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A Model Integrating Surface to Subsurface Models under UncertainConditions, for Optimizing Production in Santa Barbara and Pirital Fields,VenezuelaRosa Angélica Rodríguez V., PDVSA, Cesar Muziotti, PDVSA, and Nelson Caraballo, PDVSA

Copyright 2008, Society of Petroleum Engineers

This paper was prepared for presentation at the 2008 SPE Improved Oil Recovery Symposium held i n Tulsa, Oklahoma, U.S.A., 19–23 April2008. 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 beenreviewed 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, itsofficers, 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 toreproduce 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

Located at the northern area of Monagas state, Venezuela is the Santa Barbara and Pirital Fields; known to be two of the principle hydrocarbon fields in the country. Since their discovery, several characterization studies have been done forimproving the understanding of its complex geologic structure and fluid column distributions; which vary greatly within eachof the eight, independently modeled, sectors of the two fields.

Opportunities for improving exploitation plans, of these fields, have been identified based on characterization studies.Accordingly, It was realized the importance of evaluating different scenarios of hydrocarbon production, considering theunique characteristics of each of the eight reservoir sectors, as well as the surface installations capacities for handling production in the short, medium and long terms; taking into account the different levels of risk and uncertainties.

This article presents the development of a subsurface-surface model for the evaluation of multiple production scenarios, takinginto account the reservoir characteristics, well bore design and surface installations in an integrated manner scoping theunexpected out of plan deviations that was not considered in the initial planning process, as well as the inherent uncertainties

of the models used for designing the exploitation plan.

This model allows the selection of the most adequate infrastructure for handling Oil and Gas production, based on thereservoir behavior over time and considering the margin of error as a function of the possible risk and uncertainties at the

reservoir level as well as delays in drilling future wells and variations in the produced hydrocarbon densities. The results ofthis model will finally be used for the economic evaluation that decides on the profitability of the projects associated to thesefields.

This article will focus on the procedure of integrating the different surface-subsurface model components supported by a casehistory from the area.

Introduction

During oil and gas fields exploitations plans generation, different kinds of uncertainties are presented wich make a challengetaking proper decisions. According to some authors the uncertainties are associated to technological, economic, political and

environmental variables. Each one of those uncertainties can impact in a different way, in different parts of the project;however, certain standards obtained coming from several statistical studies indicate that the ones corresponding to technical

variables play the most important place in the total uncertainty of the system.

PDVSA has implemented a planning methodology to select the optimal field exploitation strategy, which considers anintegrated automatic workflow and involves uncertainties models, called MIAS (sustainable integrated asset modeling)[Acosta et al ., 2005; Khan et al ., 2006]. MIAS Santa Barbara and Pirital project’s objective is to assure optimal short-termfield operating strategies in agreement with long-term reservoir management objectives with social and environmentalresponsibility.

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Prior to Santa Barbara and Pirital MIAS project is required a platform [Rodriguez et al ., 2007] for the quantification ofsubsurface and surface uncertainty variables and the evaluation of the impact on the value creation.

An integrated surface to subsurface model was developed for multiple production scenarios evaluation, considering reservoir,wells, and surface facilities characteristics in an integrated way taking into account the effect of planning deviation along thetime and uncertainties of models used in the exploitation plan elaboration.

The automated workflow, developed in this effort, allows predict infrastructure requirements for oil and gas handing. It is

based on reservoirs behaviour, considering the error margin and the indentified reservoir risk areas as uncertainties functions.For each exploitation plan evaluated, surface subsurface model results serve as an economic evaluation input. It enables todetermine the profitability of the projects associated to Santa Bárbara y Pirital fields.

Santa Barbara and Pirital Fields Characteristics

Santa Barbara and Pirital fields are located north of Monagas State, Eastern Venezuela. Production comes from two (2)

producing formations, Naricual and Cretaceo wich are present in eight (8) separated production areas identified and modelledin an independent way (Figure 1).

SBC-08SBC-08

SBC-139SBC-139

PIC-10PIC-10

SBC-22SBC-22

SBC-06SBC-06SBC-29SBC-29

PIC-3PIC-3

SBC-01SBC-01

N

SBC-08SBC-08

SBC-139SBC-139

PIC-10PIC-10

SBC-22SBC-22

SBC-06SBC-06SBC-29SBC-29

PIC-3PIC-3

SBC-01SBC-01

N

Figure 1. Santa Barbara and Pirital Reservoirs Areas.

SBC-1 Area

SBC-1 area, presents a complex reservoir fluid columns with depth variation composition. In the top structure a typical gas

condensed behavior is observed. This zone produce 41° API light oil, while reservoir deepest area produce sub-saturated blackoil. In between, a transition area presents fluids in a near critical state. In this area exist 113 wells located strrategically for

optimazing reservoir reserves drainage.

As part of the reservoirs development, a phase of secondary recovery by high pressure gas injection is applied.

SBC-8 Area

The SBC-8 Area is located south Santa Barbara Field, in which 31 producing wells have been perforated.

This area presents a depth varying fluid column, and a continuous decrease pressure. This quick decline of the pressure makes

necessary the implementation of a secondary recovery process, this is why two new gas injector wells have been planned in

this area.

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SBC-22, PIC-10 And PIC-3 Areas.

The Santa Barbara and Pirital Fields north area have an extension of 31300 acres approximately; it is separated in three areas,namely SBC-22, PIC-10 and PIC-3, with different kind of fluids and initial pressures conditions.

At the moment the north area has a total of 25 producing wells. SBC-22 area produces black oil. PIC-10 and PIC-3 sectors are

condensate gas producers.

SBC-6, SBC-139 And SBC-29 Areas.

The Santa Bárbara and Pirital south west area is a very high uncertain area and it’s composed by three parts: SBC 139 wichhas just one active producer well, SBC-6 and SBC-29 with only one abandoned well in each of them.

The extension of this area is 4650 acres, being part of the South West flank structure that includes the fields Santa Bárbara andPirital. Pressure of the South West area reaches, at the moment around the 6800 lpc.

Reservoir Simulation Models Characteristics

The next table is a resume of reservoir simulation models characteristics involved in surface subsurface model development.

Table 1 – Available Reservoir Simulation for Santa Barbara and Pirital Fields

Reservoir Area

NameReservoir Model Type Simulator Active Cells

SBC-1 357529

SBC-8 88335

SBC-22 153940

SBC-06 9836

SBC-29 7089

SBC-139 45908

PIC-3 38883

PIC-10

Compositional Eclipse 300 (TM)

110418

Surface Facilities Description

Currently, Santa Barbara and Pirital Fields produce 1770000 STB/D. Field production is processed in three flow stations,which may have up to three operating production trains (1200, 550, and 60 psia); two-stage separation for each production

train; crude oil stabilizers; oil storage; and gas transfer pipelines. Figure 2 shows Santa Bárbara and Pirital fields crude oil production schematics.

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Figure 2. Estaciones de Flujo Área Santa Bárbara-Pirital

Amana Fl ow Estation

This Flow Station gathers oil ang gas coming from Mulata and Santa Bárbara Fields, with a design capacity of 200 Mstb/d oilcapacity and 1950 MMstcf/d gas and separation capacity. Nowadays the station processes 135 Mstb/d of 46° API oil and 465MMstcf/d of gas.

This flow station has four (4) production trains that handle two (2) kinds of oils: 30° API oil called Mesa 30 segregation, and35° API oil Called Premium Santa Barbara 35.

El Tejero F low Station

El Tejero Flow Station gathers oil ang gas coming from Santa Bárbara and Pirital Fields, with a design capacity of 200 Mstb/d

oil capacity and 2000 MMstcf/d of gas. Nowadays the station processes 85 Mstb/d of oil and 880 MMstcf/d of gas.

Santa Bárbara F low Station

This Flow Station receives oil from Santa Barbara and Pirital Fields wells, its design capacity is 113 Mstb/d oil and 1400MMstcf/d of gas. Currently, this flow station gathers 32 Mstb/d of Pemium Santa Barbara 35 segregation and 385 MMstcf/d ofgas.

Subsurface and Surface Uncertainty Analyses

Oil and gas projects are affected by technical, economical, political or environmental uncertainties in various, remarkable ways

[Saputelli et al., 2002]. These uncertainties, which are often difficult to evaluate, affect the ability to make adequate decisions.Usually, project teams are faced with several misconceived practices:

1. Expected project outcome is evaluated in comfortable ranges of uncertainties, especially in those ranges we know better. For those uncertain variables in which we barely know one single value or one model, we usually stick to thatvalue as the absolute truth; and we rarely extend our expected range.

2. Uncertainties which can not be evaluated in available models are just ignored.

3. In addition, uncertainties are often evaluated by isolated disciplines, and not all uncertainties are tested against theoverall project results.

MIAS Santa Barbara and Pirital Project

MIAS project’s objective is to assure well-informed decision-making for optimal Santa Barbara and Pirital fields development.

Optimal field development implies understanding the impact of all uncertainties over all decision criteria. Prior to project

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commencement, the MIAS Santa Barbara and Pirital project required a formal basis for quantification of the uncertaintiesassociated with subsurface models, with the wells, and with surface facilities.

Santa Barbara and Pirital Subsurface Uncertainties

In this work, most subsurface uncertainties were evaluated by both static and dynamic reservoir models with the consideration

of a multi-disciplinary team. Static model uncertainties were evaluated establishing multiple geostatistical realizations, while

dynamic model uncertainties were evaluated using different available reservoir models (Table 1). Production prediction profiles were generated in the known range of static and dynamic uncertainties.

Santa Barbara and Pirital Surface Uncertainties

Santa Barbara and Pirital fields surface operations performance are mostly related to uncertainties in project execution time,workover success, well parameters uncertainty (productivity, THP), wells and facilities uptime, and facilities spare capacity.

The surface net that gathers oil and gas from Santa Barbara and Pirital Fields is simplified and represented in this work bythree flow stations and gathering, distribution and transfer gas flow lines (15 gas flow lines). The infrastructure requirementscalculations were done through a Visual basic Routine which considers the current surface facilities configuration (Figures 1and Figures 2) and design capacities inside a statistical procedure that takes into account productivity and well head pressureuncertainties.

Tools

Subsurface Surface (2S) workflow was implemented using mainly Microsoft Excel™. Project-specific VBA code wasdeveloped to:

1. Interface with input files, external application (Eclipse 300™) and output files,2. Build the business logic of well production profiles aggregation and separator pressure level allocation3. Manage the Montecarlo simulation from an outer loop.

For each scenario, and after key variables and file formats have been declared, the workflow can be re-used by any professional in the organization who may not be computer-literate to produce multiple runs of different scenarios.

The following applications and tools were used for the implementation of the subsurface and surface integration procedure:

• Reservoir simulation application (Eclipse 300™)

• Microsoft Excel™ data base

• Microsoft Excel™ Visual Basic programming environment

Subsurface and Surface Integration

A procedure for integrating multiple numerical reservoir simulated production profiles within surface facilities net wasdeveloped. Subsurface responses were obtained from eight (08) different reservoir simulation models (Table 1) with theirassociated well constraints.

The developed procedure included the design and implementation of one integrated subsurface-surface (2S) productionoperations model for Santa Barbara and Pirital reservoirs. This procedure included the reservoirs’ response with the whole production value-chain. The evaluation tool allows fast quantification of short, mid- and long-term production scenarios,

according to MIAS Santa Bárbara and Pirital requirements.

Integrated Surface Subsurface (2S) Workflow

The yellow box inFigure shows the automated algorithm structure and procedures developed for 2S workflow. Also, in Figure 3, the input processes (green box) and the output processes (orange box) are shown. During the evaluation of each scenario, 2S Modelexecutes recurrently the integration workflow shown in Figure 3.

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Define Field

Exploitation Scenario

Corporate Guidelines, Prices, Costs,

Investments and Uncertainties

Risk

MetricsProbabilistic

methodology

Generate Well Production

Profile for each Reservoir

Define Well Locations

and Schedule

Iteration 1 to n Year 2007 to 2026

Load Well Profiles (qo, q

g , q

w , THP, THT, API )

Iteration = 1

Propagate Uncertainty in Well Profiles

Perturbed Well Profiles (q’ o, q’

g , q’

w , THP’, THT’, API’ )

1

2

3

2S Automated Workflow

yes

6

Calculate Flow Stations Infrastructure requirements

Profiles by Field Totals

Profiles by Reservoir

Profiles by Flow Station

Gas Flow Line Profiles

Profiles by Pressure Level

Infraestructure capacity

Graphics

Calculate Statistic Measure (Mean,

Standard Deviation, P10, P50, P90)

Generate 2D

& 3D Plots

Generate Efficient

Frontier Analysis

Start

Configure

Economic

Statistics

Analysis

Assign Separator Pressure Level (Low, medium, High)4

5

Iteration < n

Final 2S

yes

Define Field

Exploitation Scenario

Corporate Guidelines, Prices, Costs,

Investments and Uncertainties

Risk

MetricsProbabilistic

methodology

Generate Well Production

Profile for each Reservoir

Define Well Locations

and Schedule

Iteration 1 to n Year 2007 to 2026

Load Well Profiles (qo, q

g , q

w , THP, THT, API )

Iteration = 1

Propagate Uncertainty in Well Profiles

Perturbed Well Profiles (q’ o, q’

g , q’

w , THP’, THT’, API’ )

1

2

3

2S Automated Workflow

yes

66

Calculate Flow Stations Infrastructure requirements

Profiles by Field Totals

Profiles by Reservoir

Profiles by Flow Station

Gas Flow Line Profiles

Profiles by Pressure Level

Infraestructure capacity

Graphics

Calculate Statistic Measure (Mean,

Standard Deviation, P10, P50, P90)

Generate 2D

& 3D Plots

Generate Efficient

Frontier Analysis

Start

Configure

Economic

Statistics

Analysis

Assign Separator Pressure Level (Low, medium, High)44

55

Iteration < nIteration < n

Final 2S

yes

Figure 3 – Detailed 2S Workflow (this paper in green).

Well Production Profiles

For each reservoir and scenario, the following information had to be specified:

• Time step (e.g. years)

• Well name (e.g. “SBC-63”)

• Producing formation name (e.g. “SBC-8”)

• Oil rate (STB/D)

• Water cut (%) or water rate (STB/D)

• Gas Rate (MMSCF/D)

• oAPI gravity

• Simulator calculated wellhead pressure

• Reservoir model type or name (e.g. simulator brand)

• Well risk metrics indicator (R1, …, R5)

Production Profiles Perturbation

Monte Carlo Simulation

MIAS project objectives included the quantification of subsurface, wells and surface uncertainty variables. One methodologyused to quantify these uncertainties included the use of a stochastic simulation method, such as Monte Carlo.

Monte Carlo simulation is a technique to propagate uncertainty on a mathematical model by repetitive and random

perturbation on each of its uncertain inputs. In this technique, each variable’s probability distribution function is sampled atrandom intervals. By using such stochastic methodology in the 2S workflow, a new well profile could be generated for each of

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the iterations.

The total number of iterations could be defined by the user. At the end, it was possible to find new reservoir and field profiles by aggregation with all their statistical measures (mean, standard deviations, P10, P50 and P90).

2S wokflow permitted obtaining deterministic and probabilistic results. For the first iteration no perturbation was imposed tothe profiles, this is what it is called: the deterministic solution.

For the rest of the iterations (i.e. iterations 2 to n), production profiles were perturbed in accordance to the associatedvariability defined for each well risk, and following Montecarlo simulation rules, generating what is called the probabilistic

solutions.

Uncertainty Propagation in Production Profi les

Oil, water and gas production profile data were modified using different multipliers factors:

• Oil production multiplier

• Gas production multiplier

• Water production multiplier

• Producer well head pressure multiplier

Each of these variables corresponded to uncertainties that were previously evaluated and were supported with available fieldhistoric data.

Oil, water and gas Production Multiplier

Production multiplier affects well production from well P as follows:

p simq

pi qq ×= β ...................................................... (1)

Producer well head Pressure Multiplier

Simulated well head pressure values exhibited certain degree of uncertainty and error with respect to field observed values.

Pressure multipliers were added as input variables in the Montecarlo simulation to account for such uncertainty:

sim P pi THP THP ×= β ............................................ (2)

Multiplying Factors Generation

q β And P β factors were generated for each Montecarlo iteration using probability distributions in Table 2.

All factors were obtained independently as the inverse of the cumulative probability distribution function for a givenrandom number as shown in Figure 4.

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0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

0 0.5 1 1.5 2

Multiplying Factor

C u m u l a t i v e P r o b

a b i l i t y o f O c u r r e n c e

R5

R1

Random Number = 0.8

1.250 1.401

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

0 0.5 1 1.5 2

Multiplying Factor

C u m u l a t i v e P r o b

a b i l i t y o f O c u r r e n c e

R5

R1

Random Number = 0.8

1.250 1.401

Figure 4 – Multiplying factor generation for =1, =0.1, and for two different risk levels. Notice the variation for a random numberequal to 0.8; the resulting factor is 1.250 for R1 and 1.401 for R5.

Reservoir and Dri ll ing Risk Metri cs

MIAS project objectives include the identification and the quantification of subsurface, wells and surface uncertainty

variables. One methodology used to identify these uncertainties included the establishment of reservoir and drilling riskmetrics.

Santa Barbara and Pirital Fields were divided into several risk areas based on (a) available field data and information, (b) levelof definition of static and dynamic characterization and (c) geological and drilling complexity and impact to the exploitation plan.

Five (5) risk levels were created to standardize the results of the risk metrics: [R1] Low, [R2] medium-low, [R3] medium, [R4]

medium-high, and [R5] high risk. Higher risk levels are associated with projects with higher standard deviations, and

similarly, lower risk areas are associated in projects in lower standard deviations.

Production Prof il e Pertu rbation by Risk Metri cs

To include the effects of such risk metrics in the 2S workflow, it was necessary to add a risk factor to adjust the standard

deviation (σ) originally perceived in each uncertainty variable, as follows:

i R Ri +=σ σ .......................................................... (3)

As an example, various probability distribution functions are shown in Figure , notice the variation of one standard deviation

below the average for R1 and R5. The modificated standard deviations, used for each risk level are shown in Error! Referencesource not found.

Table 2. Risk Level used in this study

Parameter DistributionR1 R2 R3 R4 R5

Oil Normal Continuos 4% 10% 20% 37% 68%

Gas Normal Continuos 12% 23% 33% 58% 98%

Water Normal Continuos 40% 48% 59% 73% 94%

Tubing Head Pressure Uniform 24% 24% 24% 24% 24%

Standard Deviation for eachRisk Areas

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0%

5%

10%

15%

20%

25%

30%

0 0.5 1 1.5 2

Multiplying Factor

P r o b a b i l i t y

o f O c u r r e n c e

R5

R4

R3

R2

R1

1-sigmaR1

1-sigmaR5

11

Rσ −

51 Rσ −

0%

5%

10%

15%

20%

25%

30%

0 0.5 1 1.5 2

Multiplying Factor

P r o b a b i l i t y

o f O c u r r e n c e

R5

R4

R3

R2

R1

1-sigmaR1

1-sigmaR5

11

Rσ −

11

Rσ −

11

Rσ −

51 Rσ − 51 Rσ − 51 Rσ −

Figure 5 – Multiplying factor distribution for =1, =0.1, and for different risk levels. Notice the variation of one standard deviationbelow the average for R1 and R5.

Automated Workflow to Calculate Surface Equipment Requirements.

The 2S model involved the automated execution of the workflow for all the wells, reservoirs and scenarios of the MIAS SantaBarbara and Pirital Project:

The workflow involved the following tasks:

• Gather well production data from reservoir files

• Perturbate well profiles

• Assign optimal separator pressure level

• Calculate infrastructure requirements

Gather production data

Well production profiles are taken via remote passing of data and commands using the VBA environment, and they are loadedin several variant variables in VBA. For each well the following information had to be specified dynamically:

• Oil production rate (STB/D)

• Water production rate (STB/D)

• Gas Production rate (MMSCF/D)

• Well head pressure (psi)

• Well temperature pressure (psi)

• oAPI gravity

This information is stored for each well that exists in given reservoir simulation profiles and network configuration and on a particular scenario.

Separati on Pressure Level Assignment

The objective of this routine (step 4 in Figure 3) was to define a new separation pressure level for each well p and for eachtime step i. This was done to consider the effects of future well pressure level changes over the separation installed capacity(Figure 6).

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Start

For all p

From i = 2008 to 2027

THP i p

THP i

p> 2400 psi

THP i

p> 1100 psi

Separator Pressure

Level = High (1250 psi)

Separator Pressure Level =

Medium(550 psi)


Low (60 psi)

End

Yes

Yes No

No

Start

For all p

From i = 2008 to 2027

THP i p

THP i

p> 2400 psi

THP i

p> 1100 psi

Separator Pressure



Medium(550 psi)


Low (60 psi)

End

Start

For all pFor all p

From i = 2008 to 2027From i = 2008 to 2027

THP i pTHP i pTHP i p

THP i

p> 2400 psiTHP

i

p> 2400 psiTHP

i

pTHP

i

p> 2400 psi

THP i

p> 1100 psiTHP

i

p> 1100 psiTHP

i

pTHP

i

p> 1100 psi

Separator Pressure


Separator Pressure



Medium(550 psi)


Medium(550 psi)


Low (60 psi)


Low (60 psi)

End

Yes

Yes No

No

Figure 6– Separator Pressure Level Assigment Flow Diagram

Surface network model, included in 2S workflow, assumed that: (a) separator pressure changes are feasible at the field level,(b) separator capacity is limited and (c) well count is unlimited.

Separator pressure level assigment permitted the maximization of usage of available reservoir energy with time, minimizingenergy losses at the surface, hence minimizing environmental impact [Saputelli et al ., 2003; Litvak et al ., 2002, Rodriguez et all, 2007].

Calculate I nf rastructure Requir ements

Infrastructure requirements were calculated by combining resulting production profiles from each separator in the networkmodel and currently-installed base capacity in each Santa Barbara and Pirital flow station.

Dynamic Separator Capacity Modeling

Separator capacity at different pressure stages was determined as a function of equipment dimensions. A current capacityequal to zero was assigned those non-existing separation stages.

There were considered all future projects that are currently in the opportunities business plan (PDO), whether in engineering orexecution stages.

New Infrastructure Requirements Calculation

At every time step, all currently-installed base capacity in eachSanta Barbara and Pirital flow station was determined. Then, new requirements for each crude and gas processing equipment

were calculated as follows:

( ) nsnsliqnsns Capl Ql Ql Capl −=≤ Rethen,If ................. (4)

( ( ns sepns gas sepnsns Capg Qg Qg Capg −=< Rethen,If .......... (5)( ( nsdepnsdepdepnsns DepQg Qg Dep −=< Rethen,If .............. (6)

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Infrastructure Planning

Infrastructure adjustment to meet new and increasing capacity demand was planned to grow in modules. The followingassumptions were made:

• One additional first stage separator module can increase liquid handling capacity in 50000 STB/D in any flow station

• One additional first stage separator module can increase gas handling capacity in 150 MMSCF/D in any flow station

• One additional second stage separator module can increase gas handling capacity in 150 MMSCF/D in any flow stationInfrastructure adjustment could be calculated through the following equations:

5000

Reliqliq Mod = ............................................................. . (7)

20

Re gas gas Mod =

........................................................... . (8)

20

Redepdep Mod =

........................................................... . (9)

The required number of first stage separator modules is the higher number between liq Mod and gas Mod , at any time step.

Automated Work fl ow I terati ons

This workflow was repeated every time step (once per year, 2008 to 2027), for every of the 500 Montecarlo simulations, andfor every exploitation scenario (for a total of 8 scenarios)

Subsurface Surface Model Study Case

During Santa Barabara and Pirital MIAS Project, eight exploitation scenarios have been evaluated. Next, the most outstandingresults of the selected case study are presented.

Scenario 2 Profiles Production Analysis

This scenario considered following premises:

• It was considered production profiles coming from reservoir simulations models.

• Injection gas process starting in year 2012, when is considered a capacity increase of injection gas.

• 66 new wells to perforate from 2009 to 2017.

• Production distrubution in each flow station separation level was carried out using 2S Model, according to wellstubing head pressure obtained from reservoir simulation models.

Methodology

Probabilistics graphics coming from 2S Model were analyzed considering scenario 2 premises. These graphics (Figure 7,Figure 8 and figure 9) show oil, gas and water production profiles for each separation level in every Flow Station, besides,total field’s oil, gas and water production profiles. Probabilistic and deterministic results are presented. It Is important to know,that infrastructure analysis and design is done using P90 percentile.

Santa Barbara and Piri tal F ields Total Oi l, Gas and Water Pr oduction.

Santa Barbara and Pirital Fields maximum oil volume is 179 Mstb/d and it takes place in the year 2011. Maximum gas volumeis 1866 MMstcf/d and maximum water volume is 16 Mstb/d in year 2014 (Figure 7)

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Total Fields Oil Production

0

20000

40000

60000

80000

100000

120000

140000

160000

180000

200000

2008 2010 2012 2014 2016 2018 2020 2022 2024 2026

Years

S T B

/ D

P10 P50 P90 Deterministic

Total Fields Gas Production

0

200

400

600

800

1000

1200

1400

1600

18002000

2008 2010 2012 2014 2016 2018 2020 2022 2024 2026

Years

M M S C F / D


Total Fields Water Production

0

5000

10000

15000

20000

25000

30000

2008 2010 2012 2014 2016 2018 2020 2022 2024 2026

Yea rs

S T B / D


Total Fields Oil Production

0

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2008 2010 2012 2014 2016 2018 2020 2022 2024 2026

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/ D


Total Fields Gas Production

0

200

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2008 2010 2012 2014 2016 2018 2020 2022 2024 2026

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M M S C F / D


Total Fields Water Production

0

5000

10000

15000

20000

25000

30000

2008 2010 2012 2014 2016 2018 2020 2022 2024 2026

Yea rs

S T B / D


Figure 7. Santa Barbara and Pirital Fields Total Production

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Oil and Gas Handli ng in Surf ace Facili ties.

In this case study results are obtained for COA, COT and Santa Barbara Flow Stations. This paper will present only COAFlow stations results, segregation Santa Barabara 35 especifically. The same kind of results is obtained for the others flowstation, and gas flow lines. All the results are coming from the 2S Model presented in this articule.

COA Flow Station Results.

As showed in figure 8 and figure 9, COA flow station can manage oil production with current capacity, while high pressuregas production will overcome the capacity for the year 2009, this will require a system separation growth. Due to therequirement date the proposal is to make a new distribution flow in the flow station, allowing this surplus to flow to a highercapacity separation train.

COA SB-35 Segregation Production

0

10000

20000

30000

40000

50000

60000

2008 2010 2012 2014 2016 2018 2020 2022 2024 2026

Years

S T B / D

P90 P50 P10 DETERM Capacity

COA High Pressure Gas Rate

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Figure 8. Production Profiles Vs Facilities Separation Capacity. COA Flow Station, Santa Barbara 35 Segregation.

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Figure 9. Production Profiles Vs Facilities Depuration Gas Capacity. COA Flow Station

High and médium pressure depuration gas system can manage gas production with current capacity, while low pressuredepuration gas system requires an increase in 2009 (Figure 9). Due to requirement date, it is recommended as a short termsolution, to upgrade a currently out of service high pressure depurator to handle additional medium pressure gas production.

A summary of detected surface facilities requeriments using 2S Models are presented in table 3.

Table 3. Summary of Surface Facilities Requeriments

Year Requirement

2009-2017 239 flow line kilometers

2009 30 Mstb/d and 150 MMstcf/d extra capacity in hight separation level. COA Flow Station.

2009 10 Mstb/d and 100 MMstcf/d extra capacity in médium separation level. COA Flow Station.

2010 Field Manifold to handling 10 new wells. COT Flow Station. Mesa 30 segregation.

2014 Field Manifold to handling 10 new wells. COT Flow Station. Santa Barbara 35 segregation.

Conclusions

1. Subsurface Surface Model developed in this effort, allows predict infrastructure requirements for oil and gas handing.It is based on reservoirs behaviour, considering the error margin and the indentified reservoir risk areas as

uncertainties functions..

2. The 2S Model results are an input to economic evaluation that decides on the profitability of the projects associated toSanta Barbara and Pirital fields.

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3. The methodology developed has served as a platform for exhaustive evaluation of surface equipment sizing in the presence of uncertainties.

4. 2S Model enables a probabilistic and deterministic quantitative uncertainties and risk analysis.

Nomenclature

API = American Petroleum Institute P β = Well head pressure multiplier

q β = Well production multiplier

T β = Well head temperature multiplier

nsCapl =Current liquid capacity in first stage separation,

for each pressure level ns

nsCapg =Current gas capacity in first stage separation, foreach pressure level ns

ns Dep =Current gas capacity in second stage separation,for each pressure level ns

D = DayMIAS = Sustainable Integrated Asset Modeling

MSTB = Thousands Stock Tank BarrelsMMSCF = Millions Standard Cubic Feet

liq Mod =Additional first stage separator modules for liquid

handling

gas Mod =Additional first stage separator modules for gashandling

dep Mod =Additional second stage separator modules forgas handling

piq =

Perturbed production from variable i and well P ,

with i = oil, water or gas

p simq =

Simulated unperturbed production from variable i and well P , with i = oil, water or gas

p

iQ = liquid flow rate in STB/D

nsQl =Liquid rate in first stage separation, for each pressure level ns

sepnsQg =

Gas rate in first stage separation, for each pressure level ns

depnsQg =

Gas rate in second stage separation, for each pressure level ns

liqRe = New liquid capacity requirement in first stage

separation, for each pressure level ns

gasRe = New gas cap. requirement in 1st. stage separation

depRe = New gas cap. requirement in 2nd stage separation

R1,…R5 = Risk metric levels

σ = Standard Deviation piTHP = Perturbed well P head pressure for iteration i

simTHP =Simulated unperturbed well P head pressure foriteration i

piTHT = Perturbed well P head temperature for time step i

simTHT = Simulated well P head temperature for iteration i

COA = Amana Flow EstationCOT= Tejero Flow Station

2S = Subsurface and surface

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SI Metric Conversion FactorsBtu x 1.055 056 E+00 = kJDay x 8.64 E+04 = sºF(TºF-32/1.8) = ºCft x 3.0848 E-01 = mft2 x 9.290 340 E-02 = m2

ft3 x 2.831 685 E-03 = m

3

lb x 4.535 923 E-01 = Kg psi x 6.894 757 E+00 = kPa

AcknowledgementsAuthors would like to thank PDVSA for supporting this project and for permitting the publication of results.

References

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Saputelli, L.A., Mochizuki, S., Hutchins, L., Cramer, R., Anderson, M., Mueller, B., Escorcia, A., Harms, L., Sisk, C., Pennebaker, S., Han,

J., Brown, A., Kabir, C.S., Reese, R.D., Wood A., Núñez, G., Landgren K., McKie C., and Airlie C., “Promoting Real-TimeOptimization of Hydrocarbon Producing Systems”, SPE Paper No. 83978 presented at the 2003 SPE Europe Offshore TechnologyConference, 3-4 September

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