An efficient optimization framework of cyclic steam stimulation with experimental design in extra heavy oil reservoirs

Luo, Erhui; Fan, Zifei; Hu, Yongle; Zhao, Lun; Bo, Bing; Yu, Wang; Liang, Hongwei; Liu, Minghui; Liu, Yunyang; He, Congge; Wang, Jianjun

doi:10.1016/j.energy.2019.116601

Cited by 19 publications

(12 citation statements)

References 15 publications

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“…Zhang et al in 2007 introduced a framework to optimize a development design for a field-scale simulation study under various uncertainties by using an experimental design and a Monte Carlo algorithm with a global optimization search engine. 80 Luo et al in 2020 stated an optimization procedure for operation parameters in one CSS field in China based on simulation results, 81 where a two-level Plackeett−Burman experimental design was first used to select seven operation parameters, followed by a three-level Box−Behnken experimental design for optimizing cumulative oil volumes and SORs. Horizontal CSS wells have been proposed to enhance performance, as a vertical well has a limited drainage area.…”

Section: ■ Cyclic Steam Stimulation (Css)mentioning

confidence: 99%

A Critical Review of Reservoir Simulation Applications in Key Thermal Recovery Processes: Lessons, Opportunities, and Challenges

Yang

Nie

et al. 2021

Energy Fuels

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After a cyclic steam stimulation (CSS) process achieved accidental success in recovering heavy oil resources in the 1960s, thermal recovery processes started to unlock the door of in situ recovery of heavy oil and bitumen resources. After five decades of development, many thermal recovery processes have been developed, among which CSS and steam-assisted gravity drainage (SAGD) are two main commercialized processes. With the support of laboratory and field data, reservoir simulation can help engineers and researchers to understand recovery mechanisms, optimize operations, and forecast performance of either existing or emerging processes. In this paper, we present a critical review of applications of reservoir simulation on CSS, CSS follow-ups (steamflooding, liquid addition to steam for enhancing recovery (LASER), and in situ combustion), SAGD, solvent-assisted SAGD, and SAGD noncondensable gas (NCG) coinjection processes. This review and the analyses of these processes provide not only their clarifications from thermal simulation lessons but also an extensive summary of challenges still faced by industry. Moreover, they shed light on future research directions and opportunities for thermal recovery processes.

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Section: ■ Cyclic Steam Stimulation (Css)mentioning

confidence: 99%

A Critical Review of Reservoir Simulation Applications in Key Thermal Recovery Processes: Lessons, Opportunities, and Challenges

Yang

Nie

et al. 2021

Energy Fuels

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“…Since many factors affecting the development index of heavy oil steam stimulation, it is necessary to go through a systematic index analysis process to find the development index more accurately. Based on the basic theory of reservoir engineering and combined with related research [2,13,[26][27][28], we obtained the factors affecting the production of heavy oil steam stimulation, which can be divided into the following five categories:…”

Section: Feature Selectionmentioning

confidence: 99%

“…So far, reservoir numerical simulation software has made a great breakthrough in the integration of functions. For different types of oil and gas reservoirs, different mining methods can almost be used to deal with reservoir numerical simulation software [11][12][13][14]. We sample different underground conditions by reservoir numerical simulation and then describe the reservoir mining state by partial differential equations, but its accuracy is based on accurate geological models.…”

Section: Introductionmentioning

confidence: 99%

Prediction of Heavy Oil Steam Stimulation Based on Data-Driven and Mechanism Model

Zhao

Min

Wang

et al. 2021

Mathematical Problems in Engineering

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In the middle and late stages of heavy oil development, formulating a scientific and reasonable mining plan is the key to improving oilfield efficiency. At present, steam stimulation is still the main development method of heavy oil. The determination of its production is not only limited by boiler conditions, surface pipelines, and wellbore conditions but also by the steam absorption capacity of the formation. Therefore, local analysis cannot achieve the best effect in the whole process of steam stimulation. The mechanism model is the most commonly used method to predict heavy oil production, but too many idealized assumptions make the prediction results quite different from the actual production situation. With the rapid development of machine learning, people can achieve rapid prediction of production through field data. However, when the range of the actual parameter is small, the generalization ability of the model is weak and overfitting occurs. Based on the above background, this paper conducts a coupling study on surface steam pipeline flow, steam injection wellbore flow, and formation flow from the perspective of data-driven. Firstly, based on the correlation coefficient and the feature selection of Random Forest, the importance of the characteristics affecting liquid production and water content was ranked. Secondly, through the comparison of five typical machine learning algorithms, we select the optimal prediction model and optimal characteristics suitable for the sample of this paper. Finally, because of the poor generalization ability of the prediction model, we sampled the mechanism model and increased the diversity of steam dryness samples. We find that the accuracy of the optimal prediction model is improved and the generalization ability of the model is improved after the training of new samples. This paper provides a new idea for the production prediction of heavy oil steam stimulation reservoirs, which is helpful for the efficient development of heavy oil reservoirs.

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“… 10 It is one of effective physics-driven modeling methods and is considered as a dependable researching tool in the reservoir engineering field. 11 However, once the reservoir numerical simulation model is complex, the running process will be very time-consuming; meanwhile, the storage requirement of big data raises additional challenges for complicated simulation models. 12 So it is of great value building a more efficient proxy model to meet the requirements of today’s fast-paced application scenarios.…”

Section: Introductionmentioning

confidence: 99%

Data-Driven Proxy Model for Forecasting of Cumulative Oil Production during the Steam-Assisted Gravity Drainage Process

Liu

et al. 2021

ACS Omega

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The purpose of this study is to develop a data-driven proxy model for forecasting of cumulative oil (Cum-oil) production during the steam-assisted gravity drainage process. During the model building process, an artificial neural network (ANN) is used to offer a complementary and computationally efficient tool for the physics-driven model, and the von Bertalanffy performance indicator is used to bridge the physics-driven model with the ANN. After that, the accuracy of the model is validated by blind-testing cases. Average absolute percentage error of related parameters of the performance indicator in the testing data set is 0.77%, and the error of Cum-oil production after 20 years is 0.52%. The results illustrate that the integration of performance indicator and ANN makes it possible to solve time series problems in an efficient way. Besides, the data-driven proxy model could be applied to fast parametric studies, quick uncertainty analysis with the Monte Carlo method, and average daily oil production prediction. The findings of this study could help for better understanding of combination of physics-driven model and data-driven model and illustrate the potential for application of the data-driven proxy model to help reservoir engineers, making better use of this significant thermal recovery technology for oil sands or heavy oil reservoirs.

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An efficient optimization framework of cyclic steam stimulation with experimental design in extra heavy oil reservoirs

Cited by 19 publications

References 15 publications

A Critical Review of Reservoir Simulation Applications in Key Thermal Recovery Processes: Lessons, Opportunities, and Challenges

A Critical Review of Reservoir Simulation Applications in Key Thermal Recovery Processes: Lessons, Opportunities, and Challenges

Prediction of Heavy Oil Steam Stimulation Based on Data-Driven and Mechanism Model

Data-Driven Proxy Model for Forecasting of Cumulative Oil Production during the Steam-Assisted Gravity Drainage Process

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