Application of supervised machine learning paradigms in the prediction of petroleum reservoir properties: Comparative analysis of ANN and SVM models

Otchere, Daniel Asante; Ganat, Tarek Al-Arbi Omar; Gholami, Raoof; Ridha, Syahrir

doi:10.1016/j.petrol.2020.108182

Cited by 268 publications

(99 citation statements)

References 80 publications

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“…Many studies have used such drilling parameters to estimate different mechanical properties of the downhole formations such as unconfined compressive strength, elastic moduli, and Poisson's ratio [19][20][21][22]. Recently, several machine learning (ML) techniques have been applied on a wide scale in the petroleum industry [23][24][25][26]. ese applications aim at the best use of the big data available and support the fourth revolution for drilling automation and optimization.…”

Section: Introductionmentioning

confidence: 99%

[Retracted] Prediction of the Least Principal Stresses Using Drilling Data: A Machine Learning Application

Gowida

Ibrahim

Elkatatny

et al. 2021

Computational Intelligence and Neuroscience

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The least principal stresses of downhole formations include minimum horizontal stress (σmin) and maximum horizontal stress (σmax). σmin and σmax are substantial parameters that significantly affect the design and optimization of the drilling process. These stresses can be estimated using theoretical equations in addition to some field tests, i.e., leak-off test to include the effect of tectonic stress. This approach is associated with many technical and financial issues. Therefore, the objective of this study is to provide a novel machine learning-based solution to estimate these stresses while drilling. First, new models were developed using artificial neural network (ANN) to directly predict σmin and σmax from the drilling data; which are injection rate (Q), standpipe pressure (SPP), weight on bit (WOB), torque (T), and rate of penetration (ROP). Such data are always available while drilling, and hence, no additional cost is required. Actual data from a Middle Eastern field were collected, statistically analyzed, and fed to the models. First, the models’ predictions showed a significant match with the actual stress values with a correlation coefficient (R-value) exceeding 0.90 and a mean absolute average error (MAPE) of 0.75% as a maximum. Second, new empirical equations were generated based on the developed ANN-based models. The new equations were then validated using another unseen dataset from the same field. The predictions had an R-value of 0.98 and 0.93 in addition to MAPE of 0.36% and 0.96% for σmin and σmax models, respectively. The results demonstrated the outperformance of the developed ANN-based equations to estimate the least principal stresses from the drilling data with high accuracy in a timely and economically effective way.

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Section: Introductionmentioning

confidence: 99%

[Retracted] Prediction of the Least Principal Stresses Using Drilling Data: A Machine Learning Application

Gowida

Ibrahim

Elkatatny

et al. 2021

Computational Intelligence and Neuroscience

View full text Add to dashboard Cite

show abstract

“…The basic principle is to minimize the structured risk by calculating the minimum value of the empirical risk, so as to improve the learning ability and stability of machine learning. Compared with the general artificial neural network, it does not rely too much on the quantity and quality of the sample set, which has unique advantages and values (Otchere et al, 2021;Yu et al, 2021).…”

Section: Svmmentioning

confidence: 99%

Application of machine learning algorithms for the prediction of flame temperature in small-scale burner fueled with ethanol-diesel fuel blends

JIN

Zhu

2022

JTST

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Application of machine learning algorithms for the predictionof flame temperature in small-scale burner fueled with ethanol-diesel fuel blends IntroductionAs a light petroleum product, diesel fuel is widely used in ships, large-scale vehicles, and locomotives in railway. However, contaminants such as oxides of nitrogen (NOx) and particulate matter (PM) are generated with the use of diesel fuel. It was found that diesel fuel can be partially replaced by oxygenated fuels, reducing contaminant emissions from combustion. Consequently, many researchers have devoted their attention to the study of oxygenated fuels, such as methanol, ethanol and biodiesel (Hansen et al., 2005;Moon et al., 2010;Yao et al., 2010). Ethanol contains oxygen in 34.7wt% and its required air is theoretically nearly half as diesel when burned (Lu et al., 2021). Therefore, diesel fuel blended with ethanol can make combustion more fully, which reduces soot emissions.Ethanol-diesel fuel blends requires appropriate equipment to achieve efficient combustion. The small-scale burner is the key equipment of small-electromechanical system and this burner has the advantage of high energy density. However, in the small scale, many new problems and challenges arise, such as incomplete combustion, unstable flame, and large heat loss (

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“…Khan et al used deep learning, a neural network, and other technologies to predict the oil recovery rate of artificial gas lift wells, with an accuracy of up to 99% [41]. Some of the advantages of using neural network models are that it does not require any a priori assumptions about the dependence of the functional form of the underlying process and can also be used to establish relationships between complex nonlinear data problems by providing numerical models and can reduce noise in the data [42].…”

Section: Research On Smart Energy In "Internet+" Modementioning

confidence: 99%

The Impact of the Integrated Development of AI and Energy Industry on Regional Energy Industry: A Case of China

Dong

Zhang

Zhu

et al. 2021

IJERPH

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With the advent of the Energy 4.0 era, the adoption of "Internet + artificial intelligence" systems will enable the transformation and upgrading of the traditional energy industry. This will alleviate the energy and environmental problems that China is currently facing. The integrated development of artificial intelligence and the energy industry has become inevitable in the development of future energy systems. This study applied a comprehensive evaluation index to the energy industry to calculate the comprehensive development index of the energy industry in 30 provinces of China from 2000 to 2017. Then, taking Guangdong and Jiangsu as examples, the synthetic control method was used to explore the direction and intensity of the integrated development of artificial intelligence and the energy industry on the comprehensive development level of the local energy industry. The results showed that when artificial intelligence (AI) and the energy industry achieved a stable coupled development without the need to move to the coordination stage, the coupling effect promoted the development of the regional energy industry, and the annual growth rate of the comprehensive development index was above 20%. This coupling effect passed the placebo test and ranking test and was significant at the 10% level, indicating the robustness and validity of the experimental results, which strongly confirmed the great potential of AI in re-empowering traditional industries from the data perspective. Based on the findings, corresponding policy recommendations were proposed on how to promote the development of inter-regional AI, how the government, enterprises, and universities could cooperate to promote the coordinated development of AI and energy, and how to guide the integration process of regional AI and energy industries according to local conditions, in order to maximize the technological dividend of AI and help the construction of smart energy in China.

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Application of supervised machine learning paradigms in the prediction of petroleum reservoir properties: Comparative analysis of ANN and SVM models

Cited by 268 publications

References 80 publications

[Retracted] Prediction of the Least Principal Stresses Using Drilling Data: A Machine Learning Application

[Retracted] Prediction of the Least Principal Stresses Using Drilling Data: A Machine Learning Application

Application of machine learning algorithms for the prediction of flame temperature in small-scale burner fueled with ethanol-diesel fuel blends

The Impact of the Integrated Development of AI and Energy Industry on Regional Energy Industry: A Case of China

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