Prepared by:

Group 5

The Solvent & Surfactant Models

Khaled Al Shater

Mohamed Sherif Mahrous

Ramez Maher Aziz

Ahmed Kamal Khalil

George Ashraf

Hazem AL Nazer

Hameda Abd-Elmawla Mahdi

Agenda

Introduction

The Solvent Model

Objectives of the Solvent Model

Applications of the Solvent Model

Todd & Longstaff Model

Data Treatment for Using the Solvent Model

The Surfactant Model

Introduction & Application

Surfactant Distribution

Data Treatment for Using the Surfactant Model

Introduction

Flooding

MiscibleThe Solvent

Model

ImmiscibleThe Surfactant

Model

The Solvent Model

The Solvent Model

Objectives of the Solvent Model The aim of this chapter is to enable modeling of reservoir

recovery mechanisms in which injected fluids are miscible with the hydrocarbons in the reservoir.

A miscible displacement has the advantage over immiscible displacements such as water flooding, of enabling very high recoveries. An area swept by a miscible fluid typically leaves a very small residual oil saturation.

The ECLIPSE solvent extension allows you to model gas injection projects without going to the complexity and expense of using a compositional model

In Eclipse, the solvent extension implements the Todd and Longstaff empirical model for miscible floods.

Applications of the Solvent Model The solvent model is used in any scheme in which the aim is to

enhance the reservoir sweep by using a miscible injection fluid. Examples of solvent schemes are listed below:

1. High pressure dry gas processes, in which miscible flow conditions between the gas and the oil are found at the gas-oil contact .

2. A solvent such as LPG or propane may be injected as a ‘slug’ to be followed by an extended period of lean gas injection. The slug fluid is miscible with both the gas and oil.

3. Certain non-hydrocarbon gases such as carbon dioxide produce miscible displacement of oil at pressures above a threshold value.

4. All-liquid miscible displacements by fluids such as alcohol, normally injected as a slug between the in-place oil and the injected chase water.

Todd & Longstaff Model In Eclipse, the solvent extension implements the Todd and Longstaff

empirical model for miscible floods.

The model classifies the reservoir into 3 possible miscibility combinations:

1. In regions of the reservoir containing only solvent and reservoir oil (possibly containing dissolved gas) the solvent and reservoir oil components are assumed to be miscible in all proportions and consequently only one hydrocarbon phase exists in the reservoir. The relative permeability requirements of the model are those for a two phase system (water/hydrocarbon).

2. In regions of the reservoir containing only oil and reservoir gas, the gas and oil components will be immiscible and will behave in a traditional black oil manner.

3. In regions containing both dry gas and solvent, an intermediate behavior is assumed to occur, resulting in an immiscible/miscible transition region.

Todd & Longstaff Mixing Parameter ω The model introduces an empirical parameter, ω, whose value lies between 0

and 1, to represent the size of the dispersed zone in each grid cell. The value of ω thus controls the degree of fluid mixing within each grid cell. A value of ω = 1 models the case when the size of the dispersed zone is much

greater than a typical grid cell size and the hydrocarbon components can be considered to be fully mixed in each cell.

A value of ω = 0 models the effect of a negligibly thin dispersed zone between the gas and oil components, and the miscible components should then have the viscosity and density values of the pure components. In practical applications an intermediate value of ω would be needed to model incomplete mixing of the miscible components.

An intermediate value of ω results in a continuous solvent saturation increase behind the solvent front. Todd and Longstaff accounted for the effects of viscous fingering in 2D studies by setting ω = 2 /3 independently of mobility ratio. For field scale simulations they suggested setting ω = 1 /3 . However, in general history matching applications, the mixing parameter may be regarded as a useful history matching variable to account for any reservoir process inadequately modeled.

Data Treatment for Using the Solvent Model

The main differences between using the black oil simulator

without the solvent model and using it with the solvent

model are:

i. Phases present.

ii. Relative permeability data treatment.

iii. PVT data treatment.

I. Phases Present

To initiate the Solvent model, the following keywords must

be added to RUNSPEC section:

II. Relative Permeability Data Treatment

Relative permeability data treatment depends on whether the

displacement in the grids concerned is:

1. Fully miscible

2. Fully immiscible

3. Transition between miscible and immiscible regimes

1. In case of fully miscible:

In regions where solvent is displacing oil and the reservoir gas

saturation is small, the hydrocarbon displacement is miscible.

However, the 2-phase character of the water/hydrocarbon

displacement needs to be taken into account. The relative

permeabilities are given by:

2. In case of fully immiscible:

In the usual black-oil model the relative permeabilities for the

3 phases water, oil and gas are specified as follows:

2. In case of fully Immiscible:

When two gas components are present, the assumption is made

that the total relative permeability of the gas phase is a function

of the total gas saturation,

Then the relative permeability of either gas component is taken

as a function of the local solvent fraction within the gas phase,

3. In case of Transition between

miscible and immiscible:The transition algorithm has two steps:

1. Scale the relative permeability end points by the miscibility

function. For example, the residual oil saturation is

2. Calculate the miscible and immiscible relative

permeabilities, scaling for the new end points. Then the

relative permeability is again an interpolation between the

two using the miscibility function:

III. PVT Data TreatmentThe PVT data treatment is made for:

1. Viscosity

2. Density

1. Viscosity data treatment:

The following form is suggested by Todd and Longstaff for the

effective oil and solvent viscosities to be used in an immiscible

simulator.

1. Viscosity data treatment:

The mixture viscosities μmos , μmsg and μum are defined

using the 1/4th-power fluid mixing rule, as follows:

2. Density data treatment:

The effective oil and solvent densities (ρo eff , ρs eff , ρg eff )

are now computed from the effective saturation fractions and

the pure component densities (ρo , ρs , ρg ) using the

following formulae:

2. Density data treatment:

The effective saturation fractions are calculated from:

The Surfactant Model

The Surfactant Model

Introduction & Application

Most large oil fields are now produced with water-flooding

to increase recovery oil, but there’s a large volume of

unrecovered oil.

The remaining oil can be divided into two classes:

o Residual oil to the water flood

o Oil bypassed by the water flood

A surfactant flood is a tertiary recovery mechanism aimed at

reducing the residual oil saturation in water swept zones

The oil becomes immobile because of the surface tension

between oil and water; the water pressure alone is unable to

overcome the high capillary pressure required to move oil

out of very small pore volumes.

A surfactant reduces the surface tension, hence reduces

capillary pressure and allows water to displace extra oil.

Introduction & Application

The surfactant Model

To model The surfactant, we need to calculate:

Its distribution at each grid block

Its effect on:

Water PVT data (Viscosity of water-surfactant mixture).

SCAL data (Capillary pressure, Relative permeability, Wettability).

Surfactant Distribution

The surfactant is assumed to exist only in the water phase not

as a separate phase.

The user inputs the concentration of surfactant in the

injection stream of each well.

The distribution of injected surfactant is modeled by solving

a conservation equation for surfactant within the water

phase.

Water PVT properties

The surfactant modifies the viscosity of the pure or salted

water.

The surfactant viscosity is inputted as a function of surfactant

concentration.

The water-surfactant solution viscosity calculated by:

Water PVT properties

If the Brine option is active, it’s calculated as:

Where:

SCAL data

The Surfactant effects various SCAL data like:Capillary pressure, Relative permeability, Wettability

To Study its effect we need to input tables of water-oil

surface tension as a function of surfactant concentration in

the water using (keyword SURFACT)

Calculation of the capillary number

The capillary number the ratio of viscous forces to capillary

forces. The capillary number is calculated by:

The Relative Permeability model

The Relative Permeability model is essentially a transition

from immiscible relative permeability curves at low capillary

number to miscible relative permeability curves at high

capillary number. You supply a table that describes the

transition as a function of log10(capillary number).

The relative permeability used at a value of the miscibility

function between the two extremes

The Relative Permeability model

Capillary pressure

The water oil capillary pressure will reduce as the

concentration of surfactant increases and hence decreases the

residual oil saturation.

The oil water capillary pressure is calculated by:

Treatment of adsorption

The tendency of the surfactant to be adsorbed by the rock

will influence the success or failure of a surfactant flood

If the adsorption is too high, then large quantities of

surfactant will be required to produce a small quantity of

additional oil.

The quantity adsorbed is a function of the surrounding

surfactant concentration.

To model it, The user is required to supply an adsorption

isotherm as a function of surfactant concentration

Treatment of adsorption

The quantity of surfactant adsorbed on to the rock is given

by:Matrix density

List of References:

1. Eclipse Technical Description Manuel, Chapters 62 & 64.

2. Todd, M.R. Longstaff, W.J, 1972. The Development, Testing,

and Application Of a Numerical Simulator for Predicting Miscible

Flood Performance. J. Pet. Technol, 24(6): 874-882

Thank You