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IPA17-43-G

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IPA17-43-G

PROCEEDINGS, INDONESIAN PETROLEUM ASSOCIATION

Forty-First Annual Convention & Exhibition, May 2017

PERCEIVING THE UNSEEN HYDROCARBON PLAY POTENTIAL GROUNDED BY SEISMIC

RESERVOIR CHARACTERIZATION AND WELL DATA IN THE GUMAI FORMATION,

SOUTH SUMATERA, INDONESIA

Haryono Haryanto*

Wahyudin Bahri Nasifi*

Indah Edria Amorita*

I Wayan Ardana Darma*

Trisakti Kurniawan**

Fithra H. Darmawan**

Azli B. Abu Bakar**

Randy Condronegoro***

Andri Syafriya***

ABSTRACT

The South Sumatra Basin is one of the most prolific basins in Indonesia with estimated total recoverable reserves of 8,046 million barrels of oil equivalent

(IHS, 2016) from four main plays including (1) Pre-

Tertiary Fractured Basement, (2) Oligocene Talang

Akar Sandstone, (3) Lower Miocene Baturaja carbonate, and (4) Lower to Middle Miocene Gumai

Sandstone plays. Previously, most of the wells drilled in the study area were targeting the deeper intervals and did not consider the Gumai interval as one of the potential drilling objectives.

However, recent drilling campaigns in the study area have showed encouraging results from the Gumai play which encountered high gas reading in stringers sandstones which has triggered a look back analysis on the Gumai interval. The geological data evidence are aligned by seismic inversion results both deterministic and geostatistical which correlated with the presence of reservoirs. The integrated quantitative interpretation shows three sandstone units, two of which have good reservoir porosities and potential to be trapped with hydrocarbon. These promising results are of paramount significance for further hydrocarbon exploration strategic planning.

INTRODUCTION

The Gumai interval especially in Intra Gumai can be categorized as an underexplored play. Previous exploration wells treat it as a regional seal. This interval in the Jabung block has been previously penetrated by thirteen wells which indicate high value start from 300 to 2000 units of total gas reading. In the latest drilling campaign, well A was designed to test the Basement Fracture Play in the initial drilling program and the Intra Gumai interval was not identified as an objective, but indications of total gas 300 units and gas shows resulted in a decision to conduct tests; it flowed gas & condensate.

It has been succesful in unlocking this interval as an underexplored play. The effort to re-evaluate this play has therefore become crucial.

In this work, the authors performed several integrated geophysical studies such as deterministic and geostatistical seismic inversion to obtain the reservoir properties that could be filled by hydrocarbons and also to identify lateral distribution of good reservoirs.

Regional geology

The study area is located in the South Sumatra Basin in Indonesia (Figure 1). This basin is categorized as the most prolific basin which has recovered more than 8 BBOE and is yet to find more than 4 BBOE

(IHS, 2016) with new and underexplored plays

(Figure 2). It comprises of a series of grabens formed by extensional back-arc stress related to the subduction of the oceanic crust of the Indian Ocean under the Sundaland Craton in western Sumatra.

These grabens, which are essentially rift basins

(Figure 3), are the main hydrocarbon kitchen in

Sumatra containing thick, restricted syn-rift sediments, which form the primary source rocks. The sedimentary succession in the South Sumatra Basin comprises a single transgressive/regressive secondorder cycle that commenced in Late Eocene to Early

Oligocene with deposition of transgressive alluvial,

* Petronas Carigali Indonesia Operation

** Petronas Carigali Sdn Bhd

*** Petrochina Jabung Ltd

fluvio-deltaic and marginal marine facies of the

Lahat and Lower Talang Akar formations. Full marine conditions were eventually established during the continuing transgression sequence with deposition of an open marine facies in the Upper

Talang Akar, which consists of marine shales, marls and fine-grained sands. Initial uplift of the Sunda

Shield to the East in the Middle Miocene marked the end of the Early Tertiary transgression and the beginning of a regression sequence that continues to the present day, which gave rise to the deposition of prograding deltaic sediments of the Gumai and Air

Benakat formations as a result of increased sediment load from the Sunda landmass to the northeast. These sediments comprise of distal delta front shales, distributary mouth bar sands, delta bar sands, channel sands and interdistributary shales.

The Gumai Formation is more commonly known as the regional seal that sealed the Talang Akar

Formation, which is one of the most prolific reservoirs in the South Sumatra Basin. At a regional scale, the Gumai Formation could be divided into sandstone-rich upper and mud-rich lower sections.

At a local scale, the Lower Gumai could be further broken into upper and lower intervals. The lower interval comprises of predominantly transgressive marine shales (including marl) whereas the upper interval contains limestones streaks formed when sediment influx was locally restrained. Due to differential compaction within the Betara Graben, a localized deeper marine environment was superimposed on the regional subsidence and transgression during the deposition of the Lower

Gumai. In contrast, the deposition of the Upper

Gumai sandstones took place in more regressive conditions. During this depositional period (From

Early to Middle Miocene), marine conditions regressed to approximately middle neritic conditions in response to the initial Barisan Mountains Uplift.

The general stratigrapy of the South Sumatera basin is shown in figure 4.

The Intra Gumai play can be categorized as underexplored play. Previous exploration wells did not become target objective and it has been recognized as regional seal. In fact, the sourounding wells indicate high value start from 300 to 2000 unit of total gas reading. Latest drilling campaign, well A gave good contribution of test result; gas and condensate flow. It has been succed to unlock this interval as underexplored play.

Seismic Inversion

Acoustic impedance (AI) is an important rock property which describes density ( ρ ) and velocity

(Vp) of the subsurface. In zero offset or full stacked data, seismic trace can be modeled as the convolution of the acoustic impedance reflectivity (RAI) with the wavelet (W). The simple formula can be stated as below:

(1)

(1)

Most seismic inversion methods are based on minimizing the differences between synthetic seismic and real seismic responses. Synthetic seismic responses are the result of convolution of wavelet and earth reflectivity, which is a function of acoustic impedance. The inversion methods which operate in minimizing error are known as deterministic inversion (Ansri, 2014).

Deterministic seismic inversion such as sparse spikes or model-based inversion produces smooth resultsdue to its limitations. Francis discussed some of these limitations (Francis, 2006). The significant limitation is missing low frequency information due to the band-limitation of real seismic data. Since the source wavelet is band-limited and does not cover all frequencies, low and high frequency components are hidden in the seismic responses. Missing the low frequency is important due to the fact that low frequency components contain critical information about the absolute impedances values (Francis,

2010). Low frequency information can be obtained from well log data. For adding log data to seismic data, one may encounter a serious problem, which is known as the support of scale measurement of data.

Well data has a high vertical resolution compared to the seismic data. In deterministic methods, the scaleup of well data to larger support measurements is used. Scale-up is an averaging method, which reduces variability of measurements, when they are scaled up to larger supports.

Overcoming these problems, geostatistical seismic inversion is an alternative to improve deterministic inversion results. The geostatistical inversion process is more advantageous than the deterministic inversion process due to its ability to handle uncertainties. In addition, the process can also be improved by constraining it using geological information (Sams et al, 2011).

This joint impedance and facies geostatistical inversion of seismic, (geo)statistics and well data is based on a combination of Bayesian inference and the Markov Chain Monte Carlo (MCMC) sampling algorithm, to creat equally probable realizations of the subsurface reservoirs (Contreras et al., 2004)

METHODOLOGY

Reservoir Characterization

There are three stages in this work (see Figure 5).

Firstly, we gathered all initial information of geology, geophysics, petrophysics and rock physics.

Secondly, we performed predictive interpretation using deterministic inversion and geostatistical inversion (Bayesian inference and Markov Chain

Monte Carlo (MCMC) sampling algorithm). Thirdly, we analyzed the product of llithofacies definition and reservoir properties.

The initial stage of study began with petrophysical and rock physics analyses that included log data conditioning, sensitivity analysis, lithofacies log generation and depth trend analysis. The analyses not only require accurate elastic log curves, but also demand the consistency of lithology, porosity and saturation. Therefore, the first step began with data

QC and conditioning of the logs from the three available wells (Well A, B & C) (see Figure 6) to ensure that the data is free from borehole effect, etc.

The neutron, sonic and density logs were checked and found to be generally consistent across all three wells. However, the logs were conditioned in bad borehole section and invalid data was removed

(Figure 7).

The petrophysical model was developed from the density-neutron cross-plot in order to determine the lithology fractions and calculate V-clay as well as the porosity of the rocks. Rock physics analysis was then used to build a reliable set of elastic logs (e.g. pwave, s-wave and density logs) and to understand the relationships between the petrophysical properties with the elastic properties that can be measured from seismic data. A comprehensive rock classification based on mechanical behavior was also built in order to perform reservoir and fluid delineations.

The result shows a unique elastic parameter transition in vertical and lateral direction (Figure 8).

High impedance sand stringers turned out softer in acoustic impedance domain when good porosity and hydrocarbon were introduced. Therefore, the overlap between sand and shale seems to be high

(Figure 9).

In addition, Vp/Vs ratio is found to be a good lithology indicator due to the ability to detect variations of rock quality and fluid in seismic data through variation in reflectivity coefficient character as a function of offset/angle. However, this attribute is not used in this study due to the unavailability of angle stack data. Therefore, in the absence of seismic angle stack data, only acoustic impedance is usable both as a lithology discriminator and as a tool to predict reservoir properties. Since the overlap between the different lithologies is high, we have opted to use the geostatistical inversion, which could integrate geological information and statistics together as constrained input and deliver a plausible solution.

Once the well-based feasibility analysis was established, a trace-based single stack sparse spike deterministic inversion followed by geostatistical inversion were applied to the real seismic data using the proposed technique/workflow. The seismic data was rigorously conditioned to improve the reliability of the data through post stack processing. However, the main concern on the data quality of this onshore dataset is the noise level. Therefore, two processes were applied: (1) Cadzow filter; and (2) Spectral blueing (Randy, 2012). The application of those processes has resulted in the seismic having clearer event, better continuity and optimum frequency bandwidth (Figure 10).

Then, a deterministic inversion process was performed using (1) a simple, single shale compaction trend, (2) wavelet extracted from multiwell over the Intra Gumai interval. This process is critical because the result will be used as a reference to determine signal/noise level, reliability of wavelet and the data statistical range in the geostatistical inversion process.

The geological constraints include (1) three different compaction trends, used here to ensure good correlation with the regional geology trend (Figure

11), and (2) acoustic logs derived from rock physics modeling, which provide more reliable histograms

(Figure 12) and probability density function (PDF) information (Figure 13). Comparison between the deterministic and geostatistical inversion results was performed to ensure inverted impedances have consistent distribution (Figure 14). This process, among others, ensures that the geostatistical inversion generated is suitable with the seismic, well and geological data.

Fifteen realizations of inverted acoustic impedance and lithology have been produced and two reservoir properties, namely V-clay and effective porosity have been delivered as the products through a cosimulation process using the impedance and lithofacies results (Figure 15).

RESULT AND DISCUSSION

The geostatistical seismic inversion result shows three fascinating sandstone units, two of which have good reservoir porosities and potential to be charged by hydrocarbon (Figure 16). The upper part of the Intra Gumai interval has been proven by testing of well A that contained gas and condensate with total gas unit of 300. Those two good reservoirs show positive correlation with total gas finding around 500 to 1200 units. The rest is poor sand and is located in the lower interval; they also correlated with the result of tests in well B; no fuild recovery and only trace oil. The result of geostatistical seismic inversion has been successfully modeled and is appropriate with surrounding well evidence. It has captured the Intra

Gumai Sandstone in the lower interval as a clear channel feature, roughly in the NW-SE direction which could be interpreted as influx from the NW direction (Figure 17).

Those reservoirs are of paramount significance and it is recommended to explore more to support further exploration strategies.

CONCLUSIONS

South Sumatera Basin is a productive basin in

Indonesia. Currently, hydrocarbon discovery in conventional play may have already reached plateau.

Therefore, it need to review again for underexplored play especially Intra Gumai play. It is important to maintaining current hydrocarbon production from this basin.

This integrated reservoir characterization study shows three fascinating sandstone units, two of which have good reservoir porosities. Total gas evidence in the surrounding area indicates that those two good sandstone reservoirs are filled by hydrocarbon. They have been successfully modeled to unravel the underestimated hydrocarbon potential in the Intra Gumai interval. The results are of paramount significance and it is recommended to explore and discover more in this play.

ACKNOWLEDGEMENT

The authors wish to express their appreciation to the

Management of Petronas Carigali Indonesia

Operation (PCINO) especially for Mr. M Zaini Md

Nor, Mr. Jamin Jamil B Mohd Idris, Mr. Nasaruddin

B Ahmad, Rizki Krishna Pratama, and also special thanks to PetroChina International Ltd, MIGAS,

SKKMIGAS and BKPM for the approval to publish this paper. We also thank the members of

Exploration Department, CAA and Legal

Department, who assisted in internal approval.

REFERENCES

Ansri, H.R., 2014, Comparing Geostatistical Seismic

Inversion Based on Spectral Simulation with

Deterministic Inversion: A Case Study: Iranian

Journal of Oil & Gas Science and Technology, Vol.

3 (2014), No. 1, pp. 01-14.

Bishop, M.G., 2001, South Sumatra Basin Province,

Indonesia: The Lahat/Talang Akar-Cenozoic Total

Petroleum System: in U.S. Geological Survey Open-

File Report 99-50-S.

Contreras, A., Torres-Verdin, C., Kvien, K.,

Fasnacht, T. and Chesters, W., 2004, AVA stochastic inversion of pre-stack seismic data and well logs for

3D reservoir modeling,The 67th EAGE Conference

& Exhibition, Extended Abstracts, F014.

IHS, 2016, Cumulative Production of South

Sumatera Basin, IHS.

Ginger, D. and Fielding, K., 2005, The petroleum system and future potential of the south Sumatra basin, The 30th IPA Annual Convention and

Exhibition.

Pulunggono, A., 1986, Tertiary Structural Features

Related to Extensional and Compressive Tectonics in the Palembang Basin, South Sumatra, The 15th

IPA Annual Convention Proceedings.

Randy C. et. al, 2012, Seismic 3D image enhancement to extract possible thin reservoir and fractured basement in Jabung block - South Sumatra,

The 37th HAGI Annual Convention and Exhibition.

Sams, M., Millar, I., Satriawan, W., Saussus, D.,

Bhattacharyya, S., 2011, Integration of geology and geophysics trough geostatistical inversion: a case study, First Break, 29, August, 47-56, EAGE.

Figure 1 -

Location and database. Area of study is located in the South Sumatera Basin.

Figure 2 -

South Sumatera Basin Performance curve. It has produced around 8 BBOE of discovered oil and gas resources (IHS, 2016).

 

Figure 3 -

Regional schematic cross-section crossing Tungkal, Betara, and Geragai Graben.

Figure 4 -

General stratigraphic column of South Sumatra Basin.

Figure 5 -

Methodology of study.

Figure 6 -

Well A is not targeted for Intra Gumai sand. Lithologically dominated by thin sandstone and shale.

DST result confirmed gas flowed from that particular level.

Figure 7 -

Data QC example using Well A & B showing the effect of before (Blue) and after (Red) conditioning on the P-sonic and density logs.

(a)  

(b)  

(c)  

Figure 8 -

(a) Well log correlation between Well A, Well B and Well C. Rapid transitition changes both in vertical and lateral dimension representing marine depositional environment is observed.

Dominant frequency is around 42 Hz (b) and tuning thickness is between 11 – 25 m (c).

(b)  

(a)  

Figure 9 -

Histograms of acoustic impedance from individual well A, B, C (a) and Multi well (b) indicate high overlap between lithology in acoustic impedance domain. Therefore, it is recommended to perform probabilistic analysis.

Figure 10 -

The current seismic data set was applied with imaging enhancement in 2012 and the result is quite impressive compared to the legacy dataset.

Figure 11 -

Multi well depth trend analysis color coded according to (a) well, (b) lithology and (c) lithology plus porous sand category. The lithology was based on cut-off value of the Vclay log and the porous sand classification was based on effective porosity content. This classification is important to highlight and narrow down the chances of getting more precise prediction.

Figure 12 -

The graphics show P-impedance vs frequency at different layers. A general increase of impedance with depth is observed in the study area.

Figure 13 -

A 3D facies probability model was generated based on extrapolation of 3 wells in order to capture observed geological changes within the area. Top Intra Gumai marker was used as reference padding layer analysis as shown in the picture.

Figure 14 -

A relatively consistent inversion result is achieved between deterministic and geostatistical inversion. A relatively stable geostatistical inversion is achieved as well for both cases; constrained and unconstrained cases.

Figure 15 -

Seismic section and geostatistical seismic inversion products such as inverted impedance, lithofacies, volume of clay and effective porosity.

Figure 16 -

Geostatistical inversion result integrated with well data indicates that the Intra Gumai Formation potentially has several good reservoir porosities and they could be charged by hydrocarbon.

Figure 17 -

Lateral distribution of sand reservoir Intra Gumai reflects channelized feature.

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