Determination of bubble point pressure and oil formation volume factor: Extra trees compared with LSSVM-CSA hybrid and ANFIS models

Seyyedattar, Masoud; Ghiasi, Mohammad M.; Zendehboudi, Sohrab; Butt, Stephen

doi:10.1016/j.fuel.2019.116834

Cited by 62 publications

(17 citation statements)

References 105 publications

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“…Their model utilized 1248 data sets with an AAPE of 17.17%, and R 2 of 0.96. From the literature survey, it was revealed that most of the previous work utilizing AI and Machine Learning (ML) to predict PVT properties used black-box approach (Ahmadi et al 2015;Cheshmeh Sefidi and Ajorkaran 2019;Hemmati-Sarapardeh et al 2014;Ramirez et al 2017;Seyyedattar et al 2020;Shokrollahi et al 2015).…”

Section: Pvt Properties Curves Predictionmentioning

confidence: 99%

A systematic review of data science and machine learning applications to the oil and gas industry

Tariq

Aljawad

Hasan

et al. 2021

J Petrol Explor Prod Technol

101

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This study offered a detailed review of data sciences and machine learning (ML) roles in different petroleum engineering and geosciences segments such as petroleum exploration, reservoir characterization, oil well drilling, production, and well stimulation, emphasizing the newly emerging field of unconventional reservoirs. The future of data science and ML in the oil and gas industry, highlighting what is required from ML for better prediction, is also discussed. This study also provides a comprehensive comparison of different ML techniques used in the oil and gas industry. With the arrival of powerful computers, advanced ML algorithms, and extensive data generation from different industry tools, we see a bright future in developing solutions to the complex problems in the oil and gas industry that were previously beyond the grip of analytical solutions or numerical simulation. ML tools can incorporate every detail in the log data and every information connected to the target data. Despite their limitations, they are not constrained by limiting assumptions of analytical solutions or by particular data and/or power processing requirements of numerical simulators. This detailed and comprehensive study can serve as an exclusive reference for ML applications in the industry. Based on the review conducted, it was found that ML techniques offer a great potential in solving problems in almost all areas of the oil and gas industry involving prediction, classification, and clustering. With the generation of huge data in everyday oil and gas industry activates, machine learning and big data handling techniques are becoming a necessity toward a more efficient industry.

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Section: Pvt Properties Curves Predictionmentioning

confidence: 99%

A systematic review of data science and machine learning applications to the oil and gas industry

Tariq

Aljawad

Hasan

et al. 2021

J Petrol Explor Prod Technol

101

View full text Add to dashboard Cite

show abstract

“…(ii) LSSVM applies the principle of risk minimization to transform the inequality constraints into equality ones. These two parts are conducive to reducing the calculation complexity and promoting the operation speed [41].…”

Section: Modified Lssvm (Mlssvm)mentioning

confidence: 99%

Sustainability Evaluation of Renewable Energy Incubators Using Interval Type-II Fuzzy AHP-TOPSIS with MEA-MLSSVM

2021

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With the development of renewable energy, renewable energy incubators have emerged continuously. However, these incubators present a crude development model of low-level replication and large-scale expansion, which has triggered a series of urgent problems including unbalanced regional development, low incubation efficiency, low resource utilization, and vicious competition for resources. There are huge challenges for the sustainable development of incubators in the future. A scientific and accurate evaluation approach is of great significance for improving the sustainability of renewable energy incubators. Therefore, this paper proposes a novel method combining an interval type-II fuzzy analytic hierarchy process (AHP) with mind evolutionary algorithm-modified least-squares support vector machine (MEA-MLSSVM). The indicator system is established from two aspects: service capability and operational efficiency. TOPSIS integrated with an interval type-II fuzzy AHP is employed for index weighting and assessment. In the least-squares support vector machine (LSSVM), the traditional radial basis function is replaced with the wavelet transform function (WT), and the parameters are fine-tuned by the mind evolutionary algorithm (MEA). Accordingly, the establishment of a comprehensive sustainability evaluation model for renewable energy incubators is accomplished in this paper. The experimental study reveals that this novel technique has the advantages of scientificity and precision and provides a decision-making basis for renewable energy incubators to realize sustainable operation.

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“…ANFIS is an integrated neuro-fuzzy modeling technique that embeds a Fuzzy Inference System (FIS) into the framework of adaptive neural networks (ANN). Based on a set of input-output pairs and the human reasoning process in the form of if-then rules, ANFIS has been used to construct many practical models [24,37]. In this study, we apply this method to combine the unitary algorithms to estimate illuminant color for a given image.…”

Section: Anfis Modelingmentioning

confidence: 99%

“…The consequent parameters of defuzzification are identified using least square methods in the forward pass, and the premise parameters are adjusted using the gradient descent method in the backward pass. For more details, refer to [36,37].…”

Section: Anfis Modelingmentioning

confidence: 99%

Illuminant Estimation Using Adaptive Neuro-Fuzzy Inference System

Luo

Wang

et al. 2021

Applied Sciences

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Computational color constancy (CCC) is a fundamental prerequisite for many computer vision tasks. The key of CCC is to estimate illuminant color so that the image of a scene under varying illumination can be normalized to an image under the canonical illumination. As a type of solution, combination algorithms generally try to reach better illuminant estimation by weighting other unitary algorithms for a given image. However, due to the diversity of image features, applying the same weighting combination strategy to different images might result in unsound illuminant estimation. To address this problem, this study provides an effective option. A two-step strategy is first employed to cluster the training images, then for each cluster, ANFIS (adaptive neuro-network fuzzy inference system) models are effectively trained to map image features to illuminant color. While giving a test image, the fuzzy weights measuring what degrees the image belonging to each cluster are calculated, thus a reliable illuminant estimation will be obtained by weighting all ANFIS predictions. The proposed method allows illuminant estimation to be dynamic combinations of initial illumination estimates from some unitary algorithms, relying on the powerful learning and reasoning capabilities of ANFIS. Extensive experiments on typical benchmark datasets demonstrate the effectiveness of the proposed approach. In addition, although there is an initial observation that some learning-based methods outperform even the most carefully designed and tested combinations of statistical and fuzzy inference systems, the proposed method is good practice for illuminant estimation considering fuzzy inference eases to implement in imaging signal processors with if-then rules and low computation efforts.

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Determination of bubble point pressure and oil formation volume factor: Extra trees compared with LSSVM-CSA hybrid and ANFIS models

Cited by 62 publications

References 105 publications

A systematic review of data science and machine learning applications to the oil and gas industry

A systematic review of data science and machine learning applications to the oil and gas industry

Sustainability Evaluation of Renewable Energy Incubators Using Interval Type-II Fuzzy AHP-TOPSIS with MEA-MLSSVM

Illuminant Estimation Using Adaptive Neuro-Fuzzy Inference System

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