Data-driven model for hydraulic fracturing design optimization: focus on building digital database and production forecast

Morozov, Aleksandr; Popkov, Dmitry; Duplyakov, Victor M.; Mutalova, R.; Осипцов, А. Н.; Vainshtein, Albert; Burnaev, Evgeny; Shel, Egor; Paderin, Grigory

doi:10.1016/j.petrol.2020.107504

Cited by 47 publications

(27 citation statements)

References 26 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…However, several field cases have justified that this cannot guarantee the maximum economic benefits. Therefore, to the best of our knowledge, there is little research on the optimization of controllable fracturing parameters to maximize oil production or economic benefit (Morozov et al, 2020). Furthermore, due to the special phase behavior and flow mechanisms of shale oil, the optimization result may be different from that without considering shale oil characteristics (Feng et al, 2019).…”

Section: Current Limitations and Future Directionsmentioning

confidence: 99%

Advances and challenges in shale oil development: A critical review

Feng¹,

Xu²,

Xing³

et al. 2020

Adv. Geo-Energy Res.

151

View full text Add to dashboard Cite

Section: Current Limitations and Future Directionsmentioning

confidence: 99%

Advances and challenges in shale oil development: A critical review

Feng¹,

Xu²,

Xing³

et al. 2020

Adv. Geo-Energy Res.

151

View full text Add to dashboard Cite

“…Nowadays, many IOR/EOR projects are being carried out worldwide. There are already examples of successful ML applications in the literature for hydraulic fracturing [17][18][19]. For such projects, it is crucial to assess the potential and risks in advance; however, this is not easy to do.…”

Section: Discussionmentioning

confidence: 99%

“…A number of studies confirmed the effectiveness of ML models in application to oil recovery factor estimation [14][15][16]. Several other studies have reported the successful application of ML models to estimate the effects of hydraulic fracturing [17][18][19][20]. Kornkosky et al applied multivariate linear regression to estimate the waterflooding effect [11].…”

Section: Introductionmentioning

confidence: 95%

Machine Learning to Rate and Predict the Efficiency of Waterflooding for Oil Production

2022

View full text Add to dashboard Cite

Waterflooding is a widely used secondary oil recovery technique. The oil and gas industry uses a complex reservoir numerical simulation and reservoir engineering analysis to forecast production curves from waterflooding projects. The application of such standard methods at the stage of assessing the potential of a huge number of projects could be computationally inefficient and requires a lot of effort. This paper demonstrates the applicability of machine learning to rate the outcome of waterflooding applied to an oil reservoir. We also explore the relationship of project evaluations by operators at the final stages with several performance metrics for forecasting. Real data about several thousand waterflooding projects in Texas are used in the current study. We compare the ML models rankings of the waterflooding efficiency and the expert rankings. Linear regression models along with neural networks and gradient boosting on decision threes are considered. We show that machine learning models allow reducing computational complexity and can be useful for rating the reservoirs, with respect to the effectiveness of waterflooding.

show abstract

“…In the face of a large amount of fracturing well data, data analysis technology is getting a lot of attention [21][22][23][24]. When the time of refracturing is taken as the target parameter after quantitative characterization treatment, there should be a complex nonlinear relationship between it and the geological and engineering parameters of horizontal wells.…”

Section: Introductionmentioning

confidence: 99%

Prediction of Refracturing Timing of Horizontal Wells in Tight Oil Reservoirs Based on an Integrated Learning Algorithm

Zhang

Ren²,

Feng

et al. 2021

Energies

View full text Add to dashboard Cite

Refracturing technology can effectively improve the EUR of horizontal wells in tight reservoirs, and the determination of refracturing time is the key to ensuring the effects of refracturing measures. In view of different types of tight oil reservoirs in the Songliao Basin, a library of 1896 sets of learning samples, with 11 geological and engineering parameters and corresponding refracturing times as characteristic variables, was constructed by combining numerical simulation with field statistics. After a performance comparison and analysis of an artificial neural network, support vector machine and XGBoost algorithm, the support vector machine and XGBoost algorithm were chosen as the base model and fused by the stacking method of integrated learning. Then, a prediction method of refracturing timing of tight oil horizontal wells was established on the basis of an ensemble learning algorithm. Through the prediction and analysis of the refracturing timing corresponding to 257 groups of test data, the prediction results were in good agreement with the real value, and the correlation coefficient R2 was 0.945. The established prediction method can quickly and accurately predict the refracturing time, and effectively guide refracturing practices in the tight oil test area of the Songliao basin.

show abstract

Data-driven model for hydraulic fracturing design optimization: focus on building digital database and production forecast

Cited by 47 publications

References 26 publications

Advances and challenges in shale oil development: A critical review

Advances and challenges in shale oil development: A critical review

Machine Learning to Rate and Predict the Efficiency of Waterflooding for Oil Production

Prediction of Refracturing Timing of Horizontal Wells in Tight Oil Reservoirs Based on an Integrated Learning Algorithm

Contact Info

Product

Resources

About