METHODS article

Front. Earth Sci.

Sec. Solid Earth Geophysics

Volume 13 - 2025 | doi: 10.3389/feart.2025.1601363

This article is part of the Research TopicAdvances in Petrophysics of Unconventional Oil and GasView all 13 articles

Data-Driven Intelligent Productivity Prediction Model for Horizontal Fracture Stimulation

Provisionally accepted
Qian  LiQian Li1Yong  Yi SuiYong Yi Sui1*Ying  Meng LuoYing Meng Luo2Lu  LiuLu Liu3Yuan  ZhaoYuan Zhao4
  • 1School of Petroleum Engineering,China University of Petroleum (East China), Qingdao, China
  • 2Petroleum Development Center of ShengLi Oilfield, Dongying, China
  • 3Exploration and Development Research Institute of Dagang Oilfield Company, Tianjin, China
  • 4Tianjin Branch of CNPC Logging, Tianjin, China

The final, formatted version of the article will be published soon.

Traditional methods for predicting post-fracturing productivity in horizontal fractures primarily use fracture and formation parameters for calculations. Complex fracture data are difficult to obtain, and these methods do not consider the effects of displacement mechanisms, fracturing techniques, or time factors on post-fracturing productivity. To address the limitations and shortcomings of existing postfracturing performance prediction methods for horizontal fractures, a horizontal fracture well productivity prediction model was established by combining physical mechanisms with data-driven approaches. First, based on physical mechanisms, factors influencing well productivity were selected from reservoir properties and fracturing operations. Second, relevant characteristic parameters were chosen from geological conditions, production characteristics, and fracturing techniques to perform clustering analysis on fracturing intervals in the data sample. Intervals with similar multidimensional physical features were grouped into the same category. Under the assumption of similar characteristics and mechanisms, correlation analysis was conducted for each fracturing interval category to identify the dominant controlling factors affecting post-fracturing productivity in each reservoir type. Machine learning algorithms were used to establish intelligent models describing the relationships between post-fracturing production enhancement effects, dominant factors, and production time for each reservoir category. Finally, during fracturing design, the optimal productivity prediction model was matched to each interval based on its characteristics to predict post-fracturing productivity. Additionally, the influence patterns of proppant volume on well productivity were comprehensively analyzed to optimize reasonable proppant volumes for different wells and intervals. Field validation showed that the productivity prediction model achieved an average error of 7.06%, providing a basis for horizontal fracture engineering design and achieving cost reduction and efficiency improvement in oilfield development.

Keywords: data-driven 1, horizontal fracture fracturing 2, productivity optimization 3, application 4, controlling factors 5

Received: 27 Mar 2025; Accepted: 30 Jun 2025.

Copyright: © 2025 Li, Sui, Luo, Liu and Zhao. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

* Correspondence: Yong Yi Sui, School of Petroleum Engineering,China University of Petroleum (East China), Qingdao, China

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