Machine-learning-based well production prediction under geological and hydraulic fracture parameters uncertainty for unconventional shale gas reservoirs

Xiao, Cong; Wang, Guangdong; Zhang, Yayun; Deng, Ya

doi:10.1016/j.jngse.2022.104762

Cited by 12 publications

(2 citation statements)

References 28 publications

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“…According to the revenue estimation formula, it can be seen that the production parameter Q is an important factor affecting revenue from shale gas development [28]. Combined with the decreasing law of single wells year-by-year [46,47], the shale gas production forecasts for the two blocks are shown in Figures 3 and 4.…”

Section: Single Well Production Analysismentioning

confidence: 99%

China’s Shale Gas Development Subsidy Analysis and Optimization considering Resource Endowment Characteristic

Huang

Zheng

Zhang

et al. 2023

International Journal of Energy Research

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The currently implemented indiscriminate subsidy policy in China is difficult to provide effective incentives for a firm’s investment behavior because it does not take into account the differences in resource characteristics. Thus, this paper puts forward a type of differentiated subsidy that considers resource conditions, consisting of a basic guaranteed subsidy and a variable incentive subsidy. First, from the perspective of the economic efficiency of enterprises, the amount of basic subsidy required by enterprises is calculated by the discounted cash flow method. Then, from the government’s perspective, a variable incentive subsidy amount is calculated by establishing an expectation benefit maximization function using principal-agent theory. The case application shows that due to the superior resources and development conditions of the Fuling block, it requires a significantly smaller subsidy amount than the Weiyuan block which verifies the need to consider regional differences in the design of shale gas development subsidies.

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Section: Single Well Production Analysismentioning

confidence: 99%

China’s Shale Gas Development Subsidy Analysis and Optimization considering Resource Endowment Characteristic

Huang

Zheng

Zhang

et al. 2023

International Journal of Energy Research

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“…Shale gas, known for its abundant reserves and lower carbon emissions compared to traditional fossil fuels like coal and oil, has gained worldwide attention [4,5]. Hydraulic fracturing is the primary method for efficient shale gas extraction [6][7][8]. However, hydraulic fracturing increases ground instability and the likelihood of induced seismicity due to changes in pore pressure and variations in porous elastic stress induced by high-pressure fluid injections [9][10][11].…”

Section: Introductionmentioning

confidence: 99%

Deep-Learning-Based Seismic-Signal P-Wave First-Arrival Picking Detection Using Spectrogram Images

Choi,

Lee,

Kim

et al. 2024

Electronics

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The accurate detection of P-wave FAP (First-Arrival Picking) in seismic signals is crucial across various industrial domains, including coal and oil exploration, tunnel construction, hydraulic fracturing, and earthquake early warning systems. At present, P-wave FAP detection relies on manual identification by experts and automated methods using Short-Term Average to Long-Term Average algorithms. However, these approaches encounter significant performance challenges, especially in the presence of real-time background noise. To overcome this limitation, this study proposes a novel P-wave FAP detection method that employs the U-Net model and incorporates spectrogram transformation techniques for seismic signals. Seismic signals, similar to those encountered in South Korea, were generated using the stochastic model simulation program. Synthesized WGN (White Gaussian Noise) was added to replicate background noise. The resulting signals were transformed into 2D spectrogram images and used as input data for the U-Net model, ensuring precise P-wave FAP detection. In the experimental result, it demonstrated strong performance metrics, achieving an MSE of 0.0031 and an MAE of 0.0177, and an RMSE of 0.0195. Additionally, it exhibited precise FAP detection capabilities in image prediction. The developed U-Net-based model exhibited exceptional performance in accurately detecting P-wave FAP in seismic signals with varying amplitudes. Through the developed model, we aim to contribute to the advancement of microseismic monitoring technology used in various industrial fields.

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Spatial correlation-based machine learning framework for evaluating shale gas production potential: A case study in southern Sichuan Basin, China

Yi,

Qi,

et al. 2024

Applied Energy

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Machine-learning-based well production prediction under geological and hydraulic fracture parameters uncertainty for unconventional shale gas reservoirs

Cited by 12 publications

References 28 publications

China’s Shale Gas Development Subsidy Analysis and Optimization considering Resource Endowment Characteristic

China’s Shale Gas Development Subsidy Analysis and Optimization considering Resource Endowment Characteristic

Deep-Learning-Based Seismic-Signal P-Wave First-Arrival Picking Detection Using Spectrogram Images

Spatial correlation-based machine learning framework for evaluating shale gas production potential: A case study in southern Sichuan Basin, China

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