Geological geomechanical modeling as a support for the design and monitoring of oil wells

Prévia do material em texto

Mecânica das Rochas para Recursos Naturais e Infraestrutura 
SBMR 2014 – Conferência 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 Marchesi 
PUC-Rio, Rio de Janeiro, Brazil, vivianrm@puc-rio.br 
Débora Lopes Pilotto Domingues 
PUC-Rio, Rio de Janeiro, Brazil, deborapilotto@puc-rio.br 
Alvaro Gustavo Talavera Lopez, 
PUC-Rio, Rio de Janeiro, Brazil, alvaro@ele.puc-rio.br 
Sergio Augusto Barreto da Fontoura 
PUC-Rio, Rio de Janeiro, Brazil, fontoura@puc-rio.br 
Clemente José Gonçalves 
Petrobras, Rio de Janeiro, Brazil, clemente@petrobras.com.br 
Marcos Fonseca Alcure 
Petrobras, Rio de Janeiro, Brazil, alcure@petrobras.com.br 
 
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 be 
used 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 well 
established 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 
SBMR 2014 
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 in 
others. 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), Araújo 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 the 
methodology 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 Araújo 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 possible 
connections 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 
SBMR 2014 
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 grid 
capable of providing a representative3D 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 geostatistical 
predictions, 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 geostatistical 
simulation 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 modeling – in 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 secondary 
property 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 correlation 
between 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; Araújo 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 density 
cube in depth, while horizontal stresses require 
SBMR 2014 
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: correlating 
these 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 in 
permeable 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 used 
to define representative geological horizons that 
divide zones of characteristic geomechanical 
behavior. 
 The classification of facies capable of 
dividing the overburden into more discrete and 
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 some 
shale 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 mechanics 
correlations and for in situ stress and pore 
SBMR 2014 
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 be 
a 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 from Figure 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 integrating 
densities 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. 
SBMR 2014 
 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 global 
data 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 values 
are 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 analysis 
of 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 on Figure 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 data 
analysis 
Filter shale 
points 
Global trend 
Shale pore 
pressure 
Filter 
Sand pore 
pressure 
Special filters 
SBMR 2014 
 
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 atypical 
measure 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 are 
made available and it can be used to guide 
real time decisions in an integrated and 
spatial analysis. 
 
 
ACKNOWLEDGEMENTS 
 
The authors thank Schlumberger for providing 
PetrelTM academic license and Petrobras for 
making data available for this case study. 
 
 
REFERENCES 
 
Al-Ruwaili, S.B.; Chardac, O. (2003) 3D Model for Rock 
Strength & In-Situ Stresses in the Khuff Formation of 
Ghawar Field, Methodologies & Applications. Middle 
East Oil Show, 9-12 June, Bahrain. SPE-81476-MS. 
Araújo, E. et al. (2010) Drilling Optimization Using 3D 
Geomechanical Modeling in the Llanos Orientales 
Basin, Colombia. SPE Latin American and Caribbean 
Petroleum Engineering Conference, Lima, Peru. SPE-
138752-MS 
Armstrong, M. (1998) Basic Lineargeostatistics. 
Springer-Verlag Berlin Heidelberg. 157 p. 
Bachrach, R. et al. (2007) From pore-pressure prediction 
to reservoir characterization: A combined 
geomechanics-seismic inversion workflow using 
trend-kriging techniques in a deepwater basin. The 
Leading Edge, pp. 590-595. 
Bowers G. (1995). Pore Pressure Estimation From 
Velocity Data: Accounting for Overpressure 
Mechanisms Besides Under compaction. SPE 27488. 
Den Boer, L.D. et al. (2011) Using Tomographic Seismic 
Velocities to Understand Subsalt Overpressure 
Drilling Risks in the Gulf of Mexico. Offshore 
Technology Conference, Houston, Texas, USA. OTC-
21546-MS. 
Eaton B. A. (1975). The Equation for Geopressure 
Prediction from Well Logs. SPE Paper 5544. 
Holland, M. et al. (2010) Value of 3D Geomechanical 
Modeling in Field Development – A new Approach 
Using Geostatistics. SPE/DGS Annual Technical 
Symposium and Exhibition. Al-Khobar, Saudi Arabia. 
Kristiansen, T.G. (1999) Minimizing Drilling Risk in 
Extended-Reach Wells at Valhall Using 
Geomechanics, Geoscience and 3D Visualization 
Technology. SPE/IADC Drilling Conference, 
Amsterdam, Holland. SPE/IADC 52863. 
Rocha, L.A.S.; Azevedo, C.T. (2009) Design of Oil Wells 
(in Portuguese). 2nd Edition. Interciência, Rio de 
Janeiro. 
Tellez, C.P. et al. (2012) Geomechanics Characterization 
of the Clastics and Carbonates Formation of Southern 
Fields of Mexico (2005-2009). SPE Latin American 
and Caribbean Petroleum Engineering Conference, 
Mexico City, Mexico. SPE 153430. 
Torres, V. et al. (2005) 3D Analysis for Wellbore 
SBMR 2014 
Stability: Reducing Drilling Risks in Oriente Basin, 
Ecuador. SPE Latin American and Caribbean 
Petroleum Engineering Conference, 20-23 June, Rio 
de Janeiro, Brazil. SPE-94758-MS. 
Turner, A.K. (2006) Challenges and trends for geological 
modelling and visualisation. Bulletin of Engineering 
Geology and the Environment. v.65, pp 109–127. 
Zoback, M.D. et al. (2003) Determination of stress 
orientation and magnitude in deep wells. International 
Journal of Rock Mechanics & Mining Sciences. V. 40 
pp 1049–1076.