Qin Qian¹, Mingjing Lu^1,2,*, Anhai Zhong¹, Feng Yang¹, Wenjun He¹, Min Li¹

Energy Engineering, Vol.121, No.8, pp. 2167-2190, 2024, DOI:10.32604/ee.2024.049430 - 19 July 2024

Abstract The production capacity of shale oil reservoirs after hydraulic fracturing is influenced by a complex interplay involving geological characteristics, engineering quality, and well conditions. These relationships, nonlinear in nature, pose challenges for accurate description through physical models. While field data provides insights into real-world effects, its limited volume and quality restrict its utility. Complementing this, numerical simulation models offer effective support. To harness the strengths of both data-driven and model-driven approaches, this study established a shale oil production capacity prediction model based on a machine learning combination model. Leveraging fracturing development data from 236 wells… More >

Rabar H. Faraj^1,*, Hemn Unis Ahmed^2,3, Hardi Saadullah Fathullah⁴, Alan Saeed Abdulrahman², Farid Abed⁵

CMES-Computer Modeling in Engineering & Sciences, Vol.138, No.3, pp. 2925-2954, 2024, DOI:10.32604/cmes.2023.029392 - 15 December 2023

Abstract Plain concrete is strong in compression but brittle in tension, having a low tensile strain capacity that can significantly degrade the long-term performance of concrete structures, even when steel reinforcing is present. In order to address these challenges, short polymer fibers are randomly dispersed in a cement-based matrix to form a highly ductile engineered cementitious composite (ECC). This material exhibits high ductility under tensile forces, with its tensile strain being several hundred times greater than conventional concrete. Since concrete is inherently weak in tension, the tensile strain capacity (TSC) has become one of the most… More >

Wenxiao Wang^1,*, Xiaoyu Li^1,*, Yin Ding¹, Feizhou Wu², Shan Yang³

Journal of Quantum Computing, Vol.3, No.1, pp. 25-33, 2021, DOI:10.32604/jqc.2021.016346 - 20 May 2021

Abstract Due to the increase in the types of business and equipment in telecommunications companies, the performance index data collected in the operation and maintenance process varies greatly. The diversity of index data makes it very difficult to perform high-precision capacity prediction. In order to improve the forecasting efficiency of related indexes, this paper designs a classification method of capacity index data, which divides the capacity index data into trend type, periodic type and irregular type. Then for the prediction of trend data, it proposes a capacity index prediction model based on Recurrent Neural Network (RNN), More >