SPE 157556

An Innovative Integrated Asset Modeling for an Offshore-Onshore Field Development. Tomoporo Field Case Fernando Pérez, Edwin Tillero, Ender Pérez, and Pedro Niño PDVSA; José Rojas, Juan Araujo, Milciades Marrocchi, Marisabel Montero, and Maikely Piña, Schlumberger

Copyright 2012, Society of Petroleum Engineers This paper was prepared for presentation at the SPE International Production and Operations Conference and Exhibition held in Doha Qatar, 14 16 May 2012. 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 necessar ily 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

A reliable future development plan of an oilfield would require that all of the elements in the petroleum system are modeled in an integrated manner if a timely response, a more realistic economical evaluation, and risk analysis are needed for better decisions making.

The main goal for future development of Tomoporo field is to change the traditional focus (petroleum system elements by separated) by enabling to multidisciplinary team members to take advantage of their expertises within a collaborative environment based on interaction among petroleum system components.

The Tomoporo field's hydrocarbon reserves have been largely developed in offshore, but barely in onshore. It has been planned to increase production twice through new producing wells in onshore area which presents several limitations for handling production. Also a plan for pressure support, and improved oil recovery have been considered by implementing a waterflooding project.

This paper shows an innovative integrated asset methodology, applied for forecasting scenarios where reservoir, surface network, geographic location aspects, economy, risk, and uncertainty analysis were considered. The evaluation of forecasting scenarios was performed by implementing an integrated asset modeling (IAM) where all of simulation scenarios were coupled with a surface network model. Such network modeling included itself three integration levels to address complexity of surface facility needed for future offshore-onshore field development. In addition, an innovative link from reservoir-surface network models to the economic model was developed for a fully assisted asset modeling, resulting in faster and more reliable scenarios evaluation.

The IAM for Tomoporo field provided valuable information for all team members of the production stream, maximizing benefits from decision making based on a fully coupled asset model. This integrated approach determined that greater recovery factor and less reservoir pressure drop are achieved if an onshore flow station is added for new onshore wells in spite of existing capabilities in offshore surface facilities.

The IAM approach triggered warnings about future needs (investment, expenses), and also to be alert in minimizing bottlenecks in order to ensure no violation of surface capacity constraints. In addition, it allowed to define operating limits of water injection plants, enabling that optimum operation conditions are set, and the added value of the Tomoporo field development be maximized.

Introduction

Lack of asset management policies for current and future production from oilfields may lead to an either overestimated or underestimated added value during any stage of asset life cycle. It might also imply loss of production opportunities. In such cases, an asset development based on an integrated modeling of all petroleum system components (reservoir model, surfaces model, and financial model) that takes into account risk and uncertainty, becomes essential.

Some oilfields may have significant reserve volumes in zones with few drilled wells and limitations in surface facilities capabilities owing to sensitive areas for exploration and production activities. Thus, an integrated approach that considers such unfavorable conditions for better decision making and improvement in project definition in terms of field operations is a must.

This paper attempts to explain how an integrated assessment of Tomoporo field was made considering its biased current development (mainly offshore). This evaluation was based on an innovative workflow considering multiple integration levels

2 SPE 157556

in asset's components, also taking into account geographic nature where asset's component are set, type of reservoir model (analytical or numerical), and so on. Also uncertainty and risk analysis are considered in order to improve project definition and the success in terms of field operations (production, drilling campaign, etc).

The case study is developed in Tomoporo field, located in Maracaibo Lake basin, Venezuela. Details about field characteristics, the methodology used for the assessment, and results derived from application of the methodology are presented.

Project Background Field Data

Tomoporo field is located southeast of Maracaibo Lake basin. This field becomes one of those which conform the production growth axis in Western Venezuela, along with Franquera and La Ceiba fields. Basic data from Tomoporo field is shown in figure 1.

Tomoporo field was discovered in 1988 with the offshore well VLG-3729. Later in 1999 an exploratory onshore well (TOM-0007) pointed out Tomoporo's onshore reserves. A plan to increase production through drilling clustered wells was visualized just after drilling some onshore delineation wells, as a result of the high productivity shown by earlier onshore wells. Such plan also has triggered opportunities of reserves development from neighboring oilfields (Franquera and La Ceiba). Tomoporo's production comes from Misoa formation (Eocene epoch), specifically from members B-1 to B-5, being B-1 and B-4 the most prolific members (figure 2). Ninety five wells have been drilled with production rates vary from 800 to 6000 STBD.

Figure 1. Geographic Location and Basic Data of Tomoporo Field.

Figure 2. Stratigraphic Column of Tomoporo Field

B-2

B-4

B-6

B-5

M I

S O

A

B-3

E O

C E

N E

PAUJI

B-1

LA ROSA

M I

O C

E N

E

LA PUERTA

Laguna

LAG

UN

ILLA

S Bachaquero

EL MILAGRO

PLEI

STO

CEN

E

PLIOCENE ONIA

Ojeda

EPOCH FORMATION

B-1.0

B-1.1

B-1.2

B-1.3

B-1.4B-1.5

B-1.6

B-4.0

B-4.1B-4.2B-4.3B-4.4B-4.5B-4.6B-4.7

B-4.8

B-4

B-1

B-2

B-3

B-1.0

B-1.1

B-1.2

B-1.3

B-1.4B-1.5

B-1.6

B-4.0

B-4.1B-4.2B-4.3B-4.4B-4.5B-4.6B-4.7

B-4.8

B-4

B-1

B-2

B-3

STOIIP : 5.217 MMbls

Recovery Factor = 22 % (Oficial)

Recoverable Oil : 1.147 MMBls

Cummulative Production : 394 MMBls

Remaining Oil : 753 MMBls

API gravity : 22.5°

Inicial Pressure= 7500 LPC

Average Pressure: 2000 - 5000 Lpc

Bubble Pressure =1494 Lpc

Formaciones Productoras: Misoa

Porosity (%): 15

Permeability (Md): 50-1100 mD

Average Depth: 16000 Pies

Natural Drive Mechanism:- Rock and liquid expansion Drive- Water Drive

Artificial Lift Method : - Electric Submersible Pumps (ESP) - Gas Lift

Production Rate (January 2011):- Oil: 93.2 MBNPD- Water : 24,4 MBD- Gas : 23.8 MMPCD

MOPORO FIELD

FRANQUERA FIELD

Coast Line

LA CEIBA FIELD

SouthAmerica

Venezuela

300 KM

SouthAmerica

Venezuela

300 KM

SouthAmerica

Venezuela

300 KM

SouthAmerica

Venezuela

300 KM

SouthAmerica

Venezuela

300 KM

SouthAmerica

Venezuela

300 KM

SouthAmerica

Venezuela

300 KM

Basic Data

SPE 157556 3

Geologic Framework and Simulation Model A 3D geo-cellular model that included estructural, stratigraphical, sedimentological, and petrophysical aspects was generated and resulted in a simulation mesh with aproximately 267,000 actives cells (figure 3). From this model an OOIP about 5,200 MMSTB was obtained after equilibrium and convergence of initial conditions were achieved. History matching of produced fluids and pressure behavior were achieved so that forecasting scenarios were generated to evaluate several exploitation strategies such as base case (no activities), drilling campaign, and flank-waterflooding scenarios at different injection rates.

Figure 3. Geocellular Model of Tomoporo Field

Surface Facilities Network

re wells completed with gas artificial lift method. Onshore production is addressed through multiphase pipeline to Cluster #7 (TOM-7) in land, and from there to 2 offshore flow stations (EF7-7 and EF11-7) which are connected to low pressure gas network along with the others 8 offshore flow stations and 2 production manifolds that handle the offshore production (figure 4).

Figure 4. Current Distribution of Tomoporo Field's Surface Facilities

TOM-1X

FRANQ

BARUA V

LA CEIBACOAST LINE

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Integrated Asset Evaluation Workflow The main goal of the assessment of Tomoporo field is to optimise both the onshore and offshore production stream from

reservoir to surface facilities handling its complexity and minimizing bottlenecks. Ensuring no violation of surface capacity, and defining operating limits are also main objetives.

All these expectations are based on the comparison among visualized exploitation scenarios in which analysis of uncertainty and risk will be considered in order to improve project definition and the success in terms of field operations (production, drilling, etc). Figure 5 shows a simplified workflow used to fully assisted asset modeling based on an innovative interface from integrated reservoir-surface network model to the economic model.

This innovative integrated asset methodology firstly set production profiles from reservoir which was coupled with surface network, and evaluated at the same time surface facilities capabilities for each scenario, and possible needs of new investment. Lately, decision making techniques were applied based on economical indicators such as risk, NPV, and investment efficiency, among other.

Figure 5. Workflow

Forecasting Scenarios Assessment One of the main strategies for Tomoporo

wells as cluster (figure 6). Some of these wells will be drilled having as objective the upper sand (B1), and anothers the lower sand (B4). Also injection wells will be drilled for implementing a waterflooding project as pressure maintenance alternative to pressure decline observed in existing wells.

Forecasting scenarios were comprised by a base case (depletion), primary recovery (new producers), and 3 possible waterflooding project scenarios: low-rate injection, medium-rate injection, and high-rate injection (figure 7). All those water injection scenarios were evaluated because of the uncertainty generated by unsuccessful results in injectivity tests performed during all field history (high surface injection pressure).

Table 1 shows all water injection scenarios along with respective regions, objectives, and rates. The most reliable injection scenario will be selected based on future injectivity tests which must be designed and performed by a multidisciplinary team taking into account learned lessons earned in the past, and minimising operational troubles.

Assessment of reservoir and surface

models

Segmentation of the surface model

Reservoir -Wells Coupling

Wells-Surface connections

Production Profile

NO

Economic Evaluation, Risk and Uncertaintly

AnalysisStrategy

accomplishment

YES

Assessment of reservoir and surface

models

Assessment of reservoir and surface

models

Segmentation of the surface model

Segmentation of the surface model

Reservoir -Wells Coupling

Reservoir -Wells Coupling

Wells-Surface connections

Wells-Surface connections

Production ProfileProduction Profile

NO

Economic Evaluation, Risk and Uncertaintly

Analysis

Economic Evaluation, Risk and Uncertaintly

AnalysisStrategy

accomplishment

YES

SPE 157556 5

Figure 6. Clustered Drilling Campaign of Future Producing and Injection Wells

Figure 7. Profile Production of Forecasting Scenarios

Table 1. Description of Water Injection Scenarios

High-Rate InjectorsMedium-Rate InjectorsLow-Rate Injectors

Primary Recovery (new producers) +80880Tasa

Baja

+125925TasaIntermedia

+150950TasaAlta

DiferenciaSin Iny.MMSTB

PetróleoAcumuladoMMSTB

+80880

+125925

+150950

MMSTB

Base Case(do nothing)

High-Rate Injectors

Medium-Rate Injectors

Low-Rate Injectors

High-Rate InjectorsMedium-Rate InjectorsLow-Rate Injectors

Primary Recovery (new producers)

Base Case(do nothing)

Field Oil Production Total

Primary Recovery Difference MMSTB

Injection Scenarios Objectives, Regions and Rates

B1_Region 1 (Rate: 4000 STB/d/Well)

B4_Region 1 (Rate: 6000 STB/d/Well)

B4_Region 3 (Rate: 6000 STB/d/Well)

B1_Region 1 (Rate: 2000 STB/d/Well)

B4_Region1 (Rate: 5000 STB/d/Well)

B4_Region 3 (Rate: 4000 STB/d/Well)

B1_Region 1 (Rate: 2000 STB/d/Well)

B4_Region 1 (Rate: 2000 STB/d/Well)

B4_Region 3 (Rate: 2000 STB/d/Well)

High-Rate Injectors

Medium-Rate Injectors

Low-Rate Injectors

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Future Surface Network Assessment

To support future production, 44 onshore new producing wells and new surface facilities will be required for production increase in Tomoporo area (figure 8). Most relevant new infraestructures that will support additional production are a new power sub-station, an onshore flow station (EF-MOP-I) with capacity of 150,000 BPD, and an onshore gas compressing plant (PC-MOP-I) with gas processing capability about 120 MMSCFD (figure 9). All produced gas will aim to support artificial lift needs. Also a water injection plant, required for IOR scenarios will be designed based on injection limits (150,000 BWPD) and evaluation of optimal location (figure 10).

Figure 8. Onshore Surface Network for New Wells in Tomoporo Field

Figure 9. Future Surface Network of Tomoporo Field

FRANQ

LA CEIBA

Electrical Substation Q

High Gas Pipeline 16´´x 16 Km

Oil Pipeline 30´´x 51 Km

Coast Line

Moporo I Flow Station

Moporo I Compressor

Onshore

Offshore

SPE 157556 7

Figure 10. Water Injection Network for Tomoporo Field

Reservoir-Surface Network Coupling Methodology Understanding the behavior of each one of the petroleum system components of Tomoporo field is vital for emulating as

close as posible real operation conditions (flow and pressure) from surface network to reservoir, also for modeling and optimising wellbore completion, flowline dimensions, among others so that future strategies of investment are less risky.

To address complexity on surface facility configuration due to a biased offshore-onshore field development, all of the simulation scenarios were coupled considering three integration levels of the surface network model using AVOCET® as software to integrate reservoir and network models (figure 11). This configuration also aimed to simplify the analysis of pressure drop and flow behavior in subelements of network and also helped to identify more easily bottlenecks through surface facilities (individual flow stations).

Figure 11. Integration Levels of Tomoporo Integration Level I Level 1 aimed to pass wells boundary conditions from reservoir model to wells model in the network. Coupling was

located on borehole so that fluids behavior can be evaluated by pressure-gradient prediction for multiphase flow in pipes (without VFP tables). Productivity Index (PI) of both existing and new producing wells was modeled and tunned according to wellbore conditions and multiphase flow patterns.

Multiphase Flow (Reservoir and Surface Models)

Ceutagas Compressor Unigas Compressor

Flow Station (FS)

PPQ

Ta Tb

QQ gas

Q multiphase

Level ILevel III Level II Level ILevel III Level II

FS1

FS3

FS5

FS4

FS6

FS2

B C

D

E

Multiphase Source (wells from neighboring

reservoirs )

Scrubber outlet

Internal Equipments of Flow Station Gas Network

Production manifold Inlet

Reservoir Wells Coupling

Liquids

Separator TcTd

Flow direction

8 SPE 157556

In this level, multiphase flow in each well was evaluated from borehole to flow station, specifically to production manifold inlet. 86 existing wells and 45 new wells were modeled into 6 flow stations (EF7-7, EF8-7, EF9-7, EF10-7, EF11-7, and EF-MOP-I). 25 injection wells were connected to a water injection plant (PI-MOP-I). Both reservoir and this level of network are solved sequentially with the goal of obtaining a balanced system.

Integration Level II In this level was modeled pressure drop in flow station, water injection plants, etc. Multiphase flow was evaluated from

production manifold inlet to gas scrubber. Wells from neighboring reservoirs were modeled as single source, hence flow streams converge into a node before arrive the production separator. Liquid flow behavior after separator (from pipeline to tank farm) was not modeled. The gas flow after gas scrubber was modeled instead. Also new flow station (EF-MOP-I) and water injection plant along with two water injection manifolds were modeled as sources with defined boundary conditions.

Integration Level III In this level, single phase flow of gas at low pressure from flow stations to gas plant (MOP-I) was modeled. Both

compressor inlet pressure and gas rate represented boundary conditions for gas plant MOP-I. Gas flow and pressure from neighboring flow stations to gas plant were also modeled, considering flow station as a source. Compressor inlet pressure governed pressure profile from integration level III to I.

Reservoir-Network Model Calibration and Predictions

Reservoir-network calibration process was performed based on base case scenario, where productivity index (PI) was the . Wells' PI from both reservoir and network were calibrated so that the

restart of reservoir conditions at the end of history matching was mimiced as similar as possible by the wells in the network. As calibration of base case scenario was obtained, all of visualized future exploitation strategies (table 2) were performed

in an integrated manner considering surface facilities's constrains (figure 12). All of future scenarios required the evaluation of both current surface network capabilities and additional investments in surface facilities, as showed in table 3. Also geographic location aspects were considered mostly in waterflooding scenarios. Elements such as ESP systems, pipelines from onshore to offshore production manifolds, optimum location of injection plan and flow station were evaluated.

Capabilities such as separation, purifying, storage, and pumping of flow stations were used as constraints in order to trigger warnings about needs of surface facility adaptations, new elements in the surface network, wells rate target control, etc.

Figure 12. Production Integrated Profiles of Tomoporo Field

FORECAST CASE NAME

Local_4

CasoRP_Eclipse2_sin_MPOI

CasoRP_Eclipse2

inj_B1_6

CasoRS_Eclipse2

CasoRS_Baja_Eclipse2_MOPI

inj_B1_7

CasoRS_Medio_Eclipse2

CasoRS_Medio_Eclipse2_MOPI

inj_B1_2

CasoRS_Alto_Eclipse2

CasoRS_Alto_Eclipse2_MOPI

MEDIUM-RATE INJECTION

HIGH-RATE INJECTION

WATERFLOODING SCENARIOS

NEW PRODUCERS

LOW-RATE INJECTION

PRIMARY RECOVERY

FORECAST CASE NAME

Local_4

CasoRP_Eclipse2_sin_MPOI

CasoRP_Eclipse2

inj_B1_6

CasoRS_Eclipse2

CasoRS_Baja_Eclipse2_MOPI

inj_B1_7

CasoRS_Medio_Eclipse2

CasoRS_Medio_Eclipse2_MOPI

inj_B1_2

CasoRS_Alto_Eclipse2

CasoRS_Alto_Eclipse2_MOPI

MEDIUM-RATE INJECTION

HIGH-RATE INJECTION

WATERFLOODING SCENARIOS

NEW PRODUCERS

LOW-RATE INJECTION

PRIMARY RECOVERY

SPE 157556 9

Table 2. Resulting Integrated Escenario of Tomoporo Field

Table 3. Capabilites of Surface Facilities Asociated to Tomoporo Field

Integrated Economic Evaluation and Risk/Uncertainty Analysis Evaluating added value of an integrated asset model becomes essential if better decision making on exploitation strategies

is the goal. Therefore, economic, risk, and uncertainty analysis must be incorporated to the petroleum system assessment. Once integrated production profiles were defined along with infrastructure capabilities for each forecasting scenarios, a

programming-assisted economic model connected with the reservoir-surface model was built in order to transfer integrated production profiles and other variables in an automatic manner to economic evaluation software. For accomplishing of that, an innovative link developed in a regular programming language was developed for having a totally coupled and integrated asset model (figure 13).

Figure 13. Configuration of Reservoir-Network-Economic Models as Integrated Asset Modeling

FORECAST CASE NAME DESCRIPTION Np (MMBN) RF (%) Gp (MMMSCF) Wp (MMBW)

Local_4 NOT INTEGRATED 802.5 11.9 189.5 172.2

CasoRP_Eclipse2_sin_MPOI INTEGRATED 745.6 11.0 168.8 128.3

CasoRP_Eclipse2 INTEGRATED WITH ONSHORE FLOW STATION 751.0 11.1 170.1 129.9

inj_B1_6 NOT INTEGRATED 879.9 13.0 202.1 235.9

CasoRS_Eclipse2 INTEGRATED 846.8 12.5 190.7 199.6

CasoRS_Baja_Eclipse2_MOPI INTEGRATED WITH ONSHORE FLOW STATION 848.5 12.5 191.0 202.8

inj_B1_7 NOT INTEGRATED 925.9 13.7 212.9 316.8

CasoRS_Medio_Eclipse2 INTEGRATED 904.3 13.4 203.8 284.0

CasoRS_Medio_Eclipse2_MOPI INTEGRATED WITH ONSHORE FLOW STATION 909.2 13.4 205.0 284.0

inj_B1_2 NOT INTEGRATED 949.6 14.0 214.6 366.3

CasoRS_Alto_Eclipse2 INTEGRATED 949.4 14.0 213.3 345.9

CasoRS_Alto_Eclipse2_MOPI INTEGRATED WITH ONSHORE FLOW STATION 954.1 14.1 214.3 344.2

MEDIUM-RATE INJECTION

HIGH-RATE INJECTION

WATERFLOODING SCENARIOS

20 YEARS EVALUATION PERIOD

NEW PRODUCERS

LOW-RATE INJECTION

PRIMARY RECOVERY

N°FLOW

STATION / MANIFOLD

N° OF EQUIPMENTS

LIQUID CAPACITY

(MBD)

GAS CAPACITY (MMSCFD)

N° OF EQUIPMENTS

GAS CAPACITY (MMSCFD)

N° OF EQUIPMENTS

LIQUID CAPACITY

(MBD)

N° OF EQUIPMENTS

LIQUID CAPACITY

(MBD)

1 EF-1-7 4 60 72 1 55 2 2000 2 204002 MP-2-7 0 0 0 0 0 0 0 0 03 EF-4-7 3 45 54 1 55 2 2000 3 369004 MP-5-7 0 0 0 0 0 0 0 0 05 EF-6-7 4 60 72 1 53 2 2000 4 492006 EF-7-7 4 60 72 1 75 2 2000 4 504007 EF-8-7 4 72 100 2 90 2 3000 4 639008 EF-9-7 3 54 75 2 90 2 3000 6 812009 EF-10-7 3 54 75 2 90 2 3000 6 63000

10 EF-11-7 3 75 20 1 60 2 2000 5 6250011 EF-MOPORO I 4 152 75 2 75 2 26960 8 96

PUMP CAPACITYSEPARATION CAPACITY SCRUBBER CAPACITY STORAGE CAPACITY

Reservoir ModelReservoir Model

Surface ModelsSurface Models

Multidisciplinary Integrated ModelMultidisciplinary Integrated ModelEconomic EvaluationEconomic Evaluation

Network Economics LinkNetwork Economics Link

10 SPE 157556

Assisted Economic Evaluation An innovative way for economic evaluation of assets with complexities on surface facilities was implemented taking

advantage of the buil (level of integration). This configuration aimed to simplify the analysis of pressure drop and flow behavior in subelements of network.

As production profiles from each flow stations and water injection plants were generated, economic evaluations were performed automatically through the implemented link which passed all needed variables (production and injection profile, operating costs, investments, oil and prices, number of wells, etc) to the economic evaluation software (figure 14). After that, all of economic results were consolidated in order to perform sensitivity, risk, and uncertainty analysis based on Monte Carlo Simulation.

Figure 14. Workflow for Economic Evaluation of Tomoporo Field This methodology allowed to get faster economical evaluations, and also to evaluate economical indicators as function of

time (figure 15). Also allowed visualizing how investments, profits, and others variables were being performed as functions of time. It yielded to detect differences between scenarios that impact the planning and design of exploitation strategies.

Figure 15. NVP Profiles of Forecasting Scenarios as Function of Time

Sensitivity Analysis In order to determine which variables had greater impact on profitability, a sensitivity analysis was

performed for each one of proposed scenarios. Variables such as net present value (NPV), rate of return, investment efficiency were used. It also allowed identifying critical variables in order to make efforts to mitigate risk. In this study was found that production and oil prices showed high sensitivity resulting in those of highly impact on project implementation (figure 16).

Economic Evaluation forEach Flow Station

Economic Evaluation forEach Flow Station Scenarios ConsolidationScenarios Consolidation Economic EvaluatiónEconomic Evaluatión

Deterministic Assessment Strategies

and Sensibility Analysis

Deterministic Assessment Strategies

and Sensibility AnalysisStochastic Assessment

StrategiesStochastic Assessment

Strategies ResultsResults

Forecast ProductionForecast

Production

Operation CostOperation Cost

InvestmentInvestment

PricesPrices

OPERATION COST

Low-rate InjectionLow-rate Injection

Medium-rate Injection

Medium-rate Injection

High-rate InjectionHigh-rate Injection

Base CaseBase Case

Primary RecoveryPrimary Recovery

NET PRESENT VALUE (NPV) Vs Time

Primary RecoveryBase Case Low - rate Injection Medium - rate Injection High - rate Injection

SPE 157556 11

Figure 16. Example of Sensitivity Analysis

Stochastic Analysis

After finishing sensitivity analysis, stochastic analysis was performed applying stochastic methods such as decision tree and Monte Carlo analysis.

Decision tree allowed analizing, in a schematic way, several pathways of actions that a project might take considering decision making under uncertainty (figure 17).

Figure 17. Decision Tree Analysis.

Monte Carlo simulation was evaluated under obability distributions of

variables with uncertainties such as capital, operating costs, fluids production, and oil prices were considered. A risk analysis was performed plotting net present value (NPV) vs. semi-standard deviation of NPV, which is known as

. This graph allowed classifying scenarios selecting those that present low risk (standar deviation of NPV) and high NPV.

Also cumulative probability curves were generated to show the probability of having negatives and positives NPV from forecasting scenarios. None of evaluated scenarios showed negative NPV. The high-rate scenario is one of the least risky, which represents the drilling campaign of both producing and injector wells and total injection rate of 100,000 BWPD (figure 18).

12 SPE 157556

Figure 18. Efficiency threshold Graph

Figure 19. Cumulative Probability Graph

Conclusions

Coupled models provide common asset management strategies for all disciplines of the petroleum production system. More realistic and accurate predictions were obtained for Tomoporo field from this new approach on integrated asset

modeling, reducing uncertainties, assuring control of field operations, and adding value for this asset. Integrated simulation models are robust and versatile, allowing reservoir, facilities, and processes engineers run

sensibilities over any components of the total asset, detecting their impact on the whole system. The IAM for Tomoporo field provided valuable information for all team member of the production stream, maximizing benefits from decision making based on a fully coupled asset model.

This integrated approach determined that greater recovery factor and less reservoir pressure drop are achieved if an onshore flow station is added for new onshore wells in spite of existing capabilities in offshore surface facilities.

The IAM approach also triggered warnings about future needs to minimize bottlenecks, to ensure no violation of surface capacity constraints, and to define operating limits of water injection plants, enabling that optimum operation conditions are set, and the added value of the Tomoporo field development be maximized.

VPN@10%

Curva de probabilidad AcumuladaCummulative Probability Curve

NPV @ 10%VPN@10%

Curva de probabilidad AcumuladaCummulative Probability Curve

NPV @ 10%

Semi Desviación Estándar

VPN

@10

%

Gráfico de Frontera

Semi-Standard Deviation

NP

V @

10%

Efficient Threshold Graph

Semi Desviación Estándar

VPN

@10

%

Gráfico de Frontera

Semi-Standard Deviation

NP

V @

10%

Efficient Threshold Graph

SPE 157556 13

None of evaluated forecasting escenarios showed negative NPV cumulative values (risk), hence all scenarios are profitable. Nevertheless, high-rate water injection scenario was the best case.

Production volumes and cash flow from both integrated and no-integrated models showed significant differences (from 15% to 35%).

Integrated simulation models are robust and versatile, allowing reservoir, facilities, and processes engineers run sensibilities over any components of the total asset, detecting their impact on the whole system. Nomenclature AVOCET = Integrated Asset Modeler (Schlumberger) BWPD = Barrels of Water per Day EF = Flow Station named in field operations EF-MOP_I =Flow Station MOP_I ESP = Electrical Submersible Pumo FE = Flow Station IAM = Integrated Asset Modeling IOR = Improved Oil Recovery MMSCFD =Million Standard Cubic Feet Per Day MMSTB =Million Standard Barrels NPV = Net Present Value OOIP = Original Oil in Place PC-MOP-I =Compressor Plant MOP-I PI-MOP-I = Water Injection Plant MOP-I PI = Productivity Index STBD = Standard Barrels per Day VFP = Vertical Flow Performance References

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