THE IMPACT OF DIGITALIZATION OF SCAL ON FIELD DEVELOPMENT Presented - - PowerPoint PPT Presentation

▶
the impact of digitalization of scal on field development
SMART_READER_LITE
LIVE PREVIEW

THE IMPACT OF DIGITALIZATION OF SCAL ON FIELD DEVELOPMENT Presented - - PowerPoint PPT Presentation

Sep 17, 2022 456 likes •857 views

THE IMPACT OF DIGITALIZATION OF SCAL ON FIELD DEVELOPMENT Presented at the National IOR Conference Stavanger, Norway 2018-04-23 Lesley A. James, Christopher D. Langdon, Maziyar Mahmoodi, Daniel J. Sivira www.mun.ca Acknow ledgements

slide-1
SLIDE 1

www.mun.ca

THE IMPACT OF DIGITALIZATION OF SCAL ON FIELD DEVELOPMENT

Presented at the National IOR Conference Stavanger, Norway 2018-04-23 Lesley A. James, Christopher D. Langdon,

Maziyar Mahmoodi, Daniel J. Sivira

slide-2
SLIDE 2

2

Acknow ledgements

  • National IOR Centre
  • University of Stavanger
  • The support I receive in Canada
  • My co-authors
slide-3
SLIDE 3

3

Agenda

  • The Digital Revolution
  • Conventional Core Analysis
  • Digital Rock Physics
  • Digitalization of SCAL & EOR/IOR Screening
slide-4
SLIDE 4

4

DIGITALIZATION

  • Digital revolution
  • Business Process Engineering
  • DIgitalization
slide-5
SLIDE 5

5

The Digital Revolution

slide-6
SLIDE 6

6

The Six Stages of Digital Transformation

slide-7
SLIDE 7

7

CONVENTIONAL CORE METHODS

  • Core Analysis
  • Specialized Core Analysis – SCAL
  • Enhanced & Improved Oil Recovery Screening
slide-8
SLIDE 8

8

Multiphase & Compositional

Schlumberger Services

Multiphase

  • Wellsite Services

– Catalog, Stabilize for shipment, Sidewall cores

  • Routine Core Analysis

– Porosity, Saturation, Permeability – Core Gamma Logging – CT Scanning (heterogeneity) – Photographs (White and UV Light)

  • Fluid Analysis

– Composition – PVT

  • Petrology

– Viewing Rooms, X-Ray Diffraction – Thin Sections – SEM

  • Formation Damage

– Perm after Mud Invasion – Rock-Fluid Interaction – Fluid-Fluid Interaction – Damage from T&P Change

  • Special Core Analysis

– Electrical Measurement (Archie Exponents) – Nuclear Magnetic Resonance – Capillary Pressure

  • Mercury Injection
  • Centrifuge
  • Porous Plate

– Relative Permeability – Wettability

  • EOR

– Miscible-Gas & Chemical Flood – Core Flow & Sandpack – Slim-tube & Rising Bubble – Multi Contact Miscibility – IFT, Contact Angle, Viscosity – Chemical Combinations – Optimal Salinity – Surfactant Adsorbtion

https://www.slb.com/services/characterization/reservoir/core_pvt_lab/ rock_laboratory_services/core_analysis_conventional_resources.aspx

slide-9
SLIDE 9

9

SCAL Workflow

  • SCAL Program Design is very field specific and application specific

– Beliveau (2007) details a SCAL Program for aggressive field development

  • Beliveau SCAL Program included:

– Basic Rock and Fluid Properties

  • Oil viscosity
  • Rock Characterization (grain size, deposition)

– Initial Water Saturation – Wettability – Relative Permeability – Capillary Pressure

  • Porosity
  • Permeability
slide-10
SLIDE 10

10

SCAL Workflow

  • SCAL Program Design is very field specific and application specific

– Gao, Kralik and Vo, 2010, outline a “State of the Art” SCAL Program Design

  • Large scale single study program

– High accuracy measurements – Appropriate distribution across important static reservoir rock types

slide-11
SLIDE 11

11

Timeline of an EOR Project

DOES NOT INCLUDE:

  • Core Analysis
  • SCAL
slide-12
SLIDE 12

12

ExxonMobil EOR Screening Workflow

  • G. F. Teletzke, R. C. Wattenbarger and J. R. Wilkinson, "Enhanced

Oil Recovery Pilot Testing Best Practices," in SPE International Petroleum Exhibition and Conference, Aby Dhabi, 2010.

slide-13
SLIDE 13

13

EOR Screening Workflow

  • Smart EOR Screening: Breaching the Gap between Analytical and Numerical Evaluations
slide-14
SLIDE 14

14

Conventional EOR Screening

Conventional EOR Workflow

  • R. Al-Mjeni, S. Arora, P. Cherukupalli, J. van Wunnik, J. Edwards, B. J. Felber, O. Gurpinar, G. J. Hirasaki, C. A. Miller, C. Jackson, M. R.

Kristensen, F. Lim and R. Ramamoorthy, "Has the Time Come for EOR," Oilfield Review, vol. 22, no. 4, 2011.

1-4 years

slide-15
SLIDE 15

15

DIGITAL ROCK PHYSICS

  • Workflows
  • Core Analysis
slide-16
SLIDE 16

16

Uses of Digital Rocks

  • Petrophysical Properties’ Correlations
  • Fluid Flow Properties and Calculations (pore network modelling)
  • Quality Control of Convectional Experimental and Indirect Measurements
  • Wettability and EOR Analysis
  • Formation Damage Studies
  • Testing of Brines and Surfactants
slide-17
SLIDE 17

17

Digital Rock Physics (DRP)

  • All analyses undertaken on a single sample
  • Reduction in coring cost because of the sample flexibility
  • A digital rock can be obtained from sidewall cores, cuttings,

damaged, unconsolidated, contaminated, heterogeneous, and trim ends

  • Faster answers to reduce risk
  • Pore-scale understanding of reservoir behaviour
  • Insight and properties upscaled to core plug and whole core sections
  • Improve reserves estimations
slide-18
SLIDE 18

18

Digital Rock Physics (DRP)

Schematic diagram lab-based micro-CT setup [1]

Sample Size Resolution Core 11 - 16.5 cm 400 - 500 µm Core plug 2 - 4 cm 12 - 19 µm Micro plug 0.1 – 0.5 cm 0.3 - 5 µm 50 – 300 µm 0.3 – 5 nm Definition:

  • A new approach in SCAL field is digital rock physics.
  • CT-scan platforms can develop a 3D digital X-ray

micro-tomographic images. Size and Resolution Range:

  • Wide and dependent [2].

CT axial scans of core [PERM Lab, Canada]

slide-19
SLIDE 19

19

Digital Rock Physics (DRP)

Core Scale Pore Scale

  • Pore network modeling
  • Direct pore modeling

Measured Parameters: Petrophysical Properties

  • Total porosity
  • Absolute permeability
  • Density distributions of fluid/rock phases
  • Rock minerology

Multiphase Fluid Flow

  • Capillary pressure
  • Relative permeability
  • Resistivity Index

Different scale of core CT scans [PERM Lab, Canada]

slide-20
SLIDE 20

20

Digital Rock Physics Workflow

Sample Preparation Imaging in 3D. Reconstruction STATIC Calculation of physical properties: ɸ, kh, kv, m,n, Acoustic, NMR Image in 2D/3D (Multiple States) Image quality control, registration and mineral identification in 3D. Integration of

  • ther imaging techniques

DYNAMIC: Multiphase flow and

  • displacement. Estimation of

OOIP & residual saturation. 3D Visualisation & Description of 3D pore structure

https://www.fei.com/videos/webinar-Bringing-Core-Analysis-into-the-Digital-Age/

slide-21
SLIDE 21

21

Properties from Digital Rocks

Solid Matrix Pore Network

Stress - Strain Vp & Vs

Pore Space

Formation Factor NMR relaxation Permeability Pc Relative Permeability

Andrä, H., et al. (2013) Lopez, O., et al. (2012)

https://www.fei.com/videos/webinar-Bringing-Core-Analysis-into-the-Digital-Age/

slide-22
SLIDE 22

22

Properties from Digital Rocks

https://www.fei.com/videos/webinar-Bringing-Core-Analysis-into-the-Digital-Age/

3D Digital Rock Digital Rock Properties Qualitative

Petrophysical Fluid Flow 4D Imaging

  • Porosity
  • Absolute permeability
  • Formation resistivity factor
  • Cementation exponent “m”
  • Elastic moduli
  • Acoustic velocities
  • NMR relaxation times
  • Mercury injection
  • Oil in Place
  • Enhanced Oil Recovery permeability
  • Formation damage
  • Fluid sensitivity
  • Unconventional reservoirs
  • Geochemical reactivity
  • Wettability mapping
  • Capillary pressure
  • Relative permeability
  • Resistivity index
  • Saturation exponent “n”
  • Wettability analysis
  • Sw sensitivity
  • Interfacial sensitivity
  • Rate sensitivity
slide-23
SLIDE 23

23

Upscaling from Digital Rocks

Hibernia Field

Size Comparison

20 Krones Coins New York City

slide-24
SLIDE 24

24

Roles & Tasks

Typical workflow of performing DRP in SCAL [4].

Workflow::

A multidisciplinary process:

  • High-resolution images (step 1-2) of

rock are typically

  • btained

in 1-24 hours depending on spatial and time resolutions [4,5].

Roles include:

  • Petrophysicists
  • Lab technicians
  • Imaging experts
  • Computer scientists
  • Reservoir engineers
slide-25
SLIDE 25

25

Digital Rock Physics Summary

  • Based on core or sub-core samples

– Is it representative of the field?

  • Formation properties are:

– Directly measured/calculated: volumes, porosity, saturations – Correlated from measurements and conventional correlations: permeability, resistivity, capillary pressure, relative permeabilities

  • Pore network modelling:

– Pore scale material balances based direct and Lattice Boltzman Models – Can consider reactive transport, adsorption/dissolution – Intermolecular forces – Computationally intensive – Scaling is a challenge

slide-26
SLIDE 26

26

DIGITALIZATION of SCAL & SCREENING

  • Challenges
  • Possibilities?
slide-27
SLIDE 27

27

Artificial Intelligence in EOR

https://www.capgemini.com/2016/05/machine-learning-has-transformed-many-aspects-of-our-everyday-life/

  • Data Mining is used to extract

important parameters in successful EOR fields

  • Large volume of data (365 successful

EOR projects) required to train and validate model

  • Machine Learning algorithms are

used to draw screening rules and interpret relationship between input and output

  • 80% of the data-set selected at

random for the training and the remaining 20% used as the validation

  • r prediction set
  • G. Ramos and L. Akanji, “Technical Screening of Enhanced Oil Recovery Methods – A Case

Study of Block C in Offshore Angolan Oilfields," in EAGE Workshop on Petroleum Exploration, Luanda, Angola, 2017.

  • V. Alvarado, A. Ranson, K. Hernandez, E. Manrique, J. Matheus, T. Liscano and N. Prosperi,

“Selection of EOR/IOR Opportunities Based on Machine Learning,” in SPE 13th European Petroleum Conference, Aberdeen, 2002.

slide-28
SLIDE 28

28

Artificial Intelligence

  • Five layered feed forward –

backpropagation neural network

  • Input Layer

– Input variables

  • Hidden Layers

– Input/Output membership functions – Fuzzy logic AND/OR rules

  • Output Layer

– Defuzzification – Resulting output is decision signal

A typical 5 layer neuro-fuzzy framework (Ramos, 2017)

slide-29
SLIDE 29

29

Artificial Intelligence

  • Operations occur on individual neurons
  • Each neuron applies an activation function to its net

input to produce its output after receiving signal from the proceeding neurons

  • During learning, knowledge is extracted and expressed

as fuzzy rules.

  • Engineers can also input parameters to tune the

algorithm

  • Back-propagation tunes the parameters to reduce error

A typical 5 layer neuro-fuzzy framework (Ramos, 2017)

  • G. Ramos and L. Akanji, “Technical Screening of Enhanced Oil Recovery Methods – A Case

Study of Block C in Offshore Angolan Oilfields," in EAGE Workshop on Petroleum Exploration, Luanda, Angola, 2017.

  • V. Alvarado, A. Ranson, K. Hernandez, E. Manrique, J. Matheus, T. Liscano and N. Prosperi,

“Selection of EOR/IOR Opportunities Based on Machine Learning,” in SPE 13th European Petroleum Conference, Aberdeen, 2002.

slide-30
SLIDE 30

30

ISI Rock Typing Using SCAL Data

Workflow

http://www.intelligentsolutionsinc.com/Workflows/Workflow-Characterization.shtml#SCALWell

slide-31
SLIDE 31

31

ISI Rock Typing Using SCAL Data

Intelligent Solutions Inc: http://www.intelligentsolutionsinc.com/Workflows/Workflow-Characterization.shtml#SCALWell

Correlations

slide-32
SLIDE 32

32

Rock Typing Using SCAL Data

http://www.intelligentsolutionsinc.com/Workflows/Workflow-Characterization.shtml#SCALWell

slide-33
SLIDE 33

33

Other Estimation using AI

slide-34
SLIDE 34

34

Commercial Field Screening

https://daks.ccreservoirs.com/

slide-35
SLIDE 35

35

EOR Screening Methods

Conventional Screening Geologic Screening Advanced EOR Screening

  • Go - no go screening
  • Depend on expert
  • pinions
  • Taber et al. 1997
  • PRIze
  • Sword
  • Focus on critical

Geological Aspects

  • Reservoir geologic

analogies

  • Artificial Intelligence
  • Neural Networks
  • Fuzzy Logic
  • Experts Systems
  • Simulation
slide-36
SLIDE 36

36

Conclusions

  • Conventional Core Analysis, SCAL, EOR Screening:

– Based on laboratory studies on core samples – Heuristic correlations – Calculated properties based on measured laboratory data – Grouping, physical and statistical comparisons – Upscaling is a challenge

  • Digital Rock Physics (DPR):

– Micro-cores taken from core samples – Altered workflow and technical skills’ requirements – In-situ saturation monitoring is possible – Upscaling is a challenge

  • Digitalization

– Are we ready to give up on some traditional or digital core analysis?

slide-37
SLIDE 37

37

Conclusions

Conventional Core Analysis, SCAL, EOR Screening Current Digital Rock Physics Future Digital Rock Physics

AI?

slide-38
SLIDE 38

38

References

1. Cnudde, V., & Boone, M. N. (2013). High-resolution X-ray computed tomography in geosciences: A review of the current technology and applications. Earth-Science Reviews, 123, 1-17. 2. Kalam, M. Z. (2012). Digital rock physics for fast and accurate special core analysis in carbonates. In New Technologies in the Oil and Gas Industry. InTech. 3. Rassenfoss, S. (2011). Digital rocks out to become a core technology. Journal of Petroleum Technology, 63(05), 36-41. 4. Koroteev, D. A., Dinariev, O., Evseev, N., Klemin, D. V., Safonov, S., Gurpinar, O. M., ... & de Jong, H. (2013, July). Application of digital rock technology for chemical EOR screening. In SPE enhanced oil recovery conference. Society of Petroleum Engineers. 5. Berg, S., Ott, H., Klapp, S. A., Schwing, A., Neiteler, R., Brussee, N., ... & Kersten, M. (2013). Real-time 3D imaging of Haines jumps in porous media flow. Proceedings of the National Academy of Sciences, 110(10), 3755-3759. 6. Kalam, M. Z. (2012). Digital rock physics for fast and accurate special core analysis in carbonates. In New Technologies in the Oil and Gas Industry. InTech. 7. Haynes, H. J., Thrasher, L. W., Katz, M. L., & Eck, T. R. (1976). Enhanced oil recovery, national petroleum council. An analysis of the potential for EOR from Known Fields in the United States-1976-2002. 8. Smalley, P.C., Muggeridge, A.H., Dalland, M., Helvig, O.S., Høgnesen, E.J., M. Hetland, A. Østhus. Screening for EOR and Estimating Potential Incremental Oil Recovery on the Norwegian Continental Shelf. SPE Improved Oil Recovery Conference, 2018.