We present a simultaneous dual-wavelength phase-imaging digital holographic technique demonstrated on porous coal samples. The use of two wavelengths enables us to increase the axial range at which the unambiguous phase imaging can be performed, but also increases the noise. We employ a noise reduction “fine map” algorithm, which uses the two-wavelength phase map as a guide to correct a single-wavelength phase image. Then, the resulting noise of a fine map is reduced to the level of single-wavelength noise. A comparison to software unwrapping is also presented. A simple way of correcting a curvature mismatch between the reference and the object beams is offered.

Automatic visual inspection allows for the identification of surface defects in manufactured parts. Nevertheless, when defects are on a sub-millimeter scale, detection and recognition are a challenge. This is particularly true when the defect generates topological deformations that are not shown with strong contrast in the 2D image. In this paper, we present a method for recognizing surface defects in 3D point clouds. Firstly, we propose a novel 3D local descriptor called the Model Point Feature Histogram (MPFH) for defect detection. Our descriptor is inspired from earlier descriptors such as the Point Feature Histogram (PFH). To construct the MPFH descriptor, the models that best fit the local surface and their normal vectors are estimated. For each surface model, its contribution weight to the formation of the surface region is calculated and from the relative difference between models of the same region a histogram is generated representing the underlying surface changes. Secondly, through a classification stage, the points on the surface are labeled according to five types of primitives and the defect is detected. Thirdly, the connected components of primitives are projected to a plane, forming a 2D image. Finally, 2D geometrical features are extracted and by a support vector machine, the defects are recognized. The database used is composed of 3D simulated surfaces and 3D reconstructions of defects in welding, artificial teeth, indentations in materials, ceramics and 3D models of defects. The quantitative and qualitative results showed that the proposed method of description is robust to noise and the scale factor, and it is sufficiently discriminative for detecting some surface defects. The performance evaluation of the proposed method was performed for a classification task of the 3D point cloud in primitives, reporting an accuracy of 95%, which is higher than for other state-of-art descriptors. The rate of recognition of defects was close to 94%.

Quantifying the stress field induced into a piece when it is loaded is important for engineering areas since it allows the possibility to characterize mechanical behaviors and fails caused by stress. For this task, digital photoelasticity has been highlighted by its visual capability of representing the stress information through images with isochromatic fringe patterns. Unfortunately, demodulating such fringes remains a complicated process that, in some cases, depends on several acquisitions, e.g., pixel-by-pixel comparisons, dynamic conditions of load applications, inconsistence corrections, dependence of users, fringe unwrapping processes, etc. Under these drawbacks and taking advantage of the power results reported on deep learning, such as the fringe unwrapping process, this paper develops a deep convolutional neural network for recovering the stress field wrapped into color fringe patterns acquired through digital photoelasticity studies. Our model relies on an untrained convolutional neural network to accurately demodulate the stress maps by inputting only one single photoelasticity image. We demonstrate that the proposed method faithfully recovers the stress field of complex fringe distributions on simulated images with an averaged performance of 92.41% according to the SSIM metric. With this, experimental cases of a disk and ring under compression were evaluated, achieving an averaged performance of 85% in the SSIM metric. These results, on the one hand, are in concordance with new tendencies in the optic community to deal with complicated problems through machine-learning strategies; on the other hand, it creates a new perspective in digital photoelasticity toward demodulating the stress field for a wider quantity of fringe distributions by requiring one single acquisition.

Well stimulation for production or injection enhancement in mature fields is a key and challenging task. Loss of reservoir energy due to pressure depletion coupled with complex damage scenarios existing in adverse petro physical environments can become restrictive factors for the proper performance of conventional liquid based chemical stimulation systems. Main limitations are normally related to high interfacial tensions preventing optimal well's clean up and cost-effective achievable penetrations. This work presents a new well stimulation concept in which the carrying system is gas instead of liquid. The overall study will be presented in 2 parts. Part I will discuss basic physical questions related to treatment durability as a function of deployment method (continuous dispersion vs liquid batch gas displacement) for at least two damage scenarios of particular interest: asphaltene deposition and condensate blockage. A basic mechanistic simulation is also presented for benefit estimations at well scale. Part II will focus on field trials design and execution using micellar and/or fluoropolymer type of chemistries that exhibited the best performance when tested under laboratory conditions. Experiments herein presented were done in formation sandstone cores simulating reservoir conditions. It is shown that natural gas when used as the carrying system to deploy conventional asphaltene dissolution and condensate removal chemistries enhances both Ko re-establishment and treatment durability as compared to equivalent liquid-based applications. Additional studies are being performed to maximize the effectiveness of the GaStim concept. Sensibilities to gas type (N2, CO2), added chemical and dosages as long as field trial documentation will be presented in part II of the present work. GaStim concept is presented as a novel chemical stimulation technique potentially allowing deeper penetrations and better chemical adsorptions. Its potential, although still not fully undiscovered, is certainly supported by higher Ko reestablishment values and longer treatment durabilities observed.