1 / 28

PERMEABILITY PREDICTIONS , PETROPHYSICAL GROUPING & RRT ASSAIGNMENT Habeeba Al Housani

PERMEABILITY PREDICTIONS , PETROPHYSICAL GROUPING & RRT ASSAIGNMENT Habeeba Al Housani Hani Al-Sahan ADCO, Bab Team Feb 2010. Presentation Outline. Why we need predictions for non cored wells? Work steps Results Key Learning. Why we need Predictions for non cored wells?.

rudolf
Download Presentation

PERMEABILITY PREDICTIONS , PETROPHYSICAL GROUPING & RRT ASSAIGNMENT Habeeba Al Housani

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript


  1. PERMEABILITY PREDICTIONS , PETROPHYSICAL GROUPING & RRT ASSAIGNMENT Habeeba Al Housani Hani Al-Sahan ADCO, Bab Team Feb 2010

  2. Presentation Outline • Why we need predictions for non cored wells? • Work steps • Results • Key Learning

  3. Why we need Predictions for non cored wells? • Limited core data coverage • Better data extrapolation • Full use of log data

  4. RRT NN- K for Non cored wells PG from Cored wells K RRT from Cored Wells NN- K for Non cored wells PG for Non Cored Wells PG PHIE OH logs SW,PHIE, RHOB Geological data PHIE Using SOM-software Using SOM-software PG for Non Cored Wells Using NN-software RRT for Non Cored Wells NN- K for Non cored wells Phase 3 Static Model Flow Charts predictions for Non cored wells

  5. Step(1) Permeability Predictions RRT NN- K for Non cored wells PG from Cored wells K RRT from Cored Wells NN- K for Non cored wells PG for Non Cored Wells PG PHIE OH logs SW,PHIE, RHOB Geological data PHIE Using SOM-software Using SOM-software PG for Non Cored Wells Using NN-software RRT for Non Cored Wells NN- K for Non cored wells Phase 3 Static Model

  6. Data Clustering Cored 12 Non cored 82 Cored 3 Non cored 15

  7. 5 5 BB - 147 4 4 3 3 2 2 1 1 0 0 -1 -1 -2 -2 -3 -3 8660 8660 8680 8680 8700 8700 8720 8720 8740 8740 8760 8760 8780 8780 8800 8800 8820 8820 8840 8840 Log K 5 5 BB - 456 - 1D 4 4 3 3 2 2 1 1 0 0 -1 -1 -2 -2 -3 -3 8950 8950 8960 8960 8970 8970 8980 8980 8990 8990 9000 9000 9010 9010 9020 9020 9030 9030 Training Results Log K Log K Log K

  8. poor good fair Blind Test Results

  9. Estimated Permeability validation( Non cored wells) Compare the Estimated K with • MDT mobility data • Twin wells core data

  10. Comparison between MDT/RFT Mobility and core “K” in 3 Cored Wells

  11. Comparison between MDT/RFT Mobility data and Predicted K in 3 Non cored wells

  12. Comparison between MDT/RFT Mobility data and Predicted K in 3 Non cored wells

  13. Comparison between Core K in none cored well & Predicted K in Twin Cored Well Non Cored Cored Estimated K in non-cored wells compared to core K in a nearby well are in the same range

  14. High Perm STK Log K Log K NNet logK Comparison between Core K in none cored well & Predicted K in Twin Cored Well Cored Non Cored Estimated K in non cored wells compared to core K in a nearby well are in the same range- except High perm streak

  15. PG’s Assignment For Cored wells

  16. Self Organizing Map SOM 5 parameters used as input in IPSOM: Slop Permeability Hyp-tangent Inflexion point Porosity

  17. PG /MICP cap curves per PG’s 1 2 5 3 6 4

  18. Step(2) Petrophysical Grouping (PG) Assignment RRT NN- K for Non cored wells PG from Cored wells K RRT from Cored Wells NN- K for Non cored wells PG for Non Cored Wells PG PHIE OH logs SW,PHIE, RHOB Geological data PHIE Using SOM-software Using SOM-software PG for Non Cored Wells Using NN-software RRT for Non Cored Wells NN- K for Non cored wells Phase 3 Static Model

  19. Data clustering Field was clustered to reduce effects of fluids and structure position Permeability cored wells

  20. Clusters Permeability Histogram Comparison 11 3 1 2 2 4 9 1 10 5 8 6 7 7 4 6 NE MD NW NW MD SW 8 10 3 DD E MD S DD SE DDSW 11 5 DDW DD N Crest 9 Permeability frequency Histogram shows Consistency between Actual and predicted permeability Varied Permeability Statistics for each cluster

  21. Results from PG’s predictions

  22. High Perm STKS High Perm STKS Cluster 1 apply wells Cored wells Non-cored wells

  23. RRT NN- K for Non cored wells PG from Cored wells K RRT from Cored Wells NN- K for Non cored wells PG for Non Cored Wells PG PHIE OH logs SW,PHIE, RHOB Geological data PHIE Using SOM-software Using SOM-software PG for Non Cored Wells Using NN-software RRT for Non Cored Wells NN- K for Non cored wells Phase 3 Static Model Step 3 RRT predictions for Non cored wells Flowchart

  24. Blind Test Validation ACTUAL Predicted

  25. Blind Test Validation

  26. Histogram plot for actual RRT and Predicted RRT RRT prediction using PG ,PHIE and K

  27. Key Learning • NN Permeability predictions were enhanced by adding geologic term to the work flow • High perm streaks are not predicted by logs (resolution problem) • To improve prediction we need to eliminate less confident data e.g. logs affected by water/gas injection • Field clustering were used in predictions to reduce heterogenity effects

  28. Thank You

More Related