A Laboratory Approach to Measure Enhanced Gas Recovery from a Tight Gas Reservoir during Supercritical Carbon Dioxide Injection

Syah, Rahmad; Alizadeh, Seyed Mehdi Seyed; Nurgalieva, Karina Shamilyevna; Guerrero, John William Grimaldo; Nasution, Mahyuddin K. M.; Davarpanah, Afshin; Ramdan, Dadan; Metwally, Ahmed Sayed M.

doi:10.3390/su132111606

Cited by 11 publications

(6 citation statements)

References 105 publications

(108 reference statements)

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“…In the past few decades, a variety of mathematical computational approaches have been implemented in different research fields such as fluid mechanic engineering [1][2][3][4][5][6][7][8][9][10][11][12][13][14][15], chemical engineering , electrical engineering , robotics and automation , urban planning engineering [73][74][75][76], petroleum engineering [77][78][79][80][81][82][83][84][85][86][87][88][89][90][91][92][93][94], energy engineering [95,96], mathematics [97][98][99][100][101][102][103][104], environmental engineering [105][106]…”

Section: Introductionmentioning

confidence: 99%

An Investigation on Spiking Neural Networks Based on the Izhikevich Neuronal Model: Spiking Processing and Hardware Approach

et al. 2022

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The main required organ of the biological system is the Central Nervous System (CNS), which can influence the other basic organs in the human body. The basic elements of this important organ are neurons, synapses, and glias (such as astrocytes, which are the highest percentage of glias in the human brain). Investigating, modeling, simulation, and hardware implementation (realization) of different parts of the CNS are important in case of achieving a comprehensive neuronal system that is capable of emulating all aspects of the real nervous system. This paper uses a basic neuron model called the Izhikevich neuronal model to achieve a high copy of the primary nervous block, which is capable of regenerating the behaviors of the human brain. The proposed approach can regenerate all aspects of the Izhikevich neuron in high similarity degree and performances. The new model is based on Look-Up Table (LUT) modeling of the mathematical neuromorphic systems, which can be realized in a high degree of correlation with the original model. The proposed procedure is considered in three cases: 100 points LUT modeling, 1000 points LUT modeling, and 10,000 points LUT modeling. Indeed, by removing the high-cost functions in the original model, the presented model can be implemented in a low-error, high-speed, and low-area resources state in comparison with the original system. To test and validate the proposed final hardware, a digital FPGA board (Xilinx Virtex-II FPGA board) is used. Digital hardware synthesis illustrates that our presented approach can follow the Izhikevich neuron in a high-speed state (more than the original model), increase efficiency, and also reduce overhead costs. Implementation results show the overall saving of 84.30% in FPGA and also the higher frequency of the proposed model of about 264 MHz, which is significantly higher than the original model, 28 MHz.

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Section: Introductionmentioning

confidence: 99%

An Investigation on Spiking Neural Networks Based on the Izhikevich Neuronal Model: Spiking Processing and Hardware Approach

et al. 2022

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“…In recent years, different advanced computational approaches have been applied in various fields of study, such as fluid mechanic engineering [20][21][22][23][24][25][26][27][28], chemical engineering [29][30][31][32][33], electrical engineering , computer engineering [60][61][62][63][64][65][66][67][68][69][70][71][72][73][74][75][76], civil engineering [77][78][79], petroleum engineering [80][81][82][83][84][85][86][87][88][89][90][91][92][93][94], energy engineering [95]…”

Section: Rbf Neural Networkmentioning

confidence: 99%

Applying Data Mining and Artificial Intelligence Techniques for High Precision Measuring of the Two-Phase Flow’s Characteristics Independent of the Pipe’s Scale Layer

et al. 2022

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Scale formation inside oil and gas pipelines is always one of the main threats to the efficiency of equipment and their depreciation. In this study, an artificial intelligence method method is presented to provide the flow regime and volume percentage of a two-phase flow while considering the presence of scale inside the test pipe. In this non-invasive method, a dual-energy source of barium-133 and cesium-137 isotopes is irradiated, and the photons are absorbed by a detector as they pass through the test pipe on the other side of the pipe. The Monte Carlo N Particle Code (MCNP) simulates the structure and frequency features, such as the amplitudes of the first, second, third, and fourth dominant frequencies, which are extracted from the data recorded by the detector. These features use radial basis function neural network (RBFNN) inputs, where two neural networks are also trained to accurately determine the volume percentage and correctly classify all flow patterns, independent of scale thickness in the pipe. The advantage of the proposed system in this study compared to the conventional systems is that it has a better measuring precision as well as a simpler structure (using one detector instead of two).

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“…An analysis of the results of special well surveys, which consist in the systemic control over the development of an oil field, showed that technogenic changes from the use of hydraulic fracturing and bottom-hole zone treatment can significantly change the hydrodynamic characteristics of the reservoir and the integrity of the production string, especially due to technologies carried out at high pressures. So, this type of complications is one of the most frequent in practice and difficult to diagnose [35,36]. The aim of the project is to develop algorithms, that allow determining the presence of behind-casing crossflow with using oil well operation data, based on machine learning methods.…”

Section: Introductionmentioning

confidence: 99%

Application of machine learning methods for predicting well disturbances

et al. 2023

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In the process of field exploration, along with regular flooding, a significant part of the wells is flooded prematurely due to leakage of the string and outer annulus. In an effort to intensify the flow of oil to the bottom of wells in field conditions, specialists often try to solve this problem by using various technologies that change the reservoir characteristics of the formation. Any increase in pressure that exceeds the strength of the rocks in compression or tension leads to rock deformation (destruction of the cement stone, creation of new cracks). Moreover, repeated operations under pressure, as a rule, lead to an increase in water cut and the appearance of behind-the-casing circulations. For that reason, an important condition for maintaining their efficient operation is the timely forecasting of such negative phenomena as behind-casing cross flow and casing leakage. The purpose of the work is to increase the efficiency of well interventions and workover operations by using machine learning algorithms for predicting well disturbances. Prediction based on machine learning methods, regression analysis, identifying outliers in the data, visualization and interactive processing. The algorithms based on oil wells operation data allow training the forecasting model and, on its basis, determine the presence or absence of disturbances in the wells. As a result, the machine forecast showed high accuracy in identifying wells with disturbances. Based on this, candidate wells can be selected for further work. For each specific well, an optimal set of studies can be planned, as well as candidate wells can be selected for further repair and isolation work. In addition, in the course of this work, a set of scientific and technical solutions was developed using machine learning algorithms. This approach will allow predicting disturbances in the well without stopping it.

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A Laboratory Approach to Measure Enhanced Gas Recovery from a Tight Gas Reservoir during Supercritical Carbon Dioxide Injection

Cited by 11 publications

References 105 publications

An Investigation on Spiking Neural Networks Based on the Izhikevich Neuronal Model: Spiking Processing and Hardware Approach

An Investigation on Spiking Neural Networks Based on the Izhikevich Neuronal Model: Spiking Processing and Hardware Approach

Applying Data Mining and Artificial Intelligence Techniques for High Precision Measuring of the Two-Phase Flow’s Characteristics Independent of the Pipe’s Scale Layer

Application of machine learning methods for predicting well disturbances

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