SPE 169923-MS

Integrated Asset Modeling in Mature Offshore Fields: challenges and successes Roman Nazarov, SPE, Pedro Zalama, SPE, Mirko Hernandez, Cenobio Rivas, Repsol E&P T&T Ltd.

Copyright 2014, Society of Petroleum Engineers This paper was prepared for presentation at the Trinidad & Tobago Energy Resources Conference held in Port of Spain, Trinidad and Tobago, 9–11 June 2014. This paper was selected for presentation by an SPE program committee following review of information contained in an abstract submitted by the author(s). Contents of the paper have not been reviewed by the Society of Petroleum Engineers and are subject to correction by the author(s). The material does not necessarily reflect any position of the Society of Petroleum Engineers, its officers, or members. Electronic reproduction, distribution, or storage of any part of this paper without the written consent of the Society of Petroleum Engineers is prohibited. Permission to reproduce in print is restricted to an abstract of not more than 300 words; illustrations may not be copied. The abstract must contain conspicuous acknowledgment of SPE copyright.

Abstract

Production management in mature fields is a very challenging task which involves a multidisciplinary technical

approach to minimize the decline rate and extend the life of the asset/field. Most of the time Integrated Asset Modeling (IAM) techniques are applied to green fields with main objectives of identifying the “bottlenecks” or to forecast production with different development cases. In the case of mature fields it is mostly considered as an optional study with less analytical value due to low operating surface pressures, already existing facilities, known well performance and studied reservoir geology. Nevertheless the processing of the reservoir, production and operational data in mature assets through one integrated workflow facilitates field management overall, thereby helping in the estimation of the remaining reserves and indicating real opportunities for optimization not seen by initial engineering scenarios. Additionally, IAM should be incorporated before getting to EOR studies.

This paper describes the applied reservoir engineering workflow and integrated production model for the TSP fields (Teak, Samaan and Poui) located in the South East of Trinidad. TSP fields are jointly owned by by Repsol (70%), Petrotrin (15%) and NGC (15%) and are operated by Repsol. Current production of TSP is 13,500 bopd. The oil produced from these fields is generally light oil, with an average range of 25-40 API and a solution GOR 200-1400scf/stb. Gas lift is the artificial lift system used in 95% of the wells. Average water cut is around 85%.

Interaction of Production Engineering, Subsurface, Drilling, HSE, Facilities, and Maintenance departments is the key aspect to sustain the efficient operability of the TSP fields and operate at peak performance in spite of ageing installations, flow assurance problems and depleted reservoirs. The implementation of Operated Asset Structure in TSP in 2013 reinforced the cooperation between departments to achieve the main goals: minimum production deferrals, production optimization, screening of new opportunities and reserves, process improvement, facilities maintenance and effective logistics. Additionally, the Integrated Asset Modeling has been incorporated as part of the engineering surveillance which includes 3 fields, 100 wells, gas lift injection network, gas compressors, water treatment plant, etc.

Real data from different sources and platforms, such as pressure temperature sensors, daily measured well parameters, reported operational figures, monthly welltests and screened remaining reserves are jointly transferred to the integrated model, built in commercial software (GAP/RESOLVE), bringing the field data processing and production management to the state-of-the-art level. Gas lift volume availability and system pressure, performed rigless intervention jobs (including recompletion of new zones), change of the fluid composition in certain wells, reconfiguration of facilities are timely reflected in the TSP integrated model. Based on the sensitivity runs and output results immediate actions are taken to comply with the production target.

1. Introduction

Teak, Samaan and Poui (TSP) are three mature oil fields located approximately 20 to 40 km offshore the south eastern coast of Trinidad in a water depth of almost 200 ft. These discoveries were made during late 1960’s, and production started in early 1970’s achieving a production peak rate of 143,300 bopd in 1978. Currently the TSP asset produces around 13,500 bopd with a total of 100 active wells with a high average water cut of 85%.

Due to the mature nature of the fields, the vast majority of the wells (95%) are produced via gas lift as the only artificial method used, with only 5 wells producing naturally namely three low pressure gas wells and two oil wells with

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associated gas. Presently Repsol is undertaking different strategies in order to maintain the oil production rate and mitigate the oil

decline rate at the current stage of the asset. These are: • Workover and new drilling campaigns: since Repsol took over operations, two workover campaigns and one

drilling campaign have been executed. Currently a new drilling campaign is being executed with two jack up rigs with the utlimate objective to bring new oil and reserves to the asset.

• Annual Non rig campaigns: where the best well candidates are selected to conduct rigless jobs and interventions such as re-perforations, acid pickles, de-wax, gas lift valve change outs, adding perforations, sand clean outs, etc.

• Gas lift optimizations: periodically well tests at different injection rates are performed to determine the current optimimun injection rate.

• Other projects to optimize production and processes: different improvement opportunities have been identified and projects are continuously being studied and eventually implemented. E.g. HP to LP project in Samaan A to improve the compressors start-up and mitigate the lack of low pressure gas to allow the compressors to operate at optimum conditions.

As clearly described in this document, integrated production modelling is a key tool to identify opportunities for the Asset to mantain the maximum production rates and improve the system processes.

2. TSP Asset Description: 2.1 General The TSP asset is comprised of three fields (Teak, Samaan and Poui) which are interconnected by a complex pipeline

network of produced fluids, high pressure and low pressure gas and export fluids. Each TSP field consists of a main central complex or alpha platform which contains processing facilities to separate oil, water and gas. Nearby satellite platforms with dry well heads and well testing facilities are connected to the alpha platform.

The high pressure gas for gas lifting is transferred by adjacent bridge-linked platforms with compression facilities. In Teak A and Poui A complexes these platforms are operated by National Gas Company (NGC), who receives the low pressure gas produced in these fields, compress and deliver back as high pressure gas. In Samaan A complex the adjacent compression platform (Samaan AC) is operated by Repsol and demands solid maintenance support due to higher discharge pressure for gas lift.

In addition, high pressure sales gas lines owned by NGC pass through Teak B and Poui A platforms. These pipelines provide additional flexibility to the systems, where external high pressure gas for field start-up and additional required gas for normal operation can be obtained.

Samaan A is connected to Teak A by a 19km 16” export line while Poui A is connected to Teak A by a 22km 18” export pipeline. Samaan and Poui export fluid streams are merged at Teak A complex. Further Teak and Samaan-Poui liquid streams are processed in the offshore oil crude treatment plant (OCOT) to achieve pipeline specifications within 0.5% water cut limit.

Downstream of the OCOT plant, all of the produced TSP oil is routed to bpTT Galeota terminal through a 37km 16” pipeline for custody transfer and sales.

2.2 Production process:

The main production process in TSP is common to the three fields and can be generally described as follows: The process essentially consists of one stage of separation for the produced fluids coming from the wells in three-

phase production separators at a low pressure (30-40 psig); depending on the field there are either two or three separators in parallel. The separated oil is then routed to the OCOT plant (at Teak A complex) from each field for export.

Samaan B

Poui B

16 " –19km

18" – 41

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Teak C

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Samaan C

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Two phase pipelines

Condensate / Oil line

Gas lines

Multiphase lines

Tanker

bpTT COP

Galeota

bpTTStabilization

Caribbean Sea

Columbus Channel

Trinidad

Port of Spain

NGC

NGC

Figure 1: Schematic of pipelines in TSP.

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On the water treatment side: separated water is handled by a secondary wash tank type vessel. The water is then sent to an air flotation unit where the skimmed oil is routed back to the export pumps header and the treated water is disposed of into the sea. By thus, the quality of water complies with the Ministry regulations.

The flashed gas at the production separators is gathered in a common header line routing the low pressure gas (25-35 psig) to the adjacent platform for compression.

Although the three fields operate following a common process, each has its own individual peculiarities as explained below.

Teak Field is the biggest field in TSP in terms of production and facilities. It is operated with 5 production platforms, one separation process facility platform and one 3rd party compressor platform (NGC). The low pressure gas from the separation process is compressed to

850 psig and then sent back to the gas lift system network to sustain the production. At Teak B platform there is a connection to a third party (NGC) 24” high pressure export gas line, which provides the flexibility to obtain additional high pressure gas as required.

Samaan Field is operated by 3 production platforms, one separation process facility platform and one compression platform operated and maintained by Repsol. To sustain the entire field production higher Gas Lift Pressure (GLP) is required. Under normal conditions gas lift is operated at 1150psi.

Poui Field is operated with 2 production platforms, one separation process facility platform and one 3rd party compressor platform (NGC). The low pressure gas from the separation process is compressed to 850 psig and then sent back to the gas lift system network to sustain the production. In addition, at Poui A platform there is a connection to a third party (NGC) 24” high pressure export gas line, which provides the flexibility to obtain additional high pressure gas as required.

2.3 Reservoirs and fluid characteristics

TSP fields are located in the Columbus Basin, which is considered as the extension of the eastern part of the eastern Venezuelan Basin. Columbus Basin is limited to the North by the Darien Ridge and to the South by the Amacuro Shelf.

The Teak field is a steeply dipping anticline structure along an east-west trending compressional ridge, bisected by a single major regional fault with a vertical displacement of up to 3000ft. This major fault appears to influence the hydrocarbon phase, dominated in the west by oil and in the east by gas. The overall structure is complicated by numerous crosscutting normal faults that divide the reservoir into multiple compartments (Fig 1). The reservoir consists of a stacked series of Pliocene sandstones in combination dip and fault closures (3 ways-closure against faults) supporting oil columns of up to 700ft.

Figure 2: TSP production flowlines and process interaction

Figure 3: Schematic section of Teak field showing highly compartmentalized reservoirs.

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The Samaan field has a trend orientation of NNE-SSW. There are two families of faults highly tilted that compartmentalize the field. One fault set is dipping to the NE-East with an average displacement of 150-500 ft and a second fault set, which is dipping to the West-SW, has an average displacement of 200 ft. Poui’s anticline has its main trend direction northeast, and similar to the Teak and Samaan fields, it has a complex faulting system. Producing reservoir units can be found as shallow as 3000 ft TVDSS (Poui field) and as deep as 10,000 ft TVDSS (Teak field). Most of the shallow reservoir units in TSP fields are unconsolidated sandstones above a depth level of around 8000 ft TVDSS.

Reservoir mechanism is predominantly aquifer support; however there are some volumetric reservoirs that in the past have been under water injection. Reservoir quality is not unique along the three fields; it varies from blocky clean well developed sandstones with excellent properties to thin bedded reservoirs with intercalation of shale bodies.Reservoir permeability for example, has been observed to be inversely proportional to the reservoir depth. Shallow reservoirs have registered permeabilities above the Darcy values; and for the deeper reservoirs permeability can be as low as 1 md.

The quality of the fluids produced from TSP fields is medium light oil (API 25-40) with viscosities lower than 5 cp. It has several flow assurance issues, especially related to wax precipitation and scale deposition along the production stream.

3. General Methodology: TSP IAM workflow and application

The implementation of IAM for TSP assets has been a challenge due to mainly technical constraints and resource availability. The data collection and quality control of the information is an important process to build the viable and reliable model. IAM is being incorporated with the help of different disciplines such as Subsurface, Production, Operations, Maintenance and Drilling. The required data has to be properly organized and structured from all of the parties to facilitate an efficient access in order to proceed with the construction of the model and continuous updating. To comply with the best practice in modeling of IAM ideally is to involve technically competitive Reservoir engineers with Production experience into the team and vice versa. The administrative resource is an issue for this type of activity; nevertheless once the objective is achieved the management of the brown field becomes straight forward.

To succeed with the project the explicit workflow should be developed, emphasizing the main strategy and objectives of its application. Coordination with the group of professionals working offshore is critical to provide support for IAM, to finetune model according to process constraints, modified flowlines, operational changes etc.

TSP IAM workflow comprises four phases, which are distinguished by time scales, data arrangement and implementation purposes:

Phase 1: Input data preparation, IPM construction with single Network Run and Field Potential Validation. Once the model matches as per the list of single well tests, it should be appropriate for optimizing short term scenarios, and making operational decisions, and certain process improvement proposals among others. Phase 2: IPM adjustment, Mid-term validation and optimization. The constructed model is positioned to simulate the dynamic field data reflecting the gas lifting environment and variations in the system pressure. The output results are subject to verification with oil production meter (Coriolis) readings. Due to the fact that gas lift pressure varies in TSP, the individual well has to be constrainted based on its Gas lift design and setting pressures of the valves. The open server script is used to mask the well once the casing pressure goes below the Pdome. This phase is one of the key steps to attain the reliable IAM. The model implies numerous field applications including the production allocation and the deferral reporting approach. The model at this stage helps one to understand if any of the stream data such as production, water handling is under- or over- estimated, and if the calibration of the measurement tools is required. Phase 3: Incorporation of the Real time data enables one to observe possible upsets in the process, un-seen failures etc. Since most of the TSP wells are tested only once a month, any daily discrepancy between IAM calculated data and Coriolis would initiate a prompt engineering response to look at the group of the wells and substract the problematic producer for further troubleshooting. The TSP daily monitoring stream and production figures are captured into the unified data base handled by Energy Components software. The PI SCADA system is fed with field data from pressure, temperature sensors and flow meters and via Resolve is transferred to the model. Downstream of the process, HYSIS simlulation of the OCOT plant could be incorporated to IAM where calculated liquid rates advise on levels of surge vessels and pumping capacities. Phase 4: The objective of this phase is for medium-to-long term forecasting. This has been under evaluation via incorporation of the reservoir models (material balance or simulation model outputs) in order to get a proper interaction with the reservoir. Then the complete system would be simulated and a proper forecasting would be achieved. For the time being this model is being used for the medium term for 1 to 2 years forecasting, in which yearly schedules of downtimes are included as well as the incorporation of well interventions and/or new wells.

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TSP IAM WORKFLOW

Daily monitoring FIELD data capturing: EC, PI

Application:Sensitivity Run against Daily Gas Lift PressureVerification of Estimated Model Production Potential vs.Coriolis Meter (Daily)OCOT upsets expected?Gas balance checks Other advantages: water handling/estimated allocation

Last Valid test (EC)

Daily Morning Report (Excel) GAP solver network match by

field per last valid test (1 Run)

Well Prosper Validation

Monthly GAP network Validation (Coriolis Data from past month vs. IAM results)

Daily RealTime GAP/RESOLVE network

Run and Sensitivity

Update of IPRs based on last BHP data (Monthly)

Subsurface Input

No

Model complies with Coriolis Daily readings?

Verify Field Data(Meters/PI data)

Submit Montly ReportRESOLVE CONNECTION

DCA (OFM) as Qc (or when WT is unavailable)

No Model OK

Match achieved?

Appication: • Coriolis data reading verification• Production allocation as per Integrated Model• Is any deferrals have been skipped out or any

production underestimated from report?• Is any Optimization seen for upcoming month?• Is any pressure back wells appeared?

Verify Field Data

VBA is used to arrange the data into specified format

Yes

Medium-Long term GAP/RESOLVE network

Forecasting

Mbal/Eclipse modelsincorporation for Long Term

Maintenance statistic failures to predict Down Time

Appication: Proper planning and production predictionReserves forecasting for new sandsEOR scenarios etc.

Submit New Reserves for Budget and Planning purposes as per IAM

Submit Daily Concern to ProdEngineering and Operation

Maintenance Input

PVT validation

Inputs for IAM:-Gas lift Pressure data-Stream data (Gas lift Injection, LP gas volume, HP gas bought from NGC, Gas Lift Fuel Consumption, HP Gas TBD Sales, Flare etc.)-Separator Pressures-Corriolis meter data (Rate, Wc%)

HYSIS(OCOT)

Facilities Input

Figure 4: TSP IAM Workflow

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4. TSP IAM construction and short term validation

This comprehensive approach to gain oil production is ongoing on a regular basis: gas lift optimization and rigless interventions. The production changes have to be immediately considered in the operational environment. Moreover the fluctuation of FTP, flow rate, gas lift usage, water cut or newly perforated zones in individual wells affect the whole system multiphase flow dynamic, header pressure and PVT of combined fluid and flow assurance. Therefore TSP IPM is considered as a multiview technical tool to encompass the well/flowline interaction at a complex level and to advise on optimization of the entire network.

Besides production, gas lift process, including the cycle of the injection gas lift through the system, its conversion from high pressure to low pressure state, merge with formation gas/liquid stream, furher separation and compression, requires thorough understanding of thermodynamic, compositional variations, compressibility effect, metering in order to achieve precise volume calculation at each stage. Based on the existing facilities and agreements with other parties gas volume balance is important for TSP: acquisition of extra volumes of gas lift usage needs to be economically justified, and any excess of the LP gas, such as at the Teak Field, will be compressed and sold to obtain the maximum value from the Asset.

For simplicity purposes the field injection gas composition was matched to the black oil model with aligned gas specific gravity.

The short term IPM modeling represents a kind of steady state modeling. All wells have been matched to their last valid well tests and joined together to platform clusters. The Integrated Production Models in TSP have been built for each individual field. Single IPM includes the production and associated gas lift networks.

4.1 Data gathering and validation During the project execution the massive scope of data has been analyzed and structured. The data management and

quality control determines the execution time and fidelity of the model. The following list of information was used to guarantee a comprehensive TSP IPM model construction:

• Process flow diagrams and platforms P&IDs • Operational SOPs • Screening reserves data: maps, well location • PVT reports for corresponded sands • Production logging information (pressure/temperature/injection point). • Reservoir Pressure history. Buildup analysis • Well completion details, deviation survey, gas lift design. • Production well performance and decline analysis. • Daily monitoring data and metering data.

The extended production history in TSP over 40 years assumes that data is available almost at any required phase. In the case of the missing or inconsistent data, such as flowing gradient surveys, reservoir pressures etc. indirect analytical engineering techniques and/or analogue information are used to comply with recent welltest records.

4.2 Well Modeling and Calibration Well modeling is the fundamental base for IPM. The accuracy in well

performance simulation can be achieved with consistent completion details, reservoir characterization data (thickness, permeability) and estimated pressure/temperature gradient from wellhead to bottomhole.

Following the best Petroleum Engineering practice the Nodal Analysis is being performed on a monthly basis for all wells to represent the production deliverability. Nodal Analysis for TSP wells is being performed with Prosper software (by Petex) using the solution node at the bottom for VLP and IPR crosssection. Drilling provides the well schematic and deviation survey.

An essential input for the well model is the productivity of the well, which depends on the reservoir properties and the fluid properties. For the fluid properties, a dedicated team of Reservoir Engineers have embarked on a full review of all the PVT experiments done in TSP2. Although the database is extensive it has been demonstrated that reliable data is limited. Therefore careful consideration has to be taken in order to select the best fluid properties Figure 5: Typical completion of the well with

gravel pack. Teak example

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for each reservoir. Then, the well productivity is obtained by evaluating well by well taking into account petrophysical parameters and buildup test derived data (such as permeability and skin). Different models are used (multilayer, composite, etc.) in order to match the production test and performance of the well.

Ninety percent (90%) of the Teak and Poui wells are shallow 4000-7000 ft’ TVDSS, and to prevent sand production from unconsolidated reservoirs the gravel pack completion is placed, while the Samaan wells are deeper, 6000-10000’ TVDSS and do not require the sand control. The completion example in a Teak wells shown in Fig.5. For such wells the additional pressure drop has to be assumed in the model by including the gravel pack option.

For VLP calculation the flowing bottomhole pressure and injection point are determined with MLT tool which one is equipped with pressure/temperature sensors and small spinner. Pressure gradient points from MLT are matched to correlation and respective coefficients are obtained. For 80% of the TSP oil producers Petroleum Expert 2 is used as a flow correlation with minor adjustment coefficients.

PE 2 is based on the Gould et al Flow Map and for the various flow regimes following the flow correlations applied:

• Bubble flow: Wallis and Griffith • Slug flow: Hagedorn and Brown • Transition: Duns and Ros • Annular Mist flow: Duns and Ros As stated by Petex the PE2 correlation mainly has been

tested for numerous high flow rate cases and it was found to provide a good estimate of the pressure drops. As well PE2 provides reliable prediction of low-rate VLPs and well stability. This correlation proved its conformance for TSP wells.Once main VLP curve is calculated sensitivity VLP pattern has to be run for GAP model to be able to extrapolate the production as conditions change in the system (Fig.6)

To prepare the well for the IPM the gas lift design for individual well has to be properly analyzed. TSP oil production is very sensitive to Gas lift system Pressure. Samaan field is the most dependent field; there are certain wells which have high PI and are operated with high CSG pressures. If the system pressure goes below operating CSG pressure certain wells stop producing due to Pdome cannot be achieved and valves remain closed. (fig.7). Injection of the gas lift into the bottom valves in such wells is not possible due to high tubing pressure and gas lift system pressure constraints. To investigate the gas lift performance and perform diagnostic analysis functionalities of PROSPER, such as “GasLift Adjustment”, “Quicklook” or “GasLift Design” could be used.

Nevertheless the setting pressure is not accounted for in GAP due to the following reasons. The setting pressure of the valve is calculated during the gaslift design based on pressure in the tubing and in the annulus at the valve depth. It is adjusted to ensure that the valve remain open during unloading. Once unloaded the assumption is that the deepest valve that can be reached for the specified conditions will be open and used for injection. The above assumption is used to generate VLP curves for GAP. Provided that the design is performed correctly the assumption will be valid and prediction in GAP will return valid results. For VLP generation it is assumed that the deepest valve that can be reached based on the pressure gradients and it will be opened and used for injection. The sensitivity of CSG pressure will assume only pressure gradient and not the Ptro of the valves.This assumption is valid and allows running prediction in GAP where the main focus is in general production trends. Therefore special algorithm applied to account the GLV design for each well in TSP (see below challenges to model the associated network).

4.3 Production and Gas Lift System Modeling

In Production network modeling there were not encountered such difficulties as in the gas lift network. Graphically, examples of Teak and Samaan IPM models are shown in Fig.8. Pressure drop predictions in production downstream flowlines are not a main objective in brown-fields due to low operating pressures, nevertheless some bottlenecks were discovered.

Figure 7: Quicklook of gas lift design indicating that well is operating at high CSG pressure

Figure 6: Nodal Analysis with Sensitivity on VLP vs. CSG pressures

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Sensitivity on flowline roughness and overall heat transfer coefficient, showed minimum impact for TSP network model, and therefore values were used as default. Beggs and Brill correlation was used for horizontal surface flowlines.

As mentioned above Samaan production relies on gas lift system pressure more than Teak and Poui. Due to compressors constraints the maximum pressure in Samaan cannot overcome 1180psi. Factors defining the stability of the system pressure are the planned Maintenance activity, conducted in time for overhaul of the compressors, operational process adjustments to supply continous volumes of LP gas for suction. Surge problems, automation, unpredicted failures and as a result Downtime on Compressor Train, directly impact Gas lift System pressure. When the Train is down in order to sustain production in Samaan, the HP gas is transported from Teak D platform at 860psi pressure. To improve the performance and to facilitate the startup of the Compressor after Shut Down, HP-LP project was implemented. This would allow after Train SD to increase the pressure to 1150psi in approximately 10-15 hours rather than waiting 2-3 days by supplying the LP formation gas from the wells. In summary, HP gas lift system pressure in Samaan ranged from 850psi (when compressor is down) to 1180psi, with erratic behavior at 1050-1180 psi.

Challenges to model the associated network: Gas lift rates in each well are regulated by a flow control valve

(commonly known as Merla choke). If the pressure drops, the merla has to be opened more to supply the recommended volume of gas to achieve the optimum performance of the well. The Qinj value should be set based on Chart recorder measurements. But due to unstable pressure behavior from compressor and availability of the injection gas the merla choke cannot be manually adjusted as frequent as pressure changes.

GL system pressure reduction can create the following problems: a. In certain wells Pcsg becomes less than Pdome of the injection valve, therefore the injection point is switching to

upper unloading valve, drawdown reduces and production rate drops. b. In certain wells Pcsg becomes less than Ptbg at the current injection depth, therefore the injection point is switching

to upper unloading valve where the Pcsg>Ptbg; then drawdown reduces and production rate drops. c. In certain wells Pcsg become less than Pdome of all of the unloading valves. Injection in these wells stops and

liquid plus associated formation gas production into the system ceases. This affects directly to the availability for LP gas required for the compressors to function properly.

d. Reduction of delta P across Merla choke and as a result decrease in injection gas rate (Q= f (ΔP)) impacts on Gas Liquid Ratio and worsen vertical lift performance of produced fluid.

To overcome the previous problems, the IAM gas lift network has to include the boundaries at the injection sink with

the set control at injection valve. The amount of the injection gas into individual well and CSG pressure defines the consistency of the whole model. Gas Injection control could be introduced into the model within 2 methods:

• Adjustable gas lift choke –assumes the choke as an automatic pressure

control valve; the supplied volume of gas is constant. Minor changes in Casing well pressures unless GLP goes close to annulus pressure. Casing pressure remained constant in those wells where the system pressure never drops below the annulus pressure. Despite of manual adjustments made on field, chosen control would not reflect the real Qinj volumes due to erratic gas lift pressure trend.

• Fixed gas lift choke- provides better estimate of the Qinj in the field. The drawback is that it underestimates the Casing pressure. But this drawback was converted to an advantage because of following reasons. To match the GAP model one injection gas rate is used with specified fixed orifice size. The model was pushed to inject the constant amount of gas at stable gas lift system pressure as per last welltest. When the pressure changes in the field the majority of the low pressure CSG wells kept the same pressure, but in certain wells CSG pressure decreases; injection rate decrease in all wells unless Merla is not adjusted manually. But due to GAP underestimated Pcsg at current settings VLP is extrapolated from

prosper sensitivity pattern to lower the production rate. In fact, the reduction in Qinj is artificially compensated by lowering of Pcsg. Moreover, lowered Pcsg will be compared with adjusted Pdome in openserver.Therefore current control was considered as an appropriate method to match TSP IAM to field data.

Figure 8: Gas lift injection control principles in IAM.

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Figure 9: Example of Samaan (above) and Teak (below) IPMs. Production networks are at the left, gas lift networks are at the right.

4.4 Short term model validation and matching Matching of the model is crucial step to attain a reliable IPM model. Due to all of the data interconnected it is

extremely difficult to obtain the ideal match in all wells at the same time. Nevertheless the iteration process is applied to get results as close to reality as possible. The relative error criteria applied to match TSP wells to the model is within +/-5% for oil rate, formation and injection gas rate, +/-10% casing and flowing pressure values (Table 1). As can be seen from Quality assurance plots coefficient of determination (R2) indicates that real field data quite well fits the model data within range 0.9763-1 range (Fig.10).

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Table 1: Quality control table.

Figure 10: Quality assurance crossplots.

ΔBOPD % ΔFG Mscfd ΔBWPD ΔGLI MsΔCSGP WHP WHT Sens vlp Choke MerlaWell - SA01 -2 -2 31 -87 0 -6.71 21 -31 Sens GAP FixedWell - SA03 -1 -1 -62 -6 0 -4.90 8 -14 Sens Prosper FixedWell - SA05 5 7 47 21 0 -4.94 4 22 Sens Prosper Delta PWell - SA06 -2 -1 -9 -6 0 -4.26 3 -13 Sens Prosper FixedWell - SA09 1 3 5 7 0 -9.08 -26 7 Sens GAP FixedWell - SA13 3 1 4 37 0 -7.18 -36 1 Sens GAP FixedWell - SA15 -1 0 -2 2 0 5.06 -36 7 Sens Prosper Delta PWell - SA16 10 3 41 128 0 -3.67 -22 -9 Sens GAP FixedWell - SA18 -2 -3 82 -1 0 -7.18 9 5 Sens GAP FixedWell - SA20 -4 0 -4 0 0 0.00 30 -11 Sens Prosper FixedWell - SB01 -1 -2 255 -25 0 10.60 20 -7 Sens GAP FixedWell - SB03 -1 0 -10 -33 0 7.67 20 0 Sens Prosper FixedWell - SB06 4 2 1 34 0 -1.56 17 20 Sens GAP FixedWell - SB07 0 -215 0 0 0.00 25 0 Sens GAP FixedWell - SB09 2 1 5 3 0 54.10 15 3 Sens GAP FixedWell - SB10 -1 0 -4 -15 0 7.68 16 2 Sens Prosper FixedWell - SB11 1 0 4 33 0 27.68 20 -5 Sens Prosper FixedWell - SB12 10 4 21 9 0 18.52 -5 -29 Sens GAP FixedWell - SB14 4 2 22 5 0 4.98 15 -11 Sens Prosper FixedWell - SB15 5 1 9 11 0 21.74 1 -4 Sens Prosper FixedWell - SC01 6 6 72 68 0 4.91 1 7 Sens Prosper Delta PWell - SC02 4 3 27 -7 0 33.03 6 5 Sens GAP FixedWell - SC03 -4 -2 -8 -5 0 13.20 11 -5 Sens GAP FixedWell - SC05 4 4 46 48 0 11.07 26 -8 Sens Prosper FixedWell - SC06 2 1 12 -10 0 12.83 1 -12 Sens GAP FixedWell - SC07 -2 -5 -11 48 0 -19.73 -9 19 Sens Prosper FixedWell - SC10 0 0 6 -14 0 19.91 6 -15 Sens Prosper Delta PWell - SC12 -1 0 0 101 0 5.06 6 -2 Sens Prosper FixedWell - SC16 4 3 15 303 0 0.00 1 1 Sens Prosper FixedLIMITS 20 10 500 300 10.00 150.00 40 35

Matching. WT data - Model data

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5. Medium term model validation and Quality control. After Phase 1 when the IPM model is built and matched, the model has to be checked with dynamic field data. The

same month period was selected, as estimated production from well tests matched in the model. Daily gas lift pressure data was entered. The Gas lift design of each well was thoroughly reviewed. In the Open script environment Pdome limits for each well were placed to constrain the production if the system pressure drops. At lower GLP but before reaching the Pdome the production is calculated as per sensitivity VLP chart. As can be seen from the chart below (Fig. 11) the status and production of the well can be determined everyday. The most sensitive wells will be grouped and analyzed for possible GLVCO to minimize downtime from lowering GLP.

Figure 11: GLP vs. Well Oil Production per well in GAP simulator The model was fine tuned until the predicted values were consistent with the readings of the field Coriolis meters.

The model essentially proved that any impact on GLP will be reflected in total oil production. The sudden humps in the GLP find the corresponding reflection in the IPM as well as metered data.

Based on the IPM, the model is consistently matched to Coriolis meter readings from 8th to end of the month and matched to Field estimated production, except 8th to 13th period (Fig.12). It is supposed that Daily field production have been overestimated in the period from 8th to 13th, because model and Coriolis indicate the same trend. But at the same time the model advised that Coriolis production was possibly underestimated. These findings have been considered by relevant field engineers to report production figures for allocation procedure correctly.

Figure 12: Comparison of Samaan Coriolis Readings, Field Production Estimates, IPM Profile vs. GL pressure Lower trends (violet and red) represent quality check on oil production deferment.

Possible overestimate of field reported production Possible underestimate of

coriolis meter

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Visualization of the simulated and real parameters in crossplots provides the confidence in the model.

Figure 13: Crossplot. Coriolis oil readings vs. IPM oil production. Attained level of the accuracy in TSP models promotes for further investigation and optimization in the process. Any outlayi from the Coriolis readings simulated trends will provide another type of control. Such operational issues

as un-seen equipment failures, breach in metering, shift in calibration can be noticed by IAM based on the divergence of the model and field data.

The IPM model provides additional support for production forecasting in maintenance planning. It is especially useful when the activity involves the downtime on the compressors etc.

6. Daily Monitoring: Incorporation of the real time data (SCADA) One of the possible challenges faced in a brown field like TSP is the lack of modern automated process and real time

data due to the inherited old days systems for metering and difficulty sometimes to implement some of these new data monitoring technologies, especially in an offshore environment.

Figure 14: Samaan Real time Gas lift pressure visualized in PI PROCESSBOOK 2012 software (left) and hourly

average values shown on PI excel add-in (right).

In a big effort to optimize the process and identify opportunities for improvement, recently TSP is gradually incorporating more and more data points tied to the PI system (SCADA). PI is a system that provides real time data from the field available anywhere with internet connection. The data is also recorded in a physical server in Repsol Port of Spain offices where proper back-up and data management is conducted.

There are two main tools used with PI, first PI PROCESSBOOK is a visualization tool that allows the user to monitor

the operating parameters. The second one is a Microsoft excel add-in (PI DataLink) that enables information retrieval from the PI System directly into a spreadsheet allowing complete data analysis

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Currently in TSP some of the key operating parameters are monitored and recorded in PI, such as: gas lift pressures, production separators pressures and levels, gross liquids and net oil export etc. For the IAM the gas lift pressure from each field has been incorporated as a first step to include real time data for automatic update of the model. Gas lift is the main driver of the production in TSP, therefore having a model with real time gas lift pressure provides a very valuable tool for the asset.

The methodology used to incorporate this data is based on the PI DataLink excel add-in, where real time data for gas lift is retrieved and placed on the excel spreadsheet. Gas lift pressure hourly averages are calculated with this software and linked to the GAP asset model by Open Server/RESOLVE, which takes the corresponding data into the model.

Once the IAM meets all matching requirements and real time data connected to system Facilities Simulator HYSIS of OCOT plant can be incorporated. The increase in liquid rates can cause the surge vessel level to upset and this can be predicted with the model thus helping the Operation to adjust pumping capacities etc. Current integration is under evaluation.

7. Medium-Long Forecasting

As described in the General Methodology section, the objective of the Phase 4 of the workflow is to get the IAM to predict the medium to long term forecasting, incorporating the subsurface models. The current stage of the IAM only includes Material Balance models for certain reservoirs. For example it has been used for forecasting the medium term production life of a recently reactived reservoir, which was found at initial reservoir pressure. There is only one well producing from that sand and according to geological interpretation the reservoir block is isolated from any other surrounding reservoirs currently on production. This case was ideal to use a Material balance model which was incorporated into the IAM. It has been already 8 months since this well was online and the predictions obtained are remarkably in line with the real production.

Cases like the one described above are few along the TSP fields. In the reservoir description section it has been noted that the reservoirs found in TSP fields are multi-stack compartmentalized ones. Different approaches are currently under evaluation: from incorporating only Decline Curve Analysis well by well, to full 3D simulation models.

Examples where simulation models are under evaluation are to be incorporated are in the Teak field. Since this field is currently under Infill drilling, there has been extensive subsurface work on building simulation models. These models are expected to be used in conjunction with the IAM.

Not only the subsurface domain is of interest in the medium-long term forecasting, also proper prediction of downtimes is required. Due to the maturity of the TSP installations, maintenance is a major issue. Although there have been a lot of efforts implementing preventive maintenance, failure of equipments could be unpredictable. For this reason, to predict downtimes past historical performance is being evaluated and statistics are obtained. From these analyses better downtime estimations are obtained and are used as input in the IAM. Planned downtimes are also incorporated.

Model Incorporation assumes Sensitivity analysis with such parameters as gas lift pressure system to forecast the possible losses or gains.

Figure 15: Snapshot example of a well in which the reservoir model was incorporated to the existing IAM. To reach this goal, very close cooperation between different disciplines is required. Any change or update of a

reservoir model has a direct impact in the ultimate output of the IAM model, therefore close communication is mandatory.

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8. IAM Findings and recommendations In summary, main findings and recommendations from TSP IAM can be described as follows:

Application Findings / Results Recommendation GL system pressure sensitivity

The model proves its dependence on GL system pressure. At any point of time the well status and its production can be known

To minimize deferrals in certain wells the adjustments in Merla choke should be made more often if the GL sys pressure is down. (based on the divergence Coriolis and GAP model evidence)

Production Optimization

Wells with high CSG pressure and high PI have been grouped and proposed for GLVCO campaign and for gas booster installation

Run Installation Booster Scenario (SB) and perform economics based on the expected gain. Integrated GLO based on the gas balance.

Production reporting Underestimated or overestimated field reported production observed. Quality control on deferral and allocation procedure.

Once the model is fully finetuned to Coriolis data, field estimation production has to be verified with IAM on daily basis

Pressure bottlenecks or Slugs/ Errosional areas

Some evidence on bottlenecks passed to F&C dept for further investigation

Based on P&IDs IAM has to be detailized for identifying the critical piping areas. Estimate errosional risk in HP gas lines.

Integration with Real time data

Real time data from field incorporated into the IAM allows close follow up of field production with minimum reaction time for engineering/operational decision. Unseen failures can be identified.

The impact of variation in the liquid production, water cut in IAM has to be properly analyzed to improve level control system in surge vessels of OCOT plant.

Mbal/Eclipse incorporation

Full Integration dynamic models are under construction. For certain wells the models were integrated with forecast production profiles obtained

Full integration of the dynamic models into the system. Model Interconnection effect to be studied, also will assist in fault sealing analysis.

9. Conclusions.

• An Integrated Asset Model was built for TSP fields. The model incorporates information from the reservoir to the production stream. A team approach was used for building the model, which required the close collaboration of several disciplines.

• The validity of the IAM was tested with real data and the benefits of the model are already evident. Some examples are related to the impact of GL pressure system in the field production. These observations were validated with real field measurement data.

• Although the current objective of the IAM is post fact quality control, there are steps being taking to bring the model to the next level: live monitoring with the aid of SCADA systems.

• The medium-long term plan is to use the IAM for proper forecasting, incorporating the full spectrum of the TSP fields: from the pore to the sales point.

Acknowledgement

The authors would like to thank the management of Repsol E&P T&T Ltd and its partners for permission to publish

this paper. The authors would also like to thank their colleagues for their contributions and support.

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Nomenclature

API – American Petroleum Institute BLPD – Barrels of Liquid per Day BOPD – Barrels of Oil per Day BHP – Bottom Hole Pressure CSG – casing EOR – Enhanced Oil Recovery FA-Flow Assurance F&C – Facilities & Construction FTP- Flowing Tubing Pressure GAP – Integrated Production Modeling Simulator by Petex GL –Gas Lift GLO – Gas Lift Optimization GLVCO – Gas Lift Valve Change Out GLP- Gas Lift Pressure GP – Gravel Pack HP – High Pressure HSE –Health Safety Environment dept. IAM –Integrated Asset Modeling IPM – Integrated Production Modelin/Model IPR – Inflow Performance Relationship LP – Low Pressure MLT – Mini Logging Tool Non-Rig – Rigless Intervention OCOT – Offshore Crude Oil Treatment Plant Pdome –dome pressure of the valve (Recalculated Ptro at injection depth, based on P and T) PI – Plant Information Software PVT – pressure volume temperature analysis PSI – Pound per Square Inch PTA – Pressure Test Analysis RESOLVE – Integrated Asset Modeling Simulator by Petex SCADA – Supervisory Control and Data Acquisition SD – Shut Down TSP – Teak, Samaan, Poui VLP – Vertical Lift Performance WHP – Well Head Pressure WHT - Well Head Temperature

References

1. C. Correa Feria, Repsol: “Integrated Production Modeling: Advanced but, not Always Better,” SPE 138888. 2. Bagoo, D.; Ramnarine, M.; Segnini, C.; Hernandez, M. “Validation and Analysis of Past PVT Studies from a Complex and

Mature Offshore Asset in Trinidad”, SPE 169928 3. S.K. Moitra and Subhash Chand, Oil & Natural Gas Corp., Santanu Barua, Deji Adenusi, and Vikas Agrawal, Schlumberger

Data & Consulting Services “A Fieldwide Integrated Production Model and Asset Management System for the Mumbai High Field”, OTC 18678

4. Gillian Bates, Danelle Bagoo, Daniel Garcia de la Calle, Andres Finol, Roman Nazarov, Cenobio Rivas, Mirko Hernandez, SPE, Clayton Bunraj, Repsol E&P T&T Ltd.”Production Optimization of a Mature Offshore Asset” SPE 158782-MS