Facies Geometry Uncertainty in Reservoir Model a Case Study

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Petroleum Geostatistics 2007Cascais, Portugal, 10 - 14 September 2007

P05

Facies Geometry Uncertainty in Reservoir Model,a Case Study

Y. Sutadiwiria* (BPMIGAS, Government Executive Agency), W. Widodo(Roxar Software Solution), E. Rukmono (JOB PT. BSP-PERTAMINA) & D.Hernadi (JOB PT. BSP-PERTAMINA)

SUMMARY

Stochastic techniques are increasingly being used to catch the heterogeneity in reservoir model and assessuncertainty for performance forecast. Although a stochastic approach offers us a variety of models, each of which is consistent with the available information, in practice only a small fraction can be considered for comprehensive flow simulations.

This paper presents the result of an integrated 3D reservoir modeling using stochastic approach of BungsuField where located in Coastal Plain Pekanbaru (CPP) area, Riau Province, approximately 75 km fromPekanbaru, Central Sumatra, Indonesia. Objective of this study is to develop a representative reservoir model to form the basis for reservoir management and future development forecast. Building this modelalso considered by uncertainty of every parameter used, and for this study is emphasizing to faciesgeometry uncertainty and their influence to STOIIP. The result are then ranked based on comprehensivematching with cumulative production and recovery factor and be used as first pace to flow simulation.




Introduction

A reservoir model is an integrated study of geophysical, petrophysical, geological and

engineering data of a reservoir relevant to management of the reservoir. The parameters

controlling this interaction are geological parameter, which include sequence stratigraphy,

depositional and facies model, and paleogeography; seismic parameter, which include fault

and horizon surface, and also seismic attribute; petrophysical properties, which include

electrofacies, porosity, permeability and initial water saturation; and engineering parameter,

which include pressure build-up analysis, SCAL data, production history from well and well

completion (Fig. 1).

Bungsu Field, the study area, is located in Coastal Plain Pekanbaru (CPP) area, Riau

Province, approximately 75 km from Pekanbaru, Central Sumatra, Indonesia (Fig. 2). The

field was discovered by PT Caltex Pacific Indonesia and now the field is being operated by

Joint Operation between PT BSP and PT Pertamina Upstream (JOB PT. BSP-PERTAMINA).

Currently 17 wells have been drilled in Bungsu field.

Geological Background

Based on information from log, core, and regional geology from this area, the objective

reservoir in the Bungsu Field is interpreted as tidal channel and tidal sand bars deposit in

estuarine/tidal delta complex of Bekasap Formation (Fig. 3). The recommended depositional

model is a marine influenced tide dominated estuarine with sediment influx from relatively

NE-ward, and also probably from the adjacent shelf by tidal current.

The results of the evaluation have been suggested that Bekasap 1900’SD which is the

objective of the reservoir can be divided into 1900’SD Upper and 1900’SD Lower sands.

These reservoirs are separated by a thin shale layer with range of thickness 1 – 8 feet.

Structure interpretation review of the Bungsu field was carried out using 3-D seismic depthstructure map of top 1900’SD and tied by 16 wells marker data. The faults generally follow

the regional N-S trend, and 30 faults were applied in building 3D structural framework (Fig.

4).

Multiple Geological Scenarios

Uncertainties in a reservoir model can be classified into 4 categories which are structural

uncertainty, scenario uncertainty, petrophysical uncertainty, and parameter uncertainty. Some

uncertainties, however, will have greater influence on the resulting model than others and

these are uncertainties that should be focused on. For this study, the uncertainty is

emphasizing to scenario uncertainty, particularly geometry of modeling sedimentary.

Facies model in Bungsu Field is generated by a stochastic method called sequential indicator

simulation and channel simulation. The sequential simulation works by visiting each point on

the grid to be simulated, calculating the conditional distribution at that point and sampling

from that distribution. The conditional distribution is the probability distribution for the facies

at the point, given knowledge of the facies at nearby well locations and of previously

simulated points nearby. Channel simulation algorithms transform the data from the 3D

(sub)grid into an orthogonal shaped ‘simulation box’ while executing the simulation. Before

the simulation, a geometrical transformation of the conditioning data to the simulation box is

performed. To capture heterogeneity in petrophysical properties distribution and conditioned

to facies distribution, Gaussian simulation and Kriging technique was applied.

Previous studies have shown that sand-body widths and lengths vary widely; widths can be

range from 106 km down to 5 m, and lengths vary from 190 km down to 160 m. By contrast,




thicknesses only vary from 152 to 0.2 m (Fig. 5). With limited well data, the geometry of the

channels and bars may be poorly defined. Multiple scenarios being modeled for facies

deposition based on above data. Distribution of data can be classified into two type, first is

using Low, Base, and High value, and second is using of P10, P50, and P90 from Normal

Distribution (Table 1).

Overall, 11 realizations were generated in this study. This number was derived by considering

particularly on Channel Volume Fraction, Channel Thickness, Channel Width, Shale Volume

Fraction, and Bar Volume Fraction (Fig. 6).

Ranking of Multiple Realizations

Nowadays, using the modeling software, it is relatively straightforward to generate many

hundreds of scenarios and realizations of geological models. Putting every realization

through the full field flow simulation process, however, will not generally be practical

because of time constraints. We need mechanisms to rank and select realizations of the

geological model to take to flow simulation.

The main ranking criterion used in this study was STOIIP. This rank is used as first pace to

take reservoir model to flow simulation. The results were calibrated also with total cumulative

production and recovery factor. Interaction of geological parameter is shown in figure 7,

which presents minimum and maximum response from each parameter to STOIIP for the

reservoir model in this study area. It may be seen that channel width has the most significant

interaction. On the other hand, volume fraction of the bar has the least significant interaction

to the reservoir model. Figure 8 present the result of STOIIP volumetric, which include P10,

P50, and P90 value that can be used to calculate weighting factor for uncertainty

quantification.

From this type of ranking a high, middle and low case can be determined and carried forwardinto a full field flow simulation analysis. A representative number of geological models,

sufficient to capture the reservoir uncertainty, may then be taken forward for further study.

Conclusion

A better understanding of reservoir and the parameter uncertainty need to be improved,

include understanding of geological condition that can be achieved at early stages, since this

will used as framework for reservoir management. Quantification of uncertainty can give a

better overview from parameter that involve in reservoir model.

Acknowledgement

We would like to thank the E&D team in JOB PT. BSP-PERTAMINA and Roxar Technical

Support for discussion.

References

Al-Daeeb, M., et.al. [2002] Fully Integrated 3D-Reservoir Characterization and Flow

Simulation Study: A Field Case Example, SPE 78510.

Aulia, K. and Heidrick, T.L. [1993] A Structural and Tectonic Model of The Coastal Plains

Block, Central Sumatra Basin, Indonesia, Proceedings Indonesian Petroleum Association,

22nd

Annual Convention, October 1993, P. 285-316.




Norman, T., et.al. [2006] Example of Applied Stochastic Modeling in a Mature Field, A Case

Study in Zamrud Field, Central Sumatra, Indonesia, EAGE 68th

Conference and Exhibition.

Pusat Penelitian dan Pengembangan Teknologi Minyak dan Gas Bumi (Lemigas) [1997]

Bungsu Field Simulation Study Volume II.

Reynolds, A.D. [1999] Dimension of Paralic Sandstone Bodies, AAPG Bulletin, V. 83, No.2

(February 1999), P. 211-229.

Roxar Software Solution, RMS User Guide Manual, unpublished.

Reservoir

Model

Geology Geophysics

Reservoir

Engineer Petrophysics

Reservoir

Model

Geology Geophysics

Reservoir

Engineer Petrophysics

Figure 1. Reservoir Model, an integrated study. Figure 2. Location Map of Bungsu Field.

Figure 3. Tertiary stratigraphic Column of CentralSumatra Basin

(Heidrick and Aulia, 1993).

Figure 4. The structural framework of the regionalTop Basement structure, Central Sumatra

(Heidrick and Aulia, 1993).

Bungsu Field




Figure 5. A log-log plot of width vs. thickness for sandstone bodies differentiated by

sandbody type (Reynolds, A.D., 1999).

51%34%17%Low/Base/HighBar Volume

Fraction

99%66%33%Low/Base/HighShale Volume

Fraction

Mean = 500 m, SD = 150 m Normal DistributionChannel Width

17 ft9 ft1 ftLow/Base/HighChannel

Thickness

37.5%25%12.5%Low/Base/HighChannel Volume

Fraction

HighBaseLowDistribution Type

51%34%17%Low/Base/HighBar Volume

Fraction

99%66%33%Low/Base/HighShale Volume

Fraction

Mean = 500 m, SD = 150 m Normal DistributionChannel Width

17 ft9 ft1 ftLow/Base/HighChannel

Thickness

37.5%25%12.5%Low/Base/HighChannel Volume

Fraction

HighBaseLowDistribution Type

Table 1. Geological ScenarioUncertainty Parameter.

Figure 6. Conceptual model of depositional environment. Model in left takes responsible for minimumvalue of STOIIP, and model in right takes responsible for maximum value of STOIIP.

Figure 7. Tornado plot of minimum andmaximum response of geological

parameter for reservoir model in thisstudy area.

STOIIP

0

0.5

1

1.5

2

2.5

3

3.5

4

4.5

4 .2 E+ 07 4. 4E+07 4. 6E+ 07 4 .8E+0 7 5 .0E+0 7 5. 2E+0 7

Bin

F r e q u e n c y

0.00%

20.00%

40.00%

60.00%

80.00%

100.00%

120.00%

Frequency

Cumulative %

P50P10 P90

Figure 8. Volume STOIIP for all realizations used

to calculate weighting factor for uncertainty quantification.

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Facies Geometry Uncertainty in Reservoir Model a Case Study