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Robust Facies Model by Combining Geostatistics and Multi-Attribute Analysis: A Case Study from
Middle Eocene Pay of Nawagam Field, Cambay Basin
Sankhadip Bhattacharya*1, Pratip Sengupta 2, Ravendra Kumar 1, T. R Joshi 2, R. K. Thakur2
Oil and Natural Gas Corporation Limited, India
(1 GEOPIC, Dehradun ONGC Ltd; 2 E&D Directorate, Dehradun ONGC Ltd)
Keywords
Nawagam, Sequential Indicator Simulation (SIS), Seismic attributes, Geostatistics.
Summary
Geologically sound facies model is of prime
importance in building any static model. The
classical variogram based property propagation
methodology has limitations when it comes to model
facies distribution in a complex geological
environment. These techniques usually fail to capture
spatial as well as temporal heterogeneity of sinuous
fluvial channels. In order to have a good facies model
which captures heterogeneities and therefore
movement of reservoir fluids, it is necessary to
integrate several sources of information in a proper
manner. This paper discusses a useful methodology
devised to build a geologically sound facies model of
a Middle Eocene pay of fluvial origin in the
Nawagam field of Cambay Basin, through blending
of seismic attributes using classical geostatistical
approach. Facies propagation was performed using
Sequential Indicator Simulation (SIS) and seismic
attribute trend was used as an external guide to
improve channel facies definition away from well
data points. Vertical proportion curves derived from
well data captured temporal heterogeneity in the
model. Results of several realizations were used to
estimate facies model, which helped to predict
suitable stratigraphic entrapment locales based on
complex facies distribution pattern. The study was
validated by recently drilled wells in the field.
Introduction
Nawagam field is located in the central Ahmedabad-
Tarapur-Broach tectonic block of Cambay basin, one
of the most explored petroleum provinces of India.
The field was discovered in 1963. Geologically, the
area comprises two prominent intra-basinal highs,
NW–SE trending Miroli–Nawagam high and NE-SW
trending Nandej–Wasna high. Both of them are
merged to EW trending Nawagam-Wasna Ridge
which divides northern Jetalpur Low from southern
Tarapur Depression. Regional tectonic map of the
Nawagam field is shown below (Fig. - 1).
Figure 1: Regional Tectonic setting of Nawagam field,
Cambay Basin (Courtesy: ONGC Bulletin)
Nawagam
Field
Not to
scale
Robust Facies Model by Combining Geostatistics And Multi-Attribute Analysis
So far, two major hydrocarbon bearing heterogeneous
reservoirs have been established in the field, namely
Palaeocene multi-layered Lower pays of Olpad
Formation consisting mainly of trap wash, formed
during the syn-rift phase and Middle to Late Eocene
arenaceous Upper Pays of Kalol Formation,
deposited mainly in the fluvio-deltaic setting during
the post-rift phase. Recently, commercial
hydrocarbon has been discovered in Middle pay
within argillaceous Cambay Shale Formation of Late
Palaeocene to Early Eocene. Exploring for fluvio-
deltaic pays of Middle Eocene in Nawagam is really
challenging because of its complex facies distribution
pattern. The same pay sand is producing in nearby
fields like- Sanand, Jhalora, Kalol, Wadu etc. The
present paper mainly deals with an attempt to build a
robust geologically sound Facies Model of the
shallower part of the Middle Eocene Pay within
Kalol Formation combining Seismic Attributes and
Geostatistics, since variogram based classical
stochastic analysis alone cannot capture the
complexities of a fluvio-deltaic reservoir 1.
Methodology
The structural framework was built using the fault
framework, and seismic guided horizons, in depth
domain. Thereafter, a 3D geological grid with 32
layers (cell dimension: 50*50*1) within the zone of
interest was constructed using the structural
framework, which acts as the basis for geostatistical
propagation of properties, like facies, porosity,
saturation etc. (Fig. - 2).
Figure 2: Structural framework of the study area
The workflow adopted here is shown in Chart – 1.
The first step was to mark four different types of
facies, namely sandstone, siltstone, shale and coal in
all the 45 wells, among which siltstone is the
dominant reservoir facies. The log motif of a Middle
Eocene section is shown in Fig. - 3.
Figure 3: Log motif of Middle Eocene Pay
The discrete facies logs were then up-scaled using the
most suitable arithmetic average method. The process
was iteratively done till we achieved a low value of
standard deviation and variance by comparing
histograms of input raw logs and output up-scaled
logs (Fig. - 4).
Figure 4: Raw vs Upscaled facies log histogram
A flexible pixel-based stochastic modeling technique,
called Sequential Indicator Simulation (SIS) was
used for propagating 4 types of upscaled discrete
facies in the reservoir layers of the 3D geological
grid. Vertical Proportion curves were also generated
from well data to control the vertical distribution of
facies in a particular zone and seismic attribute trends
of Spectral Decomposition maps tuned for particular
Middle Eocene pays at selected frequencies (22 Hz)
were used as spatial trends, which guided the
propagation of reservoir facies away from the well
data points, within the zone (Fig. - 5). Spectral
A A’
Middle
Eocene
Pay
Fault
Model
Horizon
Model
A-A’ Sectional View of the
layer model with faults
NG-X Well
Middle Eocene
Pay top
Robust Facies Model by Combining Geostatistics And Multi-Attribute Analysis
decomposition is chosen over other attributes since
subtle stratigraphic features get tuned at particular
frequencies and can be identified easily 2.
Thus, away from well data, conditional distribution
follows the values read from the seismic trends and
close to well data, the continuity expressed through
the variogram and kriging become more dominant.
After several iterations, we observed that 50:50
weightage distributions between variogram and
seismic trend give the optimum result in this case
(Fig. - 6).
Validation of Facies Model
In the present study, Stochastic facies modeling was
adopted over deterministic approach, since it has the
advantage of generating multiple realizations based
on the input data which will all be equally probable
and can assist in better understanding of the
associated uncertainties.
A detailed, geologically sound facies model for input
to rock property modeling was thus generated after
considering several realizations, which was subjected
to extensive quality checking, like uncertainty
analysis in variogram ranges, histogram analysis,
arbitrary profiles through wells etc. and the resultant
facies maps were correlated with well data. Even to
check the quality of facies model, a few blind wells
were left and after modeling actual lithology were
checked with the modeled lithology at recently
drilled well locations (like well NG-Y) and a good
match was observed (Fig. - 7). The well has
produced commercial oil on conventional testing.
Figure 5: Input trends for facies modeling (A- Vertical
proportion curve; B- Seismic trend; see text for explanation)
Figure 6: Average reservoir facies occurrence map of
Middle Eocene Pay with producer wells (NG-Y in circle)
Figure 7: Facies Model validation
Model
Extracted Saturation
Average reservoir facies occurrences within zone
NG-Y
Well
N
GR Facies
Actual
Facies
Upscaled
Model
Extracted
Facies
Model
Extracted
Porosity
Middle
Eocene Pay
Top
A B
Robust Facies Model by Combining Geostatistics And Multi-Attribute Analysis
Chart-1: Facies modeling flowchart
Figure 8: N-S Sectional view of Facies Model
Conclusions
Heterogeneity in facies distribution influences the
flow pattern of hydrocarbon in a reservoir. 3D
geological models that account for facies variations
are more reliable since basic depositional trends are
taken into consideration. Modeling fluvial reservoir
facies is itself a challenging task using conventional
geostatistical techniques of facies modeling. The
methodology described herein helps to capture both
spatial as well as temporal heterogeneity in the
complex facies distribution pattern of fluvio-deltaic
Middle Eocene Kalol pay sand by integrating
different types of data, like- well data, seismic data,
petrophysics and geostatistics (Fig. - 8). Such
geologically sound facies model provides more
confidence in the facies biased petrophysical property
propagations. The model has already been tested by a
number of recently drilled wells as well as some
missed opportunities were identified based on
complex facies architecture of the pay zone. It
ultimately facilitated in achieving a reliable
estimation of the in-place volume of hydrocarbon,
aiding in more comprehensive and suitable
management strategies.
References
1. Ronny Meza, et. al., 2015, Combining
Geostatistics with Seismic Attributes to
Improve Reservoir Management Strategies: A
Case Study from the Faja Petrolifera del
Orinoco; WHOC15- 327, 1-14.
2. Chopra, S., 2011, Extracting meaningful
information from Seismic Attributes; p- 8,
CSEG Distinguished Lecture, Canada, 2011
Acknowledgments
Authors would like to thank Sri. A. K. Dwivedi,
Director (Exploration) of ONGC Ltd. for giving the
permission to publish this paper in 12th Biennial
International Conference and Exposition of SPG,
2017. Authors would like to express their sincere
gratitude to Sri Ashutosh Bhardwaj, ED- HOI
GEOPIC for allowing the work to publish. Authors
greatly acknowledge Sri Anand Sahu, Retired ED-
Chief E&D Directorate for continuous support and
motivation during the entire course of the study.
Authors would like to thank the respective teams
from Asset and Block for providing all the necessary
G&G inputs. Sincere thanks to Mr. K. Vasudevan,
GM (Geology) for critically reviewing the paper.
Thanks are due to all the colleagues who are directly
or indirectly involved and contributed in various
means in this study. The views expressed herein are
solely of the authors and do not necessarily reflect the
views of the organization.
Data QC and
Loading
Seismic
Attributes
Structural
Framework
Upscaling
SIS
Gridding
Facies Model Model QC
N S
NG-A
NG-B
NG-C NG-D
NG-E
NG-F NG-G