Application of Machine Learning Models in Gas Hydrate Mitigation

Suresh, Sachin Dev; Lal, Bhajan; Qasim, Ali; Foo, Khor Siak; Sundramoorthy, Jega Divan

doi:10.1007/978-981-16-2183-3_12

Cited by 2 publications

(2 citation statements)

References 19 publications

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“…As far as hydrate formation conditions and other related subjects are concerned, Yu et al [208] developed RFs, Naive Bayes, and SVR models to determine the formation conditions of natural gas hydrates, and Qasim et al [209] presented four different case studies involving the use of ML methods for gas hydrates prediction purposes. Suresh et al [210] developed three ML algorithms based on ANNs, LSSVMs, and Extremely Randomized Trees (ERTs) to evaluate their accuracy in predicting the gas hydrate formation conditions when the input parameters consist of gas composition, pressure, the concentration of the inhibitor, and the output of hydrate formation temperatures. Kumari et al [211] examined LSSVM and ANN models in conjunction with Genetic Programming (GP) and GA to predict the stability conditions of gas hydrates, and Hosseini et al [212] developed MLP, DTs, and ERTs to estimate the methane-hydrate formation temperature in brines.…”

Section: Machine Learning Methods For Flow Assurance Problemsmentioning

confidence: 99%

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

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 which guide engineers in better decision making. The most powerful tool that most production development decisions rely on is reservoir simulation with applications in multiple modeling procedures, such as individual simulation runs, history matching and production forecast and optimization. However, all of these applications lead to considerable computational time and computer resource-associated costs, rendering reservoir simulators as not fast and robust enough, and thus introducing the need for more time-efficient and intelligent tools, such as ML models which are able to adapt and provide fast and competent results that mimic the simulator’s performance within an acceptable error margin. In a recent paper, the developed ML applications in a subsurface reservoir simulation were reviewed, focusing on improving the speed and accuracy of individual reservoir simulation runs and history matching. This paper consists of the second part of that study, offering a detailed review of ML-based Production Forecast Optimization (PFO). This review can assist engineers as a complete source for applied ML techniques in reservoir simulation 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 Flow Assurance Problemsmentioning

confidence: 99%

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

Samnioti,

Gaganis

2023

Energies

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“…Similarly, Qasim and Lal [34] presented four different case studies involving the use of ML methods for gas hydrates prediction purposes. Suresh et al [35] developed three ML algorithms based on Artificial Neural Networks, the Least Square version of Support Vector Machines (LSSVM), and Extremely Randomized Trees. They evaluated their accuracy in predicting gas hydrate formation conditions by using natural gas composition, pressure and inhibitor concentration as input to predict hydrate formation temperature.…”

Section: Introductionmentioning

confidence: 99%

Rapid Hydrate Formation Conditions Prediction in Acid Gas Streams

Samnioti,

Kanakaki,

Fotias

et al. 2023

Fluids

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Sour gas in hydrocarbon reservoirs contains significant amounts of H2S and smaller amounts of CO2. To minimize operational costs, meet air emission standards and increase oil recovery, operators revert to acid gas (re-)injection into the reservoir rather than treating H2S in Claus units. This process requires the pressurization of the acid gas, which, when combined with low-temperature conditions prevailing in subsurface pipelines, often leads to the formation of hydrates that can potentially block the fluid flow. Therefore, hydrates formation must be checked at each pipeline segment and for each timestep during a flow simulation, for any varying composition, pressure and temperature, leading to millions of calculations that become more intense when transience is considered. Such calculations are time-consuming as they incorporate the van der Walls–Platteeuw and Langmuir adsorption theory, combined with complex EoS models to account for the polarity of the fluid phases (water, inhibitors). The formation pressure is obtained by solving an iterative multiphase equilibrium problem, which takes a considerable amount of CPU time only to provide a binary answer (hydrates/no hydrates). To accelerate such calculations, a set of classifiers is developed to answer whether the prevailing conditions lie to the left (hydrates) or the right-hand (no hydrates) side of the P-T phase envelope. Results are provided in a fast, direct, non-iterative way, for any possible conditions. A set of hydrate formation “yes/no” points, generated offline using conventional approaches, are utilized for the classifier’s training. The model is applicable to any acid gas flow problem and for any prevailing conditions to eliminate the CPU time of multiphase equilibrium calculations.

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Application of Machine Learning Models in Gas Hydrate Mitigation

Cited by 2 publications

References 19 publications

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

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

Rapid Hydrate Formation Conditions Prediction in Acid Gas Streams

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