SPE-196867-MS

Integrated Asset Modelling and Automated Workflows from Pre-Productionto Full Field Development Stage for a Giant Green Field West Qurna-2

Liliya Kunakbayeva, LUKOIL Mid-East Limited; Denis Gauder, IPCOS

Copyright 2019, Society of Petroleum Engineers

This paper was prepared for presentation at the SPE Russian Petroleum Technology Conference held in Moscow, Russia, 22 – 24 October 2019.

This paper was selected for presentation by an SPE program committee following review of information contained in an abstract submitted by the author(s). Contentsof the paper have not been reviewed by the Society of Petroleum Engineers and are subject to correction by the author(s). The material does not necessarily reflectany position of the Society of Petroleum Engineers, its officers, or members. Electronic reproduction, distribution, or storage of any part of this paper without the writtenconsent 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 maynot be copied. The abstract must contain conspicuous acknowledgment of SPE copyright.

AbstractThe West Qurna-2 field is one of the biggest undeveloped oil fields in Iraq. The development of the field ishandled by the Iraqi Basra Oil Company and a consortium between LUKOIL and the National Iraqi NorthOil Company. The production of the field started in 2014. This paper describes how the integrated assetmodel (IAM) and automated workflows have been used to support the field's development from first oilto full field development.

Along the field's development stages, the IAM system has assisted LUKOIL in its operations.Its availability for first oil allowed refining the assumptions made about the productivity of the wells and

reservoir characteristics for issuing realistic production profiles, hence playing a crucial role in achievingthe production's targets.

Subsequently, the wells have been gradually converted to artificial lift (electrical submersible pumps)and the IAM system has allowed for extension of the natural flowing mode of the wells, optimization ofworkover operations, short term optimization of the ESP operations and extension of their lifetime.

Today, the IAM system allows for mitigating the water handling constraints through tailor-made field-wide optimization algorithms. The latter optimizes not only the field operations through integrated assetplanning but also the water production and injection system continuously.

Overall, the IAM system has proven to be instrumental for the development and optimisation of WestQurna II. It has allowed to de-risk the initial production, optimize the workovers in the field, enhance the ESPperformance and lifetime, optimize the pressure maintenance systems, etc. It has also provided a platformto empower multi-disciplinary teams.

The saving figures presented in this paper alone amount to around 7 million dollars in 2015-2016 andit is not unreasonable to ultimately expect total savings of tens of million dollars. Such savings make theimplementation of IAM systems very attractive for similar green field operations.

LUKOIL has made critical choices for ensuring the success of the initiative, such as allowing forcustomization and flexibility of its IAM system, putting the necessary efforts in developing and maintainingaccurate hydraulic models and starting simple and keeping focusing on the continuous and organicdevelopment and improvement of its IAM system.

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This paper demonstrates how IAM systems can be practically used for optimization activities in a greenfield, and the associated direct benefits. It also details how to maintain such systems continuously up todate for a field in development phase, where the layout is continuously changing. A series of best practicesare identified for ensuring successful embedment of these advanced approaches within the day-to-dayoperations.

West Qurna II introductionThe West Qurna-2 field was discovered by Basra Petroleum Company in August, 1973 following seismicoperations. In terms of OIP, West Qurna-2 is one of the biggest undeveloped oil fields in Iraq. Thefield's initial recoverable reserves come to around 14 billion barrels. More than 90% of the reserves areconcentrated in Mishrif and Yamama accumulations.

On December 12th, 2009, a consortium of LUKOIL and Norway's Statoil won a tender for thedevelopment of the West Qurna-2 field. The current participants of the project are the Iraqi Basra OilCompany (on behalf of the state) and a consortium of contractors including LUKOIL (75%) and the NationalIraqi North Oil Company (25%).

First Oil production was commenced on March 29, 2014. The first development stage, Early Oil Mishrif,with production of 120 MBPD was achieved in June 2014.

Current well stock comprises of 8 production wellpads, 121 producing wells (45 naturally flowing, 70ESPs and 6 wells NF via ESP), 23 water injection wells and 13 water source wells with oil production rateof approximately 400 MBPD. 49 Wells are equipped with pressure downhole gauges.

Objectives and components of the IAM systemFor supporting its operations in West Qurna II, LUKOIL has implemented a state-of-the art digital MESsystem. Figure 1 below shows the different components of this system.

Figure 1—IAM system components

While all these systems are tightly integrated, this paper focuses on the IAM system.The IAM system leverages the hydraulic models representing the reservoir, wells and facilities by

bringing them to an "on-line" environment. Data is automatically acquired, aggregated and cleansed fromthe data historian systems, hydrocarbon accounting systems, laboratory information systems and geologicalinformation management systems. This data is subsequently fed to the hydraulic models. A sequence oftechnical calculations is orchestrated by the IAM system and the output of the models is stored and exposedback to third party systems. This whole sequence is referred to as a "workflow". Some of the workflows

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are fully automated (e.g. virtual metering), and some of the workflow are semi-automated, which meansthat the engineers or field personnel action or validation is required as part of the workflow sequence (e.g.well test validation).

The IAM system provides benefits at all stages of field development. LUKOIL has divided the fielddevelopment plan in several phases represented in Figure 2 below.

Figure 2—Field development stages

Before production started, it was essential to establish best practices to avoid the development of bespoke,unmaintainable engineering solutions and to align all stakeholders around a single production platform. Thereadiness of the IAM system for first oil ensured that the hydraulic models were available immediately,leading to increased accuracy of the production predictions.

During early production, the main objectives of the IAM system were to validate the assumptions usedduring the FEED phase. With clearly stated production targets in the operating contract, gaining confidenceabout the ability to meet these targets was key. The availability of the system for first oil also ensured thatbest practices were applied from the start, avoiding the development of individual custom solutions andwork processes.

When reaching the first production targets, the IAM system objectives shifted to validation of productionfigures and production optimization. West Qurna II facilities enable extremely frequent production welltests (up to 100 well tests per day). These well tests constitute the basis for production accounting. Whilethe sheer amount of well tests ensures great visibility on the actual production, the management of thisinformation load is challenging. The IAM system has been designed to automatically qualify the well tests,drawing the attention of the engineers to exceptions and unexpected behavior. The production optimization

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needs to account for legal constraints, such a limited drawdown pressure, and focuses not only on liquidproduction maximization, but also on limiting the water production, itself constrained by surface facilities.

During the following field development phases, electrical submersible pumps (ESP) have been activated,and the water injection campaign started, again adding specific objectives to the IAM system. The well testvalidation approach has been extended to the water injectors, and advanced ESP surveillance workflowshave been put in place to ensure safe operations and maximum lifetime of the ESP's. Several workflows havebeen developed to extend the natural flowing period of the wells by accurately monitoring that operatingconditions remain within a defined operating envelope. The IAM system predicts the exact point in timewhen the wells will stop flowing naturally, allowing for optimization of the workover rig scheduling.

At the moment, LUKOIL is pursuing an ever more ambitious production target of 480 kBOPD through thefull development of the Mishrif formation and pilots in the Yamama formation (phase II). With the increasedproduction, the water handling constraints become even more important. The ESP operations also becomeincreasingly important because of the reservoir depletion and constraints on drawdown pressure. Foraddressing these challenges, optimization of existing wells through workovers and new ESP installationswill be required. This leads to the development of specific dashboards mixing completion information,long term production figures, drawdown pressures, ESP performance parameters, production events and aclear representation of network routing options. These dashboards allow engineers from multiple disciplinesto assess the performance of individual wells through a single platform, with all relevant informationintegrated. As a result, the decision for well intervention is faster, more accurate and can be planned furtherin advance.

Along the way, the ability of the platform to scale up has been put to the test. During the initial productionphase, the system needed to handle only 12 producing wells. There are currently more than 150 wells inoperation (producers and injectors) and counting, and therefore the system had to allow easy addition of newpieces of equipment and new models. The moving objectives as a function of the stage of field developmentalso put the flexibility of the system to the test, requiring specific workflows to be developed quickly.

The main achievements

Before first oil: Implement best practices from the startThe development of the IAM system and the associated hydraulic models has started over a year before thefirst oil, with the clear objective of being operational from the start. As detailed before, validation of thefeasibility of agreed production levels was a major objective, but not the only one. Providing an industrialplatform to the engineers and field personnel from the start aimed at avoiding the over development ofcustom, often Excel-based, solutions for handling processes which require transparency and maintainability.

While these two objectives have clearly been met, there are also many challenges associated to thatapproach.

First, the hydraulic models cannot be fully accurate in absence of actual field data. These models arenevertheless crucial for testing the operation of the IAM platform before first oil, as well as actual data.

Second, there is uncertainty about the exact scope of work required by the operations. There are a fewworkflows which are mandatory for any oil and gas operation (validation of well tests, virtual metering,etc.) and these ones were easy to scope and develop. Other workflows tackle very specific field issuesthough, and these ones were difficult to fully scope out beforehand. In a brown field, the challenges arewell known and the necessary workflows can be developed or adapted according to the difficulties facedby the engineers. A typical example for West Qurna II is the optimisation of the water production subjectto water handling capacity constraints.

For tackling these issues, pre-production models have been developed on the basis of exploration data.This phase has allowed to spend the necessary time to develop clear modelling guidelines, to prepare modelsfor all future wells and facilities in a consistent manner, to investigate the PVT modelling options carefully,

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etc. The uncertain parameters have been identified, and automated workflows developed to offer automaticcalculation of these parameters, and subsequent model update, when new well test data is available. On firstoil, minor issues arose with the workflows, but these could be fixed in a matter of hours, and the modelswere all calibrated within a week. Rigorous solution testing on virtual data before the first oil allowed toensure that the workflows were mostly bug-free before the critical first oil phase. The IAM project was splitbetween a phase before and after first oil, which allowed to adjust the behaviour of the workflows to thereality of the field, as well as to develop new workflows quickly according to the issues witnessed in reality.

The full system documentation, including standard operating procedures was available for first oil.Training sessions for both system administrators and users were organised before and after first oil, ensuringa record adoption. The full necessary IT infrastructure and product licensing was provided upfront.

Early production (phase I)Early validation of production profilesBefore first oil, the production predictions and field potential evaluation were based only on explorationdata, and therefore subject to great uncertainty. It is crucial to be able to recalibrate the reservoir, wells andfacility models as soon as production data is available. Such re-calibration is typically tedious, requiringthe engineers to gather a lot of data from different systems, to load this data into their models, comparewith the actual results, tune the parameters, etc. All these activities have to happen in a high-pressurecontext; unforeseen operational issues take the priority, management is expecting a validation of the realisticproduction profiles as soon as possible, and the organization is not yet fully mature.

For this purpose, specific workflows have been developed to allow for extra fast model calibration. Theworkflow automatically detects that new data is available (e.g. a new well test), processes the model againstthis new data, assesses the discrepancy and pre-calculates a combination of parameters allowing the model toreproduce the measurements. In case of alignment between the measurements and the model prediction, thesystem carries on automatically. In case of large discrepancies, engineers are invited to review the suggestedparameters before updating the models. This is done through a user-friendly step-by-step interface thatpresents all relevant data together; real-time data during the test, previous well test values, type of instrumentused for the measurement, previous values of model parameters, etc. The user can easily assess whetherthe model is responsible for the discrepancy (need for re-calibration) or the data itself (bias on a meter).In a couple of mouse clicks, the user can choose to update the models, or to alter the well test figures.The updated models are stored into a model management system, allowing to track all the model versionsthrough time, and the modified well tests are automatically updated in the hydrocarbon accounting database.

This workflow, combined with the very high well testing pace at West Qurna II, allowed to fully calibratethe IAM system within only a week.

Accurate production figures and hydraulic models (well test validation) – 2013LUKOIL started to use WTV workflow from first oil onwards to update the well models and automaticallyvalidate well measurements.

The well test validation workflow is a proven workflow which most oil and gas operators implement oneway or the other. What sets West Qurna II apart is the large amount of daily well tests (up to 100 per day).This amount requires the workflow to process the well tests automatically as much as possible (concept of"well test review by exception"), and when human intervention is required, the validation process shouldbe as fast as possible. This involves pre-gathering all the relevant information to completely eliminate thenon-productive time in the process and to provide the highest level of integration with the hydraulic models(access the model in 1 click, automatically update the model and store the new version into the modelmanager, etc.) and the hydrocarbon accounting system (altered well tests are automatically updated in thehydrocarbon accounting system without the need for any human intervention).

The overall work sequence is presented in Figure 3 below.

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Figure 3—Well test validation work sequence

The user is presented with a summary screen showing all the recent well tests along with theirautomatically calculated statuses. A tree map presented in Figure 4 is used to visualize the conformance ofthe model prediction for each well (color green/red), the production level of each well (area of the individualrectangles) and their belonging to a particular well pad (grouping of the individual rectangles).

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Figure 4—Well test summary screen

He can therefore dive in detail for the non-conform well tests and is guided through the process by astep-by-step workflow shown in Figure 5.

Figure 5—Well test validation screen

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When altering parameters such as the productivity index or the reservoir pressure, the system is able togenerate heatmaps dynamically based on the bottom hole pressures of the well, corrected or not at datum.

Overall, while the well test validation methodology is not new in itself, its careful implementation atWest Qurna II has allowed the engineers to maintain their models continuously up-to-date, day by day, forthe past 5 years, which is a significant achievement.

Virtual meters for temporary commingled wells – 2013The first benefits of using the IAM model in an on-line environment appeared clearly when tackling thechallenge of flow estimation for temporary commingled wells.

During the field development, new wells were drilled faster than the surface network could be extended.Therefore, the new wells shared manifolds with existing wells, leading them to be "commingled" throughtemporary lines demonstrated in Figure 6.

Figure 6—Simplified schematic of temporary line wells hook-up

To measure the production of these wells, the other wells on the same temporary line had to be shut-down. LUKOIL was originally performing this operation every 3 days.

By using the virtual meter workflow, taking into account the system back-pressure through the networkmodel, the rates could be continuously estimated with high accuracy for every well, reducing the need toshut down wells from once every 3 days to once every 15 days.

The virtual metering workflow is very classic and mature, being part of most IAM or Digital Oil Field(DOF) systems implemented in the oil industry. The system captures all measured inputs it requires forperforming a rate simulation using the intersection of the VLP and IPR curves. With regular calibration,such systems can reach high accuracy.

The results from the virtual metering workflow are used to perform real-time model-based allocation.By comparing the results from the virtual metering workflow to the export meters, allocation factors arecalculated and applied to each well's flow estimate.

Long term forecast – 2015Once the models encompassing the wells and production network have demonstrated accurate behaviourover an extended period of time, LUKOIL launched the integration of these models with the reservoirsimulation model.

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These integrated models (reservoir-wells-surface facilities) were subsequently used for generatingaccurate and realistic production forecasts. This allowed not only to gain confidence about the ability toreach the target, but also to identify the most efficient and economical way to reach it.

The model consists of a reservoir simulation model using Tempest software, coupled with a productionsystem and a simplified water injection model with inflows using GAP. The Petroleum Experts Resolveproduct ensures the integration of these models. The details of the integrated model are presented in Table1 below.

Table 1—IAM Reservoir simulation model dimension

The models are calibrated using well test and PLT data twice a year. History matching is performed on amonthly basis. The VLP's are re-calibrated twice a year for wells without ESP and PDHG gauges installed.

It was a company standard to run long-term production forecast scenarios using the Reservoir Simulationmodel standalone. With the rising overall field production in 2015, a series of problems related to naturallyflowing wells significantly depleting arose, leading to high demand for ESP conversions. A weak pointof using a reservoir simulation model alone is the inability to capture the effects of limitations from thesurface network and facilities. Phenomena such as back pressure, dynamic change of wellhead pressure dueto pressure hydraulics from the HP separator up to the well choke, interlayer crossflow and increasing watercut caused wells to operate in unstable conditions or to even stop flowing.

Using the integrated model rather than the reservoir simulation alone has allowed for optimization of theESP conversion plan, as detailed in the next sections.

Lessons learnt – flexibility of the IAM platformThe main lesson learnt during the early production phase is the need for flexibility form the IAM platform.

As an example, for ensuring a correct calibration of the models, it was necessary to offer a choice betweendifferent sources of measurements, such as laboratory information (from samples), multi-phase flow metersor previous model parameters.

The same flexibility was required for the forecast workflow. Originally, the workflow had been designedfor handling all steps of the forecast exercise, including input gathering, calculation and output storage. It hasquickly appeared that every month, a specific calculation or setting needed to be applied to the models, andthe pre-configured options in the workflow could not account for these specific adjustments. The workflowhas been subsequently used to store and visualize the inputs and outputs of the forecasting scenarios forcomparison and communication purposes, while the actual calculations have been run using the simulationsoftware interface directly.

Scale-up, artificial lift and pressure maintenance (phase I) −2015Extension of natural flow operating modesBy using the integrated model to generate long term forecast scenarios (as opposed to using only a reservoirsimulation model), the period where the wells could flow naturally has been considerably extended. Notonly has this led to important CAPEX and OPEX reduction, it also allowed the workover rigs to spread theworkload over a longer period, enabling for optimal scheduling.

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For achieving these results, the IAM system was used to evaluate the well operating parameters requiredto produce the wells above the saturation pressure and keeping a wellhead pressure above pipeline pressureas long as possible. The conversion schedule of oil producers into water injector was also part of theoptimization problem.

As a result, the decline in production due to the end of the natural flow period has been delayed by 6.5months. Figure 7 below shows the production levels predicted by the reservoir model alone against thoseoptimized with the IAM system.

Figure 7—Production decline calculation by IAM and Reservoir Simulation model standalone

Figure 8 below shows the delay in ESP conversion on well-by-well basis.

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Figure 8—Delay in ESP conversion on well-by-well basis calculated by IAM

A rough economic model has been run on these figures to evaluate the gains related to this approach. Itsresults are presented in Table 2 below.

Table 2—Estimated gain by implementing IAM approach in 2015-2016

Workover rig optimizationThrough its detailed IAM model presented in Figure 9, LUKOIL has been able to apply a "just-in-time"policy for the ESP conversion. Converting an well flowing naturally into an ESP well too early leadsto unnecessary CAPEX and OPEX, as well as mobilizing workover rigs that could be used elsewhere.Letting the wells reach an unstable production zone, and eventually die leads to difficulties in operationsand interrupted production.

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Figure 9—Long-term IAM model elements

LUKOIL has used its IAM models to evaluate the exact moment when the naturally flowing wells reachthe unstable operating mode. This information was communicated to the planning department who was ableto optimally schedule the workover rigs interventions. A total of 19 preventive ESP conversion operationswere run in 2018 and 9 operations have been scheduled to prevent wells stop to flow conditions.

Details of the optimization workflowEvery 3 months, LUKOIL calculates the production profiles and schedule workover operations for the

next 3 years. It is divided in several stages:

1. Calculation of the production profiles with naturally flowing wells and ESP converted wells toestimate a base case scenario (do nothing case). The schedule includes all planned events: new wellstart up, conversion to injection, field shut downs and pipeline upsizes.

2. The ESP selection is handled by a multi-disciplinary team; the selection of candidates for ESPconversion is done with the reservoir engineering team while the selection of an appropriate ESPtype is done with the production team. The planning team is then engaged for creating a workoverschedule and estimating a number of workover rigs required. This is then cross-checked with the rigsavailability in a complex planning exercise.

3. Running a forecast incorporating recommendations for ESP conversion, facilities and fieldconstraints. Once the model confirms the ability to produce a target rate over the next 3 years, thisinformation is used by all involved departments for the development of the operational budget.

One of the most recent enhancement was creating a customized Resolve workflow shown in Figure10 that prioritizes oil production and penalizes high water producing wells. Once the constraint on waterhandling capacity is reached, the workflow identifies the wells with a high water-to-oil ratio (more than85%) and low oil rate (less than 400 sm3/d). Through an iterative process, such wells are masked in thesimulation until the water handling constraints are satisfied.

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Figure 10—Water optimisation workflow in Resolve

The water penalty factor is increased based on the water constraint: the lower the water constraint, thehigher the penalty factor. Once the water production constraint is eliminated all the wells are getting openfor production.

The implementation of this workflow has reduced the calculation time from 5 days to 8 hours. Previously,the production engineers had to select manually wells to be shut-in due to water rate limitation and run 5-6additional scenarios before getting an oil production profile meeting production targets and honoring thewater production constraints.

Technical challengesSome water injection wells have very high injectivity potential, however wellhead pressures are 0 bar(vacuum effect). Therefore, the VLP curves cannot be matched and the fracture pressure constraint cannot berespected, due to the incorrect tubing pressure gradient calculation using the IPR+VLP estimation method. Ithas been decided to model water injectors as an inflow only and run the surface network potential calculationoff-line (single solve, snapshot, no dynamic calculation).

Many wells are producing from highly permeable layers with a permeability higher than 400 mD. Aminor inaccuracy in the reservoir pressure transferred from the reservoir simulation model to the IAM modelcaused a significant error in the calculation of well rates. Therefore, a dpShift was used to adjust the IPRtransferred from the reservoir model. Those values are calculated based on an historic run, with well liquidrates set as the maximum constraint over 2 months and left in the GAP model for forecast calculating.

Optimisation of ESP mean time between failure and lifetime (Panda) – 2017The "Production Assurance and Daily Adjustment" tool (PANDA) is the West Qurna II exception-basedsurveillance (EBS) system. It has been implemented in 2013 during the field startup and extended in 2017to leverage the IAM calculations aimed at monitoring the ESP operating point on near real-time basis.

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The system is designed to detect deviations from the target parameters in real time. An alarming systemnotifies the production engineers about events that may affect production. A complex logic called "Rulebased decision algorithm" has the ability to create a warning alarm based on statistic data. This tool isused by ESP engineers on a daily basis. An action is required only against the wells performing out ofrecommended operating envelope (for example, out of recommended operating range, ESP overload, ESPshutdown detected, ambient temperature higher than threshold, etc.). In such cases, an alarm is createdand a preventive maintenance action is initiated to prevent ESP shutdown and production losses. This is asignificant efficiency gain, allowing for preventive action, but also maximizing the productivity of the ESPengineers by scanning continuously very large amounts of streaming data.

This EBS system is also integrated with the well test validation module. Once a new well test is available,the well test validation workflow automatically calculates the pump operating point. If the well test isaccepted and validated, the position of the operating point is stored in the data historian (OSIsoft PI). Theposition of the point within the ESP operating envelope is converted into one single number expressed in%, where 100% is the best efficiency line and 0% represents the up thrust and down thrust limits. A valuebelow 0% generates a "Out of Recommended Range" alarm. An immediate action can be taken, before theESP suffers a forced shut-down.

Such a system allows for continuous improvement of ESP meantime between failures and overalllifetime, as shown in Figure 11 below.

Figure 11—ESP runlife history

Short-term optimization of ESP operating parametersSeveral production events require the ESP team to adapt the ESP operating points quickly. This is typicallythe case when the water production constraints are reached, or the processing plant is subject to unexpectedconstraints (unplanned maintenance, bad weather conditions, etc.).

In such situations, the ESP team has to react within hours to issue new operating setpoints for the ESP's.For enabling them to perform such a complex task easily, the ESP monitoring and optimization moduleshown in Figure 12 has been developed. The users input a target liquid rate into the OVS module withoutopening the underlying prosper models. The ESP engineers are therefore able to define setpoints for thewell head pressures and ESP frequencies for 70 wells in a short period of time.

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Figure 12—ESP optimisation workflow

Lessons learnt – scalability of the IAM platformThe IAM system has been designed and developed for accommodating the rapid evolution of the field.Specific user interfaces have been developed for adding new pieces of equipment to the system, in particularnew wells. The link to real-time data has been templated so all data points can be reconstructed automaticallybased on the well identifier. The associated well testing devices are associated to the wells in a single clickand can also be added to the system without the need for any coding. The parametrization of the workflowsis also covered through a specific interface, where the users can change the source of information for everypiece of equipment if required. The ability to recognize the lift mechanism of the well automatically hasbeen factored into the solution from the start, allowing to handle seamlessly naturally flowing or ESP wells,or wells which have the ability to flow either naturally or using their ESP. This has allowed the LUKOILpersonnel to maintain their system without the need for software developers or vendor involvement.

The hydraulic models are stored and versioned using the model manager workflow, provided by the IAMplatform vendor. This allows to upload, update and track changes for all the wells and facility models usedby the IAM platform. Functionality for synchronizing the content of the IAM platform and the hydraulicnetwork model is provided, ensuring that the engineers only need to include the new wells in the modelsfor being reflected in the IAM platform. Figure 13 is an example of model management workflow.

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Figure 13—Model management overview

Development of Mishrif formation and pilot production for Yamama (phase II) −2019Well Test Validation for injection wells (IWTV)The injection well test validation (IWTV) has been implemented at the end of 2018 to ensure that theinjection well models remained always accurate, in a similar manner as the well test validation workflow isused to keep production well models up-to-date. The objective is to demonstrate the accuracy of such modelsover a proof period for integrating them within the complete IAM model (reservoir simulation, productionwells and network). The IWTV workflow presented in Figure 14 handles both the injection wells and thewater source wells. All these wells are stored in the central model repository, along the production models.This model repository, provided by the OVS platform, allows several individuals to collaborate on the samemodels, while tracking changes and rolling back versions if necessary.

Figure 14—Injection and water intake well test validation workflow

The workflow principles are similar to the production well test validation workflow. Measured Wellhead pressures and pump frequencies are used to calculate the water rates and compare them with thecorresponding real-time measurements. The reservoir pressure and productivity index are matched toreproduce the measured rate.

The calibrated models are leveraged within the virtual metering workflow for injection wells. Thisworkflow acts as a back-up to the physical metering system.

Efforts are now on-going to monitor the pump operation of the water source wells the same way as theESP are being monitored.

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Integrated Asset Planning (IAP) as part of IAMSince the first oil, LUKOIL has been using the IAM system for calculating short-term production forecastand monthly well operating guidelines. This information has been used manually as a key input to the assetplanning.

After years of iteration on the most appropriate way to integrate the IAM models prediction within theplanning exercise, LUKOIL is moving to a fully automated and integrated asset planning workflow, witha direct connection to the IAM model and a modern web-based user interface. Figure 15 is an example ofthe display for IAP workflow.

Figure 15—Integrated asset planning workflow

This workflow offers a single user interface for the definition of all functional plans. The users definethe expected activities and the workflow automatically translates those in events that can be processed bythe IAM system. The users can build several scenarios with different combinations of events and triggeron-the-fly calculations using the IAM model. Once the best scenario has been identified, the IAM modelsare leveraged again to perform a detailed calculation allowing for the prediction and categorization of alldeferments.

Once the plan has been validated and communicated to the operations for implementation, the users areable to track the progress of activities near real-time.

Integrated well lifecycle assessmentAs stated in the introduction, LUKOIL is now facing challenges with the conversion of the naturalflowing wells into ESP wells and the water management. For reaching the ambitious production targetsin time, it is necessary to make accurate decision quickly about the next steps of field development. Suchdecisions require that a multi-disciplinary team critically reviews all the available information around welland reservoir performance. To speed up this process and ensure highest levels of accuracy, dashboardsintegrating all available field technical information are being developed.

Figure 16 below illustrates how data about reservoir, production, injection and completion are being puttogether to provide a complete, instantaneous view of the field performance.

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Figure 16—Field performance analyses dashboard

While these dashboards are still under development, it is expected that they will lead to considerableefficiency improvement for continuously optimizing the field development plan.

They also provide a single platform to support integrated teams, contributing to dissolving the silo'shistorically present in il and gas production companies.

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The way forward – 2020

Move to fully web-based environmentThe project started in 2013. The OVS workflow system was selected to automate the calculations ofthe hydraulic models, using Petroleum Experts technology. At that time, none of the available workflowplatforms offered a fully web-based interface, and therefore a thick client was deployed on all users'workstations.

Today, many products are offering a fully web-based interface. The OVS platform has completely movedto the web, and therefore a migration project is undergoing.

The ability of interacting with most tools in the MES space through web-based interfaces allow for amuch better user experience. Simple web portals are created so the users can access the different applicationslike "apps" on today's smartphones. The integration of these different applications is also greatly facilitated.

Application of machine learning for optimal pressure maintenance systemLUKOIL is currently exploring the application of advanced analytics and machine learning for a bettercharacterization of the reservoir properties through production data. In particular, the interaction betweeninjectors and producers is automatically quantified through the application of cross-correlation algorithms.

ConclusionThe IAM system has proven instrumental for the development and optimisation of West Qurna II. It hasallowed to de-risk the initial production, optimize the workovers in the field, provide back-up to physicalmetering, enhance the ESP performance and lifetime, determine the optimal operating parameters on a day-to-day basis, optimize the pressure maintenance systems, etc. It has also provided a platform to empowermulti-disciplinary teams.

The saving figures presented in this paper alone amount to around 7 million dollars in 2015-2016. Thebenefits have only been calculated for some of the achievements, and it is not unreasonable to expect totalsavings of tens of million dollars ultimately. Such savings are several orders of magnitude higher than theproject costs, making the implementation of IAM systems very attractive for similar green field operations.LUKOIL can also proudly claim to have achieved world-class operational excellence by ensuring greatvisibility, control and prediction capability over its operations.

LUKOIL has been successful in the adoption of such a platform for several reasons.

– LUKOIL has allowed for significant customization and flexibility of its IAM system. Whilesome workflows can be considered standard, it is most often the ability to tackle very specific fieldchallenges which is making the difference and generate broad adoption. Keeping control over therequirements and being able to define its own roadmap has been greatly facilitated by LUKOIL'schoice to implement an open and multi-vendor architecture.

– LUKOIL has put the necessary efforts in developing and maintaining accurate hydraulicmodels, which in turn have delivered relevant and accurate predictions allowing for optimal fieldoperations. Such an approach requires vision and confidence in simulation processes, since theinvestment is significant before it can yield results. LUKOIL also made the choice to use theintegrated asset model for predictions rather than using only the reservoir model for this purpose. Thisdecision has led to significantly increased accuracy and optimization, as demonstrated in this paper.

– LUKOIL has started simple and kept focusing on the continuous and organic development andimprovement of its IAM system. This has allowed the engineers to really understand where sucha system could make the difference in their day-to-day operation and to craft an evolution scopeanswering their needs. This step-by-step approach has started with the implementation of basic toolsand workflows, continued with the extension of the solution based on user requirements and ended

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up with a fully integrated, web-based and multi-disciplinary platform. In addition, LUKOIL hascontinuously invested in high quality support of its solution, leading the user community to remainengaged and satisfied.

AcknowledgementThe authors express the gratitude to the management of LUKOIL for the permission to publish this paper.

The authors would like to thank their LUKOIL colleagues involved in Smart Field Project for theircontribution and studies: Agafonov A., Bugatayev A., Betev Yu, Domnin E., Gusev A., Kabaev M.,Kaiumov K., Khatmullin A., Kryukov M., Letunov D., Nazarenko M., Nepomiluev D., Nikiforov I.,Pozdeev L., Radchenko N., Solomin A., Shafigullin K., Vakhramov A., Valiullin I. and Zaynullov T.

The authors would also like to acknowledge Marat Salikhov, Vadim Rogachev, Artem Ushakov andRuslan Timoshenko for their effort and contribution for project management and implementation.