Estimation of the Squirt-Flow Length Based on Crack Properties in Tight Sandstones

Tight sandstones have low porosity and permeability and strong heterogeneities with microcracks, resulting in small wave impedance contrasts with the surrounding rock and weak fluid-induced seismic effects, which make the seismic characterization for fluid detection and identification difficult. For this purpose, we propose a reformulated modified frame squirt-flow (MFS) model to describe wave attenuation and velocity dispersion. The squirt-flow length (R) is an important parameter of the model, and, at present, no direct method has been reported to determine it. We obtain the crack properties and R based on the DZ (David-Zimmerman) model and MFS model, and how these properties affect the wave propagation, considering ultrasonic experimental data of the Sichuan Basin. The new model can be useful in practical applications related to exploration areas.

Silver Conductive Threads-Based Embroidered Electrodes on Textiles as Moisture Sensors for Fluid Detection in Biomedical Applications

Wearable sensors have become part of our daily life for health monitoring. The detection of moisture content is critical for many applications. In the present research, textile-based embroidered sensors were developed that can be integrated with a bandage for wound management purposes. The sensor comprised an interdigitated electrode embroidered on a cotton substrate with silver-tech 150 and HC 12 threads, respectively, that have silver coated continuous filaments and 100% polyamide with silver-plated yarn. The said sensor is a capacitive sensor with some leakage. The change in the dielectric constant of the substrate as a result of moisture affects the value of capacitance and, thus, the admittance of the sensor. The moisture sensor’s operation is verified by measuring its admittance at 1 MHz and the change in moisture level (1–50) µL. It is observed that the sensitivity of both sensors is comparable. The identically fabricated sensors show similar response and sensitivity while wash test shows the stability of sensor after washing. The developed sensor is also able to detect the moisture caused by both artificial sweat and blood serum, which will be of value in developing new sensors tomorrow for smart wound-dressing applications.

Deep Learning Assisted Detection of Abdominal Free Fluid in Morison's Pouch During Focused Assessment With Sonography in Trauma

Background: The use of focused assessment with sonography in trauma (FAST) enables clinicians to rapidly screen for injury at the bedsides of patients. Pre-hospital FAST improves diagnostic accuracy and streamlines patient care, leading to dispositions to appropriate treatment centers. In this study, we determine the accuracy of artificial intelligence model-assisted free-fluid detection in FAST examinations, and subsequently establish an automated feedback system, which can help inexperienced sonographers improve their interpretation ability and image acquisition skills.Methods: This is a single-center study of patients admitted to the emergency room from January 2020 to March 2021. We collected 324 patient records for the training model, 36 patient records for validation, and another 36 patient records for testing. We balanced positive and negative Morison's pouch free-fluid detection groups in a 1:1 ratio. The deep learning (DL) model Residual Networks 50-Version 2 (ResNet50-V2) was used for training and validation.Results: The accuracy, sensitivity, and specificity of the model performance for ascites prediction were 0.961, 0.976, and 0.947, respectively, in the validation set and 0.967, 0.985, and 0.913, respectively, in the test set. Regarding feedback prediction, the model correctly classified qualified and non-qualified images with an accuracy of 0.941 in both the validation and test sets.Conclusions: The DL algorithm in ResNet50-V2 is able to detect free fluid in Morison's pouch with high accuracy. The automated feedback and instruction system could help inexperienced sonographers improve their interpretation ability and image acquisition skills.

Prediction of sweet spots in tight sandstone reservoirs based on anisotropic frequency-dependent AVO inversion

The frequency-dependent amplitude-versus-offset (FAVO) method has become a practical method for fluid detection in sand reservoirs. At present, most FAVO inversions are based on the assumption that reservoirs are isotropy, but the application effect is not satisfactory for fractured reservoirs. Hence, we analyse the frequency variation characteristics of anisotropy parameters in tight sandstone reservoirs based on a new petrophysical model, and propose a stepwise anisotropic FAVO inversion method to extract frequency-dependent attributes from prestack seismic field data. First, we combine the improved Brie's law with the fine-fracture model to analyse frequency-dependent characteristics of velocities and Thomsen anisotropy parameters at different gas saturations and fracture densities. Then, we derive an anisotropic FAVO inversion algorithm based on Rüger's approximation formula and propose a stepwise anisotropic FAVO inversion method to obtain the dispersions of anisotropy parameters. Finally, we propose a method that combines the inversion spectral decomposition with the stepwise anisotropy FAVO inversion and apply it to tight sand reservoirs in the Xinchang area. We use P-wave velocity dispersion and anisotropy parameter ε dispersion to optimise favourable areas. Numerical analysis results show that velocity dispersion of the P-wave is sensitive to fracture density, which can be used for fracture prediction in fractured reservoirs. In contrast, anisotropic parameter dispersion is sensitive to gas saturation and can be used for fluid detection. The seismic data inversion results show that velocity dispersion of the P-wave and anisotropic parameter dispersion are sensitive to fractured reservoirs in the second member of Xujiahe Group, which is consistent with logging interpretation results.