Известия Томского политехнического университета: Инжиниринг георесурсов (Dec 2022)

ANALYSIS OF THE FEATURES OF CO2 HUFF-N-PUFF TECHNOLOGY

  • Andrey V. Soromotin,
  • Alexander V. Lekomtsev,
  • Pavel Yu. Ilyushin

DOI
https://doi.org/10.18799/24131830/2022/12/3635
Journal volume & issue
Vol. 333, no. 12
pp. 178 – 189

Abstract

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The relevance. A promising direction for the development of oil fields is the use of unconventional methods for extraction of hard-to-recover oil reserves. Carbon dioxide technologies are of particular interest in the context of a policy to reduce the hydrocarbon footprint in the global energy balance. Carbon dioxide is one of the most effective displacing agents for enhanced oil recovery, which can dissolve in light and medium fractions of oil, contributing to its swelling, reducing viscosity and increasing mobility. In this regard, the search for and practical application of effective solutions is an urgent task. One such carbon dioxide technology is Huff-n-Puff. The main aim: to assess the world experience in the application of CO2 Huff-n-Puff technology; analyze the factors affecting the effectiveness of the method; establish the relationship of technological factors when using CO2 Huff-n-Puff. Object: CO2 Huff-n-Puff technology. Methods: literature review; analysis of technology parameters. Results. CO2 Huff-n-Puff technology is effective in a wide range of reservoir and oil properties. The research revealed the main criteria for the effectiveness of Huff-n-Puff technology, the most important of which is to achieve complete solubility of carbon dioxide in oil. The main factors affecting the efficiency of this process are the operating technological parameters, natural and artificial fracturing, and molecular diffusion. The effect of increased oil recovery from the influence of the geometry and length of fractures is estimated. The authors analyze the parameters of the technology and identify statistical dependencies, a heat map and distribution characteristics were used. Gradient boosting (machine learning method) was also applied using the SHAP library to determine the effect of the parameter on the efficiency of CO2 Huff-n-Puff. An increase in oil recovery from the use of the technology in question was established to 5,4%.

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