The characteristics of unconventional tight oil reservoir and its modification technology and productivity prediction

Yang, Fan; Liu, Lifeng; Ran, Qiquan; Kong, Jinping; Xu, Mengya; Dong, Jiaxin

doi:10.15446/esrj.v24n4.91473

Cited by 1 publication

(1 citation statement)

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“…In addition, Zeng and others applied the reset potential theory and considered the seepage resistance in the fracture, which improved the calculation accuracy of the productivity of fractured horizontal wells. In terms of productivity prediction, scholars at home and abroad have established various productivity prediction models in view of the particularity of tight reservoirs [10,11]. These models not only consider the interference between fractures and the simultaneous production of fractures and horizontal sections, but also integrate the effects on productivity of multi-fracture interference, the length of each fracture, the conductivity, the interval between fractures and the angle between the fracture plane and the horizontal wellbore.…”

Section: Literature Reviewmentioning

confidence: 99%

Effect Evaluation of Staged Fracturing and Productivity Prediction of Horizontal Wells in Tight Reservoirs

Zhang,

Chen,

et al. 2024

Energies

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In this paper, the effect evaluation and production prediction of staged fracturing for horizontal wells in tight reservoirs are studied. Firstly, the basic characteristics and value of horizontal wells in tight reservoirs are introduced, their geological characteristics, flow mechanism and permeability model are analyzed and the application of grey theory in effect analysis is discussed. Considering the problems of staged fracturing effect evaluation and the production prediction of horizontal wells in tight reservoirs, a BP neural network model based on deep learning is proposed. Due to the interference of multiple physical parameters and the complex functional relationship in the development of tight reservoir fracturing, the traditional prediction method has low accuracy and it is difficult to establish an accurate mapping relationship. In this paper, a BP neural network is used to simulate multivariable nonlinear mapping by modifying the model, and its advantages in solving the coupling relationship of complex functions are brought into play. A neural network model with fracturing parameters as input and oil and gas production as output is designed. Through the training and testing of data sets, the accuracy and applicability of the proposed model for effect evaluation and yield prediction are verified. The research results show that the model can fit the complex mapping relationship between fracturing information and production and provide an effective evaluation and prediction tool for the development of the staged fracturing of horizontal wells in tight reservoirs.

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Section: Literature Reviewmentioning

confidence: 99%