Xinyu Zhao^1,2,*, Mofeng Li², Kai Yan², Li Yin³

Energy Engineering, Vol.120, No.12, pp. 2933-2949, 2023, DOI:10.32604/ee.2023.041580

Abstract This study presents an avant-garde approach for predicting and optimizing production in tight reservoirs, employing a dual-medium unsteady seepage model specifically fashioned for volumetrically fractured horizontal wells. Traditional models often fail to fully capture the complex dynamics associated with these unconventional reservoirs. In a significant departure from these models, our approach incorporates an initiation pressure gradient and a discrete fracture seepage network, providing a more realistic representation of the seepage process. The model also integrates an enhanced fluid-solid interaction, which allows for a more comprehensive understanding of the fluid-structure interactions in the reservoir. This is achieved through the incorporation of… More >

Hongtao Liu, Hongtao Jing, Zhixiong Tu^*, Shiyong Qin, Junhui Wei, Xiaotong Yu

FDMP-Fluid Dynamics & Materials Processing, Vol.19, No.7, pp. 1851-1862, 2023, DOI:10.32604/fdmp.2023.025015

Abstract Reservoirs characterized by high temperature, high-pressure, medium high cementation strength, low porosity, and low permeability, in general, are not affected by sand production issues. Since 2009, however, it is known that cases exists where sand is present and may represent a significant technical problem (e.g., the the Dina II condensate gas field). In the present study, the main factors affecting sand production in this type of reservoir are considered (mechanical properties, stress fields, production system, completion method and gas flow pattern changes during the production process). On this basis, a new liquid-solid coupled porous elasto-plastic 3D sand production model is… More >

Xiao Ya Ma^1,2,*, Jin Tong^1,2, Fei Jiang³, Min Xu⁴, Li Mei Sun¹, Qiu Yan Chen¹

CMC-Computers, Materials & Continua, Vol.74, No.3, pp. 6145-6159, 2023, DOI:10.32604/cmc.2023.034833

Abstract Production prediction is an important factor influencing the realization of an intelligent agricultural supply chain. In an Internet of Things (IoT) environment, accurate yield prediction is one of the prerequisites for achieving an efficient response in an intelligent agricultural supply chain. As an example, this study applied a conventional prediction method and deep learning prediction model to predict the yield of a characteristic regional fruit (the Shatian pomelo) in a comparative study. The root means square error (RMSE) values of regression analysis, exponential smoothing, grey prediction, grey neural network, support vector regression (SVR), and long short-term memory (LSTM) neural network… More >

Hao Qiu¹, Yi Wu¹, Min Wen¹, Xuesong Xing¹, Zening Hou¹, Nan Ma¹, Zizhen Zhang², Rui Zhang^2,*

FDMP-Fluid Dynamics & Materials Processing, Vol.19, No.3, pp. 579-592, 2023, DOI:10.32604/fdmp.2022.020297

Abstract Sand production is a critical issue during the development of offshore oil and gas fields. Certain gas fields (e.g. the AB gas field) have high porosity and high permeability, and with water at the bottom of the reservoir, the risk of sand production greatly increases at high differential pressures. Based on reservoir properties, geological conditions, production requirements, and well logging data, in this study an ultrasonic time difference method, a B index method, and a S index method are used together with a model of rock mass failure (accounting for water influx and pressure depletion) to qualitatively predict sand production.… More >

Hongfei Ma^1,*, Wenqi Zhao², Yurong Zhao¹, Yu He¹

CMES-Computer Modeling in Engineering & Sciences, Vol.134, No.3, pp. 1773-1790, 2023, DOI:10.32604/cmes.2022.020498

Abstract Accurate prediction of monthly oil and gas production is essential for oil enterprises to make reasonable production plans, avoid blind investment and realize sustainable development. Traditional oil well production trend prediction methods are based on years of oil field production experience and expertise, and the application conditions are very demanding. With the rapid development of artificial intelligence technology, big data analysis methods are gradually applied in various sub-fields of the oil and gas reservoir development. Based on the data-driven artificial intelligence algorithm Gradient Boosting Decision Tree (GBDT), this paper predicts the initial single-layer production by considering geological data, fluid PVT… More >