Optimization of the Steam Alternating Solvent Process Using Pareto-Based Multi-Objective Evolutionary Algorithms

Molina, Israel Mayo; Leung, Juliana Y.

doi:10.1115/1.4055292

Cited by 1 publication

(1 citation statement)

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“…These studies include estimating corrosion rates in pipelines [6] , predicting cumulative oil production in unconventional reservoirs, accelerating reservoir simulations [7] . Additionally, in the specific context of SAGD, considerable research has been conducted on data-driven models for predicting production performance [8] , history matching [9] , clustering [10] , and optimizing SAGD processes [11] . These studies have significantly enhanced prediction efficiency and expanded the application range of data-driven models.…”

Section: Introductionmentioning

confidence: 99%

Comparative Study of Machine Learning Algorithms for Predicting Steam-assisted Gravity Drainage (SAGD) Production Performance

Wang,

Huang,

Zhao

et al. 2024

Preprint

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Steam-assisted gravity drainage (SAGD) is one effective and well-established technology for recovering heavy oil and bitumen resources. Extensive research has been conducted on data-driven models to evaluate the production performance of the SAGD process. The artificial neural network (ANN) is a commonly used machine learning method. However, it is crucial to explore other machine learning methods such as Symbolic Regression (SR), Extreme Gradient Boosting (XGBoost) and Random Forest (RF) using field data. In this study, firstly, a data set consisting of thirteen input/output attributes describing production-related properties and production characteristics was extracted from Long Lake field data. Secondly, three different machine learning methods, including Neural Networks (ANN), Random Forest (RF), Extreme Gradient Boosting (XGBoost) and Symbolic Regression (SR), were employed to establish a relationship between the input and output parameters in the different data sets. Subsequently, a range of models were created, evaluated, and compared. Furthermore, the impact of two feature scaling methods, namely standardization and normalization, on the accuracy of a series of prediction models was explored. Lastly, the sensitivity of the input parameters was analyzed. Analysis of the forecasting results obtained from different models leads to the following conclusions. The study found that standardization and normalization significantly enhance the performance of the artificial neural network model, with standardization being more effective. However, the impact of data scaling on integrated learning models (random forest and extreme gradient boosting tree) is minimal. Interestingly, for models based on symbolic regression algorithms, not using data scaling yields the best results. Both artificial neural network and symbolic regression algorithms demonstrate significant advantages and are suitable for Energy Proceedings

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