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

Yu, Yang; Liu, Shangqi; Liu, Yang; Bao, Yu; Zhang, Lixia; Dong, Yintao

doi:10.1021/acsomega.1c00617

Cited by 14 publications

(5 citation statements)

References 48 publications

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“…Proxy models that used numerical-simulation results for model training helped to reduce the computational load. 31 , 32 Data collected from the literature were used to train a neural network that can forecast recovery in SAGD operations. 34 By combining reinforcement learning with optimization algorithms and a numerical simulator, steam injection in SAGD was optimized.…”

Section: Introductionmentioning

confidence: 99%

“…In addition to their applications for unconventional reservoirs − and secondary recovery, − successful EOR forecasting and screening models have also been developed. Evaluations of different EOR methods, chemical flooding methods, − cyclic pressure pulsing, , and thermal methods − including SAGD , were made using data-driven models. A combination of models for different EOR processes can result in an comprehensive toolbox that would help to optimize the design of potential EOR applications …”

Section: Introductionmentioning

confidence: 99%

“…Neural networks were employed to develop forecasting models for SAGD. Proxy models that used numerical-simulation results for model training helped to reduce the computational load. , Data collected from the literature were used to train a neural network that can forecast recovery in SAGD operations . By combining reinforcement learning with optimization algorithms and a numerical simulator, steam injection in SAGD was optimized. , Machine learning was also employed to efficiently optimize steam allocation in a multipad SAGD reservoir model of Athabasca formation .…”

Section: Introductionmentioning

confidence: 99%

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Machine-Learning Approach for Forecasting Steam-Assisted Gravity-Drainage Performance in the Presence of Noncondensable Gases

Canbolat¹,

Artun

2022

ACS Omega

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Steam-assisted gravity drainage (SAGD) is an effective enhanced oil recovery method for heavy oil reservoirs. The addition of certain amounts of noncondensable gases (NCG) may reduce the steam consumption, yet this requires new design-related decisions to be made. In this study, we aimed to develop a machine-learning-based forecasting model that can help in the design of SAGD applications with NCG. Experiments with or without carbon dioxide (CO 2 ) or n -butane ( n -C 4 H 10 ) mixed with steam were performed in a scaled physical model to explore SAGD mechanisms. The model was filled with crushed limestone that was premixed with heavy oil of 12.4° API gravity. Throughout the experiments, temperature, pressure, and production were continuously monitored. The experimental results were used to train neural-network models that can predict oil recovery (%) and cumulative steam–oil ratio (CSOR). The input parameters included injected gas composition, prior saturation with CO 2 or n -C 4 H 10 , separation between wells, and pore volume injected. Among different neural-network architectures tested, a 3-hidden-layer structure with 40, 30, and 20 neurons was chosen as the forecasting model. The model was able to predict oil recovery and CSOR with R 2 values of 0.98 and 0.95, respectively. Variable importance analysis indicated that pore volume injected, distance between wells, and prior CO 2 saturation are the most critical parameters that would affect the performance, in agreement with the experiments.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Machine-Learning Approach for Forecasting Steam-Assisted Gravity-Drainage Performance in the Presence of Noncondensable Gases

Canbolat¹,

Artun

2022

ACS Omega

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“…The artificial neural network (ANN), as a typical sort of deep learning, is prevalently and widely employed in the chemistry and chemical industry. 32 − 40 The ANN can learn and adapt in response to the given input–output patterns and adjust itself to minimize the fitting error. Furthermore, the ANN can ascertain the essential of relationships.…”

Section: Introductionmentioning

confidence: 99%

“…Deep learning has made a significant progress in addressing the issues that have been resisting the artificial intelligence community for many years, and it has been proven to be excellent in discovering a intricate structure of the multidimensional data. , There were kinds of successful applications in deep learning, such as crystal identification and discovery, thermodynamic properties prediction for complex materials, predictions of chemical reactions, process performances, − and so forth. The artificial neural network (ANN), as a typical sort of deep learning, is prevalently and widely employed in the chemistry and chemical industry. − The ANN can learn and adapt in response to the given input–output patterns and adjust itself to minimize the fitting error. Furthermore, the ANN can ascertain the essential of relationships .…”

Section: Introductionmentioning

confidence: 99%

Optimized Artificial Neural Network for Evaluation: C4 Alkylation Process Catalyzed by Concentrated Sulfuric Acid

et al. 2021

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In this work, an artificial neural network was first achieved and optimized for evaluating product distribution and studying the octane number of the sulfuric acid-catalyzed C4 alkylation process in the stirred tank and rotating packed bed. The feedstock compositions, operating conditions, and reactor types were considered as input parameters into the artificial neural network model. Algorithm, transfer function, and framework were investigated to select the optimal artificial neural network model. The optimal artificial neural network model was confirmed as a network topology of 10-20-30-5 with Bayesian Regularization backpropagation and tan-sigmoid transfer function. Research octane number and product distribution were specified as output parameters. The artificial neural network model was examined, and 5.8 × 10 –4 training mean square error, 8.66 × 10 –3 testing mean square error, and ±22% deviation were obtained. The correlation coefficient was 0.9997, and the standard deviation of error was 0.5592. Parameter analysis of the artificial neural network model was employed to investigate the influence of operating conditions on the research octane number and product distribution. It displays a bright prospect for evaluating complex systems with an artificial neural network model in different reactors.

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A novel approach to classify lithology of reservoir formations using GrowNet and Deep‐Insight with physic‐based feature augmentation

Mousavi,

Hosseini‐Nasab

2024

Energy Science & Engineering

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Manual interpretation of geophysical logging data can be a tedious and time‐consuming task in the case of the nonlinear behavior of well‐logging signals. This study aims to enhance lithology classification of reservoir formations through advanced machine learning (ML) and deep learning (DL) techniques, introducing and comparing three novel algorithms, GrowNet, Deep‐Insight, and blender, against traditional models like random forest (RF) and support vector machine (SVM). Data from the South and North Viking Graben regions, encompassing 12 lithological facies, was preprocessed through cleaning, normalization, transformation, and imputation of missing values using regression models. The data set was enhanced with physic‐based features and balanced using SMOTE and NearMiss algorithms. Deep‐Insight converted tabular data into images for a convolutional neural network (CNN), significantly improving classification accuracy compared to conventional models such as decision trees (DTs). GrowNet and blender models leveraged hybrid approaches for enhanced performance. These hybrid approaches successfully addressed data imbalance and enhanced model learning, outperforming classic methods. The GrowNet and blender models for lithology classification successfully increased the penalty score and accuracy compared to the FORCE 2020 competition. Additionally, introducing the class prediction error plot visualizes multiclass classification performance more effectively than using a confusion matrix. These novel models in multiclass classification contribute to the petroleum industry by providing more accurate and reliable lithology classification, thereby improving reservoir characterization and exploration efficiency.

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

Cited by 14 publications

References 48 publications

Machine-Learning Approach for Forecasting Steam-Assisted Gravity-Drainage Performance in the Presence of Noncondensable Gases

Machine-Learning Approach for Forecasting Steam-Assisted Gravity-Drainage Performance in the Presence of Noncondensable Gases

Optimized Artificial Neural Network for Evaluation: C4 Alkylation Process Catalyzed by Concentrated Sulfuric Acid

A novel approach to classify lithology of reservoir formations using GrowNet and Deep‐Insight with physic‐based feature augmentation

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