3D multi-scale characterization of fractured carbonates in field analogues on the Maltese Islands: workflow and preliminary results.

Multiscale characterization of fracturing in folded platform carbonates: the case study of the Island of Pag, External Dinarides of Croatia

Fractures are a primary factor controlling the fluid storage and transport properties of hydrocarbon reservoirs. The quantitative characterization of fractures in outcrop analogues allows a better understanding of natural fracture systems, to extrapolate predictive laws useful for integrating the limited data available for buried reservoirs....

Characterizing Dynamic Stress Sensitive Fracture Apertures in A DFN Representation: An Example From the Island of Pag (Croatia)

Improving the understanding of fluid flow through fracture networks is crucial to the optimum recovery of hydrocarbons in naturally fractured reservoirs. In explicit dual continuum representations of matrix and fractures, it is not just the intensity of fracturing and the topological relationships in the network but also connectivity of...

Multiscale 3d Prediction Of Fracture Network Geometry And Fluid Flow Efficiency In Folded Carbonate Reservoir Analogues; Case Study Of The Island Of Pag (Croatia)

Geological and structural map of the southeastern Pag Island, Croatia: field constraints on the Cretaceous - Eocene evolution of the Dinarides foreland

The sedimentary succession exposed in the Northern Dalmatia Islands mainly consists of Cretaceous to Neogene shallow water carbonates, folded and imbricated within the External Dinarides thrust belt. During Cretaceous times, carbonate sediments were deposed on a heterogeneous, tectonically-influenced carbonate platform, which was then uplifted...

A new methodology to train fracture network simulation using Multiple Point Statistics.

Natural fracture network characteristics can be establishes from high-resolution outcrop images acquired from drone and photogrammetry. Such images might also be good analogues of subsurface naturally fractured reservoirs and can be used to make predictions of the fracture geometry and efficiency at depth. However, even when supplementing...

The MPS-Based Fracture Network Simulation Method: Application to Subsurface Domain

Natural fractures conduct fluids in subsurface reservoirs. Quick and realistic predictions of the fracture network organization and its fluid flow efficiency from limited amount of data is critical to optimize resources productivity. We recently developed a method based on multiple point statistics (MPS) technique to produce geologically...

Thermomechanical Oriented Reliability Enhancement of Si MOSFET Panel-Level Packaging Fusing Ant Colony Optimization With Backpropagation Neural Network

Considerable advancements in power semiconductor devices have resulted in such devices being increasingly adopted in applications of energy generation, conversion, and transmission. Hence, we proposed a fan-out panel-level packaging (FOPLP) design for 30-V Si-based metal-oxide-semiconductor field-effect transistor (MOSFET). To achieve...

A Thin and Low-Inductance 1200 V SiC MOSFET Fan-Out Panel-Level Packaging With Thermal Cycling Reliability Evaluation

SiC MOSFET is mainly characterized by the higher electric breakdown field, higher thermal conductivity, and lower switching loss enabling high breakdown voltage, high-temperature operation, and high switching frequency. However, their performances are considerably limited by the high parasitic inductance and poor heat dissipation capabilities...

Thermal-mechanical-electrical Co-design of Fan-Out Panel-Level SiC MOSFET Packaging with a Multi-objective Optimization Algorithm

As the next generation of semiconductor devices, SiC MOSFETs have demonstrated significant performance improvements in switching loss, switching frequency, and high-temperature operation compared to Si-based MOSFETs. However, the long-term reliability of such devices and their packaging continues to be a major concern. Towards addressing this...

In-Situ early anomaly detection and remaining useful lifetime prediction for high-power white LEDs with distance and entropy-based long short-term memory recurrent neural networks

High-power white light-emitting diodes (LEDs) have demonstrated superior efficiency and reliability compared to traditional white light sources. However, ensuring maximum performance for a prolonged lifetime use presents a significant challenge for manufacturers and end users, especially in safety–critical applications. Thus, identifying...