Geological characterization of unconventional shale-gas reservoirs

Suriamin, Fnu; Ko, Lucy T.

doi:10.1016/b978-0-323-90185-7.00006-6

Cited by 1 publication

(1 citation statement)

References 92 publications

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“…Therefore, shale gas, as a major component of natural gas, contributes to reducing carbon footprint and has huge development potential in energy supply. However, owing to the ultralow porosity and permeability, stimulation methods are necessary to achieve industrial development of shale gas resources [6]. A number of shale gas stimulation methods have been proposed to increase shale gas production, such as supercritical CO 2 fracturing [7], application of micro-nanoparticles [8], nitrogen/carbon Processes 2023, 11, 806 2 of 15 dioxide injection, and thermal stimulation methods [9].…”

Section: Introductionmentioning

confidence: 99%

A Combined Gated Recurrent Unit and Multi-Layer Perception Neural Network Model for Predicting Shale Gas Production

Qin

Liu

et al. 2023

Processes

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Shale gas plays an important role in supplementing energy demand and reducing carbon footprint. A precise and effective prediction of shale gas production is important for optimizing completion parameters. This paper established a gated recurrent unit and multilayer perceptron combined neural network (GRU-MLP model) to forecast multistage fractured horizontal shale gas well production. A nondominated sorting genetic algorithm II (NSGA II) was introduced into the model to enable its automatic architectural optimization. In addition, embedded discrete fracture models (EDFM) and a reservoir simulator were used to generate training datasets. Meanwhile, a sensitivity analysis was carried out to find the variable’s importance and support the history matching. The results illustrated that the GRU-MLP model can precisely and efficiently predict the productivity of multistage fractured horizontal shale gas in a rapid and effective manner. Additionally, the model fits better at peak values of shale gas production. The GRU-MLP hybrid model has a higher accuracy within an acceptable computational time range compared to recurrent neural networks (RNN), long short-term memory (LSTM), and GRU models. The mean absolute percentage error (MAPE) and root mean square percentage error (RMSPE) for shale gas production generated by GRU-MLP model were 3.90% and 3.93%, respectively, values 84.87% and 84.88% smaller than those of the GRU model. Consequently, compared with a purely data-driven method, the physics-constrained data-driven method behaved better. The main results of the study will hopefully contribute to the intelligent development of shale gas production prediction.

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