Integration of data-driven modeling techniques for lean zone and shale barrier characterization in SAGD reservoirs

Ma, Zhiwei; Leung, Juliana Y.

doi:10.1016/j.petrol.2019.01.106

Cited by 24 publications

(11 citation statements)

References 30 publications

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“…Zhou et al (2016) derived a model of initial water mobility in intraformational water zones based on a classical capillary model, and their study provided an initial water permeability formula. Ma and Leung (2019) employed a data-driven method to study the effect of intraformational water zones and shale layers, and their study showed the effect of intraformational water zones on production curves. All the previous studies indicate that intraformational water zones are harmful to the SAGD process, and an industrial practice to deal with intraformational water zones is needed.…”

Section: Introductionmentioning

confidence: 99%

Impacts of pore size distribution on gas injection in intraformational water zones in oil sands reservoirs

Chen

2020

Oil Gas Sci. Technol. – Rev. IFP Energies nouvelles

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Intraformational water zones are widely reported in Canadian oil sands fields. In order to pressurize a thief zone, one of the initiatives is to inject gas. However, the evaluation of gas injectivity based on a pore size distribution is still a big challenge. This study provides a multi-scale approach to study the effect of a pore size distribution on gas injectivity in intraformational water zones. The results indicate the gas effective permeability increases in a less complex and more discrete pore network. The enhancement of gas effective permeability with increased gas saturation weakens with higher complexity and lower discreteness of a pore network. A less complex and more discrete pore network better benefits the gas injectivity index.

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Section: Introductionmentioning

confidence: 99%

Impacts of pore size distribution on gas injection in intraformational water zones in oil sands reservoirs

Chen

2020

Oil Gas Sci. Technol. – Rev. IFP Energies nouvelles

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“…[ 11 ] In particular, many studies utilized a series of data‐driven models based on different techniques to analyze the SAGD process. Those studies paid much attention to the impact of reservoir heterogeneity, [ 12–18 ] optimization, [ 19–23 ] production performance prediction, [ 24–28 ] and clustering, [ 29–31 ] which have significantly improved the ability to predict a SAGD process. However, studies of a data‐driven model applied to infill wells in a SAGD process are still rare.…”

Section: Introductionmentioning

confidence: 99%

Integration of machine learning and data analysis for the SAGD production performance with infill wells

2023

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There have been numerous studies on predicting the production performance of the steam assisted gravity drainage (SAGD) process by data‐driven models with different machine learning algorithms since their introduction into industry. Similar efforts on SAGD infill wells, nevertheless, remain rare for this advanced alteration in improving the classical SAGD performance. On the other hand, predictive tools to optimize an infill well start time is useful in maximizing bitumen production and minimizing its costs. In this paper, a series of SAGD infill well models are constructed with selected ranges of operational conditions. Three SAGD infill well production performance indicators, namely, an increased ratio (), a total steam–oil ratio (SORtotal), and a stolen ratio () for each SAGD infill well, are calculated based on simulated infill well cases and control models. Five different machine learning algorithms (an artificial neural network [ANN] algorithm, three gradient boosting decision tree [GBDT] algorithms, and a support vector machine [SVM] algorithm) are trained, tested, and evaluated for their effectiveness in predicting those three indicators as output parameters, given seven SAGD relevant parameters as input parameters. Comparisons of different data sets show that the ANN is the best in predicting all three performance indicators under different infill well start times among all the above machine learning algorithms, while the GBDT algorithms have a better ability to learn a variation trend in the SAGD infill well performance.

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“…Huang et al [18] studied the influence of multiple shale interlayers on the steam chamber and production profile of the SAGD process by combining a physical experiment method and numerical simulation approach; the results show that the key factor is the length of the first interlayer. Ma and Leung [13,19] designed various distribution scenarios of the shale interlayer including the variation of threedimensional spatial distribution and geological characteristics and conducted the related numerical simulation runs; the results indicate that the production rate decreases significantly when the steam chamber touches the interlayer, and this phenomenon will last until the steam chamber extends through the entire interlayer. In 2020, case studies of the Fengcheng SAGD project in China were carried out by Wang et al and Liu et al [20,21], in order to compare the SAGD performance under different distribution patterns through a statistical analysis approach and numerical simulation method; the results show that the flow capacity of fluid is weak at the area where interlayers exist, and the remaining oil zone with high oil saturation is formed above the interlayer.…”

Section: Introductionmentioning

confidence: 99%

The Adverse Impact of Muddy Laminae on Conventional SAGD Performance and Improvement Strategy with the Multilateral Well Pattern

Liu

Bao

et al. 2021

Geofluids

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With further progress of Steam-Assisted Gravity Drainage (SAGD) technology, a growing number of oil sands or heavy oil reservoirs were put into production in an efficient way. However, owing to the existence of muddy laminae within reservoirs, there are challenges associated with the expansion of the steam chamber and oil drainage during the SAGD process. The purpose of this study is to evaluate the adverse impact of muddy laminae on conventional SAGD performance and introduce an improvement strategy with multilateral well patterns to reduce the adverse impact and improve the performance. In the research reported here, the reservoir numerical simulation approach is applied to conduct the research. The analysis conducted on a prototypical reservoir reveals that the steam chamber may expand slowly in some sections due to the poor capacity of heat and mass transfer, and the expansion of the steam chamber is relatively uneven along the wellbore, when the muddy laminae are existing in the formation. The influence level of the muddy laminae on conventional SAGD performance under different distribution modes is different, but the adverse effect is mainly reflected in the delay of peak oil production, the decrease in peak oil production, the decrease in steam chamber volume, and the increase in the cumulative steam oil ratio (mainly in early and middle stages of the SAGD process). On the basis of aforementioned researches, the improvement strategy with two different multilateral well patterns, planar multilateral well and upward multilateral well, is introduced to improve the SAGD performance. The results indicate that the combination of a planar multilateral injector and planar multilateral producer has the best performance. By adopting such kind of combination, the recovery factor can be increased from 31.36% to 47.08%, and the cumulative steam oil ratio can be decreased from 5.29 m3/m3 to 4.64 m3/m3 under the combined distribution mode of muddy laminae. It can be known that the branches of the planar multilateral well are very helpful for the expansion of the steam chamber and oil drainage, once the heat connection between branches of the injector and producer is well established. Overall results show that the multilateral well pattern is promising for SAGD applications at oil sands or heavy oil reservoirs which are rich in muddy laminae.

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Integration of data-driven modeling techniques for lean zone and shale barrier characterization in SAGD reservoirs

Cited by 24 publications

References 30 publications

Impacts of pore size distribution on gas injection in intraformational water zones in oil sands reservoirs

Impacts of pore size distribution on gas injection in intraformational water zones in oil sands reservoirs

Integration of machine learning and data analysis for the SAGD production performance with infill wells

The Adverse Impact of Muddy Laminae on Conventional SAGD Performance and Improvement Strategy with the Multilateral Well Pattern

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