The goal of Swift Modelling™ services is to generate digital representations of the underground geology and rock fluid dynamics that parallel the reservoir’s reality by integrating geophysical, geological, and production data. This process involves several challenges, including scale and resolution of various data sources, data quality and information content, qualitative observations, and inverse models. In addition, the validation of interpretation models across disciplines addressing complex problems that are difficult to solve in the confines of a single domain.

Our approach focuses on capturing the impact of geological complexities in production performance to overcome the drawbacks of conventional techniques in developing static and dynamic models. It combines state-of-the-art methods and workflows in structural modelling, petrophysical analysis, geological model construction, reservoir fluid and rock evaluation, classical reservoir engineering, decline curve analysis, assisted history match, and numerical simulation technologies.

Building various hierarchical realisations ensures that generating multiple geological models accounts for uncertain geological parameters. However, before following a machine learning or automatic history matching approach to make numerous simulation runs to minimise an objective function, our geoscientists and reservoir engineers initially make few geological simulation models and focus on analysing in detail how they diverge from reality.

Our reservoir engineers expertly use dynamic methods such as Streamline Simulation or Fast Marching Methods (FMM) for determining the connectivity within the reservoir without the need for full finite difference simulation runs. Thus, quickly determining the performance characteristics of the different realisations of the geological model to select those that properly quantify the entire range of uncertainty, therefore, worth carrying forward to the reservoir simulation for history matching.

In reviewing the divergences with the entire multi-discipline team, perhaps, the geophysicist contemplates an alternative interpretation of the seismic that explains why the model fails to match pressure support in a specific area. Or the production engineer recognises a possible systematic misallocation of produced fluids. Or, the petrophysicist identifies a previously unsuspected bias in core sampling.  Hence following a round table discussion approach, the team rebuilds the model, and the reservoir engineer makes further simulation runs.

While cycling around this “Big Loop” several times, the entire team uses the history matching process to gain a much-improved understanding of the reservoir, the key to enabling optimum future developments. Moreover, rather than building just one, two, or three geological models, the team develops multiple alternative subsurface realities.