Prediction of sub-critical two-phase flow through wellhead chokes of gas condensate wells using PSO-LSSVM method

Kalantariasl, Azim; Yazdanpanah, Arash; Ghanat-pisheh, Ehsan; Shahsavar, Negar

doi:10.1016/j.upstre.2021.100057

Cited by 4 publications

(1 citation statement)

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“…Additionally, the neural network's asphalt precipitation predictions align well with experimental research results [8][9]. LSSVM is commonly used in oil reservoirs to determine porosity and permeability [10][11][12][13], water in horizontal wells, well positions, oil flow prediction, and temperature-pressure relationships in natural gas production and processing [14][15][16][17].…”

Section: Introductionmentioning

confidence: 54%

Research on Production Prediction Method of Multi-stage Fractured Shale Gas Horizontal Well

Yin,

Zhang

2024

Applied Mathematics and Nonlinear Sciences

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Shale gas is a crucial component of unconventional energy. Productivity evaluation of gas wells is essential to ensure efficient and stable production. However, predicting productivity has been challenging due to the complex characteristics of shale gas reservoirs and the use of multi-stage fractured horizontal wells (MFHW) technology in the development process. This study compares traditional production prediction methods with an optimized least squares support vector machine model (LSSVM). The traditional productivity prediction method involves establishing a mathematical model of the MFHW in a fully enclosed rectangular shale formation. The model considers the effects of shale gas adsorption, diffusion, and pressure sensitivity. The model’s analytical solution is obtained using Duhamel’s principle, Laplace transform, and inverse transform. An independent production data analysis software is developed based on the analytical solution of bottom-hole pressure to predict production. To implement the LSSVM model, the model’s input parameters must be determined first. The LSSVM model’s regularization parameters and kernel parameters are obtained through the particle swarm optimization (PSO) algorithm, and the prediction model is established. The model’s matching is evaluated by calculating the coefficient of determination (R2) and the normalized root mean square error (NRMSE). The results indicate that the traditional production capacity prediction method is suitable for stable production in terms of applicability. However, the LSSVM model does not have this limitation and generally provides more accurate predictions throughout the entire production process. For complex shale gas reservoirs that frequently switch wells and use multi-stage fracturing technology, the LSSVM model is more suitable for predicting shale gas well productivity.

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