Foundational Study of Artificial Intelligence Reservoir Simulation by Integrating Digital Core Technology and Logging Data to Optimise Recovery

Abstract

Abstract In strategising development of hydrocarbon reservoirs, substantial uncertainty in recovery potential is often attributed to subsurface heterogeneity. Challenged reservoir characterisation is proposed to be directly due to the inability of correlating spatial scales: core analyses to well logging data. This study’s central goal is to propose a ‘Multiscale link’ by challenging empirical correlations of multiphase displacement and ‘upscaling’ processes of reservoir characterisation by exploiting Artificial Intelligence and ‘Digital Rock Technology’, aiming at minimising geological risk. By exploiting 40 years of a North Sea field's appraisal and production and formulating an AI-compatible ‘multiscale’ data set, petrophysical correlations have integrated a further innovative concept: borehole image processing to characterise geological features and oil potential. In binding the ‘Multiscale’, fundamental multiphase dynamics at pore-scale have been critically associated to most affine reservoir modelling ‘deep learning’ frameworks, leading to ideating an AI workflow linking field-scale rates, well logs and core analyses to the continuously-reconstructed pore network, whilst extracting invaluable multiphase dependencies. The preliminary results implementing selected Machine Learning algorithms, coupled with advanced digital technologies in reservoir simulation, have been showcased in proposing a solution to the ‘Multiscale link’ in reservoir characterisation, providing the groundworks for its programming realisation. Importantly, it was concluded that the layers of complexity within learning algorithms, which constrained its execution within this project, undoubtedly require multidisciplinary approach. By conceiving a physically and coding-robust workflow for advanced reservoir characterisation and modelling permitting ‘multiscale’ representative multiphase simulations, identification of optimal EOR becomes attainable. This leading edge represents potential to minimise geological risk, thus de-risking reservoir management (in turn FDP) of mature and live fields; but also expected to set a starting point for further developments of Artificial Intelligence in the oil and gas industry.