scenarios

cannot be fully appreciated by using the

classical methodology. Well design experts,

therefore, have been forced to develop a more

robust field characterization in order to

reproduce the geological and geomechanical

complexity of these sites.Some of the first attempts to solve this issue
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were developed by Kristiansen et al. (1999),

using a 3D integrated analysis of wellbore

instability events faced during drilling and 3D

seismic attributes in order to minimize drilling

risks in the Valhall field, North Sea. As the

authors have noted, geomechanical problems

were encountered in some wells but not inothers. Thus, they looked at potential

heterogeneous rock strength changes that could

not be picked up by relying solely on offset well

information.

They found that the geomechanical problems

could be characterized by developing a

geological model utilizing top formation

surfaces and 3D seismic coherency data for the

overburden. This model has helped in defining

safer well trajectories by avoiding fault areas

with a narrow operational mud window.

Al-Ruwaili and Chardac (2003) advanced

this methodology by modeling the spatial

distribution of rock mechanical properties and

in situ stresses as a tool for improving well

stability for future drilling at the Ghawar field,

Saudi Arabia. Similar and better methodologies,

developed to solve specific geomechanical

issues, have been employed by Torres et al.

(2005), Arajo et al. (2010), Den Boer et al.

(2011) and Tellez et al. (2012).The present paper aims at presenting the

steps involved in a 3D geological-

geomechanical characterization for drilling

purposes. A case study illustrating the benefits

of applying this technique is also presented.

2 METHODOLOGY

Defining model goal - The general goal of themethodology presented here (Figure 1) is to

better understand field behavior and improve

well design.

Before starting the 3D geological-

geomechanical modeling, it is fundamental to

have the details of the model goal well

established (Turner, 2006). Even if the general

objective has already been defined, various

levels of complexity should be reached when

considering geology, specific drilling events

experienced, and time and data available.The focus of Kristiansen et al. (1999) was to

identify areas near faults, which they found to

display potential risks for drilling, and avoid

them. In this case, the model could be a simple

one, in which it is sufficient to model

stratigraphy and integrate 3D seismic coherency

into it; thus, identifying better well paths.

Figure 1. Geological-geomechanical modeling workflow.

In some cases, there are other features that

need to be characterized, such as: rock

mechanical properties, pore pressure, and in situ

stresses. This can be observed in the works

presented by Arajo et al. (2010), Den Boer et

al. (2011) and Tellez et al. (2012).

Collecting and preparing data - Data

collection and preparation are intrinsically

connected with the final goal and complexity of

the model. Usually, data from different sources

and technical areas (i.e. stratigraphic and

structural geology, well paths and well logs,

well tests, drilling events and seismic data) are

collected and analyzed in an integrated way.

Integrated data analysis - Once all data are

spatially located on the same 3D modeling

software, it is possible to identify possibleconnections between observed drilling events

and structural geology, or with specific

geological horizons or even to conclude that

some in situ stress perturbation or some

abnormal pore pressure generating mechanism

may be present on the modeled area.

Structural and stratigraphic modeling -

Structural and stratigraphic modeling consists

of developing an integrated interpretation

between well and seismic data. Geological

zones with similar geomechanical behavior are

defined by picking up well tops and propagating

Collecting and

preparing data

Structural and

stratigraphic

modeling

Facies modeling

Property

modeling

Modeling rock

mechanics

In situ stress and

pore pressure

prediction

Integrated data

analysis

Defining model

goal


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them by seismic interpretation gap between

wells and extrapolating to border areas (Figure

2).

Figure 2. Stratigraphic and structural modeling.

After interpretation, these data are used to

model horizon and fault surfaces, generating the

geological model geometry. In order to

represent the necessary refinement of these data

inside the model, it is necessary to create a gridcapable of providing a representative 3D cell

size.

It is important to highlight that a model

prepared for drilling purposes has to be much

more accurate than cells of models for reservoir

simulation purposes. It is vital that cell height is

not so big that it loses important features

captured by well logs. Laterally, cells can have a

greater size, but one has to be careful not to

create a support effect on further geostatisticalpredictions, as discussed by Armstrong (1998).

Facies modeling Even if horizons have

been mapped to separate zones with

characteristic geomechanical behavior, they are

usually too large, especially in the case of

overburden zones. Inside these zones it is

common to find interspersed lithologies with

distinct properties. In order to capture these

features in the geological models, facies

modeling is performed for future geomechanical

drilling purposes.Facies are classified according to the specific

purpose for each model, and are distributed

inside zones. For well stability applications, the

model needs to contemplate rock properties,

which are correlated to well logs depending on

the lithology groups of similar mechanical

properties. Therefore, lithofacies are classified

and spatially distributed by using geostatisticalsimulation techniques.

Once the model is populated with lithofacies,

it is already possible to advance towards a better

global field comprehension, and identify

possible risk areas.

Property modelingin order to predict rock

mechanics, in situ stresses and pore pressure

along the field, geophysical well logs are used

as a data entry; so, their distribution is predicted

first. Similar to the procedure adopted for facies

distribution, properties are analyzed and

distributed for each geological zone modeled.

These well log properties are intrinsically

dependent upon lithology; thus, the spatial data

analysis (or variography, or structural data

analysis) is made individually in each facies

present inside a zone.

In addition to the facies discretization and

structural analysis, the spatial property

distribution can be guided by a secondary

property between wells. This secondaryproperty can be a seismic attribute or another

previously modeled property, plentiful enough

to be considered a hard data.

Modeling rock mechanics given the high

cost to collect samples, laboratory tests are not

usually available in oil and gas fields. Due to

this limitation, empirical correlations are

normally used to approximate rock properties.

Their 3D distribution can be achieved by three

principal methodologies: direct correlationbetween tests (or curves predicted on wells) and

3D seismic attributes; prediction along wells

and 3D distribution by geostatistical or neural

networks techniques; prediction by applying

correlations directly on predicted log properties

cubes (Al-Ruwaili et al., 2003; Holland et al.,

2010; Arajo et al., 2010).

In situ stress and pore pressure prediction

In situ stress prediction can be separated in

vertical and horizontal stresses. Vertical stress

is directly obtained by integrating the densitycube in depth, while horizontal stresses require


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a more complex analysis. Minimum horizontal

stress can be approximated by leak off (LOT) or

hydraulic fracture tests (Zoback et al., 2003) on

specific well depths where they were

performed.

Some techniques commonly used to spatially

distribute the horizontal stresses are: correlatingthese data with vertical stress (Rocha &

Azevedo, 2009); predict both minimum and

maximum horizontal stresses along wells by

lateral strain approach and distribute it,

correlating with seismic data (Al-Ruwaili &

Chardac, 2003); adopting a correlation with the

depth of sediments (Rocha & Azevedo, 2009).

In addition to the lateral strain technique,

maximum horizontal stress can be

approximated from well instabilities, where

induced fractures and breakouts, added up to

rock properties and failure criteria, can be used

to obtain maximum horizontal stress from the

minimum one (Zoback et al., 2003).

Taking out basin modeling, which extends

beyond the scope of this study, pore pressure

prediction in a 3D model can be accessed by

applying methods developed for 1D prediction

on shale lithologies (Eaton, 1975; Bowers,

1995). On a further step, fluid flow calculations

are applied to hydrostatically distribute fluids inpermeable lithologies. The idea is to use 3D

cubes to predict pore pressure and calibrate it

with direct measures on permeable zones.

Usually this approach is applied directly for

interval velocity cubes seismically derived (Den

Boer et al., 2011), or for high resolution

velocity cubes predicted by using compressional

transit time, Dtc, well logs and interval velocity

cubes (Bachrach et al., 2007).

3 CASE STUDY

A case study is presented here to illustrate the

methodology. Coordinates are purposely hidden

to guarantee confidentiality.

The aim of the model was to characterize the

geomechanical field behavior, to provide all

necessary data for designing new wells and to

support decisions during drilling in the area.

Well logs, stratigraphy and lithology were usedto define representative geological horizons that

divide zones of characteristic geomechanical

behavior.

The classification of facies capable of

dividing the overburden into more discreteand

representative clusters was closely observed.

The idea is to enable the possibility of picking

up geomechanical differences. Shales, marl,siltstones, and clays were divided into

individual facies, as well as sands, diamictite,

carbonates, igneous rocks and an additional

group of lithologies. The authors intend to

discretize different strength behavior inside the

overburden (into clays, less compacted, shales,

which are fissile, siltstones and marls).

Sequential indicator simulation was used to

spatially distribute facies, as it can be seen in

Figure 3. A qualitative analysis of the results

showed that the overall expected behavior of

lithology distribution agreed with local geology.

Figure 3. Facies model.

Near mudline facies are predominantly clay,

which are, then, replaced by shale and some

lenses of marl and siltstone. Inside, the second

zone diamictite prevails, but there are someshale lenses. The third zone is reservoirs and

sand predominates, followed by the fourth zone,

which comprises igneous rocks, sealing a

barrier between the upper and lower reservoirs.

Below the second reservoir there is more shale.

Properties derived from well logs, such as

density (Rhob), compressional sonic wave (Dtc)

and shear wave (Dts) were spatially distributed

by employing geostatistical simulation

techniques. These logs were chosen for the

further application of rock mechanicscorrelations and for in situ stress and pore


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pressure studies.

Spatial correlation analysis of well data was

performed individually for each facies. Rhob

and Dtc presented good results, respectively

presenting mean absolute percentage errors of

approximately 5% and 2.5% along the well

reserved for blind test. This is considered to bea small error for predictions in engineering

applications.

For predicting Dtc, interval seismic velocity

was also used as secondary data. Dts, that was

less abundant, was not guided by facies for

distribution, because there were not enough data

to analyze individual variograms. Gamma ray

(GR) did not demonstrate good spatial

correlation on variograms, so it was distributed

by using the inverse distance squared method.

Rock mechanical correlations were directly

applied to these cubes, using facies distribution

to separate the best-fit correlations. A previous

study of recommended correlations for this area

had been done. Figure 4 shows the cube

obtained for unconfined compressive strength

(UCS).

Figure 4. Spatial distribution of UCS.

Laboratory test data were not available for

validation, so the arithmetic mean of the

simulated scenarios was extracted along the

blind test well and compared to the results by

applying the same correlations to the measured

wireline logs. It can be seen fromFigure 5 that a

close approximation was achieved.

Notwithstanding to smoothness, that is a

consequence of the cell size, the model

provided a very good UCS prediction.The integration of water and sediment

densities (previously predicted density cube)

with depth defined the vertical stress cube. The

modeled area has water depths varying from

500 m to 2000 m, which reflects on the lateral

changes of vertical stresses along the field

(Figure 6).

Figure 5. Quality control of modeled UCS in a blind test

(red line is the predicted UCS and blue line is the UCS

calculated from measured logs).

Figure 6. Vertical stress cube calculated by integratingdensities on depth.

It was assumed that the case study is

allocated on an extensional stress regime,

therefore minimum and maximum horizontal

stresses are considered equal. The minimum

horizontal stress was distributed by employing

two strategies: a pseudo 3D distribution

technique, through adjusting a linear regression

between LOTs and depth of sediments and; 3D

distribution of stresses from wells to the model

guided by vertical stress.


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The first technique presented better physical

results, as the second technique resulted in a

noisy performance in some areas of the model.

The correlation obtained between minimum

stress and depth of sediments was applied to the

model in order to obtain a 3D distribution.

Figure 7 shows a comparison between globaldata and the available test on the validation

well. Note that the wellbore test validation

presents a lower value than the global field

trend. This feature could be considered a

consistent behavior in that area of the field or

not. Nevertheless, as there is only a single test

available for that part of the modeled area, it

cannot be confirmed if it is a valid behavior.

The model distribution used the global trend,

while post-mortem stability analysis of the

validation well was performed with its own

data.

Figure 7. Correlation of LOT data and depth of

sediments. Pressure and depth values were purposely

removed (red color: validation well; blue color: global

data).

Pore pressure prediction was performed as

illustrated on Figure 8. Initial data analysis

indicated that overpressures were not expected

at this field. The RFTs (repeated formation test)

histogram showed that the majority of valuesare concentrated near 8.73 lb/gal with few

exceptions varying for maximum of 9.3 lb/gal,

which is still considered a normal pore pressure

gradient.

The Eaton method was used to predict

pressures along the model. In order to map the

normal compaction trend line, Dtc was filtered

by well lithologies and the facies cube to

contain data only on clay lithologies/facies. The

trend line obtained at the wells was distributed

for the global field.

Figure 8. Pore pressure prediction workflow.

The Eaton method was applied only to shale

and propagated in sand assuming a hydrostatic

pressure distribution inside them. If the depths

of fluid contacts were available, buoyancy

effects could be computed in these models.

Obtained pressures were validated and

calibrated to RFT data in good agreement. By

analyzing the Dtc and Rhob logs of igneous

rocks, it was found that they were not highly

fractured (image logs were not available to

confirm this). Therefore, it was assumed that

the fractures were not connected, so pore

pressure on that facies is equal to zero.

The modeled cubes permitted good analysisof the global field geomechanical behavior,

including potentially low strength zones, with

low UCS.

All modeled data including facies, logs,

mechanical properties, pore pressure and in situ

stresses were extracted along the trajectory of

the validation wellbore. The necessary time to

extract data took very few minutes. This data

were further used to perform a well stability

analysis, simulating a well design. Data

acquired during drilling was used to develop a

post-mortem analysis. Results obtained are

illustrated onFigure 9.

Note that the lithology was well predicted,

even capable of discretize overburden where

data was not available on the post-mortem

analysis (the initial thick light green color),

where a clay lithology was assumed. The lower

limits of the mud weight window were well

predicted too, and it can be seen that pore

pressure was also well predicted.

Pressure dataanal sis

Filter shaleoints

Global trend

Shale poreressure

Filter

Sand poreressure

Special filters


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Figure 9. Comparison of project (3D prediction) and post-

mortem (1D) mud weight window. Blue dots are pressure

measures and the black dot is a LOT pressure.

Minimum horizontal stresses demonstrated a

considerable difference because of the atypicalmeasure of LOT pressure when compared to the

global data (pink: post- mortem; yellow: project

from 3D). Apart from this difference, where it is

not possible to confirm the robustness of the

LOT data, results obtained were considered

very satisfactory.

4 CONCLUSIONS

The main points observed during the study can

be highlighted here:

3D modeling is a good solution for

accurately predicting well log properties,

where not only one offset well is being

used to predict it, but all available wells.

In addition, picking up horizons and facies

and properties prediction are guided by

seismic interpretation between wells.

Even lithofacies can be well predicted by

using 3D techniques;The 3D modeling of rock properties can

highlight possible instability zones;

Lateral transfer techniques and fluid

buoyancy effects can be considered in pore

pressure along the entire field;

Once the 3D model is created, data can be

instantly extracted for each desired well

trajectory, making the well design process

much faster.

A model can be updated as new data aremade available and it can be used to guide

real time decisions in an integrated and

spatial analysis.

ACKNOWLEDGEMENTS

The authors thank Schlumberger for providingPetrelTM academic license and Petrobras for

making data available for this case study.

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598819 80 Geological Geomechanical Modeling Marchesi

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