Process-Oriented Modeling as a core data pre-processing tool to improve ANN permeability-log estimation

Transcription

1 Process-Oriented Modeling as a core data pre-processing tool to improve ANN permeability-log estimation Livio Ruvo, Eni E&P FORCE Reservoir Characterization Workshop Stavanger - October 16 th, 6

2 Presentation Outline Introduction K curves estimation from core data Case Study core preprocessing approach Results Conclusions

3 Introduction Permeability Prediction the pot of gold at the foot of the petrophysical rainbow P. F. Worthington,

4 Introduction Permeability Prediction - Data Sources Permeability curves: main target in reservoir characterization Logging techniques (NMR) to generate continuous curves NMR availability is not the rule Available K data: mainly from sparsely sampled core plugs usually from a few reservoir intervals/wells Conventional log recordings: nearly ubiquitous can be used as permeability predictors in uncored wells

5 Introduction Permeability Prediction Available Techniques Parametric Techniques (Regression Analysis) true functional relationships unknown LOG K DEPTH y = 1.78x R = PHI LOG K PHI

6 Introduction Permeability Prediction Available Techniques Artificial Neural Network (ANN) a-priori functional relationships not needed GR RHOB K TNPH Input Layer/Nodes Hidden Layer/Nodes Output Layer/Node

7 Introduction Permeability Prediction Cross Scaling Cores and conventional logs: not comparable in terms of scale & resolution risk of obscuring core/log correlations quite high, especially in heterogeneous formations It is commonly agreed preprocessing core plug data to offset scale differences needed to correctly integrate core data into the generation of synthetic K curves

8 Study Objectives To investigate - using data from an actual reservoir - two core plug permeability data preprocessing approaches: 1. excluding core plug measurements deemed as logincompatible (limited database technique). building digital models of the cored interval using a Process-Oriented Modeling approach (SBED Methodology) by generating the two corresponding permeability curves - using ANN - and validating them against actual dynamic data (MDT)

9 Data available Reservoir: alternating fine to very fine sandstones, siltstones, mudstones (turbidites) Conventional Logs NMR Log Verification Well- Well GR RHOB TNPH Timur-Coates Eq. Key (Training) Well-3 Well GR RHOB TNPH Timur-Coates Eq. Cores 1 ft 3 ft curve Yes Yes Nr. of core plugs with permeability measurements 37 9 Formation evaluation Yes Yes Cluster Analysis on conventional logs electrofacies-based reservoir zonation (5 Log-Facies)

10 Data available X MD (ft) gamma-ray density neutron Log-Facies Zonation DST Interval DST in Verification Well: Interpreted absolute perm = 3 md X X X X X X : permeability along the DST interval = 8.1 md X X X X X D S T curves NOT used for generating synthetic K curves X X X X but suitable for a preliminary comparison X X Verification Well Final validation carried out against dynamic data

11 K estimation from Original Dataset CKHA 1.1 CKHA 1.1 CKHA GR TNPH RHOB CKHA vs. GR, RHOB and TNPH for the key-well Synthetic K curve training an ANN with Original Dataset (CKHAfromANNs): GR, TNPH and RHOB conventional log data (as input) permeability values from non-preprocessed core plugs (as desired output)

12 K estimation from Original Dataset ftmd reservoir Key Well GR RHOB TNPH CGSZ CKHAfromANN ftmd CKHA from ANNs.1 1 reservoir Verification Well GR RHOB TNPH CGSZ CKHA from ANNs.1 1 CKHAfromANN

13 K estimation from Original Dataset 4 3 R =.44 Key Well 4 3 Verification Well R = CKHA from ANN CKHA from ANN Synth. K underestimated Overestimation on low K values High data dispersion More overestimation on low K values Estimation algorithm not to be extrapolated to new incoming data

14 Core Data Pre-Processing Log-core scale effects Core-log compatibility: both measurements agree with actual formation values at a given point 1 m Core PIGE - CPOR..5 * * * Core plugs excluded : from depths where log readings are affected by shoulder-effect (within half-width of the log vertical resolution window from a lithology break) from thin layers below the vertical resolution of the log tool * * * * * * * * * * Investigated volume of rock: Plug measurement - Log measurement Shale Sandstone

15 Core Data Pre-Processing Filtering the original database Key well: 9 CKHA available CKHA CKHA SE-Corr Filtering: 7 plug data removed (34% of the original database) GR GR y = x R =.363 GR y = x R =.6789 Remaining data Limited Database (CKHA SE-Corr data set) Log1 CKHA.4 Log1 CKHA SE-Corr y = -.1x y = -.185x +.53 R =.889 R = TNPH NPHI. NPHI Log1 CKHA y = -.8x R = Log1 CKHA SE-Corr y = -.657x R = RHOB RHOB.4 RHOB

16 K estimation from Limited Dataset ftmd reservoir Key Well GR RHOB CKHA SE- TNPH CGSZ Corr from ANN ftmd CKHA SE-Corr from ANNs.1 1 reservoir Verification Well GR RHOB TNPH CGSZ CKHA SE-Corr from ANNs.1 1 CKHA SE- Corr from ANN

17 K estimation from Limited Dataset 4 Key Well 4 Verification Well 3 R =.53 3 R = CKHA SE-Corr from ANN CKHA SE-Corr from ANN High K correctly predicted Poor accuracy on low K values High K correctly predicted Poor accuracy/precision on low K values Extrapolation of to new incoming data questionable

18 Process Oriented Modeling (POM) Simulates the process of bedform deposition 3D Rock Model Facies (Core/Outcrop) Permeability grid Frequency Mud Silt Sand Ln k (Permeability)

19 POM Preprocessing Workflow (SBED Methodology) 1. Identification of Sedimentary Building Blocks (SBB s) on cores. Attribution of petrophysical parameters to pure lithologies 3. Digital reconstruction of cores by: a. stacking SBB s according to their actual occurrence on cores b. simulating permeability within digital core model; c. moving-window upscaling using a plug-compatible moving size window to match actual plug measurements 4. Moving-window upscaling using a log-compatible moving size window to simulate the process of log acquisition

20 POM Preprocessing Workflow (SBED Methodology) 1. Identification of Sedimentary Building Blocks (SBB s) on cores. Attribution of petrophysical parameters to pure lithologies 3. Digital reconstruction of cores by: a. stacking SBB s according to their actual occurrence on cores b. simulating permeability within digital core model; c. moving-window upscaling using a plug-compatible moving size window to match actual plug measurements 4. Moving-window upscaling using a log-compatible moving size window to simulate the process of log acquisition

21 POM: Identification of Sedimentary Building Blocks (SBB) SBB: association of lithological and textural properties observed on cores f sandstone vf sandstone siltstone mudstone 1) massive fine sandstone; ) massive very fine sandstone; 3) laminated fine and very fine sandstones; 4) laminated very fine sandstones and siltstones; 5) laminated very fine sand and mudstones; 6) laminated siltstones (5%) and mudstones (5%); 7) laminated siltstones (1%) and mudstones (9%).

22 POM Preprocessing Workflow (SBED Methodology) 1. Identification of Sedimentary Building Blocks (SBB s) on cores. Attribution of petrophysical parameters to pure lithologies 3. Digital reconstruction of cores by: a. stacking SBB s according to their actual occurrence on cores b. simulating permeability within digital core model; c. moving-window upscaling using a plug-compatible moving size window to match actual plug measurements 4. Moving-window upscaling using a log-compatible moving size window to simulate the process of log acquisition

23 POM: Petrophysical Parameters Lithology Fine Sandstones Very fine sandstones Ditribution Type Median (md) Mean (md) St. Dev. (md) Log Normal Log Normal Siltstone Log Normal Mudstone Constant flexible approach to the use of these values

24 POM Preprocessing Workflow (SBED Methodology) 1. Identification of Sedimentary Building Blocks (SBB s) on cores. Attribution of petrophysical parameters to pure lithologies 3. Digital reconstruction of cores by: a. stacking SBB s according to their actual occurrence on cores b. simulating permeability within digital core model; c. moving-window upscaling using a plug-compatible moving size window to match actual plug measurements 4. Moving-window upscaling using a log-compatible moving size window to simulate the process of log acquisition

25 POM: 3D Core Reconstruction Core Sedimentological interpretation SBB attribution SBB stacking fs Fining Upward fine sandstone mdst sltst 6 3 ft Laminated siltstone & mudstone Coarsening Upward fine sandstone

26 POM: 3D Core Reconstruction Core SBB stacking Permeability attribution to pure lithotypes Permeability Simulation fs mdst 3 ft sltst Frequency Mud Silt Sand Ln k (Permeability)

27 POM: 3D Core Reconstruction SBB stacking Permeability Simulation Core Plug-size up. K 3 48 fs 3 ft mdst sltst Plug-Size Moving Window K Averaging Kh (md)

28 POM: 3D Core Reconstruction Thickness of cored interval = 3 ft Need to build several (partly overlapping) separate models Cell size (x*y*z) =.33 ft *.33 ft *.3 ft Average Grid size (x*y*z) =.165 ft *.165 ft * 3 ft Average Nr. of cells = 5 * 5 * 1 Top-Base of model (ft core depth) Core 1 Core Nr. of elementary sedimentary facies Top-Base of model (ft core depth) Nr. of elementary sedimentary facies XX444-XX48 19 XX584-XX6 14 XX478-XX XX617-XX631 4 XX51-XX536 1 XX68-XX65 17 XX534-XX564 XX65-XX676 1 XX674-XX XX693-XX71 19

29 POM Preprocessing Workflow (SBED Methodology) 1. Identification of Sedimentary Building Blocks (SBB s) on cores. Attribution of petrophysical parameters to pure lithologies 3. Digital reconstruction of cores by: a. stacking SBB s according to their actual occurrence on cores b. simulating permeability within digital core model; c. moving-window upscaling using a plug-compatible moving size window to match actual plug measurements 4. Moving-window upscaling using a log-compatible moving size window to simulate the process of log acquisition

30 POM: Log size mov.-wind. upscaling Vertical resolution of moving window = 3 (.5 ft) KSBED input for K estimation Active Depth MD (ft) Core Permeability (horz).1 1. KTIM_3ST - CMR-CalibratedFromLyne.1 1. KSBED - KSBED Active Depth MD (ft) Core Permeability (horz).1 1. KTIM_3ST - CMR-CalibratedFromLyne.1 1. KSBED - KSBED Active Depth MD (ft) Core Permeability (horz).1 1. KTIM_3ST - CMR-CalibratedFromLyne.1 1. KSBED - KSBED XX 1548 XX 1557 XX 1566 XX 1549 XX 1558 XX 1567 XX 155 XX 1559 XX 1568 XX 1551 XX 156 XX 1569 XX 155 XX 1561 XX 157 XX 1553 XX 156 XX 1571 XX 1554 XX 1563 XX 157 XX 1555 XX 1564 XX 1573 XX 1556 XX 1557 XX 1565 XX 1566 XX 1574 XX 1575

31 POM: Log size mov.-wind. upscaling Vertical resolution of moving window = 3 (.5 ft) KSBED input for K estimation Active Depth MD (ft) XX 1548 XX 1549 XX 155 XX 1551 XX 155 XX 1553 XX 1554 XX 1555 XX 1556 XX 1557 Core Permeability (horz).1 1. KTIM_3ST - CMR-CalibratedFromLyne.1 1. KSBED - KSBED Active Active R =.78 Depth MD (ft) XX 1557 XX 1558 XX 1559 XX 156 XX 1561 XX 156 XX 1563 XX 1564 XX 1565 XX 1566 Core Permeability (horz).1 1. KTIM_3ST - CMR-CalibratedFromLyne.1 1. KSBED - KSBED.1 1. Depth MD (ft) XX 1566 XX 1567 XX 1568 XX 1569 XX 157 XX 1571 XX 157 XX KSBED XX 1574 XX 1575 Core Permeability (horz).1 1. KTIM_3ST - CMR-CalibratedFromLyne.1 1. KSBED - KSBED.1 1.

32 K estimation from POM ftmd reservoir Key Well GR RHOB KSBED TNPH CGSZ KSBEDfromANN ftmd K-SBED KSBED from ANNs.1 1 reservoir Verification Well GR RHOB TNPH CGSZ KSBED from ANNs.1 1 KSBEDfromANN

33 K estimation from POM 4 3 R =.79 Key Well 4 3 Verification Well R = KSBED from ANN KSBED from ANN High K correctly predicted Good accuracy on low K values as well High K correctly predicted Good accuracy on very low K values as well Estimation algorithm can be extrapolated to new incoming data

34 Cross plot comparison R =.44 3 R =.53 3 R = Key Well CKHA from ANN CKHA SE-Corr from ANN KSBED from ANN R =.58 3 R =. 3 R =.86 Verification Well CKHA from ANN CKHA SE-Corr from ANN KSBED from ANN

35 Final Validation Predicted Permeability vs. DST Permeability K abs from DST = 3 md Permeability data type (Well-3) Median value of permeability distribution (md) K avg on DST interval (Well-) Log-Facies CKHA from ANN md CKHA SE-Corr from ANN md KSBED from ANN md

36 Conclusions Core preprocessing helped improving K prediction Limited Database technique: very fast results questionable but better than nonpreprocessed core data affected by bias on the filtered data POM (SBED method) approach: time demanding full integration of plug data and sedimentology K curves honoring and matching DST

37 Acknowledgements Mauro Cozzi, Paolo Scaglioni, Anna Maria Lyne for their contribution to the work (SPE 1748) Workshop Organizing Committee