Optimize production allocation for the oil-gas field basing on a novel grey model

Lin, Chen; Liu, Zhibin; Ma, Ning

doi:10.1016/j.jnggs.2019.03.003

Cited by 12 publications

(4 citation statements)

References 13 publications

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“…The TDPGM is one of the models with good predictive applicability. The Time-Delayed Polynomial Fractional Grey Model (TDPFOGM) was proposed based on the TDPGM model (Chen et al, 2019). The Time-Delayed Polynomial Fractional Grey Model (TDPFOGM) is mathematically represented as:…”

Section: Novel Grey Model (Gm) For Gas Lift Optimizationmentioning

confidence: 99%

Gas lift optimization in the oil and gas production process: a review of production challenges and optimization strategies

Okorocha

Chinwuko

Mgbemena

et al. 2020

Int. J. Ind. Optim.

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Gas Lift operation involves the injection of compressed gas into a low producing or non-performing well to maximize oil production. The oil produced from a gas lift well is a function of the gas injection rate. The optimal gas injection rate is achieved by optimization. However, the gas lift, which is an artificial lift process, has some drawbacks such as the deterioration of the oil well, incorrect production metering, instability of the gas compressor, and over injection of gas. This paper discusses the various optimization techniques for the gas lift in the Oil and Gas production process. A systematic literature search was conducted on four databases, namely Google Scholar, Scopus, IEE Explore and DOAJ, to identify papers that focused on Gas lift optimizations. The materials for this review were collected primarily via database searches. The major challenges associated with gas lift were identified, and the different optimization strategies available in the literature reviewed. The strategies reviewed were found to be based on artificial intelligence (AI) and machine learning (ML). The implementation of any of the optimization strategies for the gas lift will enhance profitability, reduce operational cost, and extend the life of the wells.

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Section: Novel Grey Model (Gm) For Gas Lift Optimizationmentioning

confidence: 99%

Gas lift optimization in the oil and gas production process: a review of production challenges and optimization strategies

Okorocha

Chinwuko

Mgbemena

et al. 2020

Int. J. Ind. Optim.

View full text Add to dashboard Cite

show abstract

“…The prediction accuracy of the grey model is limited by the distribution of the data. Once the accumulated data deviated from the natural exponential a b c law, underfitting or overfitting would occur and the prediction effect of the Grey model would deviate from the actual value [21][22][23][24]. The exponential smoothing model applies the linear combination of time series to represent the prediction value at the next moment.…”

Section: Introductionmentioning

confidence: 99%

Reliable Prediction on Emerging Energy Supply for National Sustainability and Stability: A Case Study on Coal Bed Gas Supply in China Based on the Dual-LSTM Model

Kang

Lao

et al. 2021

IEEE Access

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Aiming to prevent from the imbalance between supply and demand of energy in which the share of emerging type is rapidly increasing, to predict the supply of emerging energy reliably is significant. However, the expected distribution uncertain and high-noise characteristics of emerging energy supply impede the reliable prediction. The Dual-LSTM (Long Short-Term Memory) model was constructed for the characteristic extracting and effective prediction of the expected distribution uncertain high-noise emerging energy supply time series. A case study on coal bed gas supply in China was conducted. Results showed that the Dual-LSTM model effectively solved the the problem of superfluous and non-quantifiable variables in the prediction of coal bed gas supply and extracted the statistical characteristics of expected distribution uncertain and high-noise data samples effectively with a relative error major less than 5% in short-term. Besides, the Dual-LSTM model has a significantly higher prediction accuracy while comparing with ARIMA model and original LSTM model. Ultimately, it is predicted that the year-on-year growth rates of coal bed gas supply of China from January to September, 2021, approximately maintains 75% in average based on the Dual-LSTM model. The Dual-LSTM model provides a reliable statistical model for policy decision to maintain national sustainability and stability. INDEX TERMSImproved deep learning model; Uncertain expected distribution high-noise sample; Emerging energy; Supply-demand stability; Reliable prediction; Time series analysis

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“…The fluid viscosity of the oil-gas reservoir [1,2] is the key factor to determine the final development effect and economic benefit of the oil-gas reservoir. Therefore, it has become an important basis performance for formulating oil-gas field development plans [3], studying oil and gas reservoir performance, implementing plan adjustment, and evaluating stimulation [4].…”

Section: Introductionmentioning

confidence: 99%

Prediction of Fluid Viscosity in Multiphase Reservoir Oil System by Machine Learning

Shao

et al. 2021

Geofluids

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It is important to realize rapid and accurate prediction of fluid viscosity in a multiphase reservoir oil system for improving oil production in petroleum engineering. This study proposed three viscosity prediction models based on machine learning approaches. The prediction accuracy comparison results show that the random forest (RF) model performs accurately in predicting the viscosity of each phase of the reservoir, with the lowest error percentage and highest R 2 values. And the RF model is tremendously fast in a computing time of 0.53 s. In addition, sensitivity analysis indicates that for a multiphase reservoir system, the viscosity of each phase of the reservoir is determined by different factors. Among them, the viscosity of oil is vital for oil production, which is mainly affected by the molar ratio of gas to oil (MR-GO).

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Optimize production allocation for the oil-gas field basing on a novel grey model

Cited by 12 publications

References 13 publications

Gas lift optimization in the oil and gas production process: a review of production challenges and optimization strategies

Gas lift optimization in the oil and gas production process: a review of production challenges and optimization strategies

Reliable Prediction on Emerging Energy Supply for National Sustainability and Stability: A Case Study on Coal Bed Gas Supply in China Based on the Dual-LSTM Model

Prediction of Fluid Viscosity in Multiphase Reservoir Oil System by Machine Learning

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