Application of Electrical Tomography Imaging Using Machine Learning Methods for the Monitoring of Flood Embankments Leaks

This paper presents an application for the monitoring of leaks in flood embankments by reconstructing images in electrical tomography using logistic regression machine learning methods with elastic net regularisation, PCA and wave preprocessing. The main advantage of this solution is to obtain a more accurate spatial conductivity distribution inside the studied object. The described method assumes a learning system consisting of multiple equations working in parallel, where each equation creates a single point in the output image. This enables the efficient reconstruction of spatial images. The research focused on preparing, developing, and comparing algorithms and models for data analysis and reconstruction using a proprietary electrical tomography solution. A reliable measurement solution with sensors and machine learning methods makes it possible to analyse damage and leaks, leading to effective information and the eventual prevention of risks. The applied methods enable the improved resolution of the reconstructed images and the possibility to obtain them in real-time, which is their distinguishing feature compared to other methods. The use of electrical tomography in combination with specific methods for image reconstruction allows for an accurate spatial assessment of leaks and damage to dikes.

Application Investigation of Light Proppant in Hydraulic Fracturing of Marcellus Shale

Slickwater-sand fracturing design is widely employed in Marcellus shale. The slickwater- sand creates long skinny fractures and maximizes the stimulated reservoir volume (SRV). However, due to the fast settling of sand in the water, lots of upper and deeper areas are not sufficiently propped. Reducing sand size can lead to insufficient fracture conductivity. This study proposes to use three candidate ultra-lightweight proppants ULWPs to enhance the fractured well performance in unconventional reservoirs. In step 1, the current sand pumping design is input into an in-house P3D fracture propagation simulator to estimate the fracture geometry and proppant concentrations. Next, the distribution of proppant concentration converts to conductivity and then to fracture permeability. In the third step, the fracture permeability from the second step is input into a reservoir simulator to predict the cumulative production for history matching and calibration. In step 4, the three ULWPs are used to replace the sand in the frac simulator to get new frac geometry and conductivity distribution and then import them in reservoir model for production evaluation. Before this study, the three ULWPs have already been tested in the lab to obtain their long-term conductivities under in-situ stress conditions. The conductivity distribution and production performance are analyzed and investigated. The induced fracture size and location of the produced layer for the current target well play a fundamental effect on ultra-light proppant productivity. The average conductivity of ULWPs with mesh 40/70 is larger and symmetric along the fracture except for a few places. However, ULWPs with mesh 100 generates low average conductivity and create a peak conductivity in limited areas. The ULW-3 tends to have less cumulative production compared with the other ULWPs. For this Marcellus Shale study, the advantages of ultra-lightweight proppant are restricted and reduced because the upward fracture height growth is enormous. And with the presence of the hydrocarbon layer is at the bottom of the fracture, making a large proportion of ULWPs occupies areas that are not productive places. The current study provides a guidance for operators in Marcellus Shale to determine (1) If the ULWP can benefit the current shale well treated by sand, (2) what type of ULWP should be used, and (3) given a certain type of ULWP, what is the optimum pumping schedule and staging/perforating design to maximize the well productivity. The similar workflow can be expanded to evaluate the economic potential of different ULWPs in any other unconventional field.

Electrical Impedance Tomography might be a Practical Tool to Provide Information about COVID-19 Pneumonia Progression

COVID-19 induced acute respiratory distress syndrome (ARDS) could have two different phenotypes, which might have different response and outcome to the traditional ARDS positive end-expiration pressure (PEEP) treatment. The identification of the different phenotypes in terms of the PEEP recruitment can help improve the patients’ outcome. In this contribution we reported a COVID-19 patient with seven-day electrical impedance tomography monitoring. From the conductivity distribution difference image analysis of the data, a clear course developing trend can be observed in addition to the phenotype identification. This case might suggest that EIT can be a practical tool to identify phenotypes and to provide progressive information of COVID-19 pneumonia.

A two-stage image reconstruction strategy for electrical impedance tomography

Electrical impedance tomography (EIT) is a potential and promising tomographic technique. Based on a reconstruction strategy, conductivity distribution can be imaged by processing boundary measurements. It should be noticed that the process of image reconstruction involves the solution of a nonlinear ill-posed inverse problem. To tackle this problem, a novel two-stage image reconstruction strategy is proposed in this work. It combines the advantages of total generalized variation regularization method and tight wavelet approach. The solution of the proposed method is acquired by employing alternating minimization algorithm and spilt Bregman algorithm. In the numerical simulation, reconstruction of five models is studied. Aside from the visual observation, we have also validated the proposed method with quantitative comparison. Meanwhile, the impact of noise on the reconstruction is considered. Furthermore, the proposed method is evaluated by phantom experimental data. The simulation and experimental results have demonstrated the superior performance of the proposed method in visualizing conductivity distribution.