Quantification method of suspended solids in micromodel using image analysis

Abstract

Micromodel can provide valuable information to improve understanding of pore-scale transport phenomenon and can also be utilized to simulate the transport process at pore scale. This research aims to propose settlement option for quantification of suspended solids in micromodel. The micromodel is used to mimic the formation damage which occurs in reservoir formation that could simultaneously affect enhanced oil recovery. This is done by utilizing visual image interpretation through image analysis on micromodel chip. Following the quantification of suspended solids, the micromodel was injected with brine that eventually forms agglomeration. Images are taken from NIS-Element AR microscope automatically in RGB color profile and then made into grayscale and finally into binary modes. Since the micromodel is simulated in 2D form structure, the quantification method complemented with image analysis is focusing on the quantified area, µm2 region of interest categorized into 3 main groups of area B05, M45 and T50, respectively. This research will explore on segmentation and thresholding processes of the visual data acquired from micromodel experiment. An image-based computational algorithm is programmed in MATLAB Image Processing Toolbox and ImageJ; hence, suspended solids in porous media could be quantified from the visual image executed in micromodel.

Computer Vision for Kinetic Analysis of Lab- and Process-Scale Mixing Phenomena

“…For each selected region of interest, within each video frame, a matrix is produced, according to the gray level of each pixel within a set of pixel pairs. The gray level is simply the grayscale value of each pixel, split into groups, or levels, defined by user-determined thresholds . The number of gray levels used determines the size of the GLCM produced; for N gray levels, an N × N matrix is made (Scheme , right).…”

“…The gray level is simply the grayscale value of each pixel, split into groups, or levels, defined by user-determined thresholds. 51 The number of gray levels used determines the size of the GLCM produced; for N gray levels, an N × N matrix is made ( Scheme 3 , right). Therefore, when the pixels in a pair belong to levels i and j , respectively, the matrix element ij is increased by one.…”

“…From the aforementioned imaging innovations in mixing analysis, ,,,− ,,− ,,,− ,− most of these are disparately reported, differently encoded, and not comparable within a single platform. Herein, we employ a series of mixing case studies to compare analysis derived from the core Kineticolor platform, tracking an averaged color region over time, as well as newly integrated and spatially resolved mixing metrics.…”

Computer Vision for Kinetic Analysis of Lab- and Process-scale Mixing Phenomena

“…From the aforementioned imaging innovations in mixing analysis, 2,14,15,[24][25][26][27][28]30,[35][36][37][38][39]41,42,[45][46][47][47][48][49][50][51][52][53] most of these are disparately reported, differently encoded, and not comparable within a single platform. Herein, we employ a series of mixing case studies to compare analysis derived from the core Kineticolor platform, tracking an averaged color region over time, as well as newly-integrated and spatially-resolved mixing metrics.…”

“…The grey level is simply the greyscale value of each pixel, split into groups, or levels, defined by user-determined thresholds. 49 7…”