A Dew Point Pressure Model for Gas Condensate Reservoirs Based on an Artificial Neural Network

Kaydani, Hossein; Hagizadeh, A.; Mohebbi, Ali

doi:10.1080/10916466.2010.540616

Cited by 10 publications

(4 citation statements)

References 14 publications

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“…Numerous ML methods have been proposed to predict the dew point pressure such as those by Akbari et al [92] and Nowroozi et al [93] who developed ANN and ANFIS models, respectively, to predict the dew point pressure of gas condensate systems using compositional and thermodynamic parameters. Similarly, Kaydani et al [94] generated a conventional back-propagation ANN to estimate the dew point of lean retrograde gas condensates using experimentally obtained PVT data (e.g., reservoir temperature, moles fractions of volatile and intermediate gases, etc.). Gonzales et al [95] used an ANN model to estimate the dew point in retrograde gas reservoirs using experimental CVD data (gas composition, MW, specific gravity of the heavy fraction, reservoir temperature).…”

Section: Machine Learning Methods For Predicting Black Oil Pvt Proper...mentioning

confidence: 99%

Applications of Machine Learning in Subsurface Reservoir Simulation—A Review—Part I

Samnioti,

Gaganis

2023

Energies

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In recent years, machine learning (ML) has become a buzzword in the petroleum industry with numerous applications that guide engineers toward better decision making. The most powerful tool that most production development decisions rely on is reservoir simulation with applications in numerous modeling procedures, such as individual simulation runs, history matching and production forecast and optimization. However, all these applications lead to considerable computational time- and resource-associated costs, and rendering reservoir simulators is not fast or robust, thus introducing the need for more time-efficient and smart tools like ML models which can adapt and provide fast and competent results that mimic simulators’ performance within an acceptable error margin. The first part of the present study (Part I) offers a detailed review of ML techniques in the petroleum industry, specifically in subsurface reservoir simulation, for cases of individual simulation runs and history matching, whereas ML-based production forecast and optimization applications are presented in Part II. This review can assist engineers as a complete source for applied ML techniques since, with the generation of large-scale data in everyday activities, ML is becoming a necessity for future and more efficient applications.

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Section: Machine Learning Methods For Predicting Black Oil Pvt Proper...mentioning

confidence: 99%

Applications of Machine Learning in Subsurface Reservoir Simulation—A Review—Part I

Samnioti,

Gaganis

2023

Energies

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“…Multi‐gene genetic programming (MGGP) signifies a symbolic regression approach bolstered by the least squares method to elucidate explicit correlations between one or more input variables and a singular output variable by manipulating mathematical symbols, variables, and functions 281. Diverging from traditional optimization methods like the GA, which frequently yields numeric solutions, MGGP offers potential solutions in a structural form, employing a tree‐based representation.…”

Section: Advanced Algorithms For Cyclone Optimizationmentioning

confidence: 99%

Numerical Investigation of Cyclone Separators: Physical Mechanisms and Theoretical Algorithms

El‐Emam,

Zhou,

Yasser

2024

ChemBioEng Reviews

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Gas‐particle aero‐type cyclones have revolutionized biochemical processes, engineering industries, and environmental pollution protection by effectively separating particles from gas. These devices rely on gravitational energy and centrifugal force to dissipate particle phase energy, but achieving optimal energy efficiency while minimizing pressure drop remains challenging. This has led to the development of various cyclone designs in commercial industries, each with unique energy efficiency characteristics. The intricate gas‐particle flow inside cyclones is a critical issue impacted by cyclone geometry, operating conditions, and media parameters. Advanced numerical simulations have been employed to understand this complex flow pattern better, offering researchers valuable insights into the mechanisms of different cyclone separators. This comprehensive review explores the available numerical methods in the literature on cyclones and their corresponding validations. Computational numerical modeling is a promising technique for predicting cyclone energy efficiency, gas‐particle behavior, and overall performance. This investigation delves into the progress and numerical forms of gas‐particle flow cyclones, examining how different parameters impact cyclone performance and flow patterns within the two‐phase flow. The future developments and challenges that may further promote the development of aero‐type cyclone separators, providing theory and engineering support for future cyclone designs, are also covered. As a result, it can confidently be reported that aero‐type cyclone separators remain a critical component in various industrial sectors, offering energy‐efficient solutions for mitigating environmental pollutants and gas‐particle separation systems. With continued development and research, these devices will undoubtedly shape the future of energy processes and engineering industries, ushering in a new era of sustainability and efficiency.

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“…This limits the accuracy of empirical correlations especially when estimating dewpoint pressure at elevated temperature conditions. Many researchers have explored the potential of applying artificially intelligent tools and other robust techniques such as; artificial neural networks (ANNs) [10, [17][18][19], support vector machine (SVM) [16,20], genetic algorithm (GA) [21,22], multi-gene genetic programming approach Hossein [23] and swarm particle optimization (SPO) [16] to predict dewpoint pressure of gas condensate reservoirs.…”

Section: Introductionmentioning

confidence: 99%

Performance evaluation of machine learning algorithms in predicting dew point pressure of gas condensate reservoirs

et al. 2020

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Accurate knowledge of the dew point pressure for a gas condensate reservoir is necessary for optimizing mitigation operations during field development plan. This study explores the use of machine learning models in predicting the dew point pressure of gas condensate reservoirs. 535 experimental dew point pressure data-points with maximum temperature and pressure of 304 °F and 10,500 psi were used for this analysis. First, a standard multiple linear regression (MLR) was used as a benchmark for comparing the performance of the machine learning models. Multilayer perceptron Neural Networks (MLP) [optimized for the number of neurons and hidden layers], Support Vector Machine (SVM) [using radial basis function kernel] and Decision Tree [Gradient boost Method (GBM) and XG Boost (XGB)] algorithms were used to predict the dew point pressure. The performance of these algorithms was then compared with results obtained from published machine learning models. The input parameters for the model include; gas composition, specific gravity, the molecular weight of the heavier component and compressibility factor. The performances of these algorithms were analysed using root mean square error (RMSE), absolute average relative deviation percentage (AARD %) and coefficient of determination (R 2).

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A Dew Point Pressure Model for Gas Condensate Reservoirs Based on an Artificial Neural Network

Cited by 10 publications

References 14 publications

Applications of Machine Learning in Subsurface Reservoir Simulation—A Review—Part I

Applications of Machine Learning in Subsurface Reservoir Simulation—A Review—Part I

Numerical Investigation of Cyclone Separators: Physical Mechanisms and Theoretical Algorithms

Performance evaluation of machine learning algorithms in predicting dew point pressure of gas condensate reservoirs

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