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In this paper, we describe a reservoir-modelling case history of Mafumeira, a Chevron-operated field located in Offshore Angola. The field has only six well penetrations and lies within the closure of nearly 60 square kilometres; the purpose of the study was to capture a range of subsurface uncertainties for evaluation of the development options. We used a Depositional Facies Modelling scheme utilizing recent developments in Multiple Point Geostatistical Simulation and reservoir property uncertainty analysis to generate five static reservoir models. After scale-up, flow simulations were conducted on each model for different field development options using a Design of Experiments (DoE) methodology and a preferred development option was selected.

The geology of Mafumeira field is complex. The multiple point geostatistical simulation used a training image consisting of seven depositional facies. The training image is a 3D conceptual model of the facies present and the facies associations; it captures complex spatial relationships between multiple facies, and non-linear shapes such as sinuous channels. The facies simulation was conditioned by a facies probability cube, which permitted the use of a single training image for different stratigraphic intervals of the reservoir, with different combinations and proportions of the seven facies. Multiple versions of the facies probability cube were produced to model the uncertainty in the occurrence of reservoir quality rock units.

In modelling the reservoir properties, uncertainties in porosity, permeability and water saturation (‘PKS’) were taken into account. Five models were produced reflecting the combinations of high- and low-case reservoir facies, high- and low-case PKS properties and an intermediate-case. The high-, intermediate- and low-case models were then dynamically tested to ensure different flow behaviours, prior to upscaling, and the flow behaviours compared to analogue producing fields.

In order to utilize DoE simulation, upscaling of the five fine-grid models was required. Flow-based simulation was chosen as the best tool to validate the behaviour of the coarse-grid models against the fine-grid models. However, this effort demonstrated that the conventional scale-up methods utilized in other reservoir models did not adequately capture the behaviour of the fine-grid models in this heterogeneous reservoir. A new method that adjusts the Dykstra–Parsons coefficient was investigated and successfully employed to tune the coarse-scale models.

Twelve development alternatives for the field were defined and deterministic economics, based on results from the mid-case simulation model, were run in order to narrow down the number of alternatives to be carried forward into probabilistic analysis to five. The DoE approach allowed us to undertake a thorough evaluation of the key subsurface uncertainties and design an overall development plan. The probabilistic simulation results along with full Decision Analysis (DA) allowed us to identify a phased development, which would mitigate potential downside risks while preserving the ability to capture upside potential.

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