Acceleration of thermodynamic computations in fluid flow applications

Sheth, Soham; Heidari, Mohammad Reza; Neylon, K.; Bennett, James; McKee, François

doi:10.1007/s10596-021-10107-5

Cited by 6 publications

(3 citation statements)

References 13 publications

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“…In cases where complicated systems are under investigation (i.e., CO 2 -EOR), the iterative algorithm in conventional reservoir simulators may fail to converge since there are cases where the flash and the non-linear solver cannot agree on which phase (gas or liquid) is present when a stability test labels the fluid as stable. For that reason, Sheth et al [65] used stability test results and developed two ANN models, one classifier and one regressor, to accelerate EOR simulations, such as dry gas and CO 2 reinjection, by predicting the fluid's critical temperature. Hence, the authors' main goal was to devise an efficient way to accurately predict the crucial value to determine the fluid phase state, hence ascertaining the correct viscosity and relative permeability values to utilize, thus preventing any problems that may arise when simulating the phase behavior of complex fluids.…”

Section: Machine Learning Methods For Handling the Stability And Phas...mentioning

confidence: 99%

“…SVMs and ANNs [39,53,55,57,58] Phase stability/split [10,54,56,59,65] Phase split [22] Phase split Deep learning ANNs [60,64] Phase stability/split [61][62][63] Black oil PVT properties Supervised Z-factor prediction ANNs [67,[70][71][72][73][74] SVMs and LSSVM with optimization methods [75,76,78,83] Kernel Ridge Regression [80] Saturation pressure ANNs [77,85,92,95,96] SVMs [90,91] ANFIS [93] ANNs coupled with optimization methods [97,98] SVMs and LSSVM with optimization methods [99,100,102] SVMs and DTs [101] For the ML strategies concerning HM, two approaches have dominated the research area. The first entails indirect HM which defines the difference between the real and the predicted production data as the model's output.…”

Section: Phase Stabilitymentioning

confidence: 99%

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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 Handling the Stability And Phas...mentioning

confidence: 99%

Section: Phase Stabilitymentioning

confidence: 99%

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

Samnioti,

Gaganis

2023

Energies

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“…In addition, Wang et al [39] built two ANN models to handle the stability and phase-split problems. Similar processes were developed by various other authors [40][41][42][43]. Schmitz et al [44] developed a classification method using a feed-forward ANN and a probabilistic ANN to extend the previous approaches and solve the phase stability problem.…”

Section: Introductionmentioning

confidence: 97%

Enhancement of Machine-Learning-Based Flash Calculations near Criticality Using a Resampling Approach

Kanakaki,

Samnioti,

Gaganis

2024

Computation

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Flash calculations are essential in reservoir engineering applications, most notably in compositional flow simulation and separation processes, to provide phase distribution factors, known as k-values, at a given pressure and temperature. The calculation output is subsequently used to estimate composition-dependent properties of interest, such as the equilibrium phases’ molar fraction, composition, density, and compressibility. However, when the flash conditions approach criticality, minor inaccuracies in the computed k-values may lead to significant deviation in the dependent properties, which is eventually inherited to the simulator, leading to large errors in the simulation. Although several machine-learning-based regression approaches have emerged to drastically accelerate flash calculations, the criticality issue persists. To address this problem, a novel resampling technique of the ML models’ training data population is proposed, which aims to fine-tune the training dataset distribution and optimally exploit the models’ learning capacity across various flash conditions. The results demonstrate significantly improved accuracy in predicting phase behavior results near criticality, offering valuable contributions not only to the subsurface reservoir engineering industry but also to the broader field of thermodynamics. By understanding and optimizing the model’s training, this research enables more precise predictions and better-informed decision-making processes in domains involving phase separation phenomena. The proposed technique is applicable to every ML-dominated regression problem, where properties dependent on the machine output are of interest rather than the model output itself.

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

Samnioti¹,

Gaganis²

2023

Preprint

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In recent years, Machine Learning (ML) has become a buzzword in the petroleum industry with numerous applications which guide engineers in 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 computer resources associated costs, rendering reservoir simulators as not fast and robust enough, thus introducing the need for more time-efficient and smart tools, like ML models which are able to adapt and provide fast and competent results that mimic the simulator’s 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 the cases of individual simulation runs and history matching, whereas the ML-based Production Forecast Optimization applications will be 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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Acceleration of thermodynamic computations in fluid flow applications

Cited by 6 publications

References 13 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

Enhancement of Machine-Learning-Based Flash Calculations near Criticality Using a Resampling Approach

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

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