University of Groningen Numerical simulation of chemical EOR processes Druetta, Pablo

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Numerical simulation of chemical EOR processes

Druetta, Pablo

IMPORTANT NOTE: You are advised to consult the publisher's version (publisher's PDF) if you wish to cite from it. Please check the document version below.

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Publication date: 2018

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Druetta, P. (2018). Numerical simulation of chemical EOR processes. Rijksuniversiteit Groningen.

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Numerical simulation of chemical EOR processes

Druetta, Pablo

IMPORTANT NOTE: You are advised to consult the publisher's version (publisher's PDF) if you wish to cite from it. Please check the document version below.

Document Version

Publisher's PDF, also known as Version of record

Publication date: 2018

Link to publication in University of Groningen/UMCG research database

Citation for published version (APA):

Druetta, P. (2018). Numerical simulation of chemical EOR processes. Rijksuniversiteit Groningen.

Copyright

Other than for strictly personal use, it is not permitted to download or to forward/distribute the text or part of it without the consent of the author(s) and/or copyright holder(s), unless the work is under an open content license (like Creative Commons).

Take-down policy

If you believe that this document breaches copyright please contact us providing details, and we will remove access to the work immediately and investigate your claim.

Downloaded from the University of Groningen/UMCG research database (Pure): http://www.rug.nl/research/portal. For technical reasons the number of authors shown on this cover page is limited to 10 maximum.

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Chemical EOR Processes

Pablo Daniel Druetta

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be reproduced, stored in a retrieval system of any nature, or transmitted in any form or by any means, electronic, mechanical, photocopying, recording or otherwise, included a complete or partial transcription, without the prior written permission of the authors, application for which should be addressed to author.

Cover design: Magdalena Piekorz and Pablo Druetta

Printed by: ProefschriftMaken ‖ www.proefschriftmaken.nl

ISBN: 978-94-034-0983-2

ISBN: 978-94-034-0982-5 (electronic version)

The work described in this thesis was performed at the Department of Chemical Engineer-ing - Product Technology (ENgineerEngineer-ing and TEchnology institute GronEngineer-ingen - ENTEG), Faculty of Science and Engineering, University of Groningen, the Netherlands.

This research project was financially supported by the Erasmus Mundus EURICA schol-arship program (Program Number 2013-2587/001-001-EMA2) and the Roberto Rocca Education Program Fellowships.

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Numerical Simulation of

Chemical EOR Processes

Proefschrift

ter verkrijging van de graad van doctor aan de

Rijksuniversiteit Groningen

op gezag van de

rector magnificus prof. dr. E. Sterken

en volgens besluit van het College voor Promoties.

De openbare verdediging zal plaatsvinden op

vrijdag 19 oktober 2018 om 11.00 uur

door

Pablo Druetta

geboren op 4 april 1980

te Buenos Aires, Argentinië

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Prof. dr. F. Picchioni

Prof. dr. C. De Persis

Copromotor

Dr. P. Tesi

Beoordelingscommissie

Prof. dr. ir. H.J. Heeres

Prof. dr. V. Tricoli

Prof. dr. A.N. Menéndez

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Prof. dr. A. Broekhuis

Prof. dr. ir. H.J. Heeres

Prof. dr. V. Tricoli

Prof. dr. A.N. Menéndez

Prof. dr. A. Vakis

Dr. P. Raffa

Dr. J. Yue

Prof. dr. F. Picchioni

Prof. dr. C. De Persis

Dr. P. Tesi

Paranimfen

Drs. A. Kamphuis

Drs. N. Migliore

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...and Magda

Also to the few brave readers

who will dare to read all the pages,

especially to those who will voluntarily

check the equations in Appendices 4 and 11

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List of Abbreviations xv

List of Symbols xvii

Preface xix

P.1 Introduction . . . xx

P.2 Chemical EOR and Reservoir Simulation . . . xx

P.3 Nanotechnology in EOR . . . xxiii

P.4 Thesis Outline . . . xxv

References . . . xxvi

1 Chemical EOR and the Role of Chemical Product Design 1 1.1 Introduction . . . 2

1.1.1 General Considerations . . . 2

1.1.2 Oil Recovery Mechanisms . . . 7

1.1.3 Enhanced Oil Recovery . . . 8

1.2 Chemical Enhanced Oil Recovery . . . 10

1.2.1 General Considerations . . . 10

1.2.2 Polymer Flooding . . . 18

1.2.3 Surfactant Flooding . . . 38

1.2.4 Polymeric Surfactants . . . 46

1.2.5 Chemical EOR Combined Techniques . . . 51

1.3 Conclusions . . . 61

1.4 Acknowledgments . . . 62

References . . . 62

2 Methods in Oil Recovery Processes and Reservoir Simulation 91 2.1 Introduction . . . 92

2.1.1 Oil Extraction . . . 92

2.1.2 Reservoir Characterization and Formation . . . 94

2.1.3 Fluid Flow Models . . . 97

2.2 Numerical Techniques for Fluid Flow in Porous Media . . . 107

2.2.1 Numerical Schemes . . . 107

2.2.2 Numerical Dissipation and Dispersion . . . 111

2.2.3 Flux Limiters . . . 113 ix

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2.2.4 Consistency and Stability . . . 116

References . . . 119

3 Influence of Rheology on the Oil Sweeping at Micro- and Macroscales 127 3.1 Introduction . . . 128

3.1.2 Aim of this Work . . . 133

3.2 Model Description . . . 134

3.2.1 Mathematical Model . . . 134

3.2.2 Physical Model . . . 136

3.2.3 Boundary Conditions . . . 138

3.3 Results and Discussion . . . 139

3.3.1 Macroscopic Model . . . 139 3.3.2 Microscopic Model . . . 148 3.4 Conclusions . . . 155 3.5 Acknowledgments . . . 155 References . . . 155 3.6 Supplementary Information . . . 160

4 Polymer Flooding Simulator: Numerical Model and Validation 161 4.1 Introduction . . . 162

4.2 Model Description . . . 166

4.2.3 Physical Properties . . . 169

4.2.4 Boundary Conditions . . . 171

4.2.5 Nondimensionalization of the Transport Equations . . . 172

4.2.6 Discretization of the Partial Differential Equations . . . 172

4.2.7 Solution Algorithm . . . 176

4.3.1 Data . . . 178

4.3.2 Validation of the model . . . 179

4.3.3 Waterflooding . . . 180

4.6 Appendix . . . 193

4.6.1 Discretization of the PDE . . . 193

4.6.2 Interactive 3D PDF . . . 198 x

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5 Polymer Flooding Simulator: Influence of the Polymer Properties 199

5.1 Introduction . . . 200

5.2 Physical Properties . . . 201

5.2.1 Chemical Component Partition . . . 201

5.2.2 Interfacial Tension . . . 201

5.2.3 Residual Saturation . . . 202

5.2.4 Inaccessible Pore Volume . . . 203

5.2.5 Phase Viscosities . . . 204

5.2.6 Adsorption . . . 207

5.2.7 Disproportionate Permeability Reduction . . . 207

5.3.1 Introduction . . . 208

5.3.2 Influence of the Degradation Rate . . . 209

5.3.3 Influence of the Numerical Scheme . . . 215

5.3.4 Influence of the Chemical Adsorption . . . 218

5.3.5 Influence of the Dispersion . . . 221

5.3.6 Influence of Viscoelasticity on the Residual Saturation . . . 226

5.3.7 Waterflooding and Polymer Flooding Combined . . . 228

5.6 Appendix . . . 235

5.6.1 Interactive 3D PDF . . . 235

6 Surfactant Flooding Simulator and its Stability Analysis 237 6.1 Introduction . . . 238

6.1.1 Surfactant Flooding . . . 238

6.1.2 Previous Numerical Work . . . 240

6.2.4 Adsorption . . . 248

6.3.1 Introduction . . . 249 6.3.2 Three-Component System . . . 250 6.3.3 Four-Component System . . . 268 6.4 Stability Analysis . . . 275 6.4.1 Explicit Method . . . 275 6.4.2 Implicit Method . . . 278 6.5 Conclusions . . . 280 xi

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7 Surfactant-Polymer Flooding Simulator 285 7.1 Introduction . . . 286

7.1.1 Polymer-Surfactant Flooding . . . 286

7.1.2 Previous Numerical Work . . . 289

7.2.4 Adsorption . . . 298

7.3.2 Influence of the Injection Scheme . . . 301

7.3.3 Influence of the Adsorption and the Surfactant-Polymer Interaction 310 7.3.4 Adsorption in a Five-Component System . . . 314

7.6 Appendix . . . 325

7.6.1 Interactive 3D PDF . . . 325

8 Nanotechnology in EOR: Nanofluids 327 8.1 Introduction . . . 328 8.1.1 Nanotechnology . . . 328 8.1.2 Nanotechnology in EOR . . . 329 8.2 Nanofluids . . . 330 8.2.1 Introduction . . . 330 8.2.2 Properties of Nanoparticles . . . 332

8.2.3 Environmental Effect of Nanoparticles . . . 333

8.2.4 Nanofluids and Enhanced Oil Recovery . . . 334

8.2.5 Effect of Nanoparticles on Reservoir and Fluid Properties . . . 337

8.3 Review of Experiments and Field Trials . . . 348

References . . . 363 xii

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9 Nanotechnology in EOR: Nanoemulsions and Nanocatalysts 373 9.1 Introduction . . . 374 9.2 Nanoemulsions . . . 374 9.2.1 Introduction . . . 374 9.2.2 Nanoemulsion Stability . . . 378 9.2.3 Preparation of Nanoemulsions . . . 381

9.2.4 Review of Experiments and Field Trials . . . 384

9.3 Nanocatalysts . . . 388

9.3.2 Chemical Kinetics . . . 391

9.3.3 Factor Affecting Nanocatalysts in EOR Applications . . . 396

9.3.4 Characterization of Upgraded Oil . . . 399

9.3.5 Inhibition of Formation Damage . . . 402

9.3.6 Review of Experiments and Field Trials . . . 402

10 Nanotechnology Enhanced Polymer Flooding Simulator 425 10.1 Introduction . . . 426

10.1.1 Nanotechnology in EOR . . . 426

10.1.2 Aim of this Work . . . 429

10.2.3 Aggregation of Nanoparticles . . . 433

10.2.5 Diffusion of Nanoparticles . . . 436

10.2.6 Retention and Adsorption . . . 438

10.2.7 Change in Absolute Porosity and Porosity . . . 440

10.3.2 Separate Flooding Processes . . . 443

10.3.3 Polymer Flooding Enhanced by Nanoparticles . . . 466

10.4 Conclusions . . . 480 10.5 Acknowledgments . . . 482 References . . . 482 10.6 Appendix . . . 487 10.6.1 Interactive 3D PDF . . . 487 xiii

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11 Technology Assessment and Further Research 489 11.1 Introduction . . . 490 11.2 Numerical Improvements . . . 490 11.3 Physical Improvements . . . 500 11.4 Conclusions . . . 509 11.5 Acknowledgments . . . 510 References . . . 510 11.6 Appendix . . . 513

11.6.1 Finite Volume Method . . . 513

11.6.2 Interactive 3D PDF . . . 521 Addendum 523 Summary . . . 525 Samenvatting . . . 531 Resumen . . . 537 Riassunto . . . 543 Podsumowanie . . . 549 Acknowledgements . . . 555

List of Publications, Conferences and Teaching Activities . . . 559

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Abbreviation

Description

AA Acrylic Acid AA-Na Sodium Acrylate

AM Acrylamide

AMPS 2-acrylamide-2-methylpropane sulfonic acid API American Petroleum Institute

APS Alkaline/Polymeric Surfactant Flooding ASP Alkaline/Surfactant/Polymer Flooding ATBS Acrylamide-tertiary-butyl sulfonate ATP Adenosine triphosphate coenzyme

ATRP Controlled Atomic Transfer Radical Polymerization BHMPAM Branched hydrophobically modified polyacrylamide

BHPMP Bis-Hexamethylene Triamine-Penta (Methylene Phosphonic) CAC Critical Aggregation Concentration

CATIN Cationic Inulin

CDC Capillary Desaturation Curve CDG Colloidal Dispersion Gel CMC Critical Micelle Concentration CMI Carboxymethylinulin

CTL Coal to Liquids Technology

DETPMP Diethylenetriaminepenta (Methylene Phosphonic Acid) DPEA Dodecyl Polyoxyethylene Acrylate

DPR Disproportionate Permeability Reduction EA Ethyl-Acrylate

EIA Energy Information Administration EOR Enhanced Oil Recovery

FAWAG Foam Assisted Water Alternating Gas Flooding FENE Finitely-Extensible Nonlinear Elastic

GTL Gas-to-Liquids Technology

HAPAM Hydrophobically modified associating polyacrylamide HASE Alkali-swellable emulsion

HDTMP Hexamethylene Diamine-Tetra (Methylene Phosphonic) Acid HEUR Ethoxylated Urethane

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Abbreviation

Description

HMBHAP Partially Hydrolyzed Microblock Hydrophobically Associating Polyacrylamide

HMHEC Hydroxyethylcellulose

HMPAM Hydrophobically Modified Polyacrylamide HPAM Hydrolyzed Polyacrylamide

IAPV Inaccessible Pore Volume IEA International Energy Agency IFT Interfacial Tension

IOR Improved Oil Recovery LPS Linked Polymer Solution MAA Methacrylic Acid

MENA Middle East and North Africa region MTOE Million Tons of Oil Equivalent NNDAM N,N-dimethyl acrylamide NVPMTOE N-Ethenyl-2-pyrrolidone OOIP Original Oil in Place PAA Poly-(Acrylic Acid)

PAG Polymer Alternating Gas Flooding PAM Polyacrylamide

PASP Polyaspartates

PEO Poly-(Ethylene Oxide)

PMES Polymeric methyl ester sulfonate PNP Polymer Coated Nanoparticles ppb Parts per Billion

PPCAP Poly Phosphino Carboxylic Acid ppm Parts per Million

PVP Polyvinylpyrrolidone RF Oil Recovery Factor

RPM Relative Permeability Modification SAG Surfactant Alternating Gas Flooding SDS Sodium Dodecyl Sulfate

SG Specific Gravity

SRB Sulfate Reducing Bacteria

TBA N-(1,1,3,3-tetramethyl butyl) Acrylamide TDS Total Dissolved Solids

TVD Total Variation Diminishing UVM Unified Viscosity Model VGO Vacuum Gas Oil WEO World Energy Outlook

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Symbol

Description

Bbl Oil Barrel (approximately 0.159 m3) c* Overlapping Concentration

Cr Courant Number D Dispersion Tensor

De Deborah Number

dl Longitudinal Dispersion Coefficient [m2/s] dm Molecular Diffusion Coefficient [m2/s] dt Transversal Dispersion Coefficient [m2/s] K Absolute Permeability [m2] or [Darcy] KM H Mark-Houwink Parameter

kr Relative Permeability

M Mobility Ratio

Mw Molecular Weight n Power Law Exponent n₂ UVM Parameter Nvc Capillary Number p Pressure [P a]

pwf Bottomhole Pressure [P a]

Pd _{Brooks-Corey Capillary Pressure Parameter} rw Well Radius [m] S Phase Saturation s Skin Factor V Volumetric Concentration v Darcy Velocity z Overall Concentration

Greek Letters

α Dynamic Capillary Pressure Parameter αM H Mark-Houwink Parameter

Γ Boundary of the Domain

˙γ Shear Rate

δij Kronecker Delta η Intrinsic Viscosity

λ Brooks-Corey Capillary Pressure Parameter

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Symbol

Description

λ₂ UVM Parameter

λj _{Phase Mobility [m}₂_{/(P a · s)]} λi Amplification Matrix Eigenvalues µ Dynamic Viscosity [P a · s] µM AX UVM Parameter

ρ Fluid Density

σ Interfacial Tension [mN/m]

σow Interfacial Tension of the Water-Oil System τ Dynamic Capillary Pressure Coefficient τ₂ UVM Parameter

τr Critical Shear Rate (Carreau Model) φ Rock Formation Porosity

Ω Reservoir Physical Model

Superscripts

a Aqueous Phase

dyn Dynamic

H Water-Oil System (no Chemical)

j Phase

[k] Number of Iterations < n > Time Step Number

o Oleous Phase

qs Quasi-static

r Residual

Subscripts

c Capillary, Chemical Component

i Component

in Injection

L Lower Carreau Viscosity m,n Grid Blocks

np Nanoparticles Component nw Non-Wetting Phase p Petroleum Component pol Polymer Component

r Residual

salt Salt Component

t Total

U Upper Carreau Viscosity

w Wetting Phase, Water Component

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Abstract

The purpose of this Ph.D. research is the development of numerical simulators aimed at un-derstanding and predicting the physical mechanisms behind Chemical Enhanced Oil Recovery (EOR) processes. It is focused on analyzing and proposing new numerical correlations relating the structure of the chemicals being used and the modification of the chemical and physical properties of the fluids and rock formations. Moreover, it includes an analysis of a cutting-edge research topic, the nanotechnology, and how this can be used to boost existing EOR techniques, applying specifically this new knowledge to chemical EOR. This research involved the develop-ment of new numerical methods and the analysis of the mathematical behavior of the systems being studied in order to maximize the numerical efficiency of the models as well as to determine the desired properties of the future chemicals to be used in EOR.

I am among those who think that science has great beauty.

Maria Skłodowska-Curie

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P.1 Introduction

Oil has been the main source of energy used by mankind during the last 120 years and the global economy largely depends on its continuous supply. Oil is a fossil fuel originated from organic substances now trapped in subsurface porous media called reservoirs. Hence, it is a non-renewable source of energy.1,2Oil reservoirs go through a series of stages along their lifespan: an initial stage, where oil is produced by natural driven mechanisms, known as primary recovery. Then, water begins to be injected in order to keep the bottomhole pressure constant and to sweep trapped oil to producing wells. This mechanism is known as secondary recovery. However, after the latter, about half of the original oil in place (OOIP) still remains in the reservoir. Researchers and engineers have focused their work for the last 40 years testing new, more advanced methods to recover the trapped oil, which are known as Enhanced Oil Recovery (EOR). EOR includes numerous techniques aimed primarily at achieving a change in the physical properties of water, rock and/or oil, thus recovering a part of what it was still trapped after waterflooding.1,3,4 The most common techniques of Enhanced Oil Recovery are: thermal, such as O₂ (fire-flooding) and steam injection; chemical, comprising polymers, gels, surfactants and alkali; miscible, like CO₂ injection; and other processes, such as microbial EOR.

The Enhanced Oil Recovery technique used depends on many factors (oil viscosity, reser-voir geology, etc.), which should be previously analyzed and accurately measured in order to achieve optimal efficiency in the recovery process. This is why numerous lab experi-ments should be carried out beforehand to test new products. Flow cells, core flooding experiments (e.g., Berea sandstone) and rheological behavior are vital in order to assess the efficiency of new chemicals. Nevertheless, many of these products failed in the past when they were tested straightforwardly at larger scales in operational oilfields. Thus, engineers started paying attention to reservoir simulation, assigning it a major role as a previous step before the chemicals’ operational deploy.

P.2 Chemical EOR and Reservoir Simulation

In the present thesis the aim is developing mathematical models for chemical Enhanced Oil Recovery (CEOR) techniques, illustrating the different processes and mechanisms involved in the exploitation. The former, both at macroscopic and microscopic level, play a vital role in the recovery efficiency in porous media. The study consists of a theoretical and simulation part coupled with a validation process with experimental data. The following activities are pursued: definition of a correct experimental model setup in order to simulate (on a laboratory- and reservoir-scales) CEOR processes; execution of different EOR numerical experiments with a variety of EOR chemical agents; preliminary modeling of the EOR process and comparison with experimental data, refining of the numerical model to fit the experimental results; and preliminary study on the further application of the proposed models to more advanced EOR processes.

Another vital part of this thesis is the numerical treatment of the fluid equations in porous media, a pivotal part of the reservoir simulation. There are several approaches nowadays regarding numerical methods and the Finite Difference Method (FDM) was chosen in this

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thesis to discretize the differential equations. The improvements regarding the numerical part of the model can be divided in the use of different, more accurate discretization schemes in order to reduce numerical errors, and a different mathematical modeling of the physical processes present in porous media. Both approaches are addressed during this thesis and also novel techniques are simulated and proposed in order to improve the accuracy of the simulators.

The first part of the thesis consists in the numerical simulation of oil recovery in a flooding cell adapted to represent a “dead-end” pore model, frequently seen in porous media, with different models of Newtonian and non-Newtonian fluids (Figure P.1). This part is simulated using the ‘Navier-Stokes’ or direct approach. The Newtonian fluid taken as reference is water, followed by different Newtonian fluids with higher viscosities (affecting the mobility ratio and increasing therefore the macroscopic efficiency factor). Finally, the oil displacement is simulated with solutions of polymers and polymeric surfactants of different properties, taking into account the characteristics of non-Newtonian flow and viscoelasticity, investigating as well the dependence of the interfacial properties on the recovery process, since it is considered that not only the viscoelasticity but also the interaction at an interfacial level plays an important role in recovering the remaining oil.

Figure P.1: Plan view of the flooding cell (top) and the microscopic model (bottom

-dimensions in µm) used during the simulations (Adapted from Wever5).

A polymer flooding is the first process studied during the thesis following a ‘Darcy’ or continuum approach. As it is well known, one way to increase the macroscopic

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ment is to reduce the mobility ratio increasing the water phase viscosity.6 The solution viscosity generally depends, among others, on the polymer concentration and molecular weight, temperature, water salinity, total dissolved solids (TDS) and the concentration of divalent ions. Furthermore, it has become important to consider the polymer architecture as well, since recent developments of novel polymers for EOR applications with complex architectures (e.g., hyperbranched) resulted in an improved recovery efficiency compared to traditional ones.5,7–13 The architecture of the polymer chains affects the rheology be-havior of the aqueous phase and thus, the recovery performance. At high shear rates, standard polymers used in oil industry have been well-documented to show shear thick-ening after a critical shear rate,14–23 whilst at low velocities some authors reported mild shear-thinning trend24–26 or a near-Newtonian behavior.27At even higher shear rates, a second shear thinning region appears, mainly due to mechanical degradation of the poly-mer’s chains. These processes affect then the viscosity and the recovery efficiency, and thus they are considered in the simulator by modeling the polymer’s molecular weight as a function of time. Moreover, it is known that polymer solutions exhibit viscoelastic properties to variable extents. The pure shear stress state from viscous fluids is replaced by a general one, with extra normal and shear stresses provoking the oil to be “pushed and pulled” out of dead-end pores.28–35

Continuing with the objective of modeling chemical EOR processes, the focus is on de-veloping a model for a surfactant flooding. This technique is not new but have been used for more than 40 years in oil reservoirs after waterflooding became economically non-profitable.36–40Surfactant molecules act primarily on the oil/water interfaces. They are used either for wettability alteration but mostly for lowering the interfacial tension (IFT) between oil and water, which is responsible for the trapped oil in the pores (cap-illary trapping). The target in designing surfactants is to achieve low interfacial tension at low surfactant concentrations, and acceptable adsorption rates when compared to the oil recovered.

The last part of this research comprising different chemical EOR agents and processes involves what it is known as combined EOR techniques. So far only standard CEOR flooding methods were presented and modeled, involving only one chemical species in the reservoir. However, it is reported the increase in efficiency when two chemical agents (or more) are used in a single, combined process. These comprise a number of techniques in which the influence and synergy of both chemicals affect the properties of fluids and rock, rendering an increase in the recovery factors. Combined techniques involve different chemical products or the use of several EOR processes together (Figure P.2). It is also important to add in this case the presence of salt in order to determine how the properties are affected by the latter. This will allow determining a set of desired properties when synthesizing new chemical products.

The numerical simulation of the EOR processes was carried out mainly in macroscopic, field-scale models, such as the five-spot pattern (Figure P.3 - left). There are currently several patterns of injection/production wells used by the industry. The most commonly used during reservoir simulation is the spot (and its simplification, the quarter five-spot), in which a injection well is placed in the center of a square formed by four pro-ducing wells. The injection fluid will then sweep the oil towards these producers and the

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Figure P.2: Scheme of a WAG (Water-Alternating-Gas) flooding.41

wavefront will be determined by the rock physical properties as well as the relationship between the fluids’ rheological characteristics. The front may be also interrupted by the presence of faults, which is one of the main reasons why better reservoir characterization techniques are necessary. The reservoir is represented by a 2D model of known geometric characteristics, with an absolute permeability and porosity (Figure P.3 - right). In order to mathematically simplify the model, a quarter of five-spot was used as the medium to be flooded. Chemical EOR flooding involves the flow of several components (water, chemical and petroleum) in different phases (aqueous and oleous). These models are represented by a system of non-linear differential equations in partial derivatives including the mass conservation equation for each component and Darcy’s equation.4,43–45

P.3 Nanotechnology in EOR

The last main line of research in this thesis deals with the use of nanotechnology for EOR. The mechanisms mentioned, such as changing the properties of the displacing agent, altering the wettability of the porous media, lowering the interfacial tension (IFT), decreasing the oil viscosity in-situ by means of catalysts, emulsion improvement, and increasing the mobility of the capillary-trapped oil, are all physical and chemical properties that can be enhanced by means of nanotechnology applied to EOR. An important property at these scales is that the size of the nanomaterials makes them suitable for injection into

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Figure P.3: Scheme of a chemical EOR flooding using a five-spot configuration (left),42

and model of a 2D quarter of five-spot used for the simulations during this thesis (right).

porous media, even with low permeabilities, since pore throat openings commonly range between 100 and 10,000 nanometers in width, which is large enough for nanofluids to flow through relatively freely. The purpose of this study is to analyze the developments in the field of nanotechnology applied to EOR and their implementation in laboratory experiments. This study is focused on nanofluids, which have shown promising results both in laboratory and trial field tests.46–56 This includes the way nanoparticles alter both the fluids and rock properties in order to increase the oil recovered. Nanofluids are dilute liquid colloidal suspensions of nanoparticles with, at least, one of their principal dimensions smaller than 100 nm (Figure P.4), with the purpose of enhancing one or several of the carrier fluid’s properties. Since this term was coined by Choi,57 there has been numerous publications and research about the many applications of nanofluids in different fields.58,59

Figure P.4: Microscopic observations for the formation of a colloidal wedge film at

the interface of n-decane and nanofluids. Microscope images show: on preparation (a), after 60 s (b), and fluorescence microscope image for the wedge film with the associative nanoparticles (ASNP) (c).60

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P.4 Thesis Outline

This Ph.D. project can be divided in three main research topics which have been reviewed during this thesis. From these central topics, several improvements and novel approaches were presented and discussed, which are briefly introduced here (Figure P.5). In chapter 1 the current and forecast energetic situation is presented and reviewed, along with the dependency from oil sources and possible solutions to the peak-oil problem including the oil production stages, focusing on the chemical EOR processes. This is complemented with a discussion of the most common chemicals for EOR and their main and desired properties, followed by a description of the combined techniques in order to improve the oil recovery. This analysis continues in chapter 2 with the review of the reservoir simulation techniques, focusing firstly on explaining the oil formation and reservoir characterization and secondly presenting the mathematical formulation used in porous media, followed by the proposed techniques to solve the discretized differential equations.

Figure P.5: General outline of this thesis showing the central topics and their

subtopics/chapters, including also the connections among the different numerical models (red dashed line).

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tions, using a direct approach. The objective of this, presented in chapter 3, is to prove both at micro- and macro-scales the role of viscoelasticity in the recovery process in order to demonstrate whether polymers can affect the sweeping efficiency at a microscopic level, which has already been shown experimentally. These results were used as a benchmark in our model in order to validate the numerical procedure adopted. Subsequently, the Darcy approach is used in a porous medium to model a polymer flooding, which is the first chemical process studied during the thesis. This is presented in chapters 4 and 5, in which the basis for the following models was also set (including a validation in a 2D oilfield, comparing the results against both commercial and academic simulators). The rheology, degradation and viscoelasticity are considered in a novel way for polymer flood-ing, which allows increasing the accuracy of the model. Subsequently, a new formulation for a surfactant recovery process is discussed in chapter 6. The phase behavior, critical in this kind of CEOR technique, is based on a model presented by previous authors, expanding it and adding the presence of a fourth component, which is the salt dissolved in the aqueous phase. This affects both the phase behavior and the surfactant adsorp-tion rates in the porous medium. After presenting the standard CEOR techniques, the research continues by modeling a combined surfactant-polymer simulator in chapter 7. It is well known that the combination of different chemicals or EOR processes increases the recovery efficiency due to the synergy between the chemical species. The objective is to study this interaction between surfactant and polymers (with the salt present), and look for ways to optimize the injection schemes.

Finally, the last major research topic of this thesis involves the use of nanotechnology as a way to boost EOR processes, which is reviewed in chapters 8 and 9. The study of nanotechnology in EOR can be divided according to the functions the nanoparticles have in the porous medium. This classification comprises nanofluids (chapter 8), and nanoemulsions and nanocatalysts (chapter 9). These processes have different charac-teristics, which are presented during the review of nanotechnology techniques in EOR, limiting their applicability only to a certain type of reservoirs and crude oils. Lastly, the last reservoir simulator of this thesis consists in the study of a novel combined method using nanoparticles and polymers, presented in chapter 10. The presence of nanoparti-cles and the properties modified by them can be further enhanced with the viscosifying and viscoelastic properties of polymeric solutions, rendering a novel method capable of increase the efficiency of standard EOR techniques. During this chapter the synergy between polymers and nanoparticles is also discussed, and how this affects the aqueous phase properties. Moreover, in this chapter the architecture of the polymers is considered in a novel way in order to determine the aqueous phase properties, representing a new approach in reservoir simulation. The last part of the thesis, discussed in chapter 11, is aimed at presenting in a brief way all the results achieved during the project, underlining their novelties, the problems faced during the simulations, and also proposing further research topics and analyses in order to tackle the remaining problems, which are divided in physical and mathematical ones.

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1. Chemic al EOR and the R Chemic al Pr o duct Design 1. Chemic al EOR and the R Chemic al Pr o duct Design

1

Chemical EOR and the Role of

Chemical Product Design

Abstract

The current and prospective worldwide energy demand has led either to start exploiting the more difficult and costly unconventional oil reserves, or to maximize the exploitation of conventional oil sources. The latter triggered the development of Enhanced Oil Recovery (EOR) processes in order to improve the efficiency and lifetime of mature oilfields. Chemical EOR is one of the most interesting techniques nowadays. The use of chemical products such as polymers, surfactants, alkalis and polymeric surfactants has been continuously increasing during the last decades. However, these chemicals should be designed to withstand the harsh conditions present in the reservoir (e.g., dissolved salts, pH, temperature, presence of bacteria) and increase the efficiency of the process. One of the key factors in this development is the (macro)molecules’ architecture and its influence on the physical properties of the fluids being injected: from linear to branched polymers, from monomeric to gemini surfactants. Furthermore, the combination of these chemicals has showed a great potential due to the synergy between them, creating a new spectrum of techniques in chemical EOR. This chapter presents the work done in this field with an analysis of the products and technologies employed, including their limitations and possible ways to improve their performance. All in all, the need of advanced products for oil recovery has set off a new field of research wherein chemical product engineering plays a major role.

The most beautiful thing we can experience is the mysterious. It is the source of all true art and science.

Albert Einstein

This chapter is based on: Druetta, P., Raffa, P. & Picchioni, F. Chemical enhanced oil recovery and the role of chemical product design. Submitted to Applied Energy (2018).

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1. Chemic al EOR and the R ole of Chemic al Pr o duct Design 1. Chemic al EOR and the R ole of Chemic al Pr o duct Design

1.1 Introduction

It is undeniable that oil has changed radically how society lives and how the economy works in such a way that any kind of resource in the past has ever done. The changes generated in both the society and economy over the last 200 years are more than obvious. Although oil was known long before by mankind, its use was only primarily limited to military purposes.1 It was at the beginning of the Industrial Revolution in the mid-nineteenth century that oil began to have a more widespread use. The demand for energy began to increase sharply along with the population growth and industrial activity. This had the immediate consequence of beginning the search for new energy sources so as to complement the existing ones, which consisted mainly in coal and whale oil. It was then in 1849 that Abraham Gesner developed the process of refining kerosene from petroleum, which led to the development of a cheaper source of the energy than those used at that time.2From that moment on, the oil consumption has been steadily increasing to become, during the twentieth and the first decade of the twenty-first centuries, the most utilized source of energy. The direct consequence of the last statement is that the global economy depends heavily on these resources. This escalation in the demand during the period mentioned above originated an era of progress in the exploration and exploitation of new oilfields, as well as in the oil refining processes. The global cumulative oil production has followed a positive trend during the last 50 years and, according to current energy reports, it is expected to continue with this trend at least for the following years (Figure 1.1).3

Figure 1.1: World cumulative oil production in (giga)barrels since 1965 (Adapted from

BP4).

1.1.1 General Considerations

Oil is a fossil fuel, composed essentially by organic substances that undergo chemical and thermal processes for long periods of time (in the order of several millions of years),

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mi-1. Chemic al EOR and the R ole of Chemic al Pr o duct Design 1. Chemic al EOR and the R ole of Chemic al Pr o duct Design

grating firstly and then being trapped into porous media formations, called “reservoirs”.5 Besides the oil in the reservoir, an overlying gas cap as well as a saline water (brine) layer can be found. The water phase can be present either as an aquifer or as connate water (trapped during the formation of the rock). It can then be inferred that oil is not a renewable energy source. It will eventually dry up while the human race exploits, as time passes by, the different reservoirs discovered. This concept of non-renewable energy is based on the fact that, although each year petroleum is generated due to the mentioned processes, the generation rate (estimated at a few million barrels per year) was widely surpassed long ago by the annual world consumption (e.g., during 2014 this value was roughly 32 × 1012barrels).4,6As obvious conclusion from this reality, many authors have speculated about the capability of a non-renewable source to supply a constantly growing demand. Hubbert’s7 initial theory of “peak oil” predicted an increase in oil production until reaching a peak and then a steady decline until the full depletion of economically exploitable oil resources. Later on, several publications8–16 took into account the cur-rent production capacity, the increase in consumption, the discovery of new exploitable reserves and the development of new technologies on their analysis. In all these analyses the question of how much longer the human race will be able of exploiting oil resources until its total depletion arises. Two well-known parameters associated with the topics ex-amined on these studies are the reserve/production ratio (R/P) and the oil reserve flux. The first allows to estimate, by taking into account reserves and production up to the present moment, how many years these current reserves can meet the needs of present time consumption. In 2014 this ratio was estimated at 53.3 years.4 The second makes allowances for the difference between the discovery of new economically exploitable fields and the oil consumption. Since 1980 this difference has become negative and ever since continued a downward trend (Figure 1.2).10

Figure 1.2: World “proven + probable” oil reserve flux in (giga)barrels (Adapted from

Owen12).

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are used in defining plans, since they just reflect a picture up to certain point in time. Merely using them without taking into account more dynamic parameters (e.g., global economy’s ups-and-downs, discovery of new energy sources, development of new envi-ronmental policies) might constitute a major mistake and could lead to inappropriate strategies. Different oils can be primarily classified according to their API gravity, calcu-lated using Eq. 1.1, and their dynamic viscosity (Table 1.1).17

Table 1.1: General oil classification according to their physical properties.

Oil Classification API Gravity Dynamic Viscosity

°API cP

Light oils > 31.1 < 100 cP Medium oils 22.3 < °API < 31.1 < 100 cP Heavy oils 10 < °API < 22.3 > 100 cP Extra-heavy oils < 10 < 10000 cP Natural bitumen (or tar sands) < 10 > 10000 cP

It is generally considered that the first two kinds (light and medium oils) are what is known as “conventional oil”, whilst the others belong to the category of “unconventional oil”. This classification corresponds to the present time, since the definition of these categories change as time goes on and new technologies are developed or economic factors alter the current scenario.18

AP I gravity = 141.5

SG − 131.5 (1.1)

As of 2014, the current proven reserves and resources for the different types of oil (this means, reserves that have a 90% chance of exploitation) are presented in Figure 1.3.4,10,19

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Figure 1.3: Conventional (top) and unconventional (bottom) oil resources expressed in

(giga)barrels (Adapted from BP4 and OECD/IEA20).

Currently oil is the most used energy source in the world supplying 32.9% of the total. In the period 2015 − 2035 an increase in the total energy required of 38% can be projected, associated with population growth and development of the global economy. In this sce-nario oil will continue to occupy, although to a lesser extent, the first place by supplying 27.4% of the total (Figure 1.4).4 This implies an increase in the production of almost 15% in the next years to comply with the future energetic requirements (provided that current energy policies remain in force).

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Figure 1.4: World Energy Supply during 2014 (top) and forecast for 2035 (bottom)

(Adapted from BP4,21). MTOE stands for “Million Tons of Oil Equivalent”, a unit of energy equivalent to 41.868 PJ.

As a conclusion of this analysis, it is obvious that the world economy is not ready to stop relying on oil. This fact is also supported on the evidence that newer, cleaner sources of energy (e.g., wind, solar, fuel cells) are not ready to take a step forward and replace oil as the main source for the world’s demands.14,15 This scenario is further complicated by the general estimation that in the near future there will be no more significant discoveries of conventional oilfields.13–16 This implies two possible outcomes: either start exploiting unconventional oil sources, with the immediate consequence of the increase in the price of refined products, or make a better use of conventional oil reservoirs. The objective in EOR is to develop the second strategy, i.e., developing new and more efficient methods in order to improve the recovery of the oil still trapped in the fields.

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The International Energy Agency (IEA) in its World Energy Outlook (WEO) has fore-cast a significant increase in the oil production by means of both EOR methods and non-conventional sources during the next 15 years (Figure 1.5). The upsurge of these processes will be inevitably coupled to an increase in the price of crude oil. The latter has showed, even with some ups-and-downs due to economy fluctuations/crisis (e.g., the 2008 World Crisis), a steady upward trend (Figure 1.6).

Figure 1.6: Oil Price in dollars per barrel as a function of time (Adapted from BP4).

1.1.2 Oil Recovery Mechanisms

Throughout the operation period of an oilfield three different stages can be distinguished, corresponding to particular physical operating mechanisms. The first stage (primary re-covery) is based on the production of oil by natural drive-mechanisms and, later on, by means of pumping devices (e.g., pumpjacks) until the pressure inside the well is no longer enough to render the operation profitable. This production stage is performed with no injection whatsoever of displacing fluids into the reservoir.23–25 The natural mechanisms referred to above are the following:25 expansion of the fluid phases and/or rock forma-tion (rock/liquid expansion drive and gas cap drive), gas release from the crude oil and its subsequent expansion (depletion drive), water drive when the reservoir is communi-cated with an aquifer (the most efficient without considering multiple drive mechanisms), gravity drainage drive due to differences in the density of each phase, and multiple drive combination. On the other hand, each reservoir is different and, in order to achieve the most efficient recovery, its natural driving-mechanisms, its rock properties and geological potential abnormalities must be identified. All in all, it is considered that at the end of primary recovery an estimate ranging from 10 to 25% of original oil in place (OOIP) can be recovered.23,24,26,27

After the primary recovery is considered no longer economically profitable, the stage of secondary recovery takes place. A fluid (water or an immiscible gas) is injected in order to re-pressurize the rock formation and act as a displacing agent pushing oil from injectors

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to producers. The water sweeps the oil from the injection wells. As time passes on, the water begins to come out in the producing wells (water breakthrough) and hereinafter the percentage of produced water (water-cut) increases. It has been reported that some wells remain economically operational with water-cuts as high as 99%.26 The main reason to use water as the displacing agent is due to the fact that it is a cheap and easy available fluid. At the end of economic life of the secondary recovery, generally an additional quota ranging from 15 to 25% of the OOIP can be recovered.23,25,26,28These two recovery stages can then account for 50/55% of the OOIP. It is at this moment when the tertiary stage or Enhanced Oil Recovery (EOR) begins. EOR is formally considered as a subgroup of what is known as Improved Oil Recovery (IOR). This stage-wise approach is an ideal one since it allows exploiting separately their advantages, maximizing the recovery without increasing the operational costs. In most cases, secondary recovery and EOR begin long before the economic limits are reached (Figure 1.7).

According to several authors,24,29,30 IOR comprises, besides EOR, all the advanced oil recovery techniques employed in any of the three stages of production of a reservoir to increase, by any means, the oil recovery. Some examples of IOR methods are, among others: hydraulic fracturing, scale-inhibition treatments, horizontal wells, acid stimula-tion procedures, new drilling and well technologies, and advanced reservoir monitoring techniques.

Figure 1.7: Production scheme as function of time in an oilfield.29

1.1.3 Enhanced Oil Recovery

The main objective in all Enhanced Oil Recovery processes is to diminish the oil saturation below the Residual Oil Saturation (Sor). The latter is defined as the oil that remains in the swept zone of a waterflood when the produced ratio of water to oil has reached its economic limit.31It can be also defined as the saturation at which the oil production becomes discontinuous and is immobilized by capillary forces under ambient-groundwater flow conditions.32

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or other agents in order to modify determinate physical and/or chemical properties either in the fluids or in the reservoir. The main objective is to improve the oil recovery factor, thus increasing the lifetime of the oilfield. These mechanisms can be categorized according to the following concepts:24

• Increase the mobility of the displacement medium by increasing the viscosity of the water, decreasing the oil viscosity (or both simultaneously).

• Increase the oil recovery at a microscopic scale by means of using viscoelastic fluids, which displace in a more effectively way the oil trapped in the porous media. • Extract the oil with a solvent.

• Reduce the interfacial tension (IFT) between oil and water and alteration of the reservoir rock wettability.

Enhanced Oil Recovery projects then are strongly influenced by economics and crude oil price. The cheapest oil currently comes from the conventional oilfield in primary or secondary stages. Fields already in EOR show an incremented cost and unconventional oilfields have the highest production costs to date (Figure 1.8). The number of EOR processes skyrocketed in the mid-1970s and early 1980s due to the rise in the price of crude oil. Then, with the collapse of the latter during the mid-1980s, the EOR projects plunged drastically simply because they became unprofitable with the attention shifting to waterflooding and other IOR techniques. In recent years there has been a resurgence of EOR due to the increment in the price of oil (Figure 1.6).22

Figure 1.8: Oil production costs in U$D per barrel for various resource categories.3

Enhanced Oil Recovery processes can be classified into three main branches, depending on the principles and physical properties on which the injected fluids act (Figure 1.9). In addition to the processes listed in Figure 1.9, improved and more advanced meth-ods have been developed combining several of them, such as: Polymer-Alternating-Gas

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(PAG), Alkaline-Polymeric Surfactant (APS) and Surfactant-Alternating-Gas (SAG). Furthermore, the use of nanotechnology in Chemical EOR has been lately reported, such as polymer nano-composites (PNP’s or polymer coated nanoparticles)33–39 and silica nanoparticles.39–50These developments have shown promising results both in laboratory and trial field tests. A detailed description of other EOR techniques (see Figure 1.9) is out of the scope of this thesis. The interested reader is kindly referred to existing literature on the topic.23,29,51–62

Figure 1.9: Oil Recovery Mechanisms (Adapted from Schmidt24).

1.2 Chemical Enhanced Oil Recovery

1.2.1 General Considerations

The main objective of the chemical EOR processes is acting on one (or several) of the fol-lowing factors: mobility (using viscosity-increasing water-polymer solutions), rock wetta-bility and interfacial tension (IFT) (by adding surfactants and/or alkalis to the displacing agent). Thus, the factors influencing the oil recovery are analyzed before stepping into the overview of the fluids and techniques used to increase oil production after waterflooding. The objective of this analysis is to understand what the mechanisms involved are and how they could be improved. This is often done by looking at the recovery factor (RF),

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which stands for the ratio between the oil produced and the OOIP (both measured at surface conditions),53,63,64

RF = Eps× Es× Ed× Ec (1.2) where Epsrepresents the microscopic displacement efficiency; Esis the macroscopic sweep efficiency; Ed is the connected volume factor; and Ec is the economic efficiency factor. The objective of EOR methods is to increase the first two factors, whilst the last two are influenced by the IOR techniques inasmuch as they cannot be improved by the mechanisms involved in EOR processes. The connected volume factor represents the proportion of the reservoir volume connected to wells: this is due to the fact that the occurrence of sealing faults or low-permeability areas may cause some regions of the reservoir to remain isolated from the rest of the field, without possibilities of displacing the oil trapped therein. On the other hand, the economy efficiency factor takes into account the physical and/or commercial constraints during the field life such as surface facilities and reservoir energy which are out of the scope of EOR processes. In order to improve the hydrocarbon recovery, it is important to have a clear understanding of the static and dynamic behavior of the whole system on various scales, from the field scale to the microscopic one (Figure 1.10).

Figure 1.10: Scales involved in oil recovery.65

Equation 1.2 provides a very simple and elegant way to calculate the recovery factor of a reservoir. However, there are several problems related to the factors involved, which de-pend on parameters or conditions unknown at the beginning, or during the exploitation. The incorrect appreciation of any of them can lead to the failure of a project. Despite these obvious considerations, only a very limited number of reports mention and deal with these problems and uncertainties.66–68In addition, during the EOR process, as specified later, the injected fluids might affect more than one factor, in both positive and negative ways, thus rendering an accurate quantification of this almost impossible. In order to use RF values appropriately, it is important to understand what these parameters represent