History Matching Using 4D Seismic in an Integrated Multi-Disciplinary Automated Workflow
This paper presents a case study in 4D seismic history matching using an automated, ensemble-based workflow that tightly integrates the static and dynamic domains. Subsurface uncertainties captured at every stage of the interpretative and modeling process are used as inputs within a repeatable workflow. By adjusting these inputs, an ensemble of models is created, and their likelihood is constrained by observations within an iterative loop. The result is multiple realizations of calibrated models that are consistent with the underlying geology, the observed production data, and the seismic signature of the reservoir and its fluids. It is effectively a digital twin of the reservoir with an improved predictive ability that provides a realistic assessment of uncertainty associated with production forecasts.
The example used in this study is a synthetic 3D model mimicking a real North Sea field. Data assimilation is conducted using an Ensemble Smoother with multiple data assimilations (ES-MDA). This paper has a significant focus on seismic data, with the corresponding result vector generated via a petro-elastic model. 4D seismic data proves to be a key additional source of measurement data with a unique volumetric distribution, creating a coherent predictive model. This allows recovery of the underlying geological features and more accurately models the uncertainty in predicted production than was possible by matching production data alone.
A significant advantage of this approach is the ability to simultaneously utilize multiple types of measurement data, including production, RFT, PLT, and 4D seismic.
Newly acquired observations can be rapidly accommodated. This is often critical, as the value of most interventions is reduced by delay.
Sasan Ghanbari, Team Lead - Modeling
Sasan Ghanbari has extensive experience in offshore exploration and in the Canadian Oil Sands. He has also worked on various field studies and geostastical subsurface modeling in North America, South America, and the Middle East. This work includes analyzing relevant data such as regional studies and uncertainty analysis to build realistic geological, geostatistical and reservoir simulation models. Sasan has been working with Emerson since 2004. He earned a degree in Petroleum Engineering from the University of Alberta and is a Professional Engineer.