Seismic history matching uncertainty with weighted objective functions

History matching using 4D seismic time-lapse data provide a method to manage uncertainties of multi-models, but it remains a great challenge without reaching any clear statements or practical methodology or good practise to follow. What makes the SHM so difficult is mainly the nature of the seismic data. Indeed, acquisition, interpretation, processing, make this data embedded with uncertainties, due to the physics and measurement issues. One of the challenge is to be able to extract and quantify the uncertainties carried over by the seismic data and use it to guide decisions. The way we insert seismic data and its inherent uncertainty into the workflow is the key to further enforce progress in 4D seismic history matching. A comprehensive workflow is implemented which allows shape to estimate uncertainties using weighted factor. In the proposed history matching workflow, 4D seismic attributes are converted to binary images which are representative of fluid saturations, then binary maps are compared using a pattern-matching objective function which can capture the main feature of the seismic data. Novelty is that weighted binary maps are generated on different estimation of the uncertainty within seismic attribute, to explore and screen performance on the seismic history matching procedure. Weighted maps are associated with error/uncertainty quantification of 4D seismic signature, which allow us to identify even specific governing region of seismic reflecting fluid properties, also it shows greatest general-usage potential. An adaptive derivative free optimisation has been applied for the history matching process. Global and local properties are parameterised in the history matching loop, which includes permeability, porosity, fault transmissibility and net-to-gross. Numerical experiments with a UKCS field shows this methodology is quite flexible and efficient, which circumvent large uncertainty in seismic data and use of uncertain petro-elastic model. This study also implies that the seismic history matching achieves a reasonable production matching while constrains saturation changes derived from time-lapse data using weighted binary seismic history matching method.