... mismatch metrics. Mismatch evaluation is a crucial part of any history matching approach, and co-visualization of data, models and mismatch was required to develop and validate 4D seismic mismatch metrics for the case study. Key visualizations were also needed to identify time-lapse intervals and locations...

... and drainage history very complex. Thus, achieving a full field history match in the Fensfjord reservoir through deterministic modeling has always been a challenge. Current STOIIP estimates indicate that there is room for several more producers. However, identification of target areas for future wells...

... methods may be used in a fruitful way for development of improved and computationally fast well inflow estimation models. reservoir characterization well performance reservoir simulation artificial intelligence dimension upstream oil & gas computational time history matching posterior...

... and practical applications. Tracer has been incorporated into history matching by a few authors (Allison, Pope, & Sepehrnoori, 1991; Datta-Gupta, Lake, & Pope, 1995; Iliassov, Datta-Gupta, & Vasco, 2001; Huseby, Andersen, Svorstol, & Dugstad, 2007; Valestrand, Sagen, Nævdal, & Huseby, 2010...

... CPU (Central Processing Unit) simulators as the results cannot be achieved within an acceptable time frame and simulation may last days or weeks. These long run times cause a delay in achieving a robust history matched or production forecast model as it constrains the ability to effectively...

..., that could explain this discrepancy. The most likely scenario gives a good history match for both pressure and tracers and gives extra insight into key reservoir parameters, zonal inflow and the effective behavior of the AICDs during multi-phase flow. The well has exceeded pre-drill production rate...

... that will be used as the observations for model updates later on, through a certain history matching algorithm. Therefore, data-size reduction through sparse representation may help mitigate the issue of high-demanding requirements on computational resources (e.g., computer memory) in big data assimilation problems...

... history from one test producer. A key objective of the modelling effort was to take an uncertainty-centric approach that could integrate all available data. The methodology of this approach is presented, and its benefits and challenges are discussed. The quality of the automated history matching is shown...

... Abstract Ensemble-based methods are among the state-of-the-art history matching algorithms. In practice, they often suffer from ensemble collapse, a phenomenon that deteriorates history matching performance. To prevent ensemble collapse, it is customary to equip an ensemble history matching...

... Abstract Ensemble-based history matching methods have received great attention in reservoir engineering. In real applications, small ensembles are often used in reservoir simulations to reduce the computational costs. A small ensemble size may lead to ensemble collapse, a phenomenon in which...

.... In recent years, ensemble-based history matching methods, for instance, the ensemble Kalman filter (EnKF, see, for example, Aanonsen et al., 2009 ; Nædal et al., 2005 ), the ensemble smoother (ES, see, for example, Van Leeuwen and Evensen, 1996 ), the ensemble Kalman smoother (EnKS, see, for example...

... is mitigated by conditioning the model with data through data assimilation, a process known in the oil industry as history matching. Several recent advances are being used to improve history matching reliability, notably the use of time-lapse data and advanced data assimilation techniques. One of the most...