What question did this study set out to answer?

To evaluate data smoothing techniques as an alternative to outlier removal for preserving periodic signals and improving decline curve analysis in noisy shale gas production records.

November 14, 2024Journal of Natural Gas GeoscienceOpen Access

Investigating curve smoothing techniques for enhanced shale gas production data analysis

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Authors

TYTaha YehiaUniversity of Petroleum SMSondos MostafaSuez University MGMoamen Gasser

Discussion

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Implication

Methodological study reveals curve smoothing improves signal extraction in shale gas production data, indicating enhanced decline curve analysis accuracy.

Key Points

To evaluate data smoothing techniques as an alternative to outlier removal for preserving periodic signals and improving decline curve analysis in noisy shale gas production records.
Evaluated seven smoothing methods, including LOWESS, Fast Fourier Transform (FFT), and Binomial Smoothing, across three shale gas datasets with varying noise levels.
Benchmarked smoothing against the cluster-based local outlier factor (CBLOF) algorithm used for noise removal.
Applied seven distinct decline curve analysis (DCA) models to raw, smoothed, and CBLOF-processed production datasets to measure reserve forecasting performance.
LOWESS and Fast Fourier Transform (FFT) demonstrated superior goodness of fit compared to Binomial Smoothing while effectively subduing noise spikes.
Duong's DCA model yielded the highest correlation coefficient (R²), whereas Wang's model recorded the lowest correlation across smoothed datasets.
Fitting DCA models to smoothed data consistently shifted production projections toward reserve underestimation, even for models that typically overestimate reserves.

Cite This Study

Yehia et al. (2024) studied this question.

synapsesocial.com/papers/6a7c94ddacea3dc62ec8911a https://doi.org/10.1016/j.jnggs.2024.10.004

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November 14, 2024Journal of Natural Gas GeoscienceOpen Access

Investigating curve smoothing techniques for enhanced shale gas production data analysis

View Full Paper

Ask AI

Bookmark

Authors

TYTaha YehiaUniversity of Petroleum SMSondos MostafaSuez University MGMoamen Gasser

Discussion

Member takes

Implication

Methodological study reveals curve smoothing improves signal extraction in shale gas production data, indicating enhanced decline curve analysis accuracy.

Key Points

To evaluate data smoothing techniques as an alternative to outlier removal for preserving periodic signals and improving decline curve analysis in noisy shale gas production records.
Evaluated seven smoothing methods, including LOWESS, Fast Fourier Transform (FFT), and Binomial Smoothing, across three shale gas datasets with varying noise levels.
Benchmarked smoothing against the cluster-based local outlier factor (CBLOF) algorithm used for noise removal.
Applied seven distinct decline curve analysis (DCA) models to raw, smoothed, and CBLOF-processed production datasets to measure reserve forecasting performance.
LOWESS and Fast Fourier Transform (FFT) demonstrated superior goodness of fit compared to Binomial Smoothing while effectively subduing noise spikes.
Duong's DCA model yielded the highest correlation coefficient (R²), whereas Wang's model recorded the lowest correlation across smoothed datasets.
Fitting DCA models to smoothed data consistently shifted production projections toward reserve underestimation, even for models that typically overestimate reserves.

Cite This Study

Yehia et al. (2024) studied this question.

synapsesocial.com/papers/6a7c94ddacea3dc62ec8911a https://doi.org/10.1016/j.jnggs.2024.10.004

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