SPE 130783

Fluid Delumping Applied to the Goliat field: A Black-oil Model Coupled with a Process Design Emanuele Vignati, William Bonotto, Alberto Cominelli, eni E&P; Elena Stano, Claire Le Maitre, eninorge

Copyright 2010, Society of Petroleum Engineers This paper was prepared for presentation at the SPE EUROPEC/EAGE Annual Conference and Exhibition held in Barcelona, Spain, 14–17 June 2010. This paper was selected for presentation by an SPE program committee following review of information contained in an abstract submitted by the author(s). Contents of the paper have not been reviewed by the Society of Petroleum Engineers and are subject to correction by the author(s). The material does not necessarily reflect any position of the Society of Petroleum Engineers, its officers, or members. Electronic reproduction, distribution, or storage of any part of this paper without the written consent of the Society of Petroleum Engineers is prohibited. Permission to reproduce in print is restricted to an abstract of not more than 300 words; illustrations may not be copied. The abstract must contain conspicuous acknowledgment of SPE copyright.

Abstract It is a common practice to use a limited number of components to describe hydrocarbon fluids in reservoir simulation, even though process engineering needs a richer set of components to model the top-side separation process. This requires delumping of the well streams from reservoir models to meet the detailed description needed by the process engineers. We have tackled this problem on the reservoir model of the Goliat field, located in the south-western part of Barents Sea. Economically producible hydrocarbons have been proven in the Realgrunnen and Kobbe formations. Production strategy consists of periferic water injection for pressure maintenance and gas injection in the gas cap for disposal purpose. The development plan includes the distillation of intermediate components from the separated gas phase. This can not be easily modelled within a black-oil reservoir simulator, because the process efficiency depends on gas volumes and gas molar composition that change along with the depletion of the reservoir. A possible solution is to build a reservoir-process model to provide corrected production profiles, and the efficiency of the solution relies on accurate and robust method to delump the black-oil streams. We modified a well-known delumping method to account for Goliat peculiarity. Detailed fluid composition is calculated at each well completion. Phase molar flow is characterized interpolating the composition measured in differential liberation test. Since the gas is expected to be partially produced from the gas cap, the original method has been modified to distinguish between dissolved and dry gas composition, following the latter in the simulation as a tracer carried by the gas phase. The application of this delumping algorithm offers several advantages. First, this method is cost-effective because it can be implemented efficiently by post-processing black-oil well streams, avoiding a much more time-consuming compositional simulation. Secondly, it increases the accuracy of forecast profiles since it takes into account the variation of the produced fluid composition with time. Finally, it provides sale-gas composition forecast according to the surface process facilities. Introduction Reservoir engineers usually prefer black-oil simulation because it is far less computational demanding compared to compositional models. Simplifying thermodynamic problems allows running models with more gridblocks or to solve the same model in less CPU time. One of the main limitations of black-oil models is that they provide results in term of volumetric flow of oil and gas phases referenced to surface conditions without any information about composition. On the other hand, process models use results of reservoir simulations to design surface equipments and to provide rates and compositions of stabilised oil and export/injection gas. Surface process simulators always model hydrocarbon fluid according to a two-parameter equation of state, with a large numbers of components (15-40). Thus, it is necessary to convert the reservoir fluid stream in a suitable input for process fluid model. This issue occures even in compositional reservoir simulation since the equation of state used in the reservoir is usually based on fewer components (5-10) in order to reduce the complexity of the model and the computational effort. The operation which allows converting a simplified fluid model in a more detailed one is called delumping.

Several methods have been proposed in the literature about delumping compositional streams since the 90’[1]-[10]. Danesh et al.[1] described a method based on corresponding state principle to compute the equilibrium coefficients for the detailed components. Leibovici et al.[2] developped a method based on the properties of the cubic equation of state, to process the results of a flash computation. Faissat and Duzan[3] proposed a post processing method based on compositional material balance computed with both lumped and detailed equations of state. The delumping of compositional data is realised according to the proportions observed in the lumped material balance.

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There are few methods in literature about delumping black-oil fluids[7], [11]-[13]. The easiest and most usual solution is to assign a fixed composition to stock tank oil and gas and to mix them according to the GOR in order to calculate the total stream composition. This method can be simply applied either at field level or at well level. Although this approach does not require any additional computation, it is a rough approximation because it does not consider the composition modifications due to the depletion. Hoda proposed another method[7]-[8], based on stream conversion starting from experimental data. Recently, Ghorayeb et al.[11] presented a new method that preserves the black-oil fluid description. This method is suitable for natural depletion and re-pressurization by water injection. They suggested computing phases composition at reservoir condition as a function of saturation pressure. This approach has been used to post-process results of black-oil simulations and to couple multiple reservoirs[13].

Process design is a fundamental step in the development of a field even from a reservoir point of view, since it helps to fix constrains of production. Moreover, the process can mix fluids coming from different reservoirs, in order to obtain a production fluid or/and an injection gas. A better characterization of the production fluids at the sale point allows evaluating economical and commercial parameters that cannot be assessed by reservoir simulation only, i.e.:

• the value of gas recycling, which is strongly dependant on the efficiency of heavy components end recovery; • the sale gas composition, required to fullfill contractual specifications; • design and long term operation of processing facilities, since they must be optimised in advance to minimize

bottlenecks. Although this kind of issues is usually tackled with the Integrated Asset Model tool, it is still required to provide robust

methods to convert fluid description from the reservoir to the process.models. A secondary issue, that usually rises, is the difference between oil production profiles estimated with reservoir and process

models. Process models are normally producing more oil since they recover intermediate fractions from the gas through the use of distillation columns. This feature is not commonly included in commercialised reservoir simulators, where surface stabilization is modeled with multi-staged thermodynamic flash to bring production fluid from reservoir to stock tank conditions. A possible solution is to estimate a constant correction factor for reservoir profiles, calculating the process separation efficiency at a known composition (usually at the beginning of production). Several commercialised reservoir simulators implement this approach by defining a correction factor for oil volume and GOR to be applied at standard condition volumes. Nevertheless, process separation efficiency is not constant during production since it depends on both production fluid composition and gas-oil ratio.

We faced these problems in the framework of development plan of the Goliat field. The base production scenario of the Goliat field is based on water injection for pressure maintenance while gas is injected for disposal purpose in the gas-cap. Other gas sale production scenarios have also been investigated in order to maximize the oil and gas recovery, assuming a gas export facilities possibility. Black oil modeling is deemed adequate to simulate these processes by well established reservoir engineering best-practices. To fully evaluate these different scenarios, it was first necessary to determine the detailed production fluid composition: since an efficient compositional model can usually provide 8/12 components for the production streams, we have decided to use the delumping approach associated to a black-oil model in order to obtain a more precise composition. Secondly, it was also crucial to couple the reservoir model with the surface process model: in this way the production profiles directly take into account the real effect of process efficiency.

In this paper, we present an improvement over Ghorayeb black-oil delumping method. Our approach is suited for scenarios

where production is supported by gas injection in the gas cap and takes in account gas cap blowdown, where the injected gas is produced. Although this method is tailored for Goliat field and we support its validity with examples related to this field, we believe its applicability may be further extended.

After a brief description of Goliat field, we are going to present the formulation of our approach and assess its validity in a model case and in one of the Goliat formations. Finally, we are going to how this approach improves the value of reservoir simulation and helps to evaluate the different scenarios of Goliat development.

Goliat presentation

The Goliat field is situated 85 km from the town of Hammerfest and the closest island is around 50 km from the field. The water depth is in the range of 320 m to 420 m (Figure 1).

Hydrocarbons have been proven in two Triassic formations: Realgrunnen and Kobbe. The Realgrunnen formation is characterized by a proximal fluvial environment with some tidal influences, and the Kobbe formation represents a prograding deltaic system with mouth bars and tidal influenced lobes. The Kobbe reservoir is split into Lower Kobbe and Upper Kobbe.

Both Realgrunnen and Kobbe reservoirs are divided into several lateral compartments: • Realgrunnen Main and Central; • Realgrunnen South; • Kobbe Main, Central and South compartments. (Figure 1 and Figure 2 )

Saturated oil is present in Kobbe and Realgrunnen South compartments while undersaturated oil is present in Main and Central Realgrunnen.

SPE 130783 3

Figure 1 – Left: Location map of the Goliat field in the Barents Sea. Right: Overview of the Goliat reservoirs, with the fluids contacts and the template location.

Figure 2 – The different compartments of the Goliat field, and well layout.

Geochemical studies and formation pressure data clearly indicate a lateral separation into several compartments. Also

stratigraphic barriers reducing or preventing vertical communication have been identified within Kobbe and taken into account.

Looking at the results of the different PVT analyses and at the MDT pressure points, we have decided to characterize the Goliat field using 3 different PVT sets: one for Realgrunnen Main and Central Segments, one for Realgrunnen South (in this area the reservoir fluid is saturated oil with original gas cap) and one for the whole Kobbe.

The Realgrunnen Main & Central and the Realgrunnen South compartments have very similar properties, as we can see in the phase envelope and in the Bo and Rs plots, in Figure 3 . Even thought Kobbe is much deeper than the Realgrunnen

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Figure 3 – Goliat fluids properties. Top left: phase envelops. Top right: oil relative volumes (Bo). Bottom left: gas relative volumes (Rs). Bottom right: table of the fluids properties values.

reservoir, the Figure 3 shows that the Bo, the Rs and the API gravities are higher in Kobbe. The Kobbe oil is then lighter: the reasons of this are because these two oils have different source rocks, and because the Realgrunnen hydrocarbon is biodegradated.

The reservoir fluids were modeled as black-oil. In both Realgrunnen and Kobbe reservoirs, the input PVT tables, used in the simulation model, have been obtained directly from the differential liberation experiments. A constant correction factor per PVT set has been used in the model to convert the production flow rates to flash conditions[1], in order to take into account the surface process.

The Goliat drainage strategy has been developed to optimize the expected value of the field, reduce the effect of possible downsides, and at the same time retain flexibility to capture potential upsides. For effective drainage, pressure maintenance is required, and water injection was selected as the primary means for pressure maintenance. This also offers an opportunity for handling of produced water which will be injected into the reservoirs in addition to sea water.

The Goliat field will be developed with subsea wells drilled from subsea templates and tied back to a Floating Production Storage and Offloading (FPSO) unit. The well streams shall be directly routed to the FPSO without intermediate processing. Stabilized crude oil and rich gas will be delivered from the FPSO unit, after treatment in several separators; the fluid production is then limited by the gas handling capacity of the first of these separators.

Since a commercial solution for export and sale of gas could not be available at production start, the produced gas could instead be re-injected into the reservoir: the reservoir drainage plan includes two gas injection wells in the gas cap of the Upper Kobbe reservoir.

Bo Rs API gravity(m3/Sm3) (Sm3/Sm3) (°)

Realgrunnen ~ 1,13 ~ 60 ~ 32

Kobbe ~ 1,56 ~ 200 ~ 43

SPE 130783 5

Initial pressure 189 bar@GOC Saturation pressure 189 bar

Initial GOR 193 n° of cells 200

n° of producers 2 n° of injectors G:1

Initial fluid Injected fluid

CO2 0.0003 0 C1N2 0.4430 0.95

C2 0.0575 0.05 C3 0.0736 0 C4 0.0650 0 C5 0.0412 0

C6C8 0.1113 0 C9C12 0.0734 0 C13+ 0.1347 0

Figure 4 – Left: saturation map of sinthetic model after initialization. Right: tables with reservoir data, composition of initial and injected fluids.

The geometry of the Goliat reservoirs calls for horizontal production wells for effective drainage. In the base case reservoir

model a total of eleven horizontal production wells is found to be optimal. Gas lift will be needed for the Realgrunnen Main and Central wells in accordance with the production profile data. The nine water injectors have selective completions with downhole flow control on two zones. The two gas injectors inject in Upper Kobbe only.

The base case production scenario assumes full life gas re-injection. Various alternatives for gas export have been studied. By limiting gas injection to the Upper Kobbe reservoir, it will be relatively simple to back-produce the gas by converting the two gas injection wells into producers at a later stage when a commercial gas export solution will be established.

Alternative gas export scenarios have different gas export rates, including the possibility of full gas export from day one. The gas management strategy has a direct impact only on Upper Kobbe segments South3, South4 and Main where produced

gas is re-injected in the base case. Depending on the amount of gas re-injected, the main recovery mechanism in these

segments is either gas drive (base case), water drive (full gas export from day one), or a combination of the two. For the Goliat reservoir, it was fundamental to evaluate the real process efficiency: this is the reason why an integrated

model, based on the delumping approach, was developed coupling the reservoir model with the surface process. Black-oil delumping procedure

The delumping method proposed by Ghorayeb[11] requires to compute the composition of the fluid entering in well completion according to the results of a differential liberation (DL) or a constant volume depletion (CVD) tests. Ghorayeb’s paper reports a detailed description and the mathematical formulation of the algorithm: in the following, we will quickly summarize the main steps.

Togheter with volumetric behaviour, DL and CVD tests provides compositional characterization of oil and gas liberated as the pressure decreases. Depletion experiments take usually part in the building of the fluid model, since the equation of state (EoS) parameters are adjusted by regression in order to match experimental tests. Thus, matched EoS can be used to fill tables predicting oil and gas compositions as functions of the saturation pressure. Starting from both free and solution gas/oil rates and fluid saturation pressure, it is required to compute:

1) the mass flows using standard densities, 2) the composition of the different phases, by interpolating data in the composition versus saturation pressure tables, 3) the phase molar weights and molar rates, 4) the fluid composition, by weighting phase composition by molar rates.

This approach is suitable for primary depletion and water injection scenarios. A different approach is required for gas injection where information from swelling test should be included to improve the simulation. Standard PVT experiments and simulators usually report tabulated liquid and vapour compositions versus initial gas/oil ratio (GOR) of the swelled fluid. Ghorayeb suggests to interpolate phase composition from liquid phase GOR in gas injection simulation. Although this approach is suitable for gas injection in undersaturated fluid, it is not able to model scenarios where production from gas cap and gas injection coexist. Indeed, when free gas from gas cap enters in a completion, the GOR increases and exceeds the corresponding value associated to the fluid bubble point. Vapour phase composition is then computed as if breakthrough of injection gas has occured. Since injected gas composition is usually different from gas cap, the overall fluid composition might be badly estimated.

We tackled this issue for delumping results with the Goliat black-oil model. In several scenarios, gas is injected in the Kobbe gas cap during the main development phase, where oil is the primary target, and it is subsequently produced during gas cap blowdown. Moreover, production wells in the Kobbe and Realgrunnen South formations will produce the gas from the gas

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Psat CO2 C1N2 C2 C3 C4+ C5+ C6+ C9+ C13+ 160 0.0003 0.3983 0.0579 0.0770 0.0688 0.0450 0.1227 0.0808 0.1492 165 0.0003 0.4065 0.0578 0.0764 0.0681 0.0443 0.1206 0.0794 0.1466 171 0.0003 0.4161 0.0578 0.0757 0.0673 0.0435 0.1182 0.0779 0.1434 175 0.0003 0.4223 0.0577 0.0752 0.0667 0.0430 0.1166 0.0768 0.1415 180 0.0003 0.4298 0.0576 0.0746 0.0661 0.0423 0.1147 0.0755 0.1390 185 0.0003 0.4372 0.0576 0.0741 0.0655 0.0417 0.1128 0.0743 0.1366 189 0.0003 0.4430 0.0575 0.0736 0.0650 0.0412 0.1113 0.0734 0.1347 300 0.0003 0.4430 0.0575 0.0736 0.0650 0.0412 0.1113 0.0734 0.1347

Table 1: Liquid composition versus saturation pressure, for fluid in example 1

Psat CO2 C1N2 C2 C3 C4+ C5+ C6+ C9+ C13+ 160 0.0004 0.8621 0.0539 0.0415 0.0287 0.0056 0.0046 0.0031 0.0000 165 0.0004 0.8605 0.0539 0.0418 0.0292 0.0059 0.0049 0.0035 0.0000 171 0.0004 0.8582 0.0538 0.0423 0.0297 0.0062 0.0054 0.0040 0.0000 175 0.0004 0.8565 0.0539 0.0426 0.0301 0.0064 0.0058 0.0044 0.0000 180 0.0004 0.8542 0.0539 0.0431 0.0306 0.0067 0.0063 0.0049 0.0000 185 0.0004 0.8517 0.0540 0.0436 0.0311 0.0070 0.0068 0.0054 0.0000 189 0.0004 0.8496 0.0540 0.0440 0.0316 0.0073 0.0072 0.0059 0.0000 300 0.0004 0.8496 0.0540 0.0440 0.0316 0.0073 0.0072 0.0059 0.0000

Table 2: Vapour composition versus saturation pressure, for fluid in example 1

cap even in the main phase. We have therefore modified the black-oil delumping algorithm in order to recognize the nature of the free gas between:

• the gas liberated from oil, • the gas from the gas cap, • the injection gas.

We take advantage of the passive tracer tracking capability, implemented in most reservoir simulators, to mark the gas from the gas cap and the injected gas with different tracers. Then, volume of free gas entering the connection can be split according to the tracer fractions detected by the simulator.

Liquid and vapour phases are delumped by interpolating composition versus saturation pressure. Since, at this stage, the vapour composition represents only the gas liberated from the oil around the producer, it is corrected accounting for the different sources of free gas. The liquid composition is not modified. Starting from the tracer concentrations in gas phase, we compute both the molar flows of injected gas and gas from the gas cap and calculate the vapour composition weighting each composition with the respective molar flow. Then, the overall gas composition is normalized and the new phase molar weight is computed. Finally the overall fluid composition is computed according to the original method. Overall fluid composition is then computed similarly to the original approach.

Since the tracer concentration is usually related to the stock tank gas, we renormalize it for referring only to the free gas in reservoir, Vgf. We can write the volume of gas entering a completion, Vg, as:

goiggcgogfg VVVVVVV +++=+= lg ( 1 )

where Vgf is the volume of gas free at reservoir conditions and Vgo is the volume of gas liberated by the oil in the well. The former can be written as the sum of gas from gas cap, Vgc, injected gas, Vig, and gas liberated from the oil outside the well, Vlg. We assumed that cgc is the concentration of the tracer associated to gas cap and cig is the concentration of the tracer linked to injection gas, as computed by the simulator, while gcc and igc are the renormalized ones, defined as:

gf

igig

gf

gcgc

g

igig

g

gcgc V

Vc

V

Vc

V

Vc

V

Vc ==== ,,, ( 2 )

Our method comprises the following steps: 1. to compute the molar flows as:

glggfgcigigggfigiggcggfgcgc MVccnMVcnMVcn ρρρ )1(lg −−=== ( 3 )

where gρ is the gas stock tank density and M is the molecular weight.

2. to compute the vapour phase molar fraction as:

gligciggcigcigiigi yffyfyfy ,,, )1( −−++= ( 4 )

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Figure 5 – Results of the example 1 for both black-oil and compositional models. Left: comparison of GOR between compositional and black oil models. Right: comparison of oil and gas production rate between compositional and black oil models.

where iy is the vapour mole fraction of the component i and alpha is the mole fraction defined by:

glgcig

gcgc

glgcig

igig nnn

nf

nnn

nf

++=

++= ( 5 )

3. to compute the vapour phase molecular weight. It is worthile to add that, as well as the original implementation, our method is not equivalent to a compositional

simulation. Fluid properties and phase equilibrium are estimated from pre-processed tables instead of thermodynamic computations based on the equation of state. Thus, a good level of agreement between black-oil and compositional models must be reached before applying the delumping method.

Test cases

We present two examples in order to verify our implementation and to asses the improvement over the Ghorayeb’s method. We focused these tests on cases where oil production is supported by gas injection and where initial gas-oil contact is present. Delumping algorithm can be applied either at the end of the simulation, as post-processing results, or while the simulation is running. Both of them require adding tracer tracking options to the original black-oil dataset, without further modifying it. Runtime delumping necessitates using a controller interfaced with the black-oil simulator to cyclically halt the simulator at required dates, run the delumping algorithm and then continue with the simulation. In the following tests we have used the runtime approach, applied at completion level, with the following applications:

• Resolve (Petroleum Experts) as a global controller[14], • Eclipse 100 (Schlumberger) as the reservoir simulator, • The delumping algorithm which is implemented by means of Visual Basic macros calling OpenServer commands.

When the algorithm is performed as post-process, no additional CPU-time is practically required and that is not dependant on the gridding of the reservoir model[11]. On the other hand, delumping applied while simulating may require a longer time since it involves data exchanges between different softwares which may be running on different machines. So, network performances must be taken in account. The additional time is not measured as CPU-time, since there are no differences in the algorithms, nevertheless it implies a much longer elapsed time.

For these test cases, we have developed compositional models as the reference solutions. We have performed a comparison of fluid composition for:

• gas cap injection in a synthetic geometry, where hydrocarbon fluid is modeled with a 9 component equation of state based on Goliat fluid.

• a more complex application based on the Kobbe reservoir in the Goliat field. Hydrocarbon fluid is modeled according to the equation of state designed for the process model.

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Figure 6 – Comparison of molar fraction of light (top-left), intermediate (top-right) and heavy (bottom-left) components between compositional simulation and results of different delumping techniques. Bottom-right plot displays the molar flow of C1N2 component for different methods. Example 1 – Simplified geometry

We have developed a Peng-Robinson equation of state with nine components, able to reproduce standard PVT experiments performed on the fluid original in place. The compositional model is used to export black-oil tables and to simulate a differential liberation test to generate composition versus saturation pressure for both hydrocarbons phases, see Table 1 and Table 2.

The reservoir, see Figure 4 , is a vertical cross-section with 10 layers, no dip, gas cap and a bottom inactive acquifer. A producer is completed in a cell in the oil region and produces hydrocarbon fluid. After one year of production, a gas injector well is completed in the highest cell of the formation, in order to mitigate the pressure decline. After 32 years, both wells are shut and the injector is converted in a gas producer to produce from the gas cap. Gas injection is performed at fixed standard conditions rate. Producers, both oil and gas, are controlled by standard conditions rate targets and a constant bottom hole pressure (BHP) constraint:

• oil producer: 30 Sm3, BHP>160 bar, • gas producer: 500 Sm3, BHP>160 bar, • gas injector: 3000 Sm3.

We analysed fluid molar fraction and rates over time at field level, between compositional simulator and black-oil delumping. As a first step, we have compared the differences in volume flows between compositional and black oil simulators.

SPE 130783 9

Initial pressure 191 bar@GOC Saturation pressure 189 bar

Initial GOR 193 n° of cells 681120

n° of producers 9 n° of injectors W:8 G:2

Initial fluid Injected fluid

N2 0.0136 0.026 CO2 0.0003 0.011 C1 0.4451 0.794 C2 0.0582 0.079 C3 0.0739 0.053 iC4 0.0238 0.011 nC4 0.0412 0.026 iC5 0.0220 0 nC5 0.0190 0 C6 0.0283 0

C7C10 0.1257 0 C11C15 0.0622 0 C16C19 0.0307 0 C20C27 0.0312 0 C27+ 0.0248 0

Figure 7 – Left: map of porosity for Kobbe reservoir. Right: tables with reservoir data, composition of initial and injected fluids.

The results, displayed in Figure 5 , show that after 2500 days, the GOR in the black-oil model rises more steeply than in

the compositional model. Hydrocarbon production is greater in the compositional model so, regardless of the composition, differences are expected in the mass flow.

Figure 6 shows the differences in light, intermediate and heavy components between the compositional model (the reference result) and the different black-oil delumping techniques:

• The traditional approach, where phases composition at initial conditions is kept constant for entire production time;

• The Ghorayeb’s approach, where the composition versus GOR tables are used to interpolate the phase molar fractions;

• Our proposed approach, where tracers are used on the top of the Ghorayeb’s method to correct the gas phase composition according to both injection gas and gas cap molar fractions.

It is interesting noticing that the standard method is correctly working for heavy components but it is predicting an opposite trend for intermediate components. Moreover, this method is not able to estimate the molar fraction during the gas cap blowdown. On the other hand, at the beginning, before the GOR starts to increase, and during the blow down, the results of the Ghorayeb’s method present a trend similar to the compositional simulation. Finally, our solution gives the best results for all the components considered: there are small differences with the reference results but they are within engineering precision.

Example 2 – Kobbe formation

In order to validate our method in a more complex production situation, we applied it on the Kobbe reservoir in the Goliat field, and we compare the results with the compositional model simulation on the same reservoir and production layout. The scenario analysed is the base case scenario: full gas re-injection for the first 15 years of production, and then gas cap blow-down.

The Kobbe reservoir has the characteristics explained in the Goliat presentation paragraph. One PVT set describes the overall Kobbe reservoir fluid: for both black-oil and compositional models, the black-oil tables and the EoS have been build by tuning a Peng-Robinson equation of state with 15 pseudo-components, matching the Kobbe PVT experiments parameters. In order to be consistent with the Goliat process model, we decided to implement the same pseudo-components in both reservoir and surface models. Seven horizontal oil producers, two gas injectors, converted after 15 years of production into gas producers, and five water injectors are implemented. The reservoir is divided into two main sequences: Upper and Lower Kobbe, themselves separated in four segments. In order to optimize the reservoir drainage, in every segment are located at least one horizontal oil producer and one water injector completed in the water zone. The gas injectors/producers are situated at the crest of the structure in the gas cap area. Producers are controlled by standard conditions rate targets, at constant minimum bottom hole pressure (BHP) and tubing head pressure (THP) constraints. The water and gas injections are performed at fixed standard conditions rate and BHP constraint. The composition of the injected gas is kept constant, see Figure 7 .

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Figure 8 – Comparison of several key-parameter between compositional and black oil models for the Kobbe reservoir.

The comparisons between the results of black-oil and compositional simulators are reported in Figure 8 for GOR, field pressure and normalised phase rates. Results are in good agreement for oil rate and field pressure, gas and water rates are roughly following the same trend although for brief periods there are significant differences. Black-oil delumping algorithm is performed every 4 months. Figure 9 displays molar fraction of produced fluid for carbon dioxide, methane, an intermediate component and the heavy tail as computed by the delumping algorithm and the compositional simulator. Results are in good agreement even when oil wells are shutted and gas production from converted gas producers is started. Post-processing results of Kobbe black-oil model allows characterizing the production fluid in the thermodynamic model used in the process simulator, substantially preserving the composition estimated by the compositional model and greatly reducing simulation time (16900s for compositional model, 4800s for black-oil).

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Figure 9 – Comparison between molar fraction of several components computed by compositional simulation and the black oil delumping technique presented in this paper.

Application on Goliat For the time being, the approved Goliat development scenario is the case of full gas re-injection. Since the oil recovery is slightly better in the gas export cases and these last scenarios allow selling the produced gas, we are still strongly investigating all the different possibilities to make a gas export solution achievable. One of these possibilities is to export the gas via pipeline to the closest existing gas export network, connected with a gas treatment plant (Figure 1). The validation of this possibility required to estimate the detailed composition of the Goliat produced gas, in order to perform a handling capacity test at the plant. Moreover, the possibility to have a detailed gas composition enables to economically asses the different development scenarios. The delumping method provides a supplementary tool to identify the best technical and economical solution.

We applied our delumping approach to the different scenarios of the Goliat field model in order to realize reservoir-process coupled model. In this paper, we present the results of a gas injection and a gas sale scenarios.

For both simulators we used the fluid model formulations implemented by reservoir and process engineers. This means a set of PVT tables based on DL experiments for the reservoir model and a Peng Robinson equation of state with 15 components based on surface data. The next step was the generation of composition versus saturation pressure tables. For this purpose, surface equation of state is tuned from DL data to provide results consistent with reservoir condition. In practice, this is similar to the definition of a separate surface equation of state.

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Figure 10 – Selected results of the application of delumping approach to the Goliat field.

Delumping is implemented at completion level while simulation is running. At each equilibration time-step defined by the global controller, the following workflow is performed:

1. Eclipse black-oil model is run; 2. the delumping custom macro loops on all well connection, collecting phase flows and computing component

molar rates. At the end of the loop, mass production and fluid composition is calculated at user-defined level (well, group, field);

3. the global controller transmits phase mass rates and composition to the process simulator (namely Hysys, Aspentech), assigning them at the inflow streams of the process plant. Surface model is solved and all the required data at the output streams are stored.

We have applied our methodology to the Integrated Asset Modelling in a slighty different implementation. Wells in the reservoir model are controlled by top head pressure target set at each timestep of the controller software. Fluid is modeled as black-oil in the network simulation. Delumping is performed as previously: since no other hydrocarbon fluid is collected by the network, no difference is expected between delumping results of reservoir or network simulators.

Results are displayed in Figure 10 for normalized oil production rates, normalized gas injection rate, ethane molar fraction in gas export stream and pseudo component C7-C10 in stabilised oil stream. Although the reservoir model includes the effect of the separation train, the process production efficiency is slighty greater (up to 5%) during the intermediate part of production and it increases up to 20% during the early stages of blowdown.

Gas injection in the reservoir model takes in account the gas shrinkage subtracting a fixed volume of produced gas. Comparison of the results of reservoir injection rates and available injection gas in the process model shows that this approximation is slighty understimating (down to -6%) the amount of injection gas available.

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The commercialised reservoir simulator can not simultaneously transport passive tracers and solve multisegmented well

model –needed for inflow control devices (ICDs) modelling– in a consistent way, as requested by our methodology. For practical problems, we experienced unexeptable large mass balance error in tracer concentration, which may reach unphysical values, corrupting results of delumping algorithm. This is a critical issue for the implementation of our workflow in the Goliat full-field model, because ICDs are planned in the baseline development scenario. Although this problem requires further investigations, we have partially tackled this issue by simulating two scenarios: a pessimistic case where ICDs are not simulated and an optimistic case where ICD are modeled by closing the completions exceeding GOR limits at sand face. The results of these two simulations allow to evaluate the error introduced by mass balance error in tracer advection.

Conclusion

The development and management of modern oil and gas fields require a better and better consistency between reservoir

engineering and process engineering. Commercial solution turning a hydrocarbon reservoir into an exploitable economical resource requires a tight integration between the two quoted disciplines. In this context the evolution of Ghorayeb’s black-oil delumping represents a step forward both from practical and technical points of view, because it extends the applicability to a broader class of problems, including the key issue of the development of oil rims which surround huge gas cap.

As noted in Ghorayeb’s paper, the original delumping method is not suited for cases where gas is injected in a saturated reservoir. Provided that the recovery is correctly simulated by black-oil models, we can obtain hydrocarbon fluid composition using tracers to identify free gas sources on top of Ghorayeb implementation still used for the oil. The comparison with compositional simulations clearly proves the reliability of our innovative approach with respects to the previous one.

This approach has been used in the framework of the Goliat field development to prepare the data for the engineering design of the surface facilities according to the various development options, from full gas reinjection to full gas export scenario. The composition of the gas export stream has been used to assess the compatibility with the gas from a nearby field.

References [1] Danesh A., Xu D. and Tod A. C.; “A grouping method to optimize oil description for compositional simulation of gas injection

processing”; SPE Reservoir Engineering (1992); 343-348. [2] Leibovici C.F., Stenby E.H. and Knudsen K.; “A consistent procedure for pseudo-component delumping”; Fluid phase equilibria; 17;

(1996); 225-232. [3] Faissat B. and. Duzan M.C: “Fluid modelling consistency in reservoir and process simulations”; SPE European Petroleum Conference,

(1996), SPE 36932. [4] Schlijper A.G., Drohm J.K., “Inverse lumping estimating compositional data from lumped information”; SPE Reservoir Engineer (1998),

SPE14267. [5] Leibovici C.F., Barker J.W., Wache D.; “Method for delumping the results of compositional reservoir simulation”; SPE Journal (2000),

SPE64001. [6] Whitson C.H., Anderson T.F. and Søriede I., “C7+ characterization of related equilibrium fluids using the gamma distribution”,

Advances in Thermodynamics Volume 1, Taylor & Francis, New York, ISBN 0-8448-1565-9. [7] Hoda, M.F., “The engineering of petroleum streams”, Doktor Ingenior Thesis, Norwegian U. of Science and Technology (NTNU),

Trondheim, Norway, (2002). [8] Bassam Al-Awani, Hemanthkumar K., Fatema Al-Awami, and Mansour Mahammedali; “Application of stream-conversion methods to

generate compositional streams from the results of a multimillion-cell black-oil simulation study of the Shayabah Field”, SPEREE (2005), SPE 84361.

[9] Nichita D.V., Broseta D., Leibovici F.C.; “Consistent delumping of multiphase flash results”; Computers and Chemical Engineering (2006), 30, 1026-1037.

[10] Vignati E., Cominelli A., Rossi R., Roscini P., “Innovative implementation of compositional delumping in integrated asset modelling”, SPEREE (2009), SPE 113769.

[11] Ghorayeb K. and Holmes J.: “Black-oil delumping techniques based on compositional information from depletion processes”; SPEREE (2007), SPE 96571.

[12] Montel F., Quettier L., “Getting the best from the black-oil approach for complex reservoir fluids”, presented at ATCE (2004), Houston [13] Ghorayeb, K., Limsukhon M., Dashti Q. and Aziz R.M.: “Black oil delumping: running black oil reservoir simulations and getting

compositional wellstreams in the North Kuwait Jurassic Complex”; (2009), SPE 118850. [14] Resolve User Guide, Version 4.0, 2009.