Abstract

The development of the Central Luconia carbonate build-ups were strongly influenced by the interplay between eustatic sea-

level and basinal tectonics. The Alpha field addressed here is one of the seismically best imaged isolated carbonate platforms

in Central Luconia and dendritic features, interpreted as karst, were found to be very prominent throughout the field. Karst is a

diagenetic facies, an overprint in sub-aerially exposed carbonate bodies produced and controlled by dissolution and migration

of calcium carbonate in meteoric waters. The exploration well in the Alpha field encountered total losses while drilling which

was believed to be as a result of drilling in to karst (common in Central Luconia carbonates). Some of the best features from

Petrel, Landmark and Shell software packages have been integrated to generate an attribute volume for karst interpretation.

Using Petrel Geobody, multiple 3D box probes with short time gate and optimum clipping value were used to extract the karst

Geobodies. Using a short time gate box probe is crucial for CPU/memory performance and most importantly, to have better

control on editing noise and non-karst signal later.

Karst has a big impact on well planning (problem with losses and completion length), the hydrocarbon volume in-place as

karst provide substantial sec

ondary porosity; and well management and development strategy (wells nearer to karst are more likely to water-out quicker).

This study incorporated an integration of well data, seismic characterization and geological understanding of the field and

near-by analogues in building a subsurface model for field development.

Geology

The Central Luconia Province is located in offshore NW Borneo. The province is delineated by two major strike-slip faults,

the West Baram in the northeast and the Rajang line in the southwest (Figure 1). Seafloor spreading in the South China Basin

during the Oligocene to middle Miocene resulted in formation of a horst graben system that which controlled distribution of

subsequent reefal carbonate growth. At mid-late Miocene reefs developed preferably on horst blocks (Mohammad Yamin Ali

& Abolins[1]

). Two major factors controlling carbonate sedimentation in the Central Luconia province are the regional

tectonics and eustatic sea level changes. Tectonics played a role in creating horst and graben structures which served as

basement for the onset of carbonate deposition and exerted an influence on the size and shape of the build-ups (Ting et al[2]

).

The latter also dictated the type of the depositional facies and their distribution which governed the reservoir properties of the

carbonate platform.

IPTC 14539

Karst Modeling of a Miocene Carbonate Build-Up in Central Luconia, SE Asia: Challenges in Seismic Characterisation and Geological Model Building Elvis Chung, Ting King King and Omar AlJaaidi Sarawak Shell Berhad, Locked Bag No.1, 98009 Miri, Sarawak, Malaysia

Copyright 2011, International Petroleum Technology Conference This paper was prepared for presentation at the International Petroleum Technology Conference held in Bangkok, Thailand, 15–17 November 2011. 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

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Figure 1 : Top carbonate structure map and location of Alpha field

Field Background

The study area (Alpha field) is part of a chained build-ups located along the Bunga Pelaga High in the central part of the

Central Luconia province. The massive carbonate platform measures some 6.6 × 3.6 km wide and has a trapezoidal shape with

its long axis aligned in a W-E direction.

The Alpha field was discovered by well Alpha-1 based on vintage 2D seismic lines. The Alpha-1 encountered losses which are

potentially the result of drilling in to karst, a common problem in Central Luconia carbonates. The 3D seismic was then

acquired for the appraisal & development of the field, which revealed an extensive network of dendritic features (karst). The

appraisal well Alpha-2 was drilled based on the seismic characterization of karst (discontinuity of seismic reflector from

semblance/variance cube, see

IPTC 14539 3

Figure 2) and successfully avoided the problem.

The wells confirmed the relative thin hydrocarbon column with 85% of the field having only 150ft column length. In order to

put in place a field development plan such large field was essential to identify the facies their spatial distribution and overall

reservoir property.

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Figure 2 : Wells location w.r.t. seismic features

The karst has a big impact on future development well planning (problem with losses and completion length), field production

(well likely to water-out quicker) and the volume in-place because karst provides substantial secondary porosity. Hence, there

is a strong business case to map out the karst in details for the field development plan.

Seismic Attribute & Interpretation

The karst interpretation on 3D seismic data was carried out in Petrel but some of the best features from Landmark and Shell

proprietary tool were incorporated into the workflow. First, the Landmark FK-Fan filter was applied the 3D seismic to enhance

lateral seismic discontinuity and then followed by a Shell proprietary edge-preserving structural-oriented-filter for noise

reduction. Next, Shell proprietary Sharp Semblance (similar to Petrel’s variance) algorithm is used to generate a seismic

attribute volume (

IPTC 14539 5

Figure 2) for Petrel Geobody interpretation.

Using Petrel Geobody, the karst was picked as 3D Geobodies that are extracted based on certain clipping value of Sharp

Semblance attribute. The biggest challenge in karst interpretation is the separation of actual signal (karst) versus noise, not

only laterally but also vertically as signal and noise may be connected & disconnected in 3D space. Besides that, Geobodies

picking is a CPU/memory intensive work, especially when dealing with huge dataset. In order to overcome these issues,

multiple 3D box probes with short time gate and optimum clipping value were used to extract the 3D karst bodies in sequence

from top carbonate down to. Using a short time gate for the 3D box probe is crucial for CPU/memory performance and most

importantly, doing so give better control on noise exclusion and make it easier to edit non-karst signal later. For example, the

dendritic features (karst) and the patch reefs (non-karst) both have lateral seismic discontinuity that will be interpreted as the

Geobodies. Hence, the patch reefs (circular features on the semblance) needed to be removed & excluded from the Geobodies

(

Figure 3).

Figure 3 : Geobodies Extraction Before and After Edit/QC

6 IPTC 14539

In general, the karst had a more chaotic seismic signature (noise) that propagates deeper down, such as at around Alpha-1 area.

The 2 intervals where losses were encountered at Alpha-1 matched with semblance signals that indicate possible penetration

through the karst. On the contrary, the patch reefs show discontinuity at certain seismic loop at a very short time interval and

usually are overlaying a continuous seismic reflector, such as at Alpha-2 area. Overall, the dendritic features (karst) show a

consistent pattern as seen in

Figure 3, and are concentrated at the centre of the field near Alpha-1. It is also important to acknowledge that there could be

sub-seismic karst that could not be mapped out.

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Figure 4 : Near stack (top) and Filtered Sharp Semblance (bottom)

Finally, the interpreted Geobodies were outputted in points and are depth converted with base, low and high case velocity

model in order to be modelled in the base, low and high case static models respectively.

Static Modelling

During the static modelling, the karst property (porosity) uncertainties are tested. It is important to test the impact of the facies

on well management and development strategy. At the time of the study, Petrel2009 was the tool used for building the static

model. The only method to import and incorporate these karst Geobodies into the static model is by converting it to point data

and upscaling it. However, this method gave a ‘staircase’ look in terms of its distribution after upscaling. The issue is due to

the karst being sampled in a regular interval in time but when converted to depth, the samples become irregular and the data

points become too sparse as the model uses much finer sampling in thickness. This layered pattern of karst is an unrealistic

model (

Figure 5) and result in decrease vertical permeability due to thin bedded high permeability streak being combined with lower

permeability reservoir. Nevertheless, the actual detailed depth converted points were used for well planning.

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Figure 5 : Karst Geobodies as original point data converted from time to depth (left) and modelled in a fine-layered

grid (right)

A second method (Figure 6) was using the seismic variance volume, resampled into the model grid resolution. Then, a

calculator function was assigned to extract high attribute value from the variance cube as karst facies. The identified lagoonal

patch reef areas are excluded to be assigned as karst facies by using polygons where no extraction is instructed at calculator

function.

Figure 6 : Method of resampling seismic volume using calculator function

Cross checks between the two methods with the aforementioned method showed a lot of similarities in terms of distribution

and geometry. Therefore the modelling method as shown in Figure 6 was used to model the karst facies in the static model.

Some of the nearby producing fields in Bunga Pelaga High require high porosity value in karst (up to 60% porosity) in order to

achieve a history match. The well Alpha-1 encountered losses which are potentially a result of drilling in to karst indicate

porous medium. For Alpha field porosity model, karst enhanced porosity of 40 % was assigned over the seismically mapped

karst. Manda & Gross[3]

shows the modern fields like karstified Biscayne aquifer of South Florida yields porosity about 40%.

IPTC 14539 9

Lastly acoustic impedance volume from seismic is also used to constrain and test the uncertainty of the model; of which tends

to result a lower average porosity (only 25%) than the above analogues. All the different interpretations of the karst property

are modelled and tested to understand the behaviour of the field productivity.

Summary

The detailed interpretation of the karst is vital for the field development plan for Alpha field in terms of well placement to

avoid drilling complications and to maximise hydrocarbon recovery. The different approaches in determining the karst

property during the static modelling phase provided a better understanding of the uncertainties in the field productivity.

Acknowledgement

The authors would like to thank PETRONAS (Petroleum Nasional Berhad) and Shell Malaysia for allowing the publication of

this paper.

Reference

[1] Mohammad Yamin, bin Ali, Abolins, P., 1999. Central Luconia Province. In: The Petroleum Geology and

Resources of Malaysia. PETRONAS, Kuala Lumpur, pp.371–392.

[2] Ting, K.K., Chung, E. and Al Jaaidi, O. 2010. Evolution and controlling factors of Miocene carbonate build-up in

Central Luconia, SE Asia: Insights from integration of geological and seismic characterization. Integrated

Petroleum Engineering and Geosciences (ICIPEG) 15-17 June, Kuala Lumpur.

[3] Manda, Alex K. & Gross, Michael R, 2006. Estimating aquifer-scale porosity and REV for karst limestone using

GIS based spatial analysis;; Geological Society of America Special Paper 404.