The Greater Vanza Longui Area (GVLA) field is located in Area B of the offshore Cabinda, Angola Block 0 concession. GVLA is a multi field development and will target hydrocarbon resources in the Cretaceous Pinda formation that contain reservoirs with rich gas condensates and underlying volatile oil rims.

The objective of this paper is to demonstrate the rigorous subsurface evaluation process employed for developing a large offshore gas condensate field. The strength of the reservoir evaluation is the large amount of good quality appraisal data gathered and used to benchmark subsurface inputs. There have been 6 reservoir penetrations, 4 DSTs and 3 full reservoir cores and multiple fluid samples.

A structured workflow was followed to identify and incorporate the uncertainties in the subsurface assessment. Probabilistic forecasts helped to fully characterize and realistically assess the uncertainties. The uncertainties were grouped mainly into static and dynamic categories.

The impact of the static uncertainties was assessed using 3-D geological models. Geologic models were built upon a core-based sequence stratigraphic framework for the Pinda formation. The facies, petrophysical properties and top depth of reservoir were varied to capture the uncertainty ranges seen in the GVLA wells and analogue fields. These models captured the net-to-gross uncertainty which has the largest impact on hydrocarbons in-place and connectivity.

The dynamic uncertainties were assessed using statistical design of experiments. Monte Carlo simulations were employed to generate the probabilistic estimates. Reservoir simulation with the compositional formulation was used as the primary forecasting method. Robust equation-of-state models were built to appropriately quantify natural gas liquids (LPGs) which were value drivers for the project. Various development alternatives (primary depletion, gas injection) were evaluated and detailed economic analysis were performed for concept selection. The production forecasts were verified with material balance models, analogues.

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