Luanping Fan-deltaic Outcrop Geostatistical Characterization & Application to NY Oilfield, China

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Abstract

Fan-deltaic environment offers a particular challenge for geostatistical modelling. Over the past decades, geostatistical methods are widely accepted and applied to simulate sedimentary microfacies. I

n this study we have selected Sequential I ndicator Simulation (SI S) to generate our model and perform sensitivity analysis. Luanping fan-deltaic outcrop has been selected as a case study. The outcrop is located in Hebei province, China. The main outcrop cut by road is an excellently exposed study area, whose length and height are 1300 meters and 230 meters respectively. The orientation is nearly east-west. It was deposited from upper Jurassic to lower Cretaceous. Based on outcrop studies, 16 types of microfacies have been classified and for simplicity, they have been finally combined into 6 types of microfacies in stochastic simulation.The 2-D grid of 470×290 (?X=2.5m, ?Y=0.8m) has been designed to establish a single deterministic (prototype) model of Luanping fan-deltaic outcrop. The model honors all existing data and represents a realistic geological interpretation as closely as possibly. The input data of SI S are extracted from prototype model at different well spaces of 100,200,300,400and 500 meters. As a result, microfacies simulations are constructed under different well spaces. Comparing these simulations with prototype model, prediction accuracy and uncertainty analysis are presented in this study.

The study shows geostatistical tools can well reproduce prototype model of Luanping fan-deltaic outcrop. The prediction accuracy is largely relied on variogram model and proportion of each microfacies. Moreover, SIS can also reflect the microfacies distribution law of fan-delta, especially fan-deltaic plain can be reproduced under less information. Finally the results are applied to NY oilfield, China successfully.

Introduction

During the last 5 years, R PED,CNPC has been actively engaged in detailed Luanping fan-deltaic outcrop studies. The objective of this project is to develop a comprehensive,multidisciplinary and quantitative characterization of a fan-deltaic reservoir to allow realistic interwell and reservoir-scale models to be developed for improved oilfield development in similar reservoirs in China. Understanding reservoir heterogeneity is the key to increasing oil recovery from existing fields. Oilfield development demands generation of the best possible predictions of production profiles and the best possible basis for well/drainage strategies etc. Moreover,fan-deltaic reservoir is one of the most important reservoirs in China. However, It is difficult to buildup detailed and reliable fan-deltaic reservoir models based on available information using conventional methods.A number of geostatistical methods have been used in reservoir modelling. Although these methods enjoy wide acceptance, they have rarely been tested with realistic reservoir models. One of the reasons for this is lack of exhaustive quantitative data.

I n order to solve above problems, some scientists from RI PED, CNPC have put a lot of efforts into Luanping fan-deltaic outcrop studies for several years.

Geological description

Luanping fan-deltaic outcrop is located in Luanping county,Hebei province, China (Fig.1). The outcrop was deposited from upper Jurassic to lower Cretaceous. The primary outcrop was cut by road, the length is 1300 meters and the height is 230 meters, the orientation is nearly E-W. It’s a well-exposed fan-deltaic outcrop, except the road cut profile, eastern and western ends were also exposed. Just near the primary outcrop, three secondary outcrops (fan-deltaic front, prodelta and lacustrine environment) were exposed too. Therefore it is a good place for fan-deltaic deposition research and outcrop laboratory establishment [1,2].

Luanping fan-delta is a typical and steep small fan-delta close to the source areas. Its sedimentary facies develops well, and the depositional characteristic is representative and

comparable to many oilfields in China, so the results from the

SPE 68706

Luanping Fan-deltaic Outcrop Geostatistical Characterization & Application to NY Oilfield, China

Liang Chen, Longxin Mu, Ailin Jia, Shiyan Huang, RIPED,CNPC, P.O.Box 910, Beijing 100083,China

2LIANG CHEN, LONGXIN MU, AILIN JIA, SHIYAN HUANG SPE 68706

outcrop can be applied to these oilfields with similar depositional environment.

Outcrop prototype model

Detailed sedimentary studies [3] show there are 16 types of microfacies in Luanping outcrop. For simplicity, we group them into 6 types in this study which include mud (F1), mouth bar (F2), channel sand (F3), flood plain (F4), braided channel (F5) and debris flow (F6). These microfacies are defined based on regional geological, rock structure, and sedimentary structure.

As a first step in outcrop studies, we construct a single “deterministic ” reservoir model of Luanping outcrop based on its photograph, which was digitized and assembled into reproducible photomosaics. This model was designed in a 2-D

grid of 470×290 (?X=2.5m, ?Y=0.8m) to honor all existing

data as closely as possible (Fig.2). Therefore, the model is often called as a “prototype ” model, which represents our best understanding of the outcrop, and reflects a realistic sedimentary interpretation.

Microfacies stochastic modelling

Scheme As described above, Luanping fan-deltaic outcrop is 1300m long and 230m high. We created some “drilling ”pseudo wells along the length of the outcrop and sampled the microfacies types along the wells. Adopting this scheme, 5different scenarios are assumed. Fig.3 displays location maps and sampling of “drilling ” pseudo wells at different well spaces of 100,200,300,400 and 500 meters. I n order to investigate the capability of geostatistical simulation in fan-delta environment, Sequential I ndicator Simulation (SI S) [4]was used for this purpose.

Variogram modelling Variogram analysis is a vital step in stochastic simulation. Microfacies modelling needs indicator variogram as one of inputs in SIS. Because there are different scales of microfacies sandbody within Luanping outcrop, here we adopted nested variogram model to fit experimental variogram. I n general, the 6 microfacies-type indicator variogram models are of the same type and can be written as: )

/()/(22110a h sph C a h sph C C Var

++=In this study the long search axis is 20°, and short search axis is 110°, see GSLIB [4] about the definition of azimuth degree.Anisotropic variogram is considered in variogram modelling.The anisotropy ratio is the same as width/thickness ratio of each microfacies-type sandbody which was obtained from detailed outcrop studies [2,3]. The analogous knowledge is very useful in microfacies modelling. During variogram analysis,we first establish microfacies indicator variogram models based on prototype model. Table 1 lists variogram parameters which can guide future work in microfacies modelling. Then we make variogram structure analysis based on 5 different well spaces. The model parameters are given in table 2.

Microfacies modelling After variogram modelling, we defined the same 2-D grid as prototype model in order to compare simulated results with prototype model. SI S was employed to generate 50 microfacies simulations for 5different scenarios. Fig.3 is one of 50 realizations for 100m well space.

Uncertainty analysis Because stochastic modelling generates independent realizations, the numerous outcomes are often postprocessed to quantify uncertainty. Mean map is one of possible maps generated from a suite of realizations.However, it is inappropriate to use simple arithmetic average to obtain mean microfacies map because microfacies belongs to categorical not continuous variables. At each cell we first compute the maximum probability for each microfacies type based on the values at that location from all simulations, then assign microfacies type with maximum probability to that cell.When the number of realization is large, the map converges to the kriged solution. Fig.4 shows the outcomes of this method for 5 different well spaces. I n addition, the outcomes and prototype model have the same grid number and size, the outcomes from 5 different scenarios can be compared with prototype model at each cell. Finally the prediction accuracy is analyzed, the results are shown in table 3 from which we find that the prediction accuracy will decrease when well space increases. Moreover we have to note that fan-deltaic plain (including microfacies F4, F5 and F6) can be reproduced well whether for 100m or for 500m well space because their continuity and lateral correlation are better than that of any other microfacies.

Case study

NY Oilfield background NY Oilfield is a mature small heterogeneous reservoir located in east-China. Its depositional environment is also fan-delta system. Unit 1 of this oilfield was selected as target zone in this study. The source supplies came from southeast. Sedimentary microfacies of unit 1consists of crevasse splay (F1), interchannel sand (F2),mouth bar (F3), and braided channel (F4). Fig.6 reflects the distribution of microfacies-type and table 4 gives the proportion of each microfacies-type in the oilfield.

Microfacies stochastic modelling At this step, we first only use well data of unit 1 to construct the 4 microfacies-type indicator variogram models as shown in Fig.7. The model parameters are one of the most important inputs in SIS. Fig.8(a)-(d) are 4 microfacies-type realizations generated by SI S.Table 4 lists the proportion of each microfacies-type in 4realizations. Although their statistical results almost honor the original sample data, we find that the law of sedimentary microfacies is not well followed by these realizations due to lack of enough information in microfacies modelling.Secondly, to overcome this weakness, we borrowed some ideas from Luanping fan-deltaic outcrop and applied them in microfacies modelling [3]. More reliable microfacies model of unit 1 is obtained and displayed in Fig. 8 (e). The proportion of each microfacies-type calculated from Fig.8 (e) is also

SPE 68706 LUANPING FAN-DELTAIC OUTCROP GEOSTATISTICAL CHARACTERIZATION & APPLICATION TO NY OILFIELD, CHINA 3

given in table 4. The result is more close to that of original sample data. Production history and infilling practices have proven the simulated microfacies model can reveal the distribution of sandbody and the reservoir heterogeneity of unit 1[3].

Conclusions

We can draw the following conclusions from our studies:

(1). Outcrop studies can provide a large amount of information

for reservoir modelling. The information is very important for establishing a high-quality and reliable reservoir model.

(2). To make good use of outcrop knowledge can decrease risk of decision-making and make better returns in oilfield exploration and development.

(3). Geostatistical tools can well reproduce prototype model of Luanping fan-deltaic outcrop. The prediction accuracy is largely relied on variogram model and proportion of each microfacies. Moreover, SI S can also reflect the microfacies distribution law of fan-delta; especially fan-deltaic plain can be reproduced under less information.

(4).How to apply Luanping outcrop knowledge to other similar depositional environment reservoirs in China is our future task.

Acknowledgements

We would like to express our thanks to CNPC for financial support. Prof. Yinan Qiu and Prof. Pingping Sun gave us valuable insights and guidance.References

1. Ailin Jia, Longxin Mu, Yuyu Wang, Shiyan Huang, Liang Chen,Jian Cheng, 1998, Detailed outcrop description and establishment of out crop laborat ory--- A case st udy of Luanping fan-delt a in China , 15th International Sedimentology Congress, Spain.

2. Ailin Jia, Longxin Mu, Shiyan Huang, Liang Chen, Chuanlan Zhang, 2000, Geological knowledge dat a-base of fan-delt a , SPE 64629, the SPE I

nternational Oil and Gas Conference and Exhibition, Beijing, China, Nov.7-10.

3. Liang Chen, 1999, Luanping Fan-del t aic ou t

crop reservoir geos t a t is t ical charac t eriza t ion , Postdoctoral report, R I PED,CNPC.

4. Deutsch C.V. and Journel A.G., 1992, GSLIB: Geos t a t is t

ical software library and user’s guide , Oxford University Press, New York.

4LIANG CHEN, LONGXIN MU, AILIN JIA, SHIYAN HUANG SPE 68706 Table 1 Microfacies indicator variogram parameters based on prototype model

Facies type C0C1a1(m)C2a2 (m)

F100.176021.60.0630156.3

F200.026635.30.0063423.5

F300.179033.10.0830145.6

F400.084131.30.0297425.7

F500.051837.40.0259402.2

F600.083074.50.0076382.4

Table 2 Microfacies indicator variogram parameters for 5 different scenarios

well space

(m)facies

type

C0C1a1 (m)C2a2 (m) F100.213021.60.0289202.1 F200.029816.50.0053110.3 F300.183022.70.003895.9 F400.090324.70.0271342.3 F500.044122.70.0240426.8

100

F600.072742.30.0151194.8

F100.1700 4.510.09696.5

F200.037116.80.0078182.4

F300.183016.20.0580211.0

F400.097946.30.0056395.6

F500.035528.10.0357433.5 200

F600.070761.70.0217347.9

F100.16412.60.093594.0

F200.019811.20.0091190.8

F300.21421.70.0152206.2

F400.082221.00.0335101.7

F500.0534154.30.0190545.1 300

F600.054326.00.125303.0

F100.141 3.610.175126.3

F200.027914.40.0137107.3

F300.1387.210.036573.0

F400.09613.50.0335318.3

F500.0639 5.410.0139402.2 400

F600.038010.80.178320.1

F100.15613.20.120110.6

F200.033121.60.0088158.7

F300.17740.90.0236430.5

F400.081526.50.0489209.2

F500.064321.60.0243496.6 500

F600.15096.20.105401.6 Table 3 Prediction accuracy for 5 different scenarios

well space (m)100200300400500

accuracy (%) 81.678.369.363.254.7

Table 4 Proportion of each microfacies-type for NY Oilfield

proportion Sample No1No2No3No4Outcrop F10.07250.04230.07000.01230.09330.0517

F20.48700.50780.58000.42590.67700.5482

F30.07250.07730.08630.09480.02490.1038

F40.36790.37260.26370.46700.20480.2963

SPE 68706 LUANPING FAN-DELTAIC OUTCROP GEOSTATISTICAL CHARACTERIZATION & APPLICATION TO NY OILFIELD, CHINA5

Fig.1 Location map of Luanping outcrop

Fig. 2 Prototype model of Luanping outcrop

Fig.3 Microfacies simulation No.1 based on 100 m well space

6LIANG CHEN, LONGXIN MU, AILIN JIA, SHIYAN HUANG SPE 68706

Fig.4 “ drilling” pseudo wells and sampling locations for 5 different scenarios

SPE 68706 LUANPING FAN-DELTAIC OUTCROP GEOSTATISTICAL CHARACTERIZATION & APPLICATION TO NY OILFIELD, CHINA7

Fig.5 Microfacies models with maximum probability for 5 different scenarios

8LIANG CHEN, LONGXIN MU, AILIN JIA, SHIYAN HUANG SPE 68706

Fig.6 Microfacies distribution in NY Oilfield

F1F2

Fig.7 Microfacies-type indicator variogram model for NY Oilfield

SPE 68706 LUANPING FAN-DELTAIC OUTCROP GEOSTATISTICAL CHARACTERIZATION & APPLICATION TO NY OILFIELD, CHINA9

(a) Realization #1(b) Realization #2

(e) Realization # 1 based on

outcrop prototype model

Fig.8 Four realizations of microfacies (a-d) based on well data, and

one realization (e) based on Luanping outcrop prototype model