Petroleum Engineering
Siavash Ashoori; Ehsan Safavi; Jamshid Moghaddasi; Parvin Kolahkaj
Abstract
Formation damage is reported during the secondary and tertiary stages of reservoir lifespan. One of the unpleasant sequences of formation damage caused by fine particles is permeability reduction due to pore plugging and bridging. The fine particles might exist initially in a porous medium or be introduced ...
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Formation damage is reported during the secondary and tertiary stages of reservoir lifespan. One of the unpleasant sequences of formation damage caused by fine particles is permeability reduction due to pore plugging and bridging. The fine particles might exist initially in a porous medium or be introduced by external sources. In addition, there is a variety of particle types and sizes. The current research focuses on the effects of non-swelling clay minerals motions, such as the laminar ones found in Iranian sandstone reservoirs, on permeability. For this purpose, sand packs in various glass bead sizes and containing aluminum oxide as fine particles were designed to scrutinize the motion of fine particles under various pressure differences, flow rates, and concentrations. It was concluded that for each of the three sand packs regarded as the porous media in this study and composed of fine glass beads with different sizes, there is a critical flow rate as a function of glass bead size. For the flow rates lower than the critical flow rate, bridges form stably and lead to the most severe formation damage. After reaching the critical flow rate, the bridges weaken and break, and relative permeability will be independent of the flow rate. It was deduced that permeability reduction and formation damage are directly proportional to particle concentration and inversely proportional to glass bead size.
Mohsen Karimian; Nader Fathianpour; Jamshid Moghaddasi
Abstract
Porosity is considered as an important petrophysical parameter in characterizing reservoirs, calculating in-situ oil reserves, and production evaluation. Nowadays, using intelligent techniques has become a popular method for porosity estimation. Support vector machine (SVM) a new intelligent method with ...
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Porosity is considered as an important petrophysical parameter in characterizing reservoirs, calculating in-situ oil reserves, and production evaluation. Nowadays, using intelligent techniques has become a popular method for porosity estimation. Support vector machine (SVM) a new intelligent method with a great generalization potential of modeling non-linear relationships has been introduced for both regression (support vector regression (SVR)) and classification (support vector classification (SVC)) problems. In the current study, to estimate the porosity of a carbonate reservoir in one of Iran south oil fields from well log data, the SVR model is firstly constructed; then the performance achieved is compared to that of an artificial neural network (ANN) model with a multilayer perceptron (MLP) architecture as a well-known method to account for the reliability of SVR or the possible improvement made by SVR over ANN models. The results of this study show that by considering correlation coefficient and some statistical errors the performance of the SVR model slightly improves the ANN porosity predictions.
