The Hibiscus field situated off the North coast of Trinidad is a large, stratigraphically isolated and well-connected gas field which has 14 years of production history. Notwithstanding this extensive production history and overall recovery, a number of key subsurface uncertainties have been identified. The scope of this study was to better understand reservoir complexity and define subsurface risk and opportunity.

An integrated and iterative multidisciplinary approach to reservoir modelling was applied in an effort to meet these objectives. A modern suite of workflows such as Monte Carlo Petrophysical analysis, conditioning of models to seismic attributes, experimental design based uncertainty analysis and assisted history matching were used to generate new static and dynamic reservoir models. The key aspect of this workflow was 20+ major static to dynamic model iterations and a large number of deterministic simulations to rank and asses the validity of static concepts. The learnings were subsequently applied to create a robust reference case, static and dynamic uncertainties framed and a static probabilistic uncertainty workflow developed to QC the deterministic case. A full probabilistic assisted history matching exercise on the dynamic model enabled a refinement of the volumetric ranges and provided critical insight through analysis of posterior uncertainty distributions.

The iterative workflow allowed concepts to be validated dynamically and it was demonstrated that high quality history matches could be achieved even after the removal of almost all dynamic multipliers – a common issue in simulation models. Significant improvements to pore volume distribution, the use of geologically derived dynamic baffles, and permeability distributions were amongst the key learnings on the static side. The probabilistic dynamic modelling was characterized by a strong GIIP convergence with a reduction of history match error, resulting in a refined volumetric range and better characterization of uncertainty ranges through posterior analysis.

The application of modern integrated and iterative workflows to a mature field has better defined uncertainty ranges, understanding of reservoir behavior and overall resulted in a more robust suite of models. Key learnings identified were highlighted to support future reservoir model rebuilds. Ultimately this process has demonstrated the value of revisiting existing datasets in late life assets by generating higher confidence in remaining reserve estimates and business plan forecasts.

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