Maximising Value of Gas Tracer Surveillance via Simulation

Mammadli, Ilkin; Ibrahimov, Turan; Sadikoglu, Kasim; Mamedov, Adil Mukhraddin; Sadigov, Samir

doi:10.2523/18168-ms

International Petroleum Technology Conference 2014

DOI: 10.2523/18168-ms

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Maximising Value of Gas Tracer Surveillance via Simulation

Ilkin Mammadli¹,

Turan Ibrahimov²,

Kasim Sadikoglu³

et al.

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“…It was noted that the injection rates into the closest water injectors and producers had remained relatively stable with no change in choke size. This observation was confirmed by gas tracer data analysed later and was modelled based on existing reservoir model (Mammadli et al, 2014). The tracer results collected from Azeri field since start of production became a very useful tool in understanding of compartmentalization along the structure in general.…”

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confidence: 56%

Surveillance Helps to Get a Better Understanding of Reservoir Connectivity in Azeri Part of ACG

Kuramshina¹,

Ibrahimov²

2015

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Accurate and timely information is essential to monitor, control and manage reservoir and well performance. At the present, production and reservoir engineers as well as production geologists have a challenging task of managing oil and gas reservoirs. This requires a broad knowledge of the reservoir supported by fit-for-purpose integrated technologies and real time access to relevant data. In the Azeri-Chirag-Gunashli (ACG) field development, downhole information is commonly acquired through uses of LWD (logging while drilling) tools to obtain a standard log suite and obtain formation pressure measurement, production logging and well testing. In addition, Downhole Temperature Sensors (DTS) has been installed on someACG wells to provide continuous temperature readings across the reservoir so as to obtain critical understanding of reservoir behaviour, connectivity and fluid distribution.ACG structure is located in the offshore of Azerbaijan part of the South Caspian Basin (Figure 1). The structure is an elongate anticline with 3 main accumulations ( Figure 2): 1). Shallow Water Gunashli (SWG) is in the northwest operated by SOCAR and on production since 1980; Deep Water Gunashli (DWG) extended from SWG to the east and became a part of ACG field operated by AIOC with production start-up in 2008; 2). Chirag situated in the centre of the structure that has been on production since 1997; 3). Azeri is in the south east which is in production since 2005(Wethington et al., 2002. ACG structure is complicated by the presence of several mud volcanoes and series of faults and seismic elements cutting field along the length. Development of field is under 30 years PSA that expires in 2024.This field contains 16 hydrocarbon-bearing reservoirs of Pliocene age. Each of the reservoirs is regionally identifiable, laterally extensive and correlatable within the long distance. They comprise stacked fluvial-deltaic sandstones deposited by the paleo-Volga delta, flowed from the north to the south into the lacustrine South Caspian Basin.The main producing interval in Azeri field is a Fasila reservoir that has been subdivided into 5 sublayers (Fasila A,B,C,D, & E) based on the first appraisal well stratigraphy. A stratigraphic section of Fasila reservoir from one of ACG wells shown on Figure 3 illustrates high net-to-gross (NTG) Fasila B and Fasila D sublayers separated by lower NTG Fasila A, C and Fasila E layers.Azeri development proceeds under crestal gas and peripheral water injection support. Therefore understanding of compartmentalization of the structure plays significant role in production efficiency.

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confidence: 56%

Surveillance Helps to Get a Better Understanding of Reservoir Connectivity in Azeri Part of ACG

Kuramshina¹,

Ibrahimov²

2015

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History of History Match in Azeri Field

Ibrahimov¹

2015

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Modern reservoir development could not happen without building and using 3-phase 3-D simulation models and predicting the future performance of the field using powerful computer technologies. Long term production and injection profile forecasts for the Azeri field have been carried out through usage of simulation models from 2001. Since production from 2005 Azeri models have been history matched on an annual basis. While matching the model data to the actual field observed data, surveillance and performance learnings have been an integral part of the process in order to obtain representative and satisfactory results.In the current work, the challenges and problems faced, as well as the success of the history match process for the Azeri field are described. Along with discussion of applied techniques and obtained results, recommendations on matching complex reservoir simulation models based on several years experience of history matching process in the Azeri field are offered.

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Uncertainty and Risk Assessment Using Ensemble Modeling for Major Development Decision

Asgarov¹,

Arslan²,

Salim-Zade³

et al. 2021

Day 2 Wed, October 06, 2021

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This paper summarizes a risk framing and assessment exercise to support a major development decision in Azeri-Chirag-Gunashli field located in Azerbaijani sector of Caspian Sea, using at scale implementation of ensemble modelling technique. A multi-disciplinary team identified a comprehensive list of geological and mechanical uncertainties and their inter-dependencies that could impact the project outcome. 20000 subsurface scenarios with unique uncertainty parameter combinations, based on Monte Carlo approach, were modeled using reservoir simulation software. Match quality to the actual performance helped to select an ensemble of unique subsurface scenarios. With the non-subsurface uncertainties in the predictive stage, the ensemble was used to inform the investment case's incremental value range. The ensemble predicted a cloud of outcomes around the reference case with -25% and +12% uncertainty range. This cloud assumed the same (reference case) sequence and location of the wells. The team selected distinct scenarios from the cloud to communicate the risks on key project metrics. Incremental recovery and production ramp-up variation against significant uncertainties guided the selection of distinct downside and upside scenarios for economic evaluation to ensure the robustness of development decision. The initial cloud was considered to be unmanaged as the well locations were exposed to reservoir property, connectivity, sweep, and other uncertainties for a specific scenario in the ensemble. The team recognized the long duration of development activities and tangible subsurface learning opportunities from drilling through multiple reservoirs and other surveillances, particularly on the key uncertainties ("heavy hitters"). This observation was leveraged to manage uncertainties and make appropriate well location and sequence decisions. Overall uncertainty range slightly narrowed down to -20 % and +16 % while providing an appropriate downside and upside profiles for the investment case. Assessing the impact of key uncertainties on investment value helped refining surveillance and risk management plan with focus areas on well and facility design to ensure risk management measures are executable.

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Maximising Value of Gas Tracer Surveillance via Simulation

Cited by 3 publications

References 0 publications

Surveillance Helps to Get a Better Understanding of Reservoir Connectivity in Azeri Part of ACG

Surveillance Helps to Get a Better Understanding of Reservoir Connectivity in Azeri Part of ACG

History of History Match in Azeri Field

Uncertainty and Risk Assessment Using Ensemble Modeling for Major Development Decision

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