Cognitive Data-Driven Proxy Modeling for Performance Forecasting of Waterflooding Process

Amirian, Ehsan; Chen, Zhangxing John

doi:10.4172/2229-8711.1000207

Cited by 11 publications

(4 citation statements)

References 45 publications

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“…With higher speed and efficiency, data‐driven models have been widely applied in the oil and gas industry, including for exploration, [ 7 ] drilling, [ 8,9 ] completion, [ 10 ] and production. [ 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.…”

Section: Introductionmentioning

confidence: 99%

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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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Section: Introductionmentioning

confidence: 99%

“…With higher speed and efficiency, datadriven models have been widely applied in the oil and gas industry, including for exploration, [7] drilling, [8,9] completion, [10] and production. [11] In particular, many studies utilized a series of data-driven models based on different techniques to analyze the SAGD process.…”

Section: Introductionmentioning

confidence: 99%

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

2023

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“…Progressive advancements in machine learning algorithms has led to their wide use in classification [1], numerical prediction [2], and pattern recognition [3]. Application of these algorithms in the fields of biology [4], engineering [5,6], environmental analysis [7], and medicine [8,9] marks their success. These algorithms learn from input data and make predictions for new unseen data.…”

Section: Introductionmentioning

confidence: 99%

Multi-Objective Optimization of Low Reynolds Number Airfoil Using Convolutional Neural Network and Non-Dominated Sorting Genetic Algorithm

Bakar

Liu

et al. 2022

Aerospace

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The airfoil is the prime component of flying vehicles. For low-speed flights, low Reynolds number airfoils are used. The characteristic of low Reynolds number airfoils is a laminar separation bubble and an associated drag rise. This paper presents a framework for the design of a low Reynolds number airfoil. The contributions of the proposed research are twofold. First, a convolutional neural network (CNN) is designed for the aerodynamic coefficient prediction of low Reynolds number airfoils. Data generation is discussed in detail and XFOIL is selected to obtain aerodynamic coefficients. The performance of the CNN is evaluated using different learning rate schedulers and adaptive learning rate optimizers. The trained model can predict the aerodynamic coefficients with high accuracy. Second, the trained model is used with a non-dominated sorting genetic algorithm (NSGA-II) for multi-objective optimization of the low Reynolds number airfoil at a specific angle of attack. A similar optimization is performed using NSGA-II directly calling XFOIL, to obtain the aerodynamic coefficients. The Pareto fronts of both optimizations are compared, and it is concluded that the proposed CNN can replicate the actual Pareto in considerably less time.

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“…With ML the computer can "learn" the complex and multifaceted relationships between dependent and independent variables via a "black box" (or neural net), which processes the data. Such applications have been used extensively in, for example; biology [12], engineering [13][14][15], environmental analysis [16], information technology [17] and medicine [18]. Such applications reveal the extent to which ML has been used to boost research and development.…”

Section: Introductionmentioning

confidence: 99%

Artificial Intelligence to Enhance Aerodynamic Shape Optimisation of the Aegis UAV

Azabi

Chai

Kipouros

2019

MAKE

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This article presents an optimisation framework that uses stochastic multi-objective optimisation, combined with an Artificial Neural Network (ANN), and describes its application to the aerodynamic design of aircraft shapes. The framework uses the Multi-Objective Particle Swarm Optimisation (MOPSO) algorithm and the obtained results confirm that the proposed technique provides highly optimal solutions in less computational time than other approaches to the same design problem. The main idea was to focus computational effort on worthwhile design solutions rather than exploring and evaluating all possible solutions in the design space. It is shown that the number of valid solutions obtained using ANN-MOPSO compared to MOPSO for 3000 evaluations grew from 529 to 1006 (90% improvement) with a penalty of only 8.3% (11 min) in computational time. It is demonstrated that including an ANN, the ANN-MOPSO with 3000 evaluations produced a larger number of valid solutions than the MOPSO with 5500 evaluations, and in 33% less computational time (64 min). This is taken as confirmation of the potential power of ANNs when applied to this type of design problem.

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Cognitive Data-Driven Proxy Modeling for Performance Forecasting of Waterflooding Process

Cited by 11 publications

References 45 publications

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

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

Multi-Objective Optimization of Low Reynolds Number Airfoil Using Convolutional Neural Network and Non-Dominated Sorting Genetic Algorithm

Artificial Intelligence to Enhance Aerodynamic Shape Optimisation of the Aegis UAV

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