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Optimization of the Water Alternating Gas Injection ... Simultaneous Water Alternating Gas (SWAG) injection scheme on the reservoir instead of the initial injection of water and gas

Apr 22, 2020




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    Optimization of the Water Alternating Gas Injection

    Compositional fluid flow simulation with Water Alternating Gas Injection optimization on the upscaled synthetic reservoir CERENA-I

    Fabusuyi, Oluwatosin John; Quintao, Maria Joao; Azevedo, Leonardo; Soares, Amílcar

    Email addresses: [email protected]; [email protected];

    [email protected]; [email protected]

    Centre for Petroleum Reservoir Modelling

    Instituto Superior Técnico

    Avenida Rovisco Pais, 1

    1049-001 Lisboa

    Abstract- This work focuses on the optimization

    of the production strategy on an up-scaled

    synthetic reservoir CERENA-I, which mimics

    some characteristics of a Brazilian Pre-Salt field.

    This reservoir has a saturated oil leg with a

    retrograde condensation gas cap, both with a

    high CO2 content. The production strategy

    involved the implementation of a simultaneous-

    water alternating gas injection scheme (SWAG).

    The objectives for this study were to increase the

    oil recovery while reducing the gas production

    and the parameters selected for optimization in

    this study were the bottom-hole pressure, the

    well position, the injection rate and WAG ratio.

    The effects of these variables were studied in

    order to achieve an optimal solution.

    Keywords: Reservoir simulation, compositional

    simulation, PVT analysis, Synthetic reservoir,

    Simultaneous WAG scheme, Particle Swarm

    Optimization, objective function.

    1. Introduction

    This study is a continued interest in the

    CERENA-I reservoir created by Pedro Pinto [10].

    from the Brazilian Pre-Salt play (figure 1), which

    has a very high content of CO2. This Brazilian

    Pre-salt reservoir poses great challenges in

    every aspect of its production, from reservoir

    modelling and management, to surface facilities.

    The reservoir covers an area of 567 km2 about

    300km offshore of Rio de Janeiro, in the Santos


    Fig 1: The Brazilian Pre-Salt Play (Source: ANP)

    It is situated in water depths of around 2000m,

    with the top of the reservoir situated at

    approximately 5200m. It has a 90m thick heavy

    oil leg with 18o API and 55% (molar) of CO2

    content. It also has a gas cap of retrograde

    condensation gas which contains approximately

    60% (molar) of CO2.

    The idea to maximize the production of oil in the

    reservoir led to the development of a single well

    Simultaneous Water Alternating Gas (SWAG)

    injection scheme on the reservoir instead of the

    initial injection of water and gas produced in 2

    different wells. SWAG is an enhanced oil

    recovery process in which gas is mixed with

    water outside the well and the mixture is then

    injected as a two phase mixture in the well or,

    alternatively, both gas and water are injected at

    the same time into the well to get better oil

    recovery. Water and gas injection are the best

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    solution to cope with the problems such as early

    breakthrough which occur only when gas is

    injected individually due to unfavorable oil-gas

    mobility ratio. Hence, simultaneous injection of

    gas and water would be of greater importance to

    improve the sweep efficiency by improving the

    displacement front [6].

    Finding an optimal depletion strategy for

    hydrocarbon production has always been a key

    subject in reservoir management. The underlying

    problem to be solved is generally the

    maximization of a key quantity such as oil

    production, net present value (NPV), etc. In the

    past, optimal settings of the optimization

    parameters were almost exclusively determined

    manually. This is generally quite time-consuming

    procedure with a high likelihood of obtaining

    suboptimal results. While manual approaches

    are still predominant strategies in the reservoir

    management practice, due to the maturity of

    most existing major oilfields and gradual

    decrease in large oil discoveries, research for

    more systematic optimization approaches has

    been initiated. The optimization technique used

    in this study was the particle swarm optimization

    which was used in the Raven software provided

    by the Epistemy Company

    The initial objective of this research work was to

    find a production strategy to optimize oil

    production and reduce the quantity of CO2 being

    produced, and as the researched progressed,

    different ideas were introduced. The different

    parameters to be optimized were introduced and

    discussed, these include parameters related to

    the production wells and others related to the

    injection wells. During the course of the thesis,

    due to computational constraints for the

    simulation and optimization procedure, the

    reservoir CERENA-I was up-scaled, and the up-

    scaled version was used henceforth.

    2. The synthetic reservoir: CERENA-I The CERENA-I model was created to replicate

    some characteristics of the Brazilian Pre-salt

    carbonate fields and it contains high-resolution

    data sets of petro-physical and petro-elastic

    properties. For the case study presented herein

    only the sets of porosity and permeability were

    used. The model is composed of two facies: a

    reservoir facies, composed by microbiolites; and

    a non-reservoir facies composed by mudstones,

    on a corner-point grid with 161x161x300 cells,

    with 25x25x1m spacing.

    Fig 2: CERENA-I porosity model

    Fig 3: Histogram of porosity for both facies

    Permeability was modelled recurring to the

    porosity model and it exhibits a dependence that

    was derived from real analogues [4].

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    3. Dynamic simulation

    Due to the lack of real data from analogue fields

    the oil composition for this study was obtained

    from a generic sample of oil from Petrel's®

    library, grouped to reduce computation time and

    memory requirements, and with the molar

    percentages re-adjusted to the known CO2

    content of the analogue field (Table 1).

    Table 1: Molar percentages of the oil with grouped components.

    Component Molar % Mol. weight

    CO2 55.00 44.01

    C1 16.56 16.043

    C2 4.46 30.037 C3 3.15 44.097

    C4-6 5.69 70.237

    C7+ 15.11 218

    For this case study, the three parameter Peng-

    Robinson equation of state was chosen, and

    tuned to match the estimated PVT observations

    (Table 2).

    Table 2: Estimated saturation pressures

    Bubble point (bar) Dew point (bar)

    493 400 Due to the huge number of cells in the reservoir,

    the choice was made to run the simulation on a

    fine grid sectoral model (figure 4) which, despite

    being considerably smaller, when compared to

    the original model, reproduces the total variability

    of the full field.

    Fig 4: Sectorial model area

    Despite doing this, the computational time and

    memory needed for the number of iterations

    needed during the optimization process was

    really enormous, hence the sectorial reservoir

    was up-scaled. The porosity and permeability

    distribution in the up-scaled reservoir were made

    to replicate the distribution in the original

    sectorial model. The new up-scaled sectorial

    model contains a combination of grid cells of

    about 22 x 22 x 154 cells compared to the 45 x

    42 x 300 cells in the original sectorial model.

    Fig 5: Up-scaled perm x and y (left), original perm x and y (right)

    Fig 6: Up-scaled perm z (left), original perm z (right)

    Fig 7: Up-scaled porosity (left), original porosity (right)

    Figures 5 to 7 show the visual differences

    between the up-scaled and original sectorial

    models of the permeability and porosity models.

    The trends, facies and distributions obtained in

    the original sectorial model can also be observed

    in the up-scaled model with variations. From this

    point on, the link to the original full field model

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    and the sectorial model is severed and the study

    object is now the up-scaled sectorial model. For

    this reason no boundary effects will be added to

    the dynamic model, to account for the influence

    of the remaining area.

    Fig 8: Well Locations

    The well pattern chosen for this study was a

    traditional five-spot configuration with four vertical

    producer wells in the corners and one vertical

    injector well in the center (Figure 8).

    Table 3: Fluids originally in place

    The model was initialized and the fluids in place

    (Table 3) were calculated for the equilibrium


    We first chose to produce the gas cap, to

    access its liquid condensate fraction. The fluid

    was condensed in surface separators and the

    resulting dry gas was re-injected back into the

    gas cap, to help keep reservoir pressure. The

    gas cap was

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