Introduction
The importance of subterranean petroleum hydrocarbons as a dependable
energy resource has intensified on global scale owing to the
ever-increasing consumption of crude oil and/or associated products in
industrial, household, transportation and technological applications
[1,2]. This has led to the exploration and production of oil from
complicated reservoir formations, wherein problems such as low
permeability, heterogeneity and less accessibility persist during
extraction processes. The initial stage encompasses the application of
primary and secondary recovery techniques to produce one-thirds to
nearly one-half of the original oil in place (OOIP) by natural drive and
water/gas injection. Enhanced oil recovery (EOR) methods have attracted
widespread attention in the last few decades to attain optimized
production of residual oil trapped by alteration of reservoir fluid
properties after conventional recovery [3,4]. Surfactant flooding is
a promising EOR technique employed by the production sector since 1970s
decade [5,6]. This type of oil recovery functions by allowing
surfactants or “surface-active agents” to adsorb onto the interface of
oil/water, thereby decreasing interfacial free energy and increasing
dimensionless capillary number [5-7]. Polymer improves the viscosity
of displacing (injected) fluid to reduce the mobility ratio between
water and oil [8,9]. Furthermore, polymer addition increases the
viscous force perpendicular to oil-water interface and responsible for
pushing the residual oil towards the production well [8,9]. When
this force exceeds the capillary forces holding crude oil within
rock-pores, residual oil detaches from the rock surface and mobilizes
forward with increasing sweep efficiencies. Nanoparticles, in
conjunction with surfactant and polymer, adsorb onto interfaces to
enhance the mechanical barrier onto displaced crude oil surfaces and
produce impulsive emulsions with improved crude oil attracting ability
[10,11]. A pivotal aspect of chemical EOR lies in proper screening
and optimization of displacing fluid, keeping in mind the effectiveness
as well as cost-profitability of the method employed [12]. Hence,
surfactant, polymer and/or nanoparticle in chemical fluid must be
introduced to create a forward-moving oil bank within porous rock
formations, which can significantly improve the oil recovery and
maintain pressure gradient during chemical fluid +/ chase water
injection.
Simulation studies are important to assess the flooding performance of
injected chemical fluids, and predict how oil displacement will occur
under specified reservoir/fluid conditions [13,14]. Prior to
simulation, the technological feasibility of different EOR routes are
tested by experimental investigations [14,15]. Such studies provide
useful input information to allow the simulator to identify reservoir
parameters, predict recovery and testing the effectiveness of different
EOR projects with similar components [13-16]. The current industry
is involved in the application of realistic chemical flood simulators
like STARS by Computer Modelling Group (CMG), UTCHEM by the University
of Texas at Austin (UT Austin), REVEAL by Petroleum Experts (Petex), and
ECLIPSE by Schlumberger (SLB). The physics associated with fluid
properties’ evaluation differ in each type of reservoir simulator
[17-19]. UTCHEM is a compositional simulator capable of simulating
different types of EOR processes owing to the provision of four
different phases (gas, aqueous, oil, microemulsion) and incorporation of
advanced numerical concepts [20]. REVEAL, a full field reservoir
expert, is similar to UTCHEM with surfactant phase behavior and mobility
control options, permeability reduction and polymer degradation
parameters [21]. However, this tool is not well known among
professionals and engineers in production areas and the existence of a
fourth phase i.e. microemulsion may cause problems in field studies
[17,20,21]. As per UTCHEM and REVEAL, the presence of a
microemulsion phase is a key parameter to model displacement efficiency,
in spite of the fact that microemulsion properties are not generally
measured in pilot tests and field operations [18-21]. Both ECLIPSE
and STARS do not consider microemulsion phase as contributor to flooding
simulation and represent oil displacement behavior via analyses of
relative permeability curves for experimental results [17,22,23].
However, ECLIPSE software, though common in the industry, does not
encompass the technical functionalities such as salinity effects,
adsorption, polymer concentration mixing, multi-component EOR, shear
thickening and degradation regimes required for accurate modelling
[22]. Another powerful flooding simulator is CMG, which is capable
of modeling flooding results and manage complex behavior of
oil-chemical-water systems in laboratory-scale and field-scale porous
media [24,25]. Goudarzi and other researchers [17,19] assessed
the performance of different reservoir simulators and developed an EOR
benchmark to improve chemical design for field-scale as well as
lab-scale operations. Pandey et al. [26] employed CMG-STARS for
coreflood modelling experiments and investigated flow parameters that
could be subsequently used in pilot field tests. Kazempour and others
[18] investigated the validity of multi-phase component EOR systems
in detail, and identified the dynamic behavior of fluid components
existing within core-flood model. Tunnish et al. [27] successfully
matched experimental flooding results using CMG to effectively tune
relative permeability curves and predict the chemical fluid’s ability to
produce in-situ crude oil. Dahbag et al. [28] reported the
performance of ionic liquid/surfactant flooding during chemical oil
recovery processes, and found the results can be used to predict future
scenarios. CMG tool is reliable and instrumental in evaluating the
potential of conventional and modern EOR methods [17-19,24-28].
In this article, a series of flooding experiments were performed to
investigate the secondary and tertiary oil recoveries using surfactant,
surfactant-polymer and surfactant-polymer-nanoparticle slugs. Initially,
physicochemical behavior of designed fluids were evaluated by a series
of experimental studies. A Cartesian grid model was developed using
CMG-STARS software, and parameters such as rock-fluid properties,
interfacial tension, viscosity, adsorption and injector/producer
geometry were entered in the simulation model. Thereafter, the
experimental results obtained in the laboratory were history-matched for
specified builder and injection pattern/time was set with CMOST tool.
Emphasis is put on the injected fluid composition, flow rate and
flooding period. Using detailed methodical approach to identify and
predict well-matched recovery data, produced recovery data were
optimized with minimal error as compared to laboratory results. This
model is useful to simulate surfactant/polymer/particle behavior on
core-scale, and optimize brine/chemical flooding from functional
viewpoint.
Experimental and Simulation
Materials
The surfactant employed in this study is
N,N′-bis(dimethyltetradecyl)-1,6-hexanediammonium bromide (abbreviated
as 14-6-14 GS) with molecular weight of 726 g/mol. This gemini
surfactant was synthesized and characterized in our earlier papers
[29,30]. Partially hydrolysed polyacrylamide (PHPA), a water-soluble
polymer was purchased from SNF
Floerger, SNF SAS, ZAC de Millieux, Andrézieux, France. It has molecular
weight of 2.1 × 107 g/mol with 26.4% hydrolysis.
Aqueous polymer solutions were prepared in accordance with the American
Petroleum Institute: Recommended practices for evaluation of polymers
used in EOR operations (API RP 63). Silica (SiO2)
nanopowder (5-15 nm) was obtained from Merck Industries. Sandstone core
employed in flooding experiments was procured from Kalol field in
Gujarat, India. Crude oil sample has total acid number (TAN) of 0.044 mg
KOH/g, kinematic viscosity of 6.147×10-5m2/s and 23.55° API gravity at 303 K. It was procured
from Ahmedabad oil field, ONGC Asset, India. Double distilled water was
extracted from distillation apparatus in our laboratory.
Physicochemical evaluation tests
Wettability behavior of 14-6-14 GS was investigated by contact angle
studies with the help of Kruss DSA25 Drop Shape Analyzer. Adsorption
behavior of gemini surfactant molecules onto sand surface was conducted
by UV spectrometric analyses to determine the amount of 14-6-14 GS
adsorbed per weight sand (in mg/g). Interfacial tension experiments were
performed by analyzing rotating crude oil drop profile in continuous
surfactant/polymer/nanoparticle containing aqueous solution with the
help of spinning drop SVT20 tensiometer (Dataphysics). Viscosity values
of aqueous chemical fluids were measured using cup and bob geometry in
Bohlin Gemini 2 Rheometer instrument at 303 K. The obtained experimental
results serve as input-data during simulation studies.
Flooding procedure
The experimental flooding apparatus (Porous Material Inc.) consists of
core-holder, positive displacement pump, chemical slug injectors and
measuring cylinder for collecting effluent samples. Sandstone core with
8.74 cm length and 3.66 cm diameter was initially saturated with 1.0%
NaCl brine for 72 h to saturate the cores; and obtain porosity values in
the range 17-18%. The core sample showed permeabilities in the range
350-400 milliDarcies (mD). When placed within core-holder apparatus, a
confining pressure of ~1000-1200 psi was employed to
hold the core in vertical position. Crude oil was injected into the
pores to displace aqueous phase, until irreducible saturation state was
achieved. This was followed by an ageing period of 6 days to obtain an
oil-saturated reservoir model in the laboratory. Secondary flooding was
investigated by brine flooding at the rate of 10 ml/h to recover a
fraction of crude oil. When water cut percentage exceeds ≥ 95%,
chemical aqueous fluid containing surfactant +/ polymer +/ nanoparticle
was flooded at 5-10 ml/h rate to sweep residual oil during EOR. Finally,
chase water was injected at the same flow-rate to maintain pressure drop
for favorable oil displacement. Effluent liquid produced during
secondary and tertiary recovery studies were collected in graduated
cylinders.
Simulation methodology
The STARS simulator package in CMG is widely employed compositional tool
in the petroleum industry, with the capacity to develop reservoir models
[24-27]. A Cartesian grid system with specified divisions along
X-axis, and the developed model was simulated to match flooding history
data using CMOST analysis tool. Prior to running the STARS simulation
for aqueous flooding model and subsequent history-matching of
experimental recovery data, the following assumptions were made to
obtain accurate findings [31,32]:
- The reservoir initially consists of two phases, namely, crude oil and
water.
- The amount of free gas/solvent gas in the core model is assumed as
zero.
- A grid-based core model is considered, with uniform properties and no
geological complexities/heterogeneities.
- Fluid flow in radial direction is negligible as compared to that in
axial direction.
- Salinity effect on phase behavior is ignored.
- Chemical reactions do not occur.
- Oil and water flowing through porous media obeys the Darcy’s Law.
Porosity, permeability and crude oil properties were introduced as input
data for reservoir characterization. In recent years, the need to
develop appropriate flooding model has paved the way to make informed
decisions during chemical fluid selection/optimization and field
implementation [33-35]. Druetta and co-workers [35] developed a
flooding simulator to investigate EOR properties of different chemical
fluid compositions. Arhuoma et al. [36] found that CMG simulation
model is useful to determine displacement phenomena governing flooding
behavior, depending on injection fluid type. The effectiveness of
numerical simulation studies on chemical flooding showed far-reaching
consequences during oilfield applications, as evident from the findings
of earlier papers [34,37]. This kind of grid-based model helps in
understanding fluid flow behavior prior to injection; as well as achieve
a sufficiently robust numerical model [33,35,36]. Table 1 presents
the core and fluid properties employed in STARS model.
Table 1. Core model and fluid parameters for flooding
simulations.