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    A General Spatio-Temporal Clustering-Based Non-local Formulation for Multiscale Modeling of Compartmentalized Reservoirs

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    Representing the reservoir as a network of discrete compartments with neighbor and non-neighbor connections is a fast, yet accurate method for analyzing oil and gas reservoirs. Automatic and rapid detection of coarse-scale compartments with distinct static and dynamic properties is an integral part of such high-level reservoir analysis. In this work, we present a hybrid framework specific to reservoir analysis for an automatic detection of clusters in space using spatial and temporal field data, coupled with a physics-based multiscale modeling approach. In this work a novel hybrid approach is presented in which we couple a physics-based non-local modeling framework with data-driven clustering techniques to provide a fast and accurate multiscale modeling of compartmentalized reservoirs. This research also adds to the literature by presenting a comprehensive work on spatio-temporal clustering for reservoir studies applications that well considers the clustering complexities, the intrinsic sparse and noisy nature of the data, and the interpretability of the outcome. Keywords: Artificial Intelligence; Machine Learning; Spatio-Temporal Clustering; Physics-Based Data-Driven Formulation; Multiscale Modelin

    Reservoir Heterogeneity: Should It Be Modelled as Conformance or Dispersion?

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    Streamline Simulation to Improve Polymer Enhanced Oil Recovery for a Mature Oil Field in Austria

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    Fine Scale Simulation of Fractured Reservoirs: Applications and Comparison

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    SIMULATION STUDY ON WATERFLOOD FRONT: BLOCK HADE OF TARIM OILFIELD IN NORTHWEST CHINA

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    Block Hade consist of a deep thin sandstone reservoir of two sub-layer reservoirs. The thickness is about 1.5 m for each layer. The two-layer “staircase” horizontal well is used for recovery. In order to determine water displacement front and edge water movement, tracer test is conducted in the reservoir. But the cycle of field tracer monitoring is about 150-360 days. This prevented the efficient monitoring of waterflood swept area and waterflood advance direction and velocity, after the cycle of tracer monitoring. Conservation of mass with respect to tracer flow and history performance matching of tracer enabled the study of water-flood front and edge-water advance. The simulation result is basically consistent with the monitored field tracer results. Therefore, numerical model can be used to conduct a longer monitoring period. It can make up for the disadvantage of the complexity of the tracer monitoring setup, its implementation, and time-consuming monitoring cycle. The water-flood front, water-flood swept area, advancing velocity and the predominant water injection direction can be obtained. Furthermore, it is possible to evaluate and predict the injection-production well interaction and can also provide a reliable basis to deploy reasonable flood patterns to enhance oil recovery

    Liquids-Rich Shale Evaluation: Modelling and Optimization of Hydraulically Fractured Liquids-Rich Shale Wells

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