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

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

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

7 Training Results Log K

8 Blind Test Results good fair poor

9 Estimated Permeability validation ( Non cored wells) Compare the Estimated K with 1.MDT mobility data 2.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 Estimated K in non-cored wells compared to core K in a nearby well are in the same range Comparison between Core K in none cored well & Predicted K in Twin Cored Well Non Cored Cored

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

15 PG ’ s Assignment For Cored wells

16 Porosity Self Organizing Map SOM 5 parameters used as input in IPSOM: PermeabilityHyp-tangentInflexion pointSlop

17 PG /MICP cap curves per PG ’ s

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

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

20 Clusters Permeability Histogram Comparison NW NE DD E DD W MD S DD SE DDSW DD N Crest MD NW MD SW Permeability frequency Histogram shows Consistency between Actual and predicted permeability Varied Permeability Statistics for each cluster

21 Results from PG ’ s predictions

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

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

24 Blind Test Validation ACTUALPredicted

25 Blind Test Validation

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

27 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 Key Learning

28 Thank You