Prediction of Microscopic Remaining Oil Distribution Using Fuzzy Comprehensive Evaluation

Abstract

A network model is established through the techniques of image reconstruction, a thinning algorithm, and pore-throat information extraction with the aid of an industrial microfocus CT scanning system. In order to characterize actual rock pore-throat structure, the established model is modified according to the matching of experimental factors such as porosity, permeability, and the relative permeability curve. On this basis, the impacts of wetting angle, pore radius, shape factor, pore-throat ratio, and coordination number as applied to microscopic remaining oil distribution after water flooding are discussed. For a partially wetting condition, the displacement result of a water-wet pore is somewhat better than that of an oil-wet pore as a whole, and the possibility of any remaining oil is relatively low. Taking the comprehensive effects of various factors into account, a prediction method of remaining oil distribution is presented through the use of fuzzy comprehensive evaluation. It is seen that this method can predict whether there is remaining oil or not in the pore space with satisfactory accuracy, which is above 75%. This method thus provides guidance for a better understanding of the microscopic causes of the remaining oil.

Numerical simulation of reservoir parameters’ synergetic time-variability on development rules

“…To build a comprehensive model describing the variability of reservoir macro-parameters, one needs to investigate the relationship between the variability of reservoir macro-parameters and that of micro-parameters. The network model applies modeling networks to substitute the complex porous space in porous media, and studies the flow mechanism in porous media using random simulations at the microscopic stage (Blunt et al 2002 ; Hou 2007 ; Mahmud et al 2007 ; Hou et al 2011a , b ). By adjusting micro-parameters of the network model, it can simulate the impact of reservoir micro-parameters’ variation on macro-parameters (Hou et al 2011a , b ), and thus, it provides an effective tool to describe the internal relationship between reservoir macro-parameters and reservoir micro-parameters.…”

“…To establish the comprehensive model for reservoir macro-parameters time-variability, the impact of different reservoir micro-parameters’ variation on macro-parameters such as porosity, permeability, and relative permeability should be studied primarily using network modeling (Hou et al 2011a , b ). The micro-parameters mainly include throat radius, throat radius uniformity coefficient, aspect ratio, wettability, shape of pore throat, and coordination number.…”

Measuring and multilevel fuzzy comprehensive predicting comfort parameters of soft materials by a new handle evaluation system

“…In practical engineering, the influence of complex factors on the evaluation object has the following characteristics: a large amount of influencing factor, different levels of factors and serious ambiguity of multiple factors. [37][38][39][40][41] FCE is a method to analyze various unclear factors and complicated human sensing to evaluate fabric tactile comfort, which is affected by multiple mechanical properties. 42 The FCE algorithm based on fuzzy sets and entropy weight is shown as follows.…”

Investigation of the Flow Intensity in an Inverted Seven-Point Well Pattern and Its Influence on the EOR Efficiency of S/P Flooding

“…Meanwhile, the intensity affects the occurrence of remaining oil [30,31]. The classification mechanism of remaining oil mainly includes the shape factor method [32,33] and the mechanical method [34,35].…”