Time-lapse seismic monitoring and inversion in a heavy oilfield. By: Naimeh Riazi PhD Student, Geophysics

Transcription

1 Time-lapse seismic monitoring and inversion in a heavy oilfield By: Naimeh Riazi PhD Student, Geophysics May 2011

2 Contents Introduction on time-lapse seismic data Case study Rock-physics Time-Lapse Calibration Time-Lapse inversion Conclusion

3 Introduction Time-lapse seismic reservoir monitoring technique has advanced rapidly over the past two decades. It is a 3D seismic imaging operation performed several times during production period of an oil or gas field to formulate an accurate model of reservoir performance over time. The 4D time-lapse seismic technology can enhance the recovery efficiency of old fields. The rock physics theory is the link between the seismic data and the reservoir processes. The change of the wave velocity is the base of the acoustic impedance concept which can be used to accentuating the production effects in a reservoir.

4 Introduction Line 14, from (a) preburn, (b) midburn, and (c) postburn 3- D seismic data volumes. (from Greaves and Fulp, 1987)

5 Acoustic Properties of Reservoir Fluids Density (API), composition, temperature, pressure, gas-oil ratio (GOR), and the bubble point are the factors which control the geophysical properties of heavy oils. Gassmann s (1951) equation: Velocity, density, bulk modulus and viscosity of oils decrease with increasing: - Temperature - GOR - API K sat K dry K Heavy oils have an effective shear modulus and can propagate a shear wave. However, this shear behavior has a strong frequency- and is temperature-dependent. fl 1 K 1 K K m dry m 2 K K dry 2 m

6 Temperature and Pressure Effect P- and S-wave velocities of the oil sands as a function of differential pressure (Kato et al (2008)). Effect of temperature on compressional and shear velocities on a core sample from Pikes Peak oil field (from Watson et al, 2002)

7 Case study Location map of GLISP (from Matthews, 1992) GLISP geologic cross-section (From Pullin et al 1987).

8 Case study Base Map of survey area

9 A seismic section from the base survey in inline 21. Color key is from Mon1 survey

10 Synthetic Seismogram Synthetic seismic traces are correlated with base seismic traces. The blue traces are the synthetic and the red are the extracted seismic

11 Fluid Replacement Modeling The QC plot for fluid replacement modeling when there is an increase in gas amount from 0 to 5 %. The black color is original and red is modified logs.

12 Variation of time-delays with change in pressure in 0.5, 1 and 1.5 MPa. Color key is P-wave velocity

13 4D seismic calibration 4D Calibrating is removing unwanted differences (spurious differences related to acquisition/processing and near surface changes). Excessive calibration is also dangerous in the interpretation of the 4D time-lapse interpretation. 4D calibration(from Hampson-Russell unpublished course notes for Pro4D)

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15 Calibration effect on a line from seismic volume in GLISP (inline 21); left is before calibration and right is after calibration.

16 Normalized amplitude of difference traces in the GLISP oilfield after applying calibration processes. High NRMS values indicate the production changes.

17 Time-lapse interpretation

18 Time-lapse interpretation Base-Mon1 Base-Mon2 Base-Mon3

19 Time-Lapse Inversion Model based inversion The convolution model is the base of the seismic inversion: Seismic trace=reflectivity*wavelet+ noise The initial model can be made by interpolating the seismic horizons and blocking the impedance of the well logs The model-based inversion attempts to modify the model until the synthetic matches the seismic trace within some acceptable bounds.

20 Time-Lapse Inversion

21 Neural Network Inversion A neural network is a computational mechanism able to compute the map from one multivariate space of information to another, given a set of data representing that mapping. Comparison of results of model based inversion and neural network inversion

22 Neural Network Inversion

23 Conclusion The 4D time-lapse seismic technology is useful and is an efficient technique for reservoir monitoring. Application of time-lapse seismic data on the GLISP oil field can give good results regarding to the petrophysical parameters such as temperature and pressure over the production time. Due to the decrease of velocity and density during the production time in the studied heavy oilfield, the seismic inversion can extract the effect of production very well. Among different methods in seismic inversion, model-based inversion and neural network inversion are applied in the 4D time-lapse data. Valuable information can be taken by applying both inversion methods. Comparing the results of these inversions show that neural network inversion can show the decrease in the acoustic impedance in the production zone better.