scholarly journals Combined artificial intelligence modeling for production forecast in a petroleum production field

2019 ◽  
Vol 9 (1) ◽  
pp. 27-35 ◽  
Author(s):  
Marco Antonio Ruiz- Serna ◽  
Guillermo Arturo Alzate- Espinosa ◽  
Andrés Felipe Obando- Montoya ◽  
Hernán Dario Álvarez- Zapata
Keyword(s):  
Artificial Intelligence ◽  
Data Mining ◽  
Fuzzy Inference ◽  
Fluid Phase ◽  
Gas Production ◽  
Production Model ◽  
Data Imputation ◽  
Production Forecast ◽  
Well Production ◽  
Inference Systems

This paper presents the results about using a methodology that combines two artificial intelligence (AI) models to predict the oil, water and gas production in a Colombian petroleum field. By combining fuzzy logic (FL) and artificial neural networks (ANN) a novelty data mining procedure is implemented, including a data imputation strategy. The FL tool determines the most useful variables or parameters to include into each well production model. ANN and FIS (fuzzy inference systems) predictive models identification is developed after the data mining process. The FIS models are capable to predict specific behaviors, while ANN models are able to forecast an average behavior. The combined use of both tools under few iterative steps, allows to improve forecasting of well behavior until reach a specified accuracy level. The proposed data imputation procedure is the key element to correct false or to complete void positions into operation data used to identify models for a typical oil production field. At the end, two models are obtained for each well product, conforming an interesting tool given the best accurate prediction of fluid phase production.

scholarly journals Building Java Intelligent Applications Data Mining for Java Type-2 Fuzzy Inference Systems

2015 ◽  
Vol 51 ◽  
pp. 2719-2728 ◽  
Author(s):  
Manuel Castañón-Puga ◽  
Josué Miguel Flores-Parra ◽  
Juan Ramón Castro ◽  
Carelia Gaxiola-Pacheco ◽  
Luis Enrique Palafox-Maestre
Keyword(s):  
Data Mining ◽  
Fuzzy Inference ◽  
Fuzzy Inference Systems ◽  
Inference Systems

scholarly journals Experimental investigation of artificial intelligence applied in MPPT techniques

2019 ◽  
Vol 10 (4) ◽  
pp. 2138
Author(s):  
Saidia Della Krachai ◽  
A. Boudghene Stambouli ◽  
M. Della Krachai ◽  
M. Bekhti
Keyword(s):  
Artificial Intelligence ◽  
Fuzzy Inference ◽  
Buck Converter ◽  
Solar Panels ◽  
Fuzzy Inference Systems ◽  
Maximal Power ◽  
Environmental Variations ◽  
Inference Systems ◽  
Space Data ◽  
Task Simulation

Nano-satellites are key features for sharing the space data and scientific researches. They embed subsystems that are fed from solar panels and batteries. Power generated from these panels is subject to environmental conditions, most important of them are irradiance and temperature. Optimizing the usage of this power versus environmental variations is a primary task. Synchronous DC-DC buck converter is used to control the power transferred from PV panels to the subsystems while maintaining operation at maximal power.  <br />In this paper, artificial intelligence techniques: neural networks and adaptive neural fuzzy inference systems (ANFIS) are used to accomplish the tracking task. Simulation and experimental results demonstrate their efficiency, robustness and tracking quality. <br /><br />

Opening Address

2017 ◽  
Vol 21 (1) ◽  
pp. 8-8
Author(s):  
Editor: Prof. Yasufumi Takama ◽  
Keyword(s):  
Artificial Intelligence ◽  
Data Mining ◽  
Computational Intelligence ◽  
Smart Grids ◽  
Fuzzy Inference ◽  
Peer Review Process ◽  
Web Intelligence ◽  
The Past ◽  
Research Areas ◽  
Institute Of Technology

The JACIII was first published in 1997, and 2017 marks its 20th anniversary. During the last two decades, the research fields in computational intelligence have rapidly evolved owing to the spread of the Internet, performance improvement of computers, and accumulation of scientific knowledge. To celebrate this 20th anniversary, we have selected 6 important research areas from the JACIII scope, and invited outstanding researchers from each of these areas to contribute papers about the progress and major topics in those areas during the past 20 years. Submitted paper went through a peer-review process by distinguished professors to further improve the quality. The research areas selected were computational intelligence, fuzzy intelligence, intelligent robots, artificial intelligence and web intelligence, data mining, and smart grids. Each of those paper covers broad topics appeared in the research areas, from which readers could grasp what happened during the past 20 years. We also hope readers could find some hints about future directions of their own researches towards the next 20 years. <strong>Invited Paper 1: Computational Intelligence: Retrospection and Future</strong> Author: Witold Pedrycz (University of Alberta, Canada) <strong>Invited Paper 2: Fuzzy Inference: Its Past and Prospects</strong> Authors: Kiyohiko Uehara (Ibaraki University, Japan) and Kaoru Hirota (Beijing Institute of Technology, China) <strong>Invited Paper 3: Relationship Between Human and Robot in Nonverbal Communication</strong> Authors: Yukiko Nakagawa and Noriaki Nakagawa (RT Corporation, Japan) <strong>Invited Paper 4: Web Intelligence and Artificial Intelligence</strong> Author: Yasufumi Takama (Tokyo Metropolitan University, Japan) <strong>Invited Paper 5: A Review of Data Mining Techniques and Applications</strong> Authors: Ratchakoon Pruengkarn, Kok Wai Wong, and Chun Che Fung (Murdoch University, Australia) <strong>Invited Paper 6: Development and Current State of Smart Grids: A Review</strong> Author: Ken Nagasaka (Tokyo University of Agriculture and Technology, Japan)

Modeling Antenna Radiation Using Artificial Intelligence Techniques

2021 ◽  
pp. 186-225
Author(s):  
Theodoros N. Kapetanakis ◽  
Ioannis O. Vardiambasis ◽  
Melina P. Ioannidou ◽  
Antonios I. Konstantaras
Keyword(s):  
Artificial Intelligence ◽  
Fuzzy Inference ◽  
Theoretical Data ◽  
Observation Angle ◽  
Circular Loop ◽  
Electromagnetic Problems ◽  
Artificial Intelligence Techniques ◽  
Neuro Fuzzy ◽  
The One ◽  
Inference Systems

The forward and the inverse problem of a thin, circular, loop antenna that radiates in free space is modeled and solved by using soft computing techniques such as artificial neural networks and adaptive neuro fuzzy inference systems. On the one hand, the loop radius and the observation angle serve as inputs to the forward model, whereas the radiation intensity is the output. On the other hand, the electric field intensity and the loop radius are the input and output, respectively, to the inverse model. Extensive numerical tests indicate that the results predicted by the proposed models are in excellent agreement with theoretical data obtained from the existing analytical solutions of the forward problem. Thus, the employment of artificial intelligence techniques for tackling electromagnetic problems turns out to be promising, especially regarding the inverse problems that lack solution with other methods.

scholarly journals Estimation of scour depth around circular piers: applications of model tree

2014 ◽  
Vol 17 (2) ◽  
pp. 226-238 ◽  
Author(s):  
Amir Etemad-Shahidi ◽  
Lisham Bonakdar ◽  
D.-S. Jeng
Keyword(s):  
Data Mining ◽  
Fuzzy Inference ◽  
Field Measurements ◽  
Bridge Piers ◽  
Scour Depth ◽  
Data Sets ◽  
Data Mining Approach ◽  
Statistical Measures ◽  
Inference Systems ◽  
Empirical Approaches

Scour around bridge piers is one of the main causes of bridge failures and is of great importance for hydraulic engineers and scientists. Prediction of the scour depth around piers is complicated, and accurate results are rarely achieved by the existing models. Recently, data mining approaches such as artificial neural networks and fuzzy inference systems have been applied successfully to predict scour depth around hydraulic structures. In this study, an alternative robust data mining approach was used for the predictions of the scour depth around piers, and the results were compared with those of three empirical approaches. Performances of developed models were tested by experimental data sets collected in laboratory experiments and field measurements, together with existing empirical approaches. Statistical measures indicate that the proposed M5′ model provides a better prediction of scour depth than the empirical approaches.

Integrated Subsurface to Surface Modeling to Assess Reservoir Uncertainty Quantification to A Carbonate Reservoir

2020 ◽  
Author(s):  
A. Chaterine
Keyword(s):  
Uncertainty Quantification ◽  
Gas Production ◽  
Carbonate Reservoir ◽  
Production Volume ◽  
Production Forecast ◽  
Uncertainty Range ◽  
Field Development ◽  
Production Forecasting ◽  
Production Scheme ◽  
Rock Compressibility

This study accommodates subsurface uncertainties analysis and quantifies the effects on surface production volume to propose the optimal future field development. The problem of well productivity is sometimes only viewed from the surface components themselves, where in fact the subsurface component often has a significant effect on these production figures. In order to track the relationship between surface and subsurface, a model that integrates both must be created. The methods covered integrated asset modeling, probability forecasting, uncertainty quantification, sensitivity analysis, and optimization forecast. Subsurface uncertainties examined were : reservoir closure, regional segmentation, fluid contact, and SCAL properties. As the Integrated Asset Modeling is successfully conducted and a matched model is obtained for the gas-producing carbonate reservoir, highlights of the method are the following: 1) Up to ± 75% uncertainty range of reservoir parameters yields various production forecasting scenario using BHP control with the best case obtained is 335 BSCF of gas production and 254.4 MSTB of oil production, 2) SCAL properties and pseudo-faults are the most sensitive subsurface uncertainty that gives major impact to the production scheme, 3) EOS modeling and rock compressibility modeling must be evaluated seriously as those contribute significantly to condensate production and the field’s revenue, and 4) a proposed optimum production scenario for future development of the field with 151.6 BSCF gas and 414.4 MSTB oil that yields a total NPV of 218.7 MMUSD. The approach and methods implemented has been proven to result in more accurate production forecast and reduce the project cost as the effect of uncertainty reduction.

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