www.mun.ca

THE IMPACT OF DIGITALIZATION OF SCAL ON FIELD DEVELOPMENT

Presented at the National IOR ConferenceStavanger, Norway

2018-04-23

Lesley A. James, Christopher D. Langdon, Maziyar Mahmoodi, Daniel J. Sivira

2

Acknowledgements

• National IOR Centre

• University of Stavanger

• The support I receive in Canada

• My co-authors

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Agenda

• The Digital Revolution

• Conventional Core Analysis

• Digital Rock Physics

• Digitalization of SCAL & EOR/IOR Screening

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DIGITALIZATION- Digital revolution- Business Process Engineering- DIgitalization

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The Digital Revolution

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The Six Stages of Digital Transformation

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CONVENTIONAL CORE METHODS- Core Analysis- Specialized Core Analysis – SCAL- Enhanced & Improved Oil Recovery Screening

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Schlumberger Services

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• 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 ht

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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

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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

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Timeline of an EOR Project

DOES NOT INCLUDE:- Core Analysis- SCAL

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ExxonMobil EOR Screening Workflow

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EOR Screening Workflow• Smart EOR Screening: Breaching the Gap between Analytical and Numerical Evaluations

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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

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DIGITAL ROCK PHYSICS- Workflows - Core Analysis

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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

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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

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Digital Rock Physics (DRP)

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

Sample Size ResolutionCore 11 - 16.5 cm 400 - 500 µm

Core plug 2 - 4 cm 12 - 19 µm

Micro plug0.1 – 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]

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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]

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Digital Rock Physics Workflow

Sample Preparation Imaging in 3D. Reconstruction

STATICCalculation 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

other 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/

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Properties from Digital RocksSolid 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/

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Properties from Digital Rocks

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

3D Digital Rock Digital Rock Properties Qualitative

PetrophysicalFluid 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

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Upscaling from Digital Rocks

Hibernia Field

Size Comparison

20 Krones Coins

New York City

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Roles & Tasks

Typical workflow of performing DRP in SCAL [4].

Workflow::

A multidisciplinary process:• High-resolution images (step 1-2) of

rock are typically obtained in 1-24hours depending on spatial and timeresolutions [4,5].

Roles include:• Petrophysicists• Lab technicians• Imaging experts• Computer scientists• Reservoir engineers

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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

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DIGITALIZATION of SCAL & SCREENING- Challenges - Possibilities?

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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 or 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.

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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)

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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.

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ISI Rock Typing Using SCAL DataWorkflow

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

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ISI Rock Typing Using SCAL Data

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

Correlations

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Rock Typing Using SCAL Data

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

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Other Estimation using AI

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Commercial Field Screening

https://daks.ccreservoirs.com/

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EOR Screening MethodsConventional Screening Geologic Screening Advanced EOR Screening

Go - no go screening Depend on expert

opinions Taber et al. 1997 PRIze Sword

Focus on critical Geological Aspects

Reservoir geologic analogies

Artificial Intelligence Neural Networks Fuzzy Logic Experts Systems Simulation

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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?

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Conclusions

ConventionalCore Analysis,

SCAL, EOR Screening

CurrentDigital Rock Physics

FutureDigital Rock Physics

AI?

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References1. 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 ofPetroleum Engineers.

5. Berg, S., Ott, H., Klapp, S. A., Schwing, A., Neiteler, R., Brussee, N., ... & Kersten, M. (2013). Real-time 3D imaging ofHaines 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 inthe 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. Ananalysis 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 andEstimating Potential Incremental Oil Recovery on the Norwegian Continental Shelf. SPE Improved Oil RecoveryConference, 2018.