Reprinted from OILFIELD TECHNOLOGYAugust 2013

A lthough reservoir simulation technology and techniques have been continuously evolving—from the nascent efforts of the early

1940s, to today’s platform-based, multidisciplinary approach—its fundamental purpose has remained the same. Critical field investment and development decisions must be based on the best possible information, predictions and intelligence—gained from accurate and detailed simulation.

Modern simulation workflows must be efficient enough to allow reservoir engineers to evaluate all relevant uncertain parameters. As the industry moves to exploit increasingly complex and unconventional reservoirs, the ability to bring subsurface disciplines together working within the same environment is crucial. Eliminating

the siloed approach still common in the industry, and benefiting from multidisciplinary development workflows, can provide a significant competitive advantage.

Effective teamwork requires a shared understanding of the Earth—a cooperative basis for more informed decisions. A recent example of this can be taken from GDF SUEZ E&P Norge’s updated reservoir simulation workflow and technology used for the highly heterogeneous Gjøa field, offshore Norway.

GjøaDiscovered in 1989, Gjøa is estimated to contain around 40 billion m3 of natural gas,

and 73 million bbls. of oil and condensate. Development was announced in December 2006 by a consortium of Statoil, GDF SUEZ E&P Norge, Petoro, Royal Dutch Shell and RWE Dea, with GDF SUEZ E&P Norge taking over operations when production commenced in November 2010. The development comprises five subsea templates tied to a semi-submersible production and processing facility, supplied with power from shore. Water depth in the area is around 370 m.

Gjøa contains gas and light oil trapped above a thin oil zone in Jurassic sandstones in the Viking group. The field, produced by pressure depletion, comprises several tilted fault blocks with variable reservoir quality. Reservoir depth is about 2200 m. Produced gas is transported through the Far North Liquids and Associated Gas System (FLAGS) pipeline to the St Fergus Gas Plant in Scotland. Produced oil is exported through a 55 km link to the Troll II trunkline on its way to the Mongstad refinery north of Bergen.

Modelling challengesThe operator needed to improve and accelerate its existing simulation workflow to integrate static and dynamic modelling for accurate uncertainty analysis, reserves reporting, and reservoir management—as well as to identify infill well targets and confidently forecast production. The company met with Schlumberger to discuss improvements to its reservoir modelling workflow. The Gjøa field is highly

Alexander Shadchnev,

Schlumberger,

Norway, and Mailin Seldal

and Lise Schiøtz,

GDF SUEZ E&P Norge, outline

how an integrated, collaborative

approach has invigorated

reservoir modelling for the

Gjøa field, offshore

Norway.

Reprinted from OILFIELD TECHNOLOGYAugust 2013

Reprinted from OILFIELD TECHNOLOGYAugust 2013

heterogeneous, with steeply dipping layers, variable rock quality and a comparatively thin oil rim and thick gas cap.

The operator also sought to adopt a more collaborative approach to reservoir modelling, bringing discipline experts together, to more accurately represent geology in simulations, capture uncertainty, and improve predictions of production performance. Such an approach allows the asset team to develop a mutual understanding of the field, incorporating multi-domain information and expertise. Further, bringing together key work processes supports preservation of accumulated asset knowledge, regardless of discipline, as the field is developed and produced.

It was agreed that the Petrel™ E&P platform would be used by the operator to drive collaboration, as well as optimise its reservoir modelling and simulation. The Petrel platform supported seismic

interpretation; geomodel construction; and pre- and post-processing of reservoir engineering data for simulation in the ECLIPSETM reservoir simulator. The Olyx assisted history matching plug-in for Petrel, providing additional automation capabilities, would enable the team to deliver the desired results.

Cluster computingA specially hosted simulation computing cluster was also required to enable engineers to undertake multiple simulations and understand dynamic reservoir behaviour in operationally viable timeframes. The operator also has seasonal periods throughout the year where simulation run volume peaks, so a cluster approach would allow the team extra scalability to efficiently handle these extra runs.

To specify and configure the cluster to best meet these requirements, the team undertook simulation run testing. GDF SUEZ E&P Norge’s existing simulation infrastructure took 54 hrs to run the Gjøa full-field model in the ECLIPSE simulator in serial, and 43 hrs in eight-way parallel arrangement. Using a high-performance cluster, on certified hardware, the team was able to complete the same simulation in 20 hrs (serial), 10 hrs in four-way parallel, and 7 hrs in eight-way parallel.

From this information, and further discussions with the operator, the team configured the cluster to allow efficient simulation at peak times as well as for more routine activity. The Schlumberger team worked to ensure that the system was fully automated; it receives simulation requests, runs them and returns results without the need for user interaction. Load sharing facility (LSF) software was implemented to manage simulation job scheduling, and EnginFrame software with ECLIPSE simulator scripts to give engineers easy cluster access and submit simulation workflows directly from the Petrel platform.

The cluster was equipped with InfiniBand interconnection to provide high performance and scalability, as well as easy future extension options. The final cluster comprised four nodes of eight CPUs, allowing significant flexibility. With this setup, the full field reservoir model could be run in 15 min. —using two years of production history—delivering the processing power required for assisted history matching workflows.

The right matchBecause the existing workflow had not supported inclusion of important well and reservoir interpretation updates, the existing geological and simulation models were not being used to their full potential. Dynamic results did not represent the most up-to-date production history. Once interpretation updates were included in the geological models, extensive history matching was undertaken to reproduce past reservoir behaviour.

The relatively thin Gjøa oil zone is produced by four multilateral wells and three monobore horizontal wells. Multiphase flowmeters are installed on the wellheads of all the wells. Downhole pressure gauges were installed to provide vital field depletion information. The key challenge in history matching production and pressure measurements from these wells was to understand the connected pore volume to the wells, the reservoir properties distribution, and the flow contribution from each reservoir zone.

To achieve a good history match, uncertainty in simulation model parameters, such as reservoir porosity, permeability and fault sealing capacity were evaluated to help match static pressures and observed production. Well-based parameters, such as skin factor and zonal productivity, were also varied to match flowing bottomhole pressures.

Figure 1. Water saturation in reservoir model.

Figure 2. Effective porosity modelling on 3D grid.

Figure 3. Initial simulated oil rate production rate versus actual production.

Assisted history matching workflows mean less manual work for engineers, and allows more efficient analysis of reservoir simulation model results—automatically tuning key parameters and consolidating equally well matched realisations. As history matching is a non-unique solution, multiple history matched models produce a range of production forecasts, which are used to assess model sensitivity, rather than using a single ‘best guess’ model.

The team used the Petrel Olyx plug-in to power the assisted history matching process, based on well production rates and bottomhole pressures from field measurements. The related workflow defined the base case geological model realisation, outlined uncertain parameters and ranges, and then generated the necessary history matching process in the E&P platform—incorporating the advanced objective function. A sensitivity study was then run on uncertain parameters; results were analysed to focus on factors that had the most impact on history match results.

An evolution strategy algorithm was used to generate simulation cases to evaluate uncertain parameter values. The ES algorithm is a population-based optimiser that generates numerous simulation cases with sampled values from an uncertain parameter’s range. It is an iterative algorithm that seeks for the

minimum or maximum objective function value in a solution space, and has the advantage of finding the global minimum or maximum, as opposed to just a local optimum. History matching was then completed, and the best matches were analysed and used for reservoir management decisions such as operational constraints definition, pipeline capacity booking, production forecast and infill drilling evaluation.

Informed decision makingBased on the history matching and modelling workflow, the team met and discussed changes to production allocations for several wells. The history matching analysis also challenged existing reservoir structure and reservoir property modelling. Based on the history matching and modelling conclusions, the production team re-evaluated the production allocation for a number of wells. The existing geological concept and property modelling was also discussed, resulting in a decision to build additional geological model realisations incorporating detailed updates to more accurately characterise the reservoir’s structure and lithology.

The Petrel environment improved collaboration between geophysicists, geologists, petrophysicists and reservoir engineers. Its integrated reservoir modelling workflow allowed for real time data updates and rapid corrections to interpreted seismic surfaces, to optimise history matching. Geological information was easily incorporated into the shared model to undertake reservoir simulation uncertainty studies, and produce history matched models for reservoir management and optimisation tasks. Reservoir property distribution algorithms were also tested in the simulation workflow. Results were analysed and optimised to further improve the history match. Finally, updated history match results were used to evaluate the geological model to improve understanding of both structure and property distribution.

The integrated modelling approach facilitated consistent model building from initial interpretation to simulation, resulting in a model that preserves meaningful geological information and accurately represents production history. These workflows can be reused to fully evaluate risk for future field development investment decisions.

Shared earth advantagesThe use of a high performance cluster allowed four times as many simulations to be run than with the previous setup, to enable better understanding of dynamic reservoir behaviour. It also meant the operator could avoid upscaling the property grid, to capture geologic features, reservoir quality and structural complexity. Simulation was undertaken on the grid, at sizes that were acceptable for both geologists and reservoir engineers. Over 2000 simulation runs were performed, in economically viable timescales, on the main model producing several history matched models for reservoir management.

The multidisciplinary workflows and a shared earth environment improved collaboration and communication between geophysicists, geologists and reservoir engineers and allowed for the incorporation of all relevant data in the final reservoir simulation model.

The newly updated geological model improved understanding of dynamic reservoir uncertainties, leading to improved prediction of dynamic gas and fluids behaviour and more confident production forecasts. History matched simulations are now used for internal and external reserves reporting. The integrated modelling workflow also allows live reservoir models to be maintained, on which history match updates can be carried out when new data becomes available.

Figure 4. Gas, oil and water contact model.

Figure 5. Model showing well placement in producing interval above oil water contact.