Study of the scale effect of filtration-capacitive properties of a complex carbonate reservoir

Abstract


One of the fundamental challenges in studying the properties of productive oil and gas reservoirs is the scale effect. Analysis of multi-scale research results often reveals discrepancies in data. For example, porosity and permeability properties determined from standard and full-size samples for the same depth interval can vary significantly. Similarly, these differences become even more pronounced when transitioning to the scale of the near-wellbore zone. At the same time, the type of reservoir significantly influences the scale effect. In porous reservoirs, the scale effect may not be pronounced, whereas in complex reservoirs, transitioning from one scale to another can result in properties changing by an order of magnitude. This is due to high heterogeneity caused by secondary processes such as leaching, dolomitization, and recrystallization. Neglecting the scale effect can adversely affect understanding reservoir structure. In this study, the scale effect of properties was examined using a complex carbonate reservoir as an example. A qualitative assessment of the scale effect was performed using mathematical statistics and petrotypification methods. To quantitatively evaluate the scale effect, a multiple regression model was developed to adjust porosity values from standard core samples to full-size samples for constructing a porosity cube. Several machine learning algorithms were used to predict the permeability values of full-size samples, including gradient boosting, random forest, multilayer perceptron, and k-nearest neighbors. It was found that the random forest-based model was the most accurate. The developed models enable highly reliable predictions of porosity and permeability when transitioning between scales (R2= 0.77–0.94).

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About the authors

A. A Kochnev

Perm National Research Polytechnic University

S. N Krivoschekov

Perm National Research Polytechnic University

N. D Kozyrev

Perm National Research Polytechnic University

О. Е Kochneva

Perm National Research Polytechnic University

E. S Ozhgibesov

Perm National Research Polytechnic University

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