A breakthrough in 3D seismic interpretation

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1. Introduction
In the last few decades, seismic interpretation 
techniques have been rapidly developed for detailed 
reservoir delineation and characterisation. The traditional 
approach is generally an intensively time-consuming 
process that is heavily reliant on manually picking or auto-
tracking of single horizons within the seismic volume. 
The tool allows tracking only one horizon at a time and 
is limited to areas with clear seismic signals or relatively 
simple geological structures.
New methods have been introduced to exploit the 
3-dimensionality of the data and simultaneously auto-
track every horizon throughout the seismic volume [2 
- 7]. In 2009, Pauget et al. proposed a global method to 
build a 3D geological model directly from the 3D seismic 
volume [1]. This innovative method optimises the seismic 
A BREAKTHROUGH IN 3D SEISMIC INTERPRETATION
Nguyen Xuan Thinh1, Ha Quang Man2
1Eliis Australia Pty, Ltd. 
2Petrovietnam Exploration and Production Corporation (PVEP)
Email: eric.nguyen@eliis.fr
interpretation workflow with greater confidence and 
accuracy. Continuous chronostratigraphic surfaces can be 
generated at every sample of the seismic data, enabling 
to overcome the limitation of seismic polarity changes. 
In this study, we have applied this advanced seismic 
interpretation method and its associated attributes for 
enhancing subsurface imaging, reservoir delineation and 
characterisation for the Maui 3D seismic volume from 
Taranaki basin, offshore New Zealand.
2. Regional geological settings
Extending 100,000 km2 along the western margin and 
filled with 10 km thick Cretaceous - Cenozoic sediments, 
the Taranaki basin is the largest offshore sedimentary basin 
in New Zealand (Figure 1). Rifting started from the Late 
Cretaceous and completely ended in the Paleocene, along 
with a rapid deposition within graben areas accompanied 
by high heat flow. During the Paleocene - Eocene period, a 
passive margin developed over the entire sub-continent; 
a slow subsidence rate allowed sediments to accumulate 
Summary
Accompanying the advancement of computer science and technologies, new techniques have been introduced to optimise the 
seismic interpretation workflow. In this study, we apply the “Global seismic interpretation method”, developed by Pauget et al. [1]. A 3D 
Relative Geologic Time (RGT) model was obtained directly from the 3D seismic volume which is the outcome of this method. Given the 
fact that in the 3D RGT model, the geologic time is continuous, a relative geologic age can be interpolated and assigned to every voxel of 
the seismic volume.
The dataset used in this study is the Maui 3D seismic volume from Taranaki basin, offshore New Zealand. A stack of 400 continuous 
stratigraphic horizons is produced from the Maui RGT model, even for complex areas where classical methods failed to achieve or would 
take a long time to complete. Integrated with seismic attribute mappings such as RMS amplitude and/or spectral decomposition, the 
horizon stack enables to navigate the seismic volume in stratigraphic order. Thus, the result enhances the identification of geological 
elements, stratigraphic insights, and paleo-depositional environments in greater detail for stratigraphic reservoir detection and 
characterisation. The novel methodology indicates a new way to conduct seismic interpretation, utilises all the information in the 3D 
seismic data, hence greatly reduces the exploration time cycle.
Key words: Seismic interpretation, seismic attributes, geologic time model, subsurface imaging, Taranaki basin.
Date of receipt: 26/10/2020. Date of review and editing: 26/10 - 3/12/2020. 
Date of approval: 11/6/2021.
PETROVIETNAM JOURNAL
Volume 6/2021, pp. 63 - 69
ISSN 2615-9902
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across the shelf and coastal plain areas in the Taranaki 
basin [8]. The Late Eocene-Early Oligocene period marked 
the starvation of clastic materials [9]. Thereafter, this basin 
underwent a significant phase of subsidence from the 
Oligocene to the Early Miocene due to the development 
of the Australia-Pacific plate boundary zone in the eastern 
area. This was followed by the widespread of limestone 
and marl deposition in the outer shelf to upper bathyal 
water depths [9]. Increasing sediment loading contributed 
to the evolution of the shelf-slope system in the Miocene, 
resulting in the deposition of sandstone, interbedded mud 
and siltstone in the outboard areas. The plate boundary 
evolution also caused the basement to overthrust the 
Taranaki fault in the Early Miocene and the formation of 
the Tarata thrust zone in the Early Miocene. By the Middle 
Miocene, the compressional effect on the northern area 
and its eastern margin had decreased, coinciding with the 
development of the submarine volcanic arc. During the 
Pliocene, the volcanic arc moved southeastward onshore 
and the northern areas of the Taranaki basin started 
extending, creating accommodation space for the Plio-
Pleistocene progradation and aggradation of the Giant 
Foresets formation in the Northern and Central grabens. 
3. Database and workflow
3.1. Subsurface data
The Maui 3D seismic data used in this study is a full 
offset, post-stack time-migrated volume which covers a 
surface area of approximately 1,000 km2. All seismic data 
are zero-phase processed where acoustic impedance 
increases are displayed by positive amplitudes (peak 
reflections) and decreases in acoustic impedance are 
indicated by negative amplitudes (trough reflections) 
on the seismic section (Figure 2). The 3D seismic survey 
was acquired with 25 × 25 bin size, 1836 samples/trace, 
3 ms sample rate and a total record length of 5,600 ms. 
In this area, the Maui gas field with 17 exploration and 
production wells is one of the largest gas condensate ... orkflow, based on the cost function minimisation 
algorithm [1], consists of two steps. 
The first step consists of computing a 3D grid of 
horizon patches, call “3D Model Grid” (Figure 3). Millions of 
grid points or nodes are distributed in 3D seismic volume, 
onto every seismic polarity such as peaks, troughs, zero 
crossings, or inflection point with a constant step of 
seismic bin size (Figure 3a). Each node is an elementary 
Figure 1. Location map of Maui gas field and Maui 3D seismic survey, Taranaki basin, 
offshore New Zealand. Modified after King and Thrasher [9], Higgs et al. [10], and Haque 
et al. [11].
Figure 2. Seismic arbitrary line from the Maui 3D traverses through Maui (M) 1, 7, 2, 6 
petroleum wells. Dashed, yellow lines are interpreted horizons N40 and N30 in the Middle 
Miocene interval from Thrasher et al. [12]. Green lines are horizons 106, 120, 126, and 248 
from the Horizon Stack (Figures 5, 6, and 7). 
Whitiki 
Fault
Cape
Egmont
Fault
Seismic 
survey
Maui
Gas Field
Region A
Taranaki Basin Wanganui 
Basin
Fault Boundary of the basin Coastline
Maui Gas Field Volcanic Maui Platform Study area
Northern 
graben
Central 
graben
Southern inversion zone
Ea
ste
rn
 m
ob
ile
 be
lt
We
ste
rn 
sta
ble
 pl
atf
orm
0 5 km
0 5 km
Maui
Gas Field
Region B
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horizon patch in 3D with the seismic bin size square (Figure 
3b). Using the afore-mentioned algorithm, all elementary 
horizon patches are linked based on the similarity of the 
seismic wavelets and their distance. For example, if two 
wavelets are 3 seismic bin size apart and 30% similar, the 
nodes on these wavelets will be linked. As a result of this 
process, all possible horizons within the seismic volume 
are auto propagated in one attempt (Figure 3a, 3b), acting 
as a framework for the 3D geological model. The fact that 
the same geologic age is assigned to patches connected 
laterally, so each auto-tracked horizon, having its relative 
age, is sorted stratigraphically and never crosses each 
other thanks to this advanced algorithm. 
In the second step, the 3D relative geologic time 
(RGT) model is computed from the interpolation of the 3D 
model grid (Figure 3), in which the relative geologic time 
is obtained for every sample of the seismic volume. The 
role of the seismic interpreter involves refining the RGT 
model by modifying and constraining the relationships of 
the horizon patches in the 3D model grid until an optimal 
result could be achieved.
3.3. Horizon stack and stratal slicing
From the RGT model, a horizon stack comprising of 
unlimited chrono-stratigraphic or iso-geological time 
surfaces can be created for imaging geological elements 
and thin stratigraphic events even at a sub-seismic scale. 
Those surfaces are only 5 - 7 ms apart and represent 
Figure 3. Summary of the workflow: (1) Maui 3D seismic volume, (2) 3D model grid creation. Auto propagation is based on a correlation threshold in the model grid when nodes (yellow 
points) are connected, showing on both 2D (a) and 3D (b) viewer, (3) Maui 3D geo-model is the result of the model grid interpolation.
Figure 4. (a) the 3D model grid where key horizon patches can be editable and highlighted in colours, following the interpreter's ideas, (b) 3D RGT model is blended with 3D seismic volume, 
showing the same geometry. Instead of having seismic amplitudes, there are relative age values assigned to each voxel of the 3D RGT model, (c) The horizon stack comprises dense strati-
graphic horizons, representing the geologic time values in the RGT model. 
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Figure 5. From the RMS amplitude attribute horizon stack, the geological features of the marginal marine depositional environment in the Giant Foresets Formation were highlighted in 
great detail on horizon 248 (see Figure 2 for a stratigraphic position).
Figure 6. Comparison between (a) horizon from N30-N40 interval using the isoproportional slicing method in Kroeger et al. [17] and (b) horizon 106 from the horizon stack with colour-
blended spectral decomposition with three different frequencies (see Figure 2 for a stratigraphic position). Note that on horizon 106 of the horizon stack, the subtle channels in the dash, red 
circle area, and the overall geometry of the Middle Miocene channel system were revealed more robustly. 
0 1 2 3 4 5 Km
(a) (b)
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stratigraphic horizons (Figure 4), contrary to horizontal 
slices (i.e. time slices) from the seismic volume. Several 
seismic attributes can be extracted on those horizons 
such as RMS amplitude and colour-blended spectral 
decomposition attribute. These attributes are calculated 
from a fixed window size in seismic samples along each 
Figure 7. Using spectral decomposition seismic attribute and the colour blending tool to detect the geological features on horizon 120 (a) and horizon 126 (b) of the horizon stack (horizon 
126 is above horizon 120 in stratigraphic order or “younger” in the relative geologic time domain). The evolution of the Middle Miocene, meandering channel system, from horizon 120 to 
126 is highlighted in both time and space (see Figure 2 for a stratigraphic position).
Horizon 120
Channel systems
Faults
Xline 3600
Xline 3600
Inl
ine
 20
0
10 km
RGT model
Inline 200
Channel systems
Horizon 126
Faulted area
RGT model
Inline 200
Xline 3600
10 km
Xline 3600
In
lin
e2
00
(a)
(b)
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horizon (e.g. a window size of 5 with a vertical sampling 
of 4 means the time window is 20 ms, the attribute will 
be mapped from 10 ms above and 10 ms below each 
horizon). This method has been used successfully on 
numerous case studies with different basin settings for 
thin-bed reservoir detection and characterisation, along 
with enhancing fault and fracture imaging [13 - 16].
4. Results and discussion
Using PaleoScanTM software, all possible horizons in 
the seismic volume are auto-tracked in one attempt either 
in peak, trough, and zero-crossing, reducing the time 
cycle on manually picking and seed-based auto-tracking 
horizon methods. A 3D RGT model is the outcome 
obtained directly from the Maui 3D seismic volume. In this 
process, the interpolation of the 3D model grid plays a key 
role, assigning relative ages to every voxel of the seismic 
volume to create both vertical and lateral continuity in the 
3D RGT model. 
In this study, 400 continuous chronostratigraphic 
surfaces representing relative geological ages are extracted 
from the 3D RGT model. This unique technique allows the 
seismic volume to be navigated stratigraphically, revealing 
stratigraphic insights at a very high resolution, even with 
thin-bed events or in complex depositional environments 
(e.g. shallow or marginal marine, Figure 5) that cannot be 
shown when using the traditional approach.
In the conventional workflow, seismic attributes are 
only mapped on manually picked key horizons or their 
shifted ones, which can be time-consuming and not 
feasible in complex intervals. Here in the same amount 
of time, hundreds or thousands of horizons with different 
seismic attributes can be extracted from the RGT model 
taking into account all the samples of the seismic volume. 
Also, different from iso-proportional horizons which are 
generated in the interval between two specific horizons, 
the horizon stack provides continuous surfaces inside the 
complex stratigraphic intervals but still strictly follows the 
geological events and seismic facies of the data, thus it is 
better for subsurface imaging in those locations (Figure 6).
This innovative workflow has made seismic 
interpretation more efficient, delivering to geologists a 
fully consistent, data-driven geo-model along with high-
quality horizons (Figures 6 and 7) and faults for further 
modelling purposes.
5. Conclusion
In this paper, a new interpretation technique has 
been presented which utilises 3D seismic data and 
directly transforms it into a 3D RGT Model. This allows 
generating a dense library of stratigraphic horizons, even 
in complex intervals to identify subtle events, unseen 
from conventional methods, which are based on manually 
picking and seed-based auto-tracking horizons [18]. 
From an example of Maui 3D, located offshore 
Taranaki basin, a 3D RGT model was obtained in a short 
time frame, producing 400 chrono-stratigraphic surfaces. 
These surfaces mapped with seismic attributes such 
as RMS amplitude and spectral decomposition using 
colour-blended method help the interpreters to build up 
a geological history of the area. The results suggest that 
this approach could be applied not only to subsurface 
imaging, the detection of subtle stratigraphic events, 
but also for modelling purposes, at both regional and 
reservoir scales. The new workflow drastically accelerates 
the entire exploration cycle and shapes a new form of 
seismic interpretation in the future [18].
Acknowledgment
The case study presented in this paper is achieved with 
PaleoScanTM software, developed by Eliis (www.eliis.fr). 
The authors would like to thank the Ministry of Business, 
Innovation, and Employment (MBIE), New Zealand, for the 
authorisation to publish the Maui 3D seismic data. 
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