scholarly journals Development of a new empirical correlation for predicting formation volume factor of reservoir oil using artificial intelligence

2021 ◽  
Vol 44 (4) ◽  
pp. 408-416
Author(s):  
E. V. Shakirova ◽  
A. A. Aleksandrov ◽  
M. V. Semykin
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Artificial Neural Network ◽  
Fuzzy Inference ◽  
Empirical Correlation ◽  
Support Vector ◽  
Volume Factor ◽  
Inference System ◽  
Study Results ◽  
Artificial Neural

It is known that oil in reservoir conditions is characterized by the content of a certain amount of dissolved gas. As reservoir pressure decreases this gas is released from oil significantly changing its physical properties, primarily its density and viscosity. In addition, the oil volume also reduces, sometimes by 50–60 %. In this regard, when calculating reserves, it is necessary to justify the reduction amount of the reservoir oil volume when oil is extracted to the surface. For this purpose, the concept of formation volume factor of reservoir oil has been introduced. The formation volume factor of oil is considered one of the main characterizing parameters of crude oil. It is also required for modeling and predicting the characteristics of an oil reservoir. The purpose of the present work is to develop a new empirical correlation for predicting the formation volume factor of reservoir oil using artificial intelligence methods based on MATLAB software, such as: an artificial neural network, an adaptive neuro-fuzzy inference system, and a support vector machine. The article presents a new empirical correlation extracted from the artificial neural network based on 503 experimental data points for oils from the Eastern Siberia field, which was able to predict the formation volume factor of oil with the correlation coefficient of 0.969 and average absolute error of less than 1 %. The conducted study shows that the prediction accuracy of the desired parameter in the developed artificial intelligence model exceeds the accuracy of study results obtained by conventional statistical methods. Moreover, the model can be useful in the prospect of process optimization in field planning and development.

METODE KOMPARASI ARTIFICIAL NEURAL NETWORK PADA PREDIKSI CURAH HUJAN - LITERATURE REVIEW

2020 ◽  
Vol 14 (2) ◽  
pp. 48-53
Author(s):  
Herlina Jayadianti ◽  
Tedy Agung Cahyadi ◽  
Nur Ali Amri ◽  
Muhammad Fathurrahman Pitayandanu
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Fuzzy Inference ◽  
Moving Average ◽  
Ensemble Empirical Mode Decomposition ◽  
Support Vector ◽  
Fast Fourier Transformation ◽  
Feed Forward Neural Network ◽  
Inference System ◽  
Artificial Neural

Abstrak - Penelitian untuk mencari model prediksi curah hujan yang akurat di berbagai bidang sudah banyak dilakukan, maka dilakukan di-review kembali guna membantu proses penyaliran dalam perusahaan tambang. Review dilakukan dengan membandingkan hasil dari setiap model yang telah dilakukan pada beberapa penelitian sebelumnya. Penelitian ini menggunakan metode kuantitatif. Model yang dibandingkan pada penelitian di antaranya yaitu model Fuzzy, Fast Fourier Transformation (FFT), Emotional Artificial Neural Network (EANN), Artificial Neural Network (ANN), Adaptive Ensemble Empirical Mode Decomposition-Artificial Neural Network (AEEMD-ANN), E-SVR-Artificial Neural Network (E-SVR-ANN), Artificial Neural Network Backpropagation (BPNN), Adaptive Splines Threshold (ASTAR), Seasonal First-Order Autoregressive (SAR), Gumbel, Autoregressive Integrated Moving Average (ARIMA), Feed Forward Neural Network (FFNN), Support Vector Machine (SVM), Adaptive Neuro-Fuzzy Inference System (ANFIS), dan Artificial Neural Network-Fuzzy (ANN-Fuzzy). Hasil dari review menyimpulkan bahwa model Artificial Neural Network memiliki beberapa kelebihan dibandingkan dengan metode yang lain, yakni ANN mampu memberikan hasil yang dapat mengenali pola-pola dengan baik dan mudah dikembangkan menjadi bermacam-macam variasi sesuai dengan permasalahan maupun parameter yang ada, sehingga ANN direkomendasikan untuk perhitungan prediksi hujan. Abstract - Various kinds of research have been carried out to find accurate models to predict rainfall in various fields, so the research that has been done previously was reviewed again to help the drainage process in mining companies. The review is done by comparing the results of each model that has been conducted in several previous studies. This research used quantitative methods. Models compared in this study include the Fuzzy model, Fast Fourier Transformation (FFT), Emotional Artificial Neural Network (EANN), Artificial Neural Network (ANN), Adaptive Ensemble Empirical Mode Decomposition-Artificial Neural Network (AEEMD-ANN), E-SVR -Artificial Neural Network (E-SVR-ANN), Artificial Neural Network Backpropagation (BPNN), Adaptive Splines Threshold (ASTAR), Seasonal First-Order Autoregressive (SAR), Gumbel, Autoregressive Integrated Moving Average (ARIMA), Feed Forward Neural Network (FFNN), Support Vector Machine (SVM), Adaptive Neuro-Fuzzy Inference System (ANFIS), and Artificial Neural Network-Fuzzy (ANN-Fuzzy). The results of the review concluded that the Artificial Neural Network model has several advantages compared to other methods, namely ANN is able to provide results that can recognize patterns well and easily developed into a variety of variations in accordance with existing problems and parameters, so ANN is recommended for rain prediction calculation.

scholarly journals A comparative assessment between artificial neural network, neuro-fuzzy, and support vector machine models in splash erosion modelling under simulation circumstances

2021 ◽  
Vol 49 (1) ◽  
pp. 23-34
Author(s):  
Mahdi Boroughani ◽  
Somayeh Soltani ◽  
Nafiseh Ghezelseflu ◽  
Iman Pazhouhan
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Support Vector Machine ◽  
Soil Erosion ◽  
Fuzzy Inference ◽  
Support Vector ◽  
Inference System ◽  
Splash Erosion ◽  
Neuro Fuzzy ◽  
Artificial Neural

Abstract Splash erosion, as the first step of soil erosion, causes the movement of the soil particles and lumps and is considered an important process in soil erosion. Given the complexity of this process in nature, one way of identifying and modeling the process is to use a rainfall simulator and to study it under laboratory circumstances. For this purpose, transported material was measured with various rainfall intensities and different amounts of poly-acryl-amide. In the next step, artificial neural network (ANN), adaptive neuro-fuzzy inference system (ANFIS), and support vector machine (SVM) were used to model the transported materials. The results showed that among the three methods, the best values of evaluation criteria were related to SVM, and ANFIS respectively. Among the three studied durations, the experiment with a duration of 30 minutes received the best results. The results based on available data showed by increasing the number of membership functions, over-fitting happens in the ANFIS method. To reduce the complexity of the model and the likelihood of over-fitting, some rules were eliminated. The results showed that the performance of the model improved by eliminating some rules.

Comparison of different modeling methods toward predictive capability evaluation of the bonding strength of wood laminated products

2021 ◽  
pp. 095440892110530
Author(s):  
Morteza Nazerian ◽  
Seyed Ali Razavi ◽  
Ali Partovinia ◽  
Elham Vatankhah ◽  
Zahra Razmpour
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Response Surface Methodology ◽  
Response Surface ◽  
Bonding Strength ◽  
Fuzzy Inference ◽  
Molar Ratio ◽  
Inference System ◽  
Neuro Fuzzy ◽  
Artificial Neural

The main aim of this study is usability evaluation of different approaches, including response surface methodoloy, adaptive neuro-fuzzy inference system, and artificial neural network models to predict and evaluate the bonding strength of glued laminated timber (glulam) manufactured using walnut wood layers and a natural adhesive (oxidized starch adhesive), with respect to this fact that using the modified starch can decrease the formaldehyde emission. In this survey, four variables taken as the input data include the molar ratio of formaldehyde to urea (1.12–1.52), nanocellulose content (0%–4%, based on the dry weight of the adhesive), the mass ratio of the oxidized starch adhesive to the urea formaldehyde resin (30:70–70:30), and the press time (4–8 min). In order to find the best predictive performance of each selected evaluation approach, different membership functions were used. The optimal results were obtained when the molar ratio, nanocellulose content, mass ratio of the oxidised starch, and press time were set at 1.22, 3%, 70:30, and 7 min, respectively. Based on the performance criteria including root mean square error (RMSE) and mean absolute percentage error (MAPE) obtained from the modeling of response surface methodology, adaptive neuro-fuzzy inference network, and artificial neural network, it became evident that response surface methodology could offer a better prediction of the response with the lowest level of errors. Beside, artificial neural network and adaptive neuro-fuzzy inference system support the response surface methodology approach to evaluate bonding strength response with high precision as well as to determine the optimal point in fabrication of laminated products.

scholarly journals Inverse Kinematic Solution of 5R Manipulator using ANN and ANFIS

2015 ◽  
Vol 4 (2) ◽  
pp. 109
Author(s):  
Panchand Jha
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Fuzzy Inference System ◽  
Inverse Kinematics ◽  
Fuzzy Inference ◽  
Robot Manipulator ◽  
Ann Model ◽  
Inference System ◽  
Artificial Neural ◽  
Neural Fuzzy

<span>Inverse kinematics of manipulator comprises the computation required to find the joint angles for a given Cartesian position and orientation of the end effector. There is no unique solution for the inverse kinematics thus necessitating application of appropriate predictive models from the soft computing domain. Artificial neural network and adaptive neural fuzzy inference system techniques can be gainfully used to yield the desired results. This paper proposes structured artificial neural network (ANN) model and adaptive neural fuzzy inference system (ANFIS) to find the inverse kinematics solution of robot manipulator. The ANN model used is a multi-layered perceptron Neural Network (MLPNN). Wherein, gradient descent type of learning rules is applied. An attempt has been made to find the best ANN configuration for the problem. It is found that ANFIS gives better result and minimum error as compared to ANN.</span>

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