Big data assimilation and uncertainty quantification in 4D seismic history matching

Size: px

Start display at page:

Download "Big data assimilation and uncertainty quantification in 4D seismic history matching"

Sabrina Parks
5 years ago
Views:

1 Big data assimilation and uncertainty quantification in 4D seismic history matching By Xiaodong Luo, NIORC/IRIS A research based on the collaborations with the following colleagues at IRIS: Tuhin Bhakta, Geir Evensen (also with NERSC), Morten Jakobsen (also with UiB), Rolf Lorentzen, Geir Nævdal, Randi Valestrand

2 Outline Seismic history matching (SHM) for reservoir management and challenges Ensemble-based SHM workflow at IRIS Application examples of the workflow Conclusion and future work

3 Seismic history matching (SHM) Input: petrophysical parameters Forward simulator Output: seismic data History matching algorithm

Integrating the results of seismic history matching for reservoir management Field development, e.g., optimize locations of new wells Production management, e.

4 Integrating the results of seismic history matching for reservoir management Field development, e.g., optimize locations of new wells Production management, e.g., optimize IOR strategies for existing wells

5 Challenges in seismic history matching (SHM) Uncertainties (input/output) Big data (output) Imperfection (simulator)

6 Outline Seismic history matching (SHM) for reservoir management and challenges Ensemble-based SHM workflow at IRIS Application examples of the workflow Conclusion and future work

7 Ensemble-based SHM workflow at IRIS Simulated seismic data Observed seismic data Forward seismic simulator Sparse data representation For both datasize reduction and UQ (output) Reservoir model Leading representation coefficients Leading representation coefficients Seismic history matching

image compression UQ (output) <=> image denoising Possible to

8 Sparse representation to handle big seismic data and UQ (output) Seismic (2D/3D) <=> image Data-size reduction <=> image compression UQ (output) <=> image denoising Possible to achieve both image compression and denoising through a single workflow

Example: workflow of wavelet-based sparse representation* Seismic data (2D/3D) Discrete wavelet transform (DWT) Wavelet coefficients Estimate noise in wavelet

the wavelet domain as a by-product Applicable to various types of image-like seismic data (AVA, impedance, time shift etc.) * Luo, X., Bhakta, T., Jakobsen, M.

9 Example: workflow of wavelet-based sparse representation* Seismic data (2D/3D) Discrete wavelet transform (DWT) Wavelet coefficients Estimate noise in wavelet coefficients* Apply thresholding to remove small wavelet coefficients Leading coefficients used as data in SHM Efficient reduction of data size UQ (output) in the wavelet domain as a by-product Applicable to various types of image-like seismic data (AVA, impedance, time shift etc.) * Luo, X., Bhakta, T., Jakobsen, M., & Nævdal, G. (2016). An ensemble 4D seismic history matching framework with sparse representation based on wavelet multiresolution analysis. SPE Journal, 22, 985-1,010

Wavelet-based sparse representation to handle big seismic data and UQ (output) Illustration: 2D amplitude

coefficients Leading coefficients used in history matching Number of leading coefficients is about 6% of the

0141 Leading coefficients Inverse transform * Luo, X., Bhakta, T., Jakobsen, M., & Nævdal, G. (2016).

10 Wavelet-based sparse representation to handle big seismic data and UQ (output) Illustration: 2D amplitude versus angle (AVA) data* Reference AVA data Noise AVA data (noise lv = 30%) Wavelet transform Wavelet coefficients Leading coefficients used in history matching Number of leading coefficients is about 6% of the original Thresholding seismic data True noise STD = ; estimated noise STD = Leading coefficients Inverse transform * Luo, X., Bhakta, T., Jakobsen, M., & Nævdal, G. (2016). An ensemble 4D seismic history matching framework with sparse representation based on wavelet multiresolution analysis. SPE Journal, 22, 985-1,010

11 UQ (input) through ensemble-based history matching algorithms Reservoir models Seismic data History matching (data assimilation) to update reservoir models Ensemble-based history matching methods provide a means of uncertainty quantification (UQ) for the estimated petrophysical parameters (inputs)

12 Poor UQ (input) performance due to ensemble collapse Desired scenario Reality: ensemble collapse Estimates Truth Ensemble collapse: a phenomenon in which estimated reservoir models become almost identical with very few varieties

13 Improving UQ (input) performance through data selection (aka localization )* Data used to update model variable Model variable Y Causal relations? N Data discarded Data Data *Luo, X., Bhakta, T., & Nævdal, G. (2018). Correlation-based adaptive localization with applications to ensemble-based 4D-seismic history matching. SPE Journal, available online January 2018.

Overcoming some long-standing issues arising in conventional data-selection schemes* Independence on physical distance Non-local observations Usability in practice ISSUES 4D observations Effect of

14 Overcoming some long-standing issues arising in conventional data-selection schemes* Independence on physical distance Non-local observations Usability in practice ISSUES 4D observations Effect of ensemble size Different types of model/data pairs *Luo, X., Bhakta, T., & Nævdal, G. (2018). Correlation-based adaptive localization with applications to ensemble-based 4D-seismic history matching. SPE Journal, available online January Luo, X, Lorentzen, R., Valestrand, R. & Evensen, G. (2018). Correlation-based adaptive localization for ensemble-based history matching: Applied to the Norne field case study. SPE Norway One Day Seminar, SPE MS

15 Ensemble-based seismic history matching (SHM) workflow at IRIS Handling challenges in SHM Big data Uncertainty quantification Imperfection

16 Outline Seismic history matching (SHM) for reservoir management and challenges Ensemble-based SHM workflow at IRIS Application examples of the workflow Conclusion and future work

Example 1: Brugge benchmark case study* Model size Parameters to estimate Experimental settings 139x48x9, with 44550 out of 60048 being active gridcells PORO, PERMX, PERMY, PERMZ.

17 Example 1: Brugge benchmark case study* Model size Parameters to estimate Experimental settings 139x48x9, with out of being active gridcells PORO, PERMX, PERMY, PERMZ. Total number is 4x44550 = 178,200 Production data (~10 yrs) BHP, OPR, WCT. Total number is 1400 Grid geometry of Brugge field 4D seismic data (1 Base + 2 monitor surveys) Leading wavelet coefficients Near and far-offset AVA data. Total number is ~ 7 x 10 6 (needing too much computer memory to be used directly) Two cases: 1. Total number is 178,332 (~2.5%); 100K case 2. Total number is 1665 (~0.02%). 1K case *Luo, X., et al. (2016). An Ensemble 4D Seismic History Matching Framework with Sparse Representation and Noise Estimation: A 3D Benchmark Case Study. 15th European Conference on the Mathematics of Oil Recovery (ECMOR), Amsterdam, Netherlands, 29 August - 01 September, 2016.

18 Reference PORO (at layer 2) Mean PORO (at layer 2) of initial guess Mean PORO (at layer 2) after history matching (100K) Mean PORO (at layer 2) after history matching (1K)

Example 2: Norne field (synthetic seismic data, proof-of-concept)* Model size Experimental settings 46 x 112 x 22 (44927/113344 active) Parameters to estimate Production data 4D seismic data (1 Base

19 Example 2: Norne field (synthetic seismic data, proof-of-concept)* Model size Experimental settings 46 x 112 x 22 (44927/ active) Parameters to estimate Production data 4D seismic data (1 Base + 3 monitor surveys) PORO, PERMX, NTG and other parameters. Total number is 148,159 WPR, GPR and OPR from producers. Total number 2623 Acoustic impedance. Total number is ~ 4.5 x 10 5 Leading wavelet coefficients ~ 2.7x10 5 * Lorentzen, Rolf, Xiaodong Luo, Tuhin Bhakta, and Randi Valestrand. History matching Norne reservoir and petroelastic models using seismic impedance with correlated noise. Under review

20 PORO map at Layer 11 Reference Initial mean Final mean

21 Ongoing activities: Norne field case study using the SHM workflow with real seimsic data* *Lorentzen, R. et al, to be presented in The 13th International EnKF Workshop, May 2018, Bergen, Norway ECMOR, September 2018, Barcelona, Spain.

22 Outline Seismic history matching (SHM) for reservoir management and challenges Ensemble-based SHM workflow at IRIS Application examples of the workflow Conclusion and future work

enhancements and developments 3 The continuous long-term supports from

23 Conclusion 1 We have developed an efficient workflow to tackle the challenges of big data and UQ in SHM 2 Still lots of room for further enhancements and developments 3 The continuous long-term supports from NIORC, RCN and industrial partners are essential for us to come to this far

24 Future work Big data UQ Imperfection More efficient solutions to tackling the challenges in SHM using multidisciplinary approaches Possible improvements on the history matching algorithms

25 The 2018 user partners and observers:

26 Acknowledgements / Thank You / Questions XL acknowledges the Research Council of Norway and the industry partners ConocoPhillips Skandinavia AS, Aker BP ASA, Eni Norge AS, Maersk Oil; a company by Total, DONG Energy A/S, Denmark, Statoil Petroleum AS, Neptune Norge AS, Lundin Norway AS, Halliburton AS, Schlumberger Norge AS, Wintershall Norge AS of The National IOR Centre of Norway for financial supports. XL also acknowledges partial financial supports from the CIPR/IRIS cooperative research project 4D Seismic History Matching, which is funded by industry partners Eni Norge AS, Petrobras, and Total EP Norge, as well as the Research Council of Norway (PETROMAKS2).

Big data assimilation and uncertainty quantification in 4D seismic history matching

Big data assimilation and uncertainty quantification in 4D seismic history matching By Xiaodong Luo, IRIS/NIORC A research based on the collaborations with the following colleagues at IRIS: Tuhin Bhakta,