Carbonate Reservoir Characterization with High-Resolution Logging-While-Drilling Dual-Images in Oil-Based Mud

Fracture evaluation and vuggy feature understanding are of prime importance in carbonate reservoirs. Commonly the related features are extracted from high resolution borehole images in water-based mud environments. To reduce the formation damage from drilling fluids, many wells are drilled with oil-based muds (OBM) in carbonate reservoirs. There are no appropriate measurements to resolve the reservoir characterization in OBM with the existing technologies in horizontal wells—especially in real-time—to make decisions at an early stage. In this paper, we would like to introduce a workflow for geological characterization using a new dual-images logging while drilling tool in oil-based mud. This new tool provides high resolution resistivity and ultrasonic images at the same time. Structural features, such as bedding boundaries, faults, fractures can be identified efficiently from resistivity images; while detailed sedimentary features, for example, cross beddings, vugs, stylolite are easily characterized using ultrasonic images. Benefiting from the dual images, an innovative workflow was proposed to estimate the vug feature more accurately; and the fractures can be identified from images and classified based on tool measurement principles. One case study from the Middle East demonstrated the benefits of this new measurement. A near well structure model was constructed from bed boundaries picked from borehole images. The fractures were picked and classified confidently using the dual images. Additionally, fracture density statistics are available along the well trajectory. The vug features were extracted efficiently, which indicates the secondary porosity development information. Rock typing is achieved by combining fracture and vug analysis to provide zonation for completion and production stimulation. The dual-images provide the capability for geological characterization in carbonate reservoir in an oil-based mud environment. The image-based rock typing helps segment the drain-hole for completion and production stimulation. The reservoir mapping with rock typing provides detailed information for in-filling well design.

Two Tales of One City: Fantasy Proneness, Authenticity, and Loyalty of On-Screen Tourism Destinations

On-screen tourism destinations provide tourists with a mixture of reality and mass-media experience. This study builds a conceptual framework of authenticity evaluating the relationship between fantasy proneness, authenticity, and destination loyalty. It is among the first to compare perceived authenticity of dual images of a destination, both the film location and the story’s setting. Using Game of Thrones and the city of Dubrovnik as a case, it applies a mixed method of interviews followed by a main survey with both closed- and open-ended questions. The primary findings show that the relationship between authenticity of the film location (Dubrovnik) and loyalty is positive, but such relationship is insignificant for the authenticity of the story’s setting (King’s Landing). This investigation enriches our understanding of the complex assessments of authenticity and expands the theory of imagination by evaluating the effects of fantasy proneness on perceived authenticity in the context of on-screen tourism.

Reversible data hiding scheme based on pixel-value differencing in dual images

In this article, a new reversible data hiding scheme using pixel-value differencing in dual images is proposed. The proposed pixel-value differencing method can embed more secret data as the difference value of adjacent pixels is increased. In the proposed scheme, the cover image is divided into non-overlapping blocks and the maximum difference value is calculated to hide secret bits. On the sender side, the length of embeddable secret data is calculated by using the maximum difference value and the log function, and the decimal secret data are embedded into the two stego-images after applying the ceil function and floor function. On the receiver side, the secret data extraction and the cover image restoration can be performed by using the correlation between two stego-images. After recovering the cover image from two stego-images, the secret data can be extracted using the maximum difference value and the log function. The experimental results show that the proposed scheme has a higher embedding capacity and the proposed scheme differs in embedding the secret data depending on the characteristics of the cover image with less distortion. Also, the proposed scheme maintains the degree of image distortion that cannot be perceived by the human visual system.

Dual Shot Face Detecting using Deep Learning

In the paper, we have used a deep learning technique to identify dual faces i.e. nothing but detecting dual shot faces. As the data is emerging day by day with high dimensionality, recognizing dual faces is a major problem. So wasting time on identifying images is like fiddling around. In order to save time and get absolute accuracy we have implemented a fast preprocessing technique named as Convolutional Neural Network (CNN) along with feature extraction technique which is used to knob the relevant features to detect and identify images/faces. By performing this robust method, our intention is to detect dual images in an efficient way. This technique results in decreased feature cardinality and preserves unique efficiency of the data. The experiment is performed on extensive well liked face detecting benchmark datasets, Wider Face and FDDB. CNN with FE demonstrates the results with superiority and the accuracy was in-depth analyzed by CNN classifier.