Decision Tree-Based Evaluation and Classification of Chemical Flooding Well Groups for Medium-Thick Sandstone Reservoirs

Zuhua Dong (), Man Li, Mingjun Zhang, Can Yang, Lintian Zhao, Zengyuan Zhou, Shuqin Zhang and Chenyu Zheng
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Zuhua Dong: Research Institute of Petroleum Exploration and Development, Liaohe Oilfield Company, CNPC, Panjin 124010, China
Man Li: Research Institute of Petroleum Exploration and Development, Liaohe Oilfield Company, CNPC, Panjin 124010, China
Mingjun Zhang: Research Institute of Petroleum Exploration and Development, Liaohe Oilfield Company, CNPC, Panjin 124010, China
Can Yang: Research Institute of Petroleum Exploration and Development, Liaohe Oilfield Company, CNPC, Panjin 124010, China
Lintian Zhao: Research Institute of Petroleum Exploration and Development, Liaohe Oilfield Company, CNPC, Panjin 124010, China
Zengyuan Zhou: State Key Laboratory of Marine Geology, Tongji University, Shanghai 200092, China
Shuqin Zhang: Research Institute of Petroleum Exploration and Development, Liaohe Oilfield Company, CNPC, Panjin 124010, China
Chenyu Zheng: Research Institute of Petroleum Exploration and Development, Liaohe Oilfield Company, CNPC, Panjin 124010, China

Energies, 2025, vol. 18, issue 17, 1-17

Abstract: Targeting the classification and evaluation of chemical flooding well groups in medium-thick sandstone reservoirs (single-layer thickness: 5–15 m), this study proposes a multi-level classification model based on decision trees. Through the comprehensive analysis of key static factors influencing chemical flooding efficiency, a four-tier classification index system was established, comprising: interlayer/baffle development frequency (Level 1), thickness-weighted permeability rush coefficient (Level 2), reservoir rhythm characteristics (Level 3), and pore-throat radius-based reservoir connectivity quality (Level 4) as its core components. The model innovatively transforms common reservoir physical parameters (porosity and permeability) into pore-throat radius parameters to enhance guidance for polymer molecular weight design, while employing a thickness-weighted permeability rush coefficient to simultaneously characterize heterogeneity impacts from both permeability and thickness variations. Unlike existing classification methods primarily designed for thin-interbedded reservoirs—which consider only connectivity or apply fuzzy mathematics-based normalization—this model specifically addresses medium-thick reservoirs’ unique challenges of interlayer development and intra-layer heterogeneity. Furthermore, its decision tree architecture clarifies classification logic and significantly reduces data preprocessing complexity. In terms of engineering practicality, the classification results are directly linked to well-group development bottlenecks, as validated in the J16 field application. By implementing customized chemical flooding formulations tailored to the study area, the production performance in the expansion zone achieved comprehensive improvement: daily oil output dropped from 332 tons to 243 tons, then recovered to 316 tons with sustained stabilization. Concurrently, recognizing that interlayer barriers were underdeveloped in certain well groups during production layer realignment, coupled with strong vertical heterogeneity posing polymer channeling risks, targeted profile modification and zonal injection were implemented prior to flooding conversion. This intervention elevated industrial replacement flooding production in the study area from 69 tons to 145 tons daily post-conversion. This framework provides a theoretical foundation for optimizing chemical flooding pilot well-group selection, scheme design, and dynamic adjustments, offering significant implications for enhancing oil recovery in medium-thick sandstone reservoirs through chemical flooding.

Keywords: sandstone reservoirs; chemical flooding; well-group classification; dynamic control (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2025
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