Lobate turbidite deposits are a dominant element of many deepwater reservoirs, but are difficult to model accurately and in a geologically-realistic manner with standard static reservoir modeling tools. Realistic geological modeling of deepwater lobate turbidite deposits is fundamental to capturing stratigraphic architectures and associated flow barriers which can strongly impact development and recovery in deepwater reservoirs. To address this, Shell has developed a tool within PetroSigns platform that enables surface-based lobe modeling. PETROSIGNS is the next step in the evolution of Shell's modelling and optimisation capabilities. This technology employs a deterministic surface-based forward modeling technique that mimics physical deposition processes at a fraction of the physics-based-modeling computational cost. Geological and depositional rules are used to guide lobe placement in a compensational stacking pattern. The tool facilitates construction of a surface-based stratigraphic grid (where the grid layers are the lobe objects/geobodies) and populates the grid with depositional trends (e.g. proximal-distal and axial-marginal trends). The resulting model trends can be integrated within standard static reservoir modeling workflows to constrain the petrophysical property modeling. The novel tool/approach presented here allows the geo-modeler to place turbidite lobes within the zone of interest either interactively or automatically by specifying limited geometrical parameters. The resulting stratigraphic grids offer significant advantages for reservoir modelling, allowing efficient representation of distal thin-beds and pinch-outs, and sharp property contrasts across lobe bounding surfaces. Models built using this tool do not have remnant erosional elements or random intra-lobe property distributions which are commonly produced by traditional approaches; rather, the tool produces proper depositional bodies with realistic property distribution. In addition, the new tool enables facies variability laterally as well as vertically within the modelled lobes (e.g. coarser facies at the bottom of the lobe with fining-upward trends which is typical in turbidite deposits). The tool has been applied in several fields. For example, it has been used in an oil field offshore Brazil where the static reservoir model built using the tool resulted in a robust history match based on geology, where previously artificial connections or flow barriers had been introduced in the dynamic model. This novel modeling technique has been used to build geologically realistic but fit-for-purpose models and has simplified turbidite lobe modeling workflows. Additionally, the resulting surface-based grid makes it possible to model flow barriers (shale drapes) by placing them between lobe surfaces. The tool is flexible and can be used in different settings; in mature fields (for model updates/history matching) as well as green fields (to examine different scenarios and capture uncertainties). Importantly, considerable reduction in cycle time is achieved when additional data becomes available and scenario based models have to be prepared, which is significant for cross-discipline integration and prompt project delivery.
Extended Abstract This paper presents a fast turnaround Integrated Reservoir Modelling (IRM) workflow for a deep water turbidite field. It is based on a static and dynamic Experiments of Design (DoE or ED) workflow that is used as a toolkit for uncertainty management. The case study shown here emphasizes the application of this "Multi Scenario Approach" methodology for field specific decisions such as short and mid-term reservoir management optimization and infill development opportunities, including impact of key subsurface uncertainties on these decisions. anonumously The objectives of Integrated Reservoir Modelling workflow presented here are: To create fully integrated models or multiple scenarios that provide realistic ranges of forecasts, e.g. geologically sound, to underpin development and operational decisions without anchoring to a single base case; andTo shorten modelling life cycle to achieve faster project delivery and allow for uncertainty workflows as proposed in this paper. There are many decisions that are dependent on model forecasts such as identifying infill well target locations and number of wells at the project level, project phasing to extend production plateau, and to identify and mitigate key projects technical risks
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