Impala Multiple Point Statistics in JewelSuite Best in Class Facies Modeling

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1 Impala Multiple Point Statistics in JewelSuite Best in Class Facies Modeling Author: Christian Höcker, Baker Hughes JewelSuite

2 What is IMPALA? Impala is an exciting next generation facies modeling module in JewelSuite, using Multiple Point Statistics (MPS) technology. MPS has significant advantages over object based and 2 point voxel based modeling techniques and allows to create realistic facies models based on geological concepts. The integration of the Impala module into JewelSuite is the result of a strategic partnership with Ephesia Consult SA.

3 Superior Performance of Impala MPS Break the stationarity paradigm: Impala supports the use of trend or auxiliary properties, which means that non stationary training images can be used to generate simulations containing complex trends. This approach is far more intuitive and easier to control than facies proportions. It also is a vital enabler for nested simulations, i.e. first modeling large scale patterns and then filling in details. Optimized for performance: Conventional MPS simulations are computation and memory intensive. The performance of Impala, however, is greatly optimized, by using a list approach rather than the common search tree method. Fully parallelized algorithm providing efficient use of multiple processors and clusters. These improvements facilitate simulation of big models with many facies.

4 Additional Benefits of JewelSuite Implementation Workflow guidance: The Impala plug in interface provides detailed access for the expert and tested defaults for newer users. Efficiency: Common controls like that for depositional azimuth are wrapped into intuitive GUI s and powerful automated methods. Ease of Use: Extensive Graphical Editor options make it easy and even fun to build 2D/3D training models. Local updates: Simulations can be updated for user defined regions, e.g. around new wells.

5 Multiple Point Simulations Generate 3D distributions of facies / lithology in Earth Models Reproducing patterns found in 2D and 3D training images (outcrops, satellite images) Honoring conditioning data Training Image Simulation

6 Scaling and Rotation Training Image (250 x 250) Zone Map zones control distribution of parameters such as scale and rotation Simulation (1000x1000)

7 Non stationary Simulation Training Image Conditioning Data Non stationary training image with associated variable distance to coast Non Stationary Simulation Simulation results are conditioned using several sets of trend information; here: distance to coast zones (direction) facies belt

8 Carbonate Build up Examples

9 Geometries of process to be modeled: (Erosive) channels cutting into platform Simulation: dark blue = infill facies Conceptual 2D Training Image

10 Auxiliary trend information to control width of facies belts Both Training Image and Simulation with overlay of Trend data; additional control: azimuth away from central watershed

11 Geometries of process to be modeled: Depositional facies during platform aggradation Lagoon with patch reefs Back reef Reef flat Reef crest Reef front Fore reef Satellite Image is cropped and discretized to 2D training image JOA Oil & Gas 11

12 Modeling depositional facies during platform aggradation Simulation Results Realization 1 of 20 Training Image based on satellite image

13 Clastics Examples

14 MPS with Complex Facies Models: Clastic Shoreline Transgression / Regression Training Image (TI): Channel Mouthbar complex (6 facies) highly non stationary TI across coastline; trend information on cross section Facies marine shale and overbank are transparent in all displays JOA Oil & Gas 14

15 Cross Sections through MPS Channel Mouthbar Simulations: along and normal to depo gradient along depo gradient strike section floodplain strike section transitional (mouthbars channels) JOA Oil & Gas 15

16 Depositional Energy as Auxiliary Variable: single channel half sided Training Image: conglomeratic turbidite with aux variable depositional energy azimuth for rotating TI in simulation calculated from aux variable

17 Depositional Energy as Auxiliary Variable: single channel half sided Training Image: conglomeratic turbidite with aux variable depositional energy azimuth for rotating TI in simulation calculated from aux variable

18 Depositional Energy as Auxiliary Variable: bifurcated channel half sided Training Image: conglomeratic turbidite with aux variable depositional energy

19 Depositional Energy as Auxiliary Variable: bifurcated channel The simulation model is actually 3D a 3D consistent result has been generated by conditioning a 2D input without getting trapped in over conditioning as when trying the same using facies proportions

20 Depositional Energy as Auxiliary Variable: fan geometry Simulations with same TI as before; 3D property Depositional Energy in simulation model has been generated to represent: decrease away from spines decrease away from branch point vertical cyclicity simulation result with colouring by energy simulation result with colouring by facies

21 Meander Belt MPS Simulation with 3 Nesting Levels 1 st level: meander belts on floodplain (2 belts in this example) 2 nd level: point bar lobes in meander belt 3 rd level: heterogeneity in point bar (4 facies in lateral accretion geometry) training image for 3 rd level JOA Oil & Gas 21

22 Details of Facies Configurations in Meander Belt Model lateral accretion surfaces Detailed shapes in point bar; only lag facies rendered in 3D size/shape of meander lobes varies within geological bounds; each lobe has its own general accretion direction Central cross section with curved lateral accretion surfaces in point bar deposits to either side of meander belt axis; horizontal slice: facies with overlay of point bar azimuth (lobes)

23 MPS Simulation of Turbidite Channel Facies single step simulation outer levee inner levee active channel with accretion pattern In single step mode it is difficult to generate continuous high sinuosity patterns in with MPS. < 10% of all realizations shows patterns with sufficient continuity selection required Nested simulation set ups are a more efficient way to create persistent curvilinear features.

24 Summary MPS combines the ease of conditioning of SIS with the credibility of patterns in object based modeling The trend approach of Impala together with the azimuth support of the JewelSuite plug in form a very powerful combination Simulation of larger scale features with internal heterogeneity can be achieved by nested simulations, at 2 3 levels Depositional energy as auxiliary/trend property is an easy method for conditioning 2D MPS simulations to honor 3D patterns The previous examples illustrate the following MPS components: Training Images: 2D and 3D distributions Stationary and non stationary TI s with trend compensation Simple and complex multi facies patterns Conceptual and satellite/seismic derived inputs Simulations: With and without azimuth regions (3D) With and without auxiliary trend information (3D)

25 Contact Information To request additional information, training or trial version of the Impala module please contact your local JewelSuite representative or send us an e mail to info@jewelsuite.com JOA Oil & Gas 25