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In many coastal areas around the world, the seagrasses provide an essential source of livelihood for many civilizations and support high levels of biodiversity. Seagrasses are highly valuable, as they provide habitat for numerous fish, endangered sea cows, Dugong dugon , and sea turtles. The health of seagrasses is being threatened by many human activities. The process of seagrass conservation requires the annotation of every seagrass species within the seagrass family. The manual annotation procedure is time-consuming and lacks objectivity and uniformity. Automatic annotation based on lightweight DeepSeagrass (LWDS) is proposed to solve this problem. LWDS computes combinations of various resized input images and various neural network structures, to determine the ideal reduced image size and neural network structure with satisfactory accuracy and within a reasonable computation time. The main advantage of this LWDS is it classifies the seagrasses quickly and with lesser parameters. The DeepSeagrass dataset is used to test LWDS's applicability.

Background Salameh et al. developed the Lebanese Waterpipe Dependence Scale (LWDS-11) that assesses nicotine dependence among adult waterpipe smokers. In view of the high waterpipe use among Lebanese youth and other neighboring countries, it was deemed necessary to check the psychometric properties of the LWDS-11, originally adapted to the Lebanese population, to measure nicotine dependence among adolescents. Methods Two cross-sectional investigations were conducted; Study 1 (January and May 2019) enrolled a total of 449 students who were exclusive waterpipe smokers; this sample was used to conduct the exploratory factor analysis. Study 2 enrolled another sample composed of 243 waterpipe smoking adolescents. This sample was independent from the first one and was used to conduct the confirmatory analysis. Results The results also showed that 312 (69.5%) [95% CI 0.652–0.738] had high waterpipe dependence (scores of ≥10). Results of the factor analysis in sample 1 showed that all LWDS-11 items were extracted following the factor analysis. Items converged over a solution of one factor; total variance explained = 70.45%, α Cronbach  = 0.96). The results of the confirmatory factor analysis were as follows: the Maximum Likelihood Chi-Square = 129.58 and Degrees of Freedom = 45, which gave a χ 2 /df = 2.88. For non-centrality fit indices, the Steiger-Lind Root Mean Square Error of Approximation (RMSEA) was 0.08 [0.071–0.106]. Moreover, the Comparative Fit Index (CFI) value was 0.77. Conclusion The preliminary results suggest that the LWDS-11 has good psychometric properties to measure waterpipe dependence among adolescents. We hope this tool would serve the benefit of research and epidemiology.

Abstract With the widespread application of the logging-while-drilling (LWD) azimuthal electromagnetic (EM) tool, the inversion of formation resistivity and boundaries has become a significant concern. However, conventional inversion methods face practical challenges, as they are often time-consuming, nonlinear, and ill-posed. To address these challenges, we designed a deep learning model based on a Bidirectional Long Short-Term Memory (BiLSTM) network to invert LWD azimuthal EM data in anisotropic formations. Initially, an anisotropic formation model of horizontal stratigraphy was established, with formation parameters (such as resistivity) assigned random values. A fast and efficient analytical method was then employed to calculate the logging response. These steps were repeated to generate a substantial number of samples. Subsequently, each sample was divided into two segments—deviate and horizontal—based on the inclination angle of the tool during drilling, resulting in two distinct sample sets. The BiLSTM network with varying hyperparameters was then trained and tested using these two sample sets, resulting in the development of two deep learning inversion models. Finally, the inversion performance of the two inversion models was analyzed. The experimental results demonstrated that the two inversion models could not only accurately invert the formation resistivity and the positions of layers boundaries, but also exhibited a rapid inversion speed, with a single-point inversion time of only 0.4 ms. This high inversion performance is crucial for reservoir detection, precise instrument targeting, and effective drilling within the reservoir. Moreover, we showcased the robustness of the inversion method by artificially introducing noise into the logging data. These results underscore the considerable potential of the intelligent inversion approach for LWD azimuthal EM inversion applications.

The historical removal of accumulations of wood on medium to large rivers in the continental United States caused a fundamental change in river corridors that has received relatively little attention in the scientific literature. Although scientific literature discusses the natural wood rafts present on the Red and the Atchafalaya Rivers in the southeastern United States, there is little awareness that similar extensive masses of wood are documented in the historical record from forested river catchments as diverse and widespread as those in the northeast, southeast, Texas Gulf Coast, Pacific Northwest, and upper Great Lakes regions of the country. While present, these natural wood rafts decreased channel conveyance, increased channel-floodplain connectivity, and facilitated anastomosing channels and floodplain lakes. Removal of natural wood rafts began in the 17th century in the eastern United States and proceeded westward with the movement of European settlers, accelerating during the 19th-century era of steamboats and floating of cut timber. Removal of the natural wood rafts likely forced many rivers from a multi thread planform with high channel-floodplain connectivity into an alternative stable state of single-thread channels with substantially reduced overbank flow, sedimentation, and avulsions. There is now widespread recognition among the geomorphic community of how upland clearance increased sediment yields and floodplain aggradation. I propose that widespread removal of instream wood for steamboat routes, timber rafts, and flood control was equally significant in decreasing floodplain sedimentation and river complexity, and in causing a fundamental, extensive, and intensive change in forested river corridors throughout the United States.

Reservoir porosity is a crucial indicator of the physical properties of reservoirs, forming the foundation for oil and gas exploration, development design, and decision-making. Currently, it is primarily obtained through core testing or logging interpretation, but the lack of quantitative evaluation methods during drilling limits the timeliness and efficiency of porosity acquisition. Based on this, this study focuses on the tight sandstone reservoir in the East China Sea shelf basin, conducting modeling and rock-breaking simulations of 5 blade and 6 blade polycrystalline diamond compact (PDC) bits commonly used in the region. It investigates the relationships between rate of penetration (ROP), torque, mechanical specific energy (MSE), physical index, and other parameters for rocks with varying physical characteristics. A real-time quantitative prediction method for reservoir porosity, based on drilling and logging engineering parameters, is proposed. The results indicate that: (1) Significant differences in the response characteristics of rate of penetration, torque, and MSE are observed when drilling formations with identical mechanical characteristics, due to the influence of bit type. Therefore, these engineering parameters are not suitable for directly predicting reservoir porosity. (2) The relationship between the physical index and elastic modulus for 5 blade and 6 blade PDC bits is highly consistent, with both increasing logarithmically as elastic modulus increases. This suggests that the physical index can eliminate the influence of bit type and more accurately reflect changes in formation characteristics during drilling. (3) Using elastic modulus as an intermediary parameter, a model is established that relates porosity to the physical index, showing that porosity decreases as a power function of the physical index. The research findings were cross-verified in well NB13-4-A, with a 91.57% agreement between the porosity predicted by engineering parameters and the logging-derived porosity. The prediction method was applied to 20 exploration wells in the NB13-4 working area, yielding an average porosity consistency rate of 85.74%. This demonstrates that the method can provide timely, efficient, and accurate support for decision-making in exploration operations, such as intermediate testing and well completion.

The compression method for wellbore trajectory data is crucial for monitoring wellbore stability. However, classical methods like methods based on Huffman coding, compressed sensing, and Differential Pulse Code Modulation (DPCM) suffer from low real-time performance, low compression ratios, and large errors between the reconstructed data and the source data. To address these issues, a new compression method is proposed, leveraging a deep autoencoder for the first time to significantly improve the compression ratio. Additionally, the method reduces error by compressing and transmitting residual data from the feature extraction process using quantization coding and Huffman coding. Furthermore, a mean filter based on the optimal standard deviation threshold is applied to further minimize error. Experimental results show that the proposed method achieves an average compression ratio of 4.05 for inclination and azimuth data; compared to the DPCM method, it is improved by 118.54%. Meanwhile, the average mean square error of the proposed method is 76.88, which is decreased by 82.46% when compared to the DPCM method. Ablation studies confirm the effectiveness of the proposed improvements. These findings highlight the efficacy of the proposed method in enhancing wellbore stability monitoring performance.

Horizontal wells have significant advantages in coal bed methane exploration and development blocks. However, its application in new exploration and development blocks could be challenging. Limited geological data, uncertain geological conditions, and the emergence of micro-faults in pre-drilled target coal seams make it hard to accurately control the well trajectory. The well trajectory prior to drilling needs to be optimized to ensure that the drilling trajectory is within the target coal seam and to prevent any reduction in drilling ratio (defined here as the percentage of the drilling trajectory in the entire horizontal section of the well located in the target coal seam) caused by faults. In this study, the well trajectory optimization is achieved by implementing the following process to drill pilot hole, acquire 2D resonance, and azimuthal gamma logging while drilling. The pilot hole drilling can obtain the characteristic parameters of the target coal seam and the top and bottom rock layers in advance, which can provide judgment values for the landing site design and real-time monitoring of whether the wellbore trajectory extends along the target coal seam; 2D resonance exploration can obtain the construction of set orientation before drilling and the development of small faults and formation fluctuations in the horizontal section, which can optimize the well trajectory in advance; the azimuth gamma logging while drilling technology can monitor the layers drilled by the current drill bit in real time, and can provide timely and accurate well trajectory adjustment methods.The horizontal well-Q in the Block-W of the Qinshui Basin was taken as a case study and underwent technical mechanism research and applicability analysis. The implementation of this new innovative process resulted in a successful drilling of a 711 m horizontal section, with a target coal seam drilling rate of 80%. Compared to previous L-type wells, the drilling rate increased by about 20%, and the drilling cycle shortened by 25%. The technical experience gained from this successful case provides valuable insight for low-cost exploration and development of new coalbed methane blocks.

Borehole resistivity measurements are routinely employed to measure the electrical properties of rocks penetrated by a well and to quantify the hydrocarbon pore volume of a reservoir. Depending on the degree of geometrical complexity, inversion techniques are often used to estimate layer-by-layer electrical properties from measurements. When used for well geosteering purposes, it becomes essential to invert the measurements into layer-by-layer values of electrical resistivity in real time. We explore the possibility of using deep neural networks (DNNs) to perform rapid inversion of borehole resistivity measurements. Accordingly, we construct a DNN that approximates the following inverse problem: given a set of borehole resistivity measurements, the DNN is designed to deliver a physically reliable and data-consistent piecewise one-dimensional layered model of the surrounding subsurface. Once the DNN is constructed, we can invert borehole measurements in real time. We illustrate the performance of the DNN for inverting logging-while-drilling (LWD) measurements acquired in high-angle wells via synthetic examples. Numerical results are promising, although further work is needed to achieve the accuracy and reliability required by petrophysicists and drillers.

Abstract Logging-while-drilling (LWD) azimuthal electromagnetic measurement (AEM), capable of detecting and imaging formation structures around the wellbore, has become a vital tool for navigating complex reservoirs. However, this technology faces challenges such as weakened signals, significant noise interference, and reduced detection scope in highly resistive formations. To address these challenges, this paper presents a new LWD AEM method for highly resistive coal mines. The key distinctions from current AEM tools are the utilization of higher frequencies and extended coil spacing to ensure proper electromagnetic (EM) wave decay in resistive media. First, by stretching the distance between receivers to 10 and 20 inches (25.4 and 50.8 cm), a notable expansion of the upper limit of resistivity range is obtained. Additionally, the operating frequency has been increased to 5 MHz to boost the strength of phase-shift (PS) signal and reduce the noise effect. A boundary detection method based on high-frequency PS in highly resistive coal mines is then established, significantly improving signal strength and depth of detection. Various operating frequencies are also optimized to increase the effective curves in highly resistive formations. Finally, a coal mine LWD AEM tool design is designed, providing five effective resistivity curves and 12 effective geological signal curves for highly resistive formations. With a resistivity detection limit of 5000 Ω·m and a depth of detection limit of 4 m, this instrument serves as a solid foundation for advancing research and development in coal mine geosteering and instrumentation.

Fractured formations are strongly heterogenous, and thus exhibit a complex logging response mechanism. By using the logging while drilling (LWD) resistivity imaging tool, fractures can be visually identified and their aperture quantitatively calculated. Because physical fracture model simulation is time consuming and costly, we propose using a 3D finite element method (FEM) numerical simulation to interpret the LWD resistivity imaging tool logging responses in conjunction with a new aperture calculation model based on the forward model. First, we used the single fracture model to investigate the effect of fracture aperture and formation resistivity contrast on the maximum current contrast at the fracture. The results showed that the aperture is linearly related to the maximum current contrast, while the formation resistivity contrast exhibits a pronounced exponential relationship with the maximum current contrast. Both of these relationships are affected by the fracture's dip angle, so segmented fitting is required when the fracture dip angles differ. Next, using the forward model, we developed the fracture aperture calculation model based on the maximum current contrast. The aperture calculation model was established in three segments in accordance with the different fracture dips, and the influence factors affecting the fracture inverting inclination were analyzed using multi-fracture simulation images. Finally, the accuracy of the new model was verified with the simulated fracture images. The novel model for calculating fracture aperture is of great significance for processing and interpreting LWD resistivity imaging logging data.