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Copyright 2007, Offshore Technology Conference

This paper was prepared for presentation at the 2007 Offshore Technology Conference held inHouston, Texas, U.S.A., 30 April–3 May 2007.

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Abstract

To increase the profitability and field production an IntegratedAsset Modeling project was initiated. A high fidelity, field-wide, Integrated Production Model (IPM), was developed forOil and Natural Gas Corporation’s (ONGC) giant offshore

field of Mumbai High. The network based IPM enablessuperior engineering analysis and more efficient decision-

making compared to classical single branch and nodal analysisapproaches. This is done by capturing, in the model, the effectof all the network interactions that occur in the productionnetwork.

Such a model helps to quickly identify and accurately quantify bottlenecks and other opportunities to improve production.Production improvements can be made by making daily or

weekly operational changes as in gas-lift-allocation, surface backpressures, pipeline lineup etc as flow conditions change inthe field. The IPM model would also assist in reliably

evaluating field operational and re-development plans, andthus expedite the engineering decision processes.

The project-plan also called for an Integrated Asset Model(IAM) by integrating the IPM to reservoir simulation modelsand to a suite of fit-for-purpose optimization tools. The IAMmodel will then enable model based field-wide futuredevelopment projection studies and planning scenario studiesover time. Network based optimization such as network-gas-

lift optimization can be easily done to find the optimumoperation set points that maximize production withoutviolating user defined operational and capacity constraints.

The model will eventually evolve into an online IAM. Here

the On-Line utilities will effortlessly update data from theapplication historian to the IAM with the latest or requesteddata set. The historian stores recurrent data from field-wide

SCADA-DCS systems and other sources.

This paper describes the development of the IPM for ONGC’sgiant offshore Mumbai High field and the technical and project management challenges faced. The IPM includes the production, water-injection, gas-lift supply and transportationnetworks. The integrated model also includes simplifiedmodels of processing facilities at each process complex.

Work to extend the IPM to an IAM is ongoing. Factors thatwere found to be critical to the success of the project areidentified and discussed in this paper. This will provide a better understanding and appreciation of the complex project

implementation issues and challenges that need to be resolved.

Introduction

ONGC’s Mumbai High field is located 160 Km W-NWoffshore from Mumbai in the Western Coastal shelf of India,with an aerial extent of 1200 sq km (Figure 1). The field wasdiscovered in 1974 and put on production in 1976; it iscurrently on a pressure maintenance scheme using waterinjection (since 1984). The field is currently in a decline phase

with problems like high water cut and high GOR (gas oilratio).

Mumbai High Field is divided into two major blocks, North

and South. The field is a multi-layered reservoir with severalexploitable zones of oil and some gas (Figure 2). At present oil

and gas is being produced through 5 processing complexes, 98unmanned wellhead platforms and 2900kms of pipelinenetwork. There are about 650 oil and 40 gas producing strings,and 200 water injection wells in Mumbai High Field. The fieldis divided into two parts; Mumbai High North and MumbaiHigh South (fig 3a and 3b) More than 80% of the oil

producers are on gas lift. Presently, around 250,000 bpd ofcrude oil and 15 MMSCMD of gas are produced from theMumbai High Field (Figure 4).

The bulk production from unmanned wellhead platforms is

transported to one of the 5 process complexes (BHS, SHP,

OTC-18678-PP

A Field-Wide Integrated Production Model and Asset Management System for theMumbai High FieldS.K. Moitra and Subhash Chand, Oil & Natural Gas Corporation Limited, India,

Santanu Barua, Deji Adenusi and Vikas Agrawal, Schlumberger Data & Consulting Services, India


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ICP, NQO and SCA) through collector lines for separation,conditioning and subsequent transport to shore. The bulkfluids received in the process complexes are separated in twostages, high pressure and low pressure, to separate crude oil,natural gas and water. The separated and conditioned crude oil

is then metered and exported to Uran onshore terminal throughtwo major trunk lines. The gas separated at each process

platform is dehydrated, compressed and sent to onshoreterminal at Uran. A part of the gas is used for internalconsumption as fuel gas and as recycle gas lift operations. Asmall quantity of low-pressure gas is flared for safe operation

of the field. Produced water is treated in the produced waterconditioner and then it is disposed off into the sea. For waterinjection, sea water is treated prior to injection.

Project Execution

The IAM Project implementations challenges are

multidimensional –(a) Data required to build the field-wide IPM resides in

different departments and further distributed in theadministration units formed by each processing platform.The data are not centralized and therefore not easily andefficiently accessible. The large size and associatedcomplexity that comes with a brown field development

and surface facilities only exacerbates the difficulties.(b) Integration of surface and sub-surface models implies that

data need to be collected from both these groups.(c) Most importantly, the introduction of the On-Line IAM

into the organization implies alignment of newtechnologies, work processes, workflows with people

within the organization to form a more efficientengineering organization. Managing this change over the

transition period is a daunting but resolvable challenge.

For success of the project, it is essential to have a proper project plan, provide sufficient strategic attention and planningtime, emphasis on coordination with relevant groups - office

and field engineers, operators and managers. Even though themodeling environment will reside in the office, the fieldsupport for such a model is critical to successfully realize thetrue potential of the Integrated Asset Model (IAM). The fieldis where the data collection begins and their sustained duediligence and trust in office based engineering decisions iscritical to the success of the project over time. It is easier to

gain sustained conscientious field support for data collectionand reporting if these groups also derive appreciable tangible benefits from the project. Potential derivable benefits that can be motivating are(a) Improved field work processes and workflows to maketheir routine data collection and reporting work easier and less

tedious and risky.(b) Provide engineering operational directives/guidance thatare accurate, timely and reliable based on modeling resultsfrom the model.(c) In case there is a need for modeling in the field-sites, makethe model easily available to the appropriately trained field personnel for them to use.

It is critical that the model data collection efforts do not

become an additional burden to already busy and hecticschedule in the field.

The project to build this modeling system was divided intothree distinct phases.

•Phase 1: Offline Integrated Production Model for the entire

MH field (MH-IPM). With the IPM, it would be possible torun modeling scenarios which can vary in scope -- field-wide

or processing-platform-wide or production-platform-wide or asingle well.

•Phase 2: The IPM will be enhanced with optimizationfunctionality and made integration ready for coupling toreservoir model(s) (ECLIPSE and/or tank models). With thisoffline Integrated Asset Model (IAM), it would be possible to

optimize production, improve production operations decisionmaking and predict future scenarios to help in field

redevelopment plans.•Phase 3: The Offline IAM model will be positioned to run inan On-line environment. Here, the latest recurrent data for the

model will be accessible via the computer network and themodel will be automatically updated with the required data.An ever-green model of the field would then be available on-

demand, boosting modeling efficiency, engineering study turnaround times and planning decision cycles several fold.

Extensive training and work association periods and fieldvisits were included in the project execution plan. The purposewas to start training the ONGC engineers across various

groups so that they familiarize and begin taking ownership ofthe models, as the models become available. This enables theengineers to become involved and begin using the modelsearly and benefit from early model results as the project progresses. Field visits helped in getting a clear understandingof the current field work processes and drivers and to betterunderstand the work realities in the field. This helped inidentifying opportunities for stream-lining and standardizing

the data collection processes better. The IAM project’s foot- print on the current field work processes should be minimizedand if possible improved and standardized field-wide. Directtangible benefits need to be realized early during the IAMimplementation to effectively build the necessary supportgroups of well co-coordinated, knowledgeable and motivated

IAM stakeholders throughout the organization before projectimplementation phase comes to a close.

The Integrated Production Model

The Integrated Production Model (IPM) of Mumbai High field

is a comprehensive, high fidelity, rigorous multi-phase flow blackoil model. The model includes wells, production network

and facilities, gas-lift delivery network, water injection systemand oil & gas transportation network which connects to the on-shore terminal at Uran. The entire IPM was developed usingwell and network modeling software PIPESIM.

The Mumbai-High field contains 5 processing complexes.

Each complex receives the production from wells connected toit and is under its administration and management. Theseparated gas is then compressed. Part of the compressed gas


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is used for gas-lift, as fuel in the offshore process-complexutilities and the remainder is transported to shore forconsumption.

Regular engineering efforts have been ongoing to maximize

production by optimizing gas lift on a well by well basis. Mostof the time, results of the recommendations made on single-

well analysis basis did not deliver the gains as expected, at the process complex level. This shortfall, which is due to theinteraction effects of adjacent wells and pipelines, could not beanalyzed in the absence of an integrated production network

model. This is a key issue in gas lifted fields, where the gas liftgas is returned along with the produced fluids to the central production facilities, adding to the ‘back pressure’ on thewells. This aspect of the behavior is naturally captured in anetwork model

The Integrated Production Model for Mumbai High was

developed in an organized and phased manner so that the hugeamount of data and modeling work involved could be

managed efficiently and smoothly. Major phases were:

1. Data Collection and Validation2. PVT Data Calibration3. Well Model Construction and Calibration4. Network Model construction (Production, Gas lift

and Water Injection)5. Network and Well Model Integration.6. History matching of the Model at different levels.

Data Collection and Validation

Since the field is structurally and otherwise a dynamic

environment it was essential that the model building andvalidation should be done by matching model results to acertain cut-off date instead of trying to match a moving target.Additionally model building exercise for such a big brownfield is time consuming and complex. Therefore, it was

decided that the model would first be made as per theconditions existing on target date to verify the model accuracyand validate the model. Subsequently the model will beupdated to current conditions during the follow-up workassociation period after Phase 1 is completed.

Data gathering is the first and foremost requirement of a

model building effort. For the physical skeletons of the wellmodels a massive data collection and data storage effort wasrequired for the 800+ wells. In order to ensure speed andefficiency on the data gathering process, a detailed list of datarequirements was prepared upfront. A networking workshopamong various disciplines and groups was organized to ensure

clear understanding of data requirements, links and noduplication. The data was manually collected from varioususer groups within ONGC. Considerable project time andeffort was spent in interfacing with the respective groups, persistently searching for required model building data. Themajor types of data collected included

o Production and Injection Plan as of cut-off targetdate,

o Well Location Maps,o Pressure Study,o PVT data and reports,o Flow Gradient Surveys (Pressure/ Temperature

surveys),o Latest Completions Diagrams, downhole equipment

details, deviation Survey of each well,o Production test history for each well,o Field Layout Diagrams and Pipeline Details, Process

Flow Schemes etc.

Before using the gathered data for modeling purposes, variousconsistency checks were applied on each data set to confirmthe quality and accuracy of the data gathered. For missing/inconsistent data engineering judgments and indirect methods

were used to generate the data.

PVT Data Calibration

Before well modeling work started, calibration of the fluid

properties was done by the software using laboratory PVTdata available for three major reservoirs viz. L-III South, L-III North and L-II North. A multi-point calibration for all the

fluid properties was carried out for the three PVT Datasets andfollowing correlations were found to be the best match.

• Lasater, for Solution gas

• Standing, for Oil FVF

• Beggs & Robinson, for Oil viscosity

• Lee et Al, for Gas viscosity

Since, multipoint calibration data was available for each of thethree reservoirs, above mentioned correlations were calibrated

to match the available PVT data using calibration coefficients.(Fig 5)

This step is a critical step because errors in PVT modelingaffect the multi-phase flow modeling results significantly and

can ultimately affect the fidelity of the model itself.

Well Model Construction and Calibration

To develop an accurate high fidelity model of the productionnetwork, it is essential to model the wells correctly because it

is the relative well performance that largely determines thenetwork performance. In order to achieve higher degree of

accuracy, it was necessary to properly model, analyze andcalibrate the well performance at each wellhead before thewell is integrated to the network model. This was done byconstructing a valid stand-alone well model for each well.

Dual string wells were considered as two separate wells for the purpose of modeling.

Well model building task was carried out in two phasesimplemented in a ‘relay’ workflow: (a) Well model skeletonconstruction and (b) calibration of the model using available pressure/temperature surveys and well tests and productionhistory.

Well Model Skeleton Construction: During the first phase,


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basic skeleton of each well was developed. It was necessary tocarry out this separately because this involves carefulhandling, scrutiny and validation of voluminous static datathat goes into making the well skeleton. Major data requiredfor this phase were reservoir description, PVT correlations,

well completion details, well deviation survey, downholeequipment details, Choke etc.

At the completion, the reservoir inflow performancerelationship (IPR) was based on PI equations, using Vogelcorrections for flow below the bubble-point pressure.

Deviation survey data was used to define well trajectory in themodel. Flow in the well was modeled starting with thereference point of flow entry in the well bore. The reference point was taken as mid-perforation depth in well bore withsingle/multiple perforation sets. In case of multilaterals, thereference point was taken at the average of true vertical depthsof the different laterals. The horizontal well completions

consisted of completed cased sections followed byuncompleted, unsupported open-hole perforated liner sections.

The reference point in such wells were at the end of thecompleted section since the uncompleted sections wereinaccessible and therefore their conditions over time wasunknowable.

Downhole equipment modeling data include SSSV details(Bean ID), ESP details (pump type, frequency of operationetc), Gas Lift Valves details (Type, Model, Manufacturer, Portsize, PTRO values), and Gas Injection Point details (Valve port diameter). The gas injection in the wells is modeled with parameters like surface injection rate, operating surface

injection pressure, specific gravity and surface injectiontemperature of the injected gas. Tubular were configured using

the latest available completion diagram. Internal diameter andwall thickness data are defined as tubular specifications. Flowtype in the different sections is selected astubing/annular/combination flow, as applicable. Once theskeleton of the model was completed, it was passed on to be

calibrated during second phase as described below.

Well Model Calibration: In the second phase, calibration ofthe well modeled was done using the Well-test and productionhistory and pressure / temperature gradient surveys.

To preserve the inherent fidelity of the model, the proper

choice of calibration parameters is important. If an acceptablematch can be obtained by tuning the most uncertain input datato the model, and these parameters are tuned within reasonable physical ranges, the inherent fidelity of the model can be preserved. For example, tuning pipe roughness or overall heattransfer coefficient to get a better pressure drop and heat

transfer match is a fidelity preserving calibration option.Tuning pipe diameter for situations where there is directevidence of diameter reduction due to scaling and/or waxdeposition will also preserve the model fidelity.

If a sufficiently close match cannot be obtained after this step, black box calibration parameters such as efficiency and other

blanket correction factors may have to be used. Care should betaken in the use of such parameters as it can lead to significant

loss in model fidelity if used unscrupulously. This canseriously impact accuracy of future predictions as modelconditions change.

In this case, no black-box type tuning parameters were used asan adequate match was obtained, which commensurate withthe data quality and quantity available. During the

identification of the most appropriate multi-phase flowcorrelation model, special attention was paid to account for the

effect of flow angle in a pipe on the pressure drop predictions.Since most of the wells were highly deviated, standard and

well respected vertical flow correlations failed to model thewells adequately. From past modeling experience, it has beenexperimentally observed and field verified that flow structureand associated flow physics changes significantly as the flow

angle changes from vertical to deviated in the vicinity of 70degrees flow angle from horizontal, other factors remaining

the same. When this was accounted for when choosing acombination of correlations for a well, based on deviationangle, a significant improvement was observed in the model’s

predictions capability for the deviated wells in the field whenmatched with measured Flow-Gradient-Survey (FGS) data.More specifically the Modified Hagedorn-Brown (Dun & Ros

Map) model or Ansari vertical upward flow correlation incombination with the Beggs & Brill model or the semi-mechanistic Mukherjee-Brill model for inclined upward flowwas found to match the FGS data very well.

During this phase, if adequate FGS data was not available, a

detailed gas lift diagnostics was carried out by the software toidentify the point of gas injection in the string. This alsohelped in identifying a number of wells producing sub-optimally due to sub-optimal performance of gas lift system inthe well. Where adequate FGS data was available point(s) ofinjection could easily be identified by inspecting plotted FGS

data. For each gas lifted well, two different well models weredeveloped. The first well model was configured for doing

detailed gas-lift analysis, design, diagnostics and trouble-shooting. The second model was derived from the first modeland was configured to inject gas through a specified pointonly. This well model was matched to well test productiondata for verification and then integrated to the IPM networkmodel. From modeling point of view, the flow mechanics and

pressure profile inside the tubing remains the same as the firstwell model as long as point of injection and gas injection rateremains unchanged.

Finally, well models (oil and gas producers; water injectors)were calibrated to the latest available measured production test

data, including lift gas injection rate and flowing wellhead pressure, available as of cut-off date. Well performance wasevaluated using the system analysis approach, on the production tests available over the last one year. For example:for each production well test data acquired over last one year,a combination of PI and reservoir pressure was generated for

respective liquid production rate, water cut, formation GOR,gas lift injection rate and pressure (Figure 6). Assuming,

reservoir pressure relatively constant over last one year,variation of PI of the well over time was analyzed to get betterunderstanding of well performance. A confidence level on the


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fidelity and accuracy of each well model was assigned bytaking into account the following factors:

• Quality of match of reservoir pressure vs. PI of thewell using the production test data acquired over thelast one year.

• Quality / consistency of the production data available.

• Gas-lift performance evaluation.

A flowchart illustrating the well model building and

calibration process is presented in figure 7

When reservoir pressure and IPR was unavailable past production test data sets were used to generate flowing bottom-hole pressure versus flow rate trends. A non-linearExcel Optimizer was then used to fit a Vogel corrected PIcurve through the selected points – essentially a constrained

auto-regression curve fit. The curve fit yielded a reservoir pressure and a PI consistent with measured data. (See figure 8)

Network Model

The Mumbai-High field contains 5 processing complexes.

Each complex receives the production from wells connected toit and is under its administration and management. Gas-lift gasfrom a given processing complex may be compressed anddelivered into wells belonging to another process complex.Similarly, the water injection may cross Process complex boundaries to wells under the purview of another Processingcomplex. To preserve the flow and administrative structure in

the model and to simplify model navigation to various parts ofthe model, the network layout was logically organized usingPIPESIM’s folder option. The IPM model was built using thefollowing six layers architecture (from the top to the bottom).This is illustrated in figure-9 and 10.

• Layer 1: Interchangers Layer containing the Mumbai-High production system and the water injection system

with platform-crossing fluid exchanges.

• Layer 2: Trunk lines Layer with inter platformconnections and the transportation network.

• Layer 3: Process Complex Layer containing the production-separation processing -gas-lift networkinterfaces.

• Layer 4: Flow lines Network Layer containing flow linesconnecting to various wellhead platforms within a process

complex.• Layer 5: Wellhead Platform Layer containing various

wells in a wellhead platform.

• Layer 6: Well Layer containing the detailed well model.

The separators at the process complex were modeled as a node

separator in IPM. A node separator enables the model toseparate the gas or water stream and re-inject it at a

downstream network location and continue to model the flowof the separated streams. This technique is used toconveniently integrate the multi-phase production network, thegas distribution and transportation networks and the oil

transportation networks. (See figure 10c)

Boundary condition considerations: Care must be taken toensure that each of these sub-network problems is well posedin order to have a solution. The boundary node sinks (well

reservoir sinks) in the water injection network model werespecified as fixed pressure sinks since the reservoir pressure

and Injectivity (IPR) is a model given. For the producing wellsource boundary nodes, the local reservoir pressure was

specified. The back pressure to the production networks werethe processing complex first stage separator pressures.

The gas flow networks consisted of two main parts.

(a) The gas-lift distribution network where the boundary sinknodes represent the well-head-platform gas-injection manifoldwhere a specified gas flow rate was delivered. Here the modelwill calculate the manifold pressure setting required for thespecified flow rate.

(b)In the gas sales network portion, where the sink is at theonshore terminal at Uran, the delivery pressure is specified inthe model.

The Integrated Network model that was constructed, includedall the wells, well head platforms, connecting flow lines

(active/ inactive), risers, down comers, and major processequipments such as separators, compressors, heaters, pumps

etc. To predict the pressure and temperature losses in all themultiphase flow lines, Beggs and Brill correlation was used. Itwas found to be sufficiently good match when compared to thelimited observed pressure drop data available. Since snap-shot

surface flow line measured flow rate and pressure data wasunavailable at this time, further calibration efforts were not

expended at this stage. Similarly moody correlation was usedfor single phase flow lines. Hagedorn-brown revised

correlation was used for vertical riser flow. Further tuning ofthe models will be justified in the future when the necessary

measured data is expected to become available more readily.

Network and Well Model Integration

Once all the well models were calibrated, they were importedinto the network model within each well head platform, providing a seamless integration between well and networkmodels. This makes the sixth layer of the layered architecture

of this model. Both the well models and surface networkmodels are solved simultaneously as an integrated network

model when solved at the IPM level. Since, each well isallowed to have its own set of PVT model, multiphasecorrelation and detailed completion details, results obtainedare more accurate when the model is solved at the network

level.

History Matching of the Model at different levels

History Matching is the process of getting a model to producePressure, Temperature and rate values in the model that match

measured values at corresponding specific points in the field ata given time. The idea is to make the model behave in the

same way that the field normally behaves, so that the field andmodel should respond similarly to specific changes. This isdone by running the network model subject to specific


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constraints. As PIPESIM models are steady state, therefore theinput data should ideally be from a system in steady-state production. It is virtually impossible to get the snap-shotsteady state data manually for such a large and ever-changingsystem, so trended data, individual branch data and

engineering judgment was used to match the IPM surfacecomponents of the model.

History matching is very critical for this type of study andrequires careful analysis of the different aspects of behavior ofeach object in the model, viz.: well performance, pressure drop

across pipes and flow lines, heat loss along main pipelines,compressors performance, etc. For matching/calibration purposes, the entire model was looked at, at the processcomplex level and wellhead platform level. All the five process complexes, gas lift network and water injectionnetwork were matched to the production / injection dataavailable on the cut-off date. The predicted production

network flow rates with backpressure set at separators in the process complexes matched well with the measured data (see

Table 1). Additional pressure and flow rate snapshot data atdifferent points in the network was unavailable for furthertuning of the model at this time.

Due to inadequate data and the inherent dynamic behavior of

the Gas lift distribution system as indicated by the availabledata no further calibration effort was expended at this time. Nofurther calibration effort was expended at this time for thewater injection system due to unavailability of sufficientadequate data. This was expected since available manifold pressure data available was not snap shot data but distributed

over a long time period making the data inconsistent andunsuitable for history matching purposes. Also, due to the

dynamic flow conditions in the gas-lift delivery networkindicated by the data and the production model’s severeslugging analysis, it was futile to try and match the steady-state flow based gas-injection network model results to whatappears to be dynamic field data.

Note that in general, the network models for single phase fluidare field proven and sufficiently accurate and robust with littlecalibration if the data input into the model is correct. So themodel can be used to predict scenarios with good accuracy andreliability. It was decided to further refine and calibrate the gasand water injection network model at a later stage when

snapshot data at steady-state operations become available inthe future.

Major Findings from IPM

During the course of modeling building and validation, a

number of production issues were identified. They may be broadly categorized into (See Table 2):

• Production optimization opportunities for gas-liftwells.

• High back-pressure (bottlenecks)

• Severe Slugging locations at Riser bases.

• Wells with unusually low Productivity (Injectivity)Index, which requires further investigation

• Gas-lifted wells experiencing multiple point injection

• Erosion risks (tubular integrity issues)

The IPM is being used by ONGC to study and analyze

alternate pipe lineups and flow connectivity’s, resizing pipelines and redesigning dysfunctional gas-lift well

installations.

Training and Work Association:

On completion of the model building and calibration phase the

model was delivered by Schlumberger, the technology provider, and made accessible to ONGC engineers. A

comprehensive training on multi-phase flow and networkmodeling using the modeling software was conducted soonafter final model and modeling report delivery.

Subsequent Phases and Challenges

Consistent with the production architecture of the field, thefield management and administration of the asset is organized

around the process complexes. Field engineers and managersare assigned tasks and responsibilities based on the processingcomplex they belong to. Field engineers within a group may belong to surface or subsurface. Inter-complex issues are

handled through group meetings. Office based field-wideengineers and managers are responsible for addressing asset

wide issues. ONGC is in the process of forming a dedicatedIAM modeling group who will be responsible for the model’supkeep and use. They will be responsible for maintaining anever-green model of the field production system which will be

made available on demand.

In the next phase (Phase 2), optimization functionality will beadded to the IPM network model. The primary optimizationtarget is gas-lift optimization where optimum and feasible gasallocation is achieved based on the compressor capacities, production network and gas-delivery network behavior.Operational constraints will be specifiable. Optimizationstudies can be done at various levels – individual well level,wellhead platform level, process complex level and field wide;

based on the need, using fit-for-purpose optimization toolsusing linear-programming, non-linear programming and Neural-network methods. Additionally, the model will be

made reservoir integration ready in an Integrated AssetModeling environment (AVOCET) so that ONGC will be ableto model and generate results for future planning and

development planning studies and analysis.

The sheer scale of the model poses a model maintenance(model updating) challenge when done manually. For exampleapprox 10 wells are single-point production tested daily inMumbai High. So the well test data, on an average, is repeated

every three months. In addition, well work over and re-drillingcontinue as per ONGC’s drilling plans.

To address this high volume updating data issue the Integrated

Asset Model for Mumbai High field will evolve into a On-


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Line model during Phase 3 of the project where data updateand model maintenance are largely automated. This is done bystoring measured and entered data in Historians andautomatically feeding the model with the requested data ondemand. The field is currently in the middle of implementing a

field-wide SCADA system for data acquisition and monitoringand storing. Data collected by the enterprise-wide SCADA

system and individual DCS systems and by other means madeaccessible in the wide-area-computer network. The culled datawill be accessed by the model and this data will be used toseamlessly update the IAM model. The model will be current

automatically, and ready for simulation and analysis.

Challenges: Phase 2 and phase 3 project activities have begun. Several issues relating to modeling specifics for these phases require further discussion, analysis and finalclarification between ONGC and the technology provider.Discussions and clarification activities have started for the

integration of Reservoir simulation models with the IPMmodel.

Design of the data and data base model and the on-linemodeling architecture is currently being designed andevaluated. Well established standard commercial software isavailable to build a historian and an automated updating

system.

Current workflows and processes vary, between processing platforms. There is error prone duplication of effort forrecording and storing data. Greater interaction with productionengineers, field engineers and reservoir engineers have been

initiated and is on-going at this time to design better andstandardized workflows and processes prior to the Phase 3

implementation of the On-line IAM model. It is imperativethat the new work flows are properly aligned with the taskrequirements and does not pose an additional burden on thefield engineers and operators.

As typical for any Brownfield, the greatest challenge is to getONGC’s engineering and management organization toefficiently move to a more modern and efficient productionmodeling and analysis workflow environment and adapt to anenvironment where all the relevant groups will have access toall the models and data. When completed the IAM modelingenvironment will enable all the related and interested groups

to work well together and be on the same page. A review ofsuch real-time optimization projects worldwide1 indicates thatcurrent work processes for engineering analysis takes 80% ofthe time to find and collect data and build models (non- productive time) and only 20% is used for actual engineeringsimulation and analysis (productive time). With a successful

online system the ratios are expected to reverse with 20% timespent on model building and data collection efforts and 80%will be available for simulation and engineering analysis. Ineffect it is expected to boost engineering productivity.

To effect and generate support for such a wide rangingchange, efforts are underway today, to familiarize both

management and engineering with where the project is todayand where it is headed tomorrow and in the future and how it

fits in with the organization’s overall engineering andmanagement vision. The transition cannot be imposedsuddenly but has to be done gradually over a transition period.

The expectation is, an early start would make the ONGCMumbai organization ready to welcome these changes by thetime the Phase 3 of the project delivery comes to a close.

Conclusions:The following conclusions can be drawn from our experience

to date in implementing a large scale field wide IntegratedAsset Modeling project

1. Collecting data for a giant offshore brown-field and building a 800+ well high fidelity network model can be successfully done with current technology.

2. Calibration of the well models where relatively moredata (test data) was available (compared to surfacelines) is critical to building a accurate high fidelity

model.3. Such field-wide Real-time modeling projects require

changes in an organization’s workflows and

processes. Ensure these changes are not burdensome.If possible the changes should be designed to makethings more rational and easier to build sustainable

field support.4. Extra care and planning need to be expended to

address people, process and workflow issues to buildthe company-wide support in order to keep the project viable, even during the implementation phases.

5. To insure the long term viability of the project anduse of the model, the model is dependent on the

sustained support and due diligence from severalallied groups in the organization. The model must

visibly show tangible benefits to all these groupshowever small to have a naturally sustainable

commitment to support, maintain and manage themodel into the future.

Acknowledgments

The Authors wish to thank the management of Mumbai HighAsset, Oil and Natural Gas Corporation Ltd (ONGCL) andSchlumberger for their encouragement and support during project execution. We would also like to thank ONGC and

Schlumberger for permission to publish this paper.

A special thanks to the IAM project engineers inSchlumberger and the ONGCL staff, from Mumbai High.They provided extensive help in data collection, model building and simulation.

References

1. Mochizuki, S., Saputelli, L.A., Kabir, C.S., Cramer,R., Lochmann, M.J., Reese, R.D., Harms, L.K., Sisk,C.D.,Hite, J.R., Escorcia, A., Real-TimeOptimization: Classification and Assessment,

November 2006, SPE Production & Operations,Volume 21, Number 4


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OTC OTC-18678-PP 9

WELL LOC TION M P MUMBAI HIGH SOUTH

ExploratoryWell

Injector

Platform

Producer

ReleasedWell

Proposed Producer

Proposed Injector

GasWell

WA

IU

SB

NL

WI6

WI7

IQ

NM

IA

IB

WI12

WI11

WI10

WI9

WI8

ID

SR

IC

SD

SM

SP

SE

SV

ST

IE

IS

SJ

WB

SA

IG

SN

IF

SI

SC

IP

SK

S1-5

EBSQ

EA

SUII

SF

SS

IH

S1-6

S1-4

EDSG

IJ

SH

EE

IK

SW

IT

SY

IW

IL

IN

IM

EC

ZA

BH-45

BH-24

BH-26

BH-21

BH-12

BH-17

BH-7

BH-35

BH-36

BH-6

BH-64

BH-37

BH-61

BH-10

BH-16

BH-5

BH-52

BH-11

BH-13

BH-23

ZB

RS8

RS7RS11

RS5

RS2RS1

RS3

RS4

Figure-3b: Mumbai High South Well Location Map

Figure-4: Mumbai High Field layout


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Reservoir Pressure Estimation (Well : XX)

0.00

500.00

1000.00

1500.00

2000.00

0. 500. 1000. 1500. 2000. 2500. 3000. 3500. 400

Liquid rate

F l o w

i n g B o t t o m h o l e P r e s s u r e

Test Data

Calculated data

Figure-6: Reservoir pressure vs PI chart

Figure-7: Well Model building and calibration Workflow Figure-8: Reservoir pressure estimation using Non-linear excel optimizer

Figure-5: Multi-point PVT Calibration

Figure-9: IPM Model layers Structure Fig 10a: Layer 1: Exchangers Layer (IPM- MH)


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Fig 10b: Layer 2: Trunk lines Layer (IPM- MH) Fig 10c: Layer 3: Process Complex Layer (IPM- MH)

Fig 10d: Layer 4: Flow line Network Layer (IPM- MH)

Fig 10e: Layer 5: Wellhead Platform Layer (IPM- MH) Fig 10e: Layer 6: Well Layer (IPM- MH)


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200705 Gas Lift Opt Otc

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