Determination of bubble point pressure &amp; oil formation volume factor of crude oils applying multiple hidden layers extreme learning machine algorithms

Rashidi, Sina; Mehrad, Mohammad; Ghorbani, Hamzeh; Wood, David A.; Mohamadian, Nima; Moghadasi, Jamshid; Davoodi, Shadfar

doi:10.1016/j.petrol.2021.108425

Cited by 45 publications

(24 citation statements)

References 68 publications

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“…This new network (MELM) can achieve higher accuracy and generalizability than concealed single-layer models. The structure of MELM can be shown in (Rashidi et al 2021).…”

Section: Multi-layer Extreme Learning Machinementioning

confidence: 99%

“…All information is transferred to the next iteration, and the position of each particle is adjusted with its velocity and the lowest G b and regarding the lowest P b of the particles of each particle is done, the velocity Vt sets them to V t + 1 , where t refers to the number of iterations. For each iteration, the particles in the swarm reach the lowest RMSE target performance values that have the greatest impact on the next generation of particles (Rashidi et al 2021). References to related velocity changes and flowcharts can be found in the articles of Singh et al (2020) andCai et al (2020).…”

Section: Particle Swarm Optimization Algorithmmentioning

confidence: 99%

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Novel hybrid machine learning optimizer algorithms to prediction of fracture density by petrophysical data

Rajabi

Beheshtian

Davoodi

et al. 2021

J Petrol Explor Prod Technol

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One of the challenges in reservoir management is determining the fracture density (FVDC) in reservoir rock. Given the high cost of coring operations and image logs, the ability to predict FVDC from various petrophysical input variables using a supervised learning basis calibrated to the standard well is extremely useful. In this study, a novel machine learning approach is developed to predict FVDC from 12-input variable well-log based on feature selection. To predict the FVDC, combination of two networks of multiple extreme learning machines (MELM) and multi-layer perceptron (MLP) hybrid algorithm with a combination of genetic algorithm (GA) and particle swarm optimizer (PSO) has been used. We use a novel MELM-PSO/GA combination that has never been used before, and the best comparison result between MELM-PSO-related models with performance test data is RMSE = 0.0047 1/m; R2 = 0.9931. According to the performance accuracy analysis, the models are MLP-PSO < MLP-GA < MELM-GA < MELM-PSO. This method can be used in other fields, but it must be recalibrated with at least one well. Furthermore, the developed method provides insights for the use of machine learning to reduce errors and avoid data overfitting in order to create the best possible prediction performance for FVDC prediction.

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“…This new network (MELM) can achieve higher accuracy and generalizability than concealed single-layer models. The structure of MELM can be shown in (Rashidi et al 2021).…”

Section: Multi-layer Extreme Learning Machinementioning

confidence: 99%

Section: Particle Swarm Optimization Algorithmmentioning

confidence: 99%

Novel hybrid machine learning optimizer algorithms to prediction of fracture density by petrophysical data

Rajabi

Beheshtian

Davoodi

et al. 2021

J Petrol Explor Prod Technol

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“…Simultaneous minimization of the weight connection norm b and learning error is the main task of the extended learning machine method. From solving the least squares problem, the output weights of the hidden layer can be easily calculated as follows [43,44]:…”

Section: Extended Learning Machinementioning

confidence: 99%

Developing an Extreme Learning Machine-Based Model for Estimating the Isothermal Compressibility of Biodiesel

Wang¹,

Heydari

2021

International Journal of Chemical Engineering

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Nowadays, the high consumption of fossil fuels has caused many pollutants and environmental problems. Biodiesel has recently been considered as a clean and renewable alternative to fossil fuels. They are found in some molecular structures including fatty acid ethyl esters (FAEEs) and also fatty methyl esters (FAMEs), having various thermophysical characteristics. Thus, it appears essential to select the suitable methods for a particular diesel engine to estimate the ester characteristics. The current research sets out to develop a new and robust method predicting isothermal compressibility of long-chain fatty acid methyl and ethyl esters directly from several basic efficient parameters (pressure, temperature, normal melting point, and molecular weight). Therefore, as a novel and prevailing mathematical method in this field, an extreme learning machine was implemented for isothermal compressibility on the massive dataset. According to statistical evaluations, this novel established model had high accuracy and applicability (R2 = 1 and RMSE = 0.0018714) which is more accurate than previous models presented by former researchers. Among various factors of the sensitivity analysis, temperature and pressure had the greatest effect on the output values, so that the output parameter has a direct relationship with temperature and an inverse relationship with pressure with relevancy factors of 22.44% and −79.81%.

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“…The oil and gas industry, which has long been the focus of the whole world, and the reason is that the extracted oil and gas has changed the world. Many people did a lot of work to optimize and find important and key parameters in the oil and gas industry [40][41][42][43][44][45][46][47][48][49][50][51][52][53][54][55][56][57][58]. The sustainability of wellbore, materials and drilling equipment's is severely threatened by well blowouts.…”

Section: Introductionmentioning

confidence: 99%

Petroleum Well Blowouts as a Threat to Drilling Operation and Wellbore Sustainability: Causes, Prevention, Safety and Emergency Response

Ghorbani¹,

Researchers²,

Abdali³

et al. 2021

JCM

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Sustainability in petroleum wells drilling operation systems strongly depends on the use of sustainable materials and a set of technical and safety measures that lead to the survival and proper operation of drilling rig equipment's and personnel. Adherence to the highest levels of standards of tools, materials and methods, although always recommended as the most important option for advancing a safe drilling operation and completing the well efficiently, low risk and stable, but drilling operation is inherently a battle with underground challenges and unexpected dangers. Learning from past such well blowout events and the problems they pose to rapidly control is essential to reduce future impacts including injuries, damage and emissions. Such analysis offers guidance for adapting working practices to improve both prevention and emergency response to such incidents. The causes of blowout during drilling and the necessary technical and safety measures to adopt are reviewed, highlighting how best practices can prevent blowout incidents by improving responses to early warning signals. The particular risks associated with potential shallow gas blowouts are identified and described with the aid of a case study associated with a catastrophic blowout of an onshore well in Iran and the methods used to ultimately control it. The multiple causes of the incident relating to defects in safety systems, equipment and operating procedures are addressed. Lessons learned from the incident reveal the complexity of well control once a blowout incident has occurred and developed into a surface fire. from the stage of the incident to fire control. There is a need for further research into top-hole well kill techniques for wells in a blowout state, as drilling bottom-hole relief wells takes substantial time, during which much surface damage, resource loss and emission typically occurs.

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Determination of bubble point pressure & oil formation volume factor of crude oils applying multiple hidden layers extreme learning machine algorithms

Cited by 45 publications

References 68 publications

Novel hybrid machine learning optimizer algorithms to prediction of fracture density by petrophysical data

Novel hybrid machine learning optimizer algorithms to prediction of fracture density by petrophysical data

Developing an Extreme Learning Machine-Based Model for Estimating the Isothermal Compressibility of Biodiesel

Petroleum Well Blowouts as a Threat to Drilling Operation and Wellbore Sustainability: Causes, Prevention, Safety and Emergency Response

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