An analysis of the effectiveness of hydraulic fracturing at YS1 of the Northern field

Karpikov, A. V.; Aliev, R I; Babyr, N. V.

doi:10.1088/1757-899x/952/1/012036

Cited by 13 publications

(11 citation statements)

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“…In the past few decades, various advanced computational methods have been applied in various fields of study such as chemical engineering [32][33][34][35][36][37], electrical and computer engineering [38][39][40][41], civil engineering [42][43][44], mechanical engineering [45][46][47][48][49][50][51], petroleum engineering [52][53][54][55][56][57][58][59][60][61][62][63], and environmental engineering [64,65], etc. The ANN has been demonstrated to be the most potent technique for classification and prediction among the aforementioned computational methods.…”

Section: Artificial Neural Networkmentioning

confidence: 99%

Extraction of Time-Domain Characteristics and Selection of Effective Features Using Correlation Analysis to Increase the Accuracy of Petroleum Fluid Monitoring Systems

et al. 2022

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In the current paper, a novel technique is represented to control the liquid petrochemical and petroleum products passing through a transmitting pipe. A simulation setup, including an X-ray tube, a detector, and a pipe, was conducted by Monte Carlo N Particle-X version (MCNPX) code to examine a two-by-two mixture of four diverse petroleum products (ethylene glycol, crude oil, gasoline, and gasoil) in various volumetric ratios. As the feature extraction system, twelve time characteristics were extracted from the received signal, and the most effective ones were selected using correlation analysis to present reasonable inputs for neural network training. Three Multilayers perceptron (MLP) neural networks were applied to indicate the volume ratio of three kinds of petroleum products, and the volume ratio of the fourth product can be feasibly achieved through the results of the three aforementioned networks. In this study, increasing accuracy was placed on the agenda, and an RMSE < 1.21 indicates this high accuracy. Increasing the accuracy of predicting volume ratio, which is due to the use of appropriate characteristics as the neural network input, is the most important innovation in this study, which is why the proposed system can be used as an efficient method in the oil industry.

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Section: Artificial Neural Networkmentioning

confidence: 99%

Extraction of Time-Domain Characteristics and Selection of Effective Features Using Correlation Analysis to Increase the Accuracy of Petroleum Fluid Monitoring Systems

et al. 2022

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“…The main methods for preventing (preventive measures) the formation of ARPDs include: (1) the use of protective coatings (coating of pipes with epoxy resins, finely crushed glass, Bakelite varnish, resins, the use of glass-reinforced plastic rods); (2) physical methods (vibrational, ultrasonic methods, exposure to magnetic, electric, and electromagnetic fields); (3) chemical methods (the use of wetting agents, modifiers, depressors, and dispersants) [34][35][36]. Modifiers and dispersants with completely different molecular structures and mechanisms of action can be effective in the fight against asphalt-resin-paraffin deposits due to their high efficiency and cost-effectiveness [37].…”

Section: Literature Reviewmentioning

confidence: 99%

“…Radial size of the BHFZ, within which the pressure is less than or equal to the saturation pressure [35]: If necessary, it is possible to perform the treatment of the BHFZ with an ARPD solvent; this requires the following calculations:…”

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confidence: 99%

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Improving the Efficiency of Oil and Gas Wells Complicated by the Formation of Asphalt–Resin–Paraffin Deposits

2021

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A number of difficulties may be encountered in the final stages of oil field exploitation, including the formation of asphalt–resin–paraffin deposits (ARPDs). It is expedient to use complex technologies to remove the already formed deposits and prevent the formation of ARPDs. This paper focuses on the complex technology of oil field exploitation. This technology combines both the removal of organic deposits and the prevention of the formation of these deposits in the well bottomhole formation zone (BHFZ) system. The calculations for determining the process parameters of selling the ARPD inhibitor solution into the BHFZ are presented in this article. This complex technology includes the process of ARPD removal by flushing the well and the subsequent injection of the developed ARPD solvent into the BHFZ. In addition, the technology is complemented by a method of preventing the formation of these deposits. This method consists of squeezing the ARPD inhibitor and then pumping it by the selling fluid from five to ten times of the volume. This article contains a detailed calculation of the methodology and provides the diagrams for the solvent and inhibitor injection.

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“…where x is the feature vector, v is the center position, σ is the spread, z j is the output prediction from the jth node of the output layer, u mj is weights, and b j is bias. It is worth mentioning that, in the past few decades, various advanced computational approaches, e.g., finite element, numerical linear algebra, statistics, numerical analysis, tensor analysis, and artificial intelligence, have been applied in various fields of study such as chemical engineering [26][27][28][29], electrical and computer engineering [30][31][32][33][34][35][36][37], civil engineering [38][39][40], mechanical engineering [41][42][43][44][45][46][47][48][49][50][51][52][53][54][55], petroleum engineering [56][57][58][59][60][61][62][63][64][65][66][67][68][69], environmental engineering [70,…”

Section: X-ray Tube Voltage Optimization Proceduresmentioning

confidence: 99%

Optimization of X-ray Tube Voltage to Improve the Precision of Two Phase Flow Meters Used in Petroleum Industry

et al. 2021

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To the best knowledge of the authors, in all the former studies, a fixed value of X-ray tube voltage has been used for investigating gas–liquid two-phase flow characteristics, while the energy of emitted X-ray radiations that depends on the tube voltage can significantly affect the measurement precision of the system. The purpose of present study is to find the optimum tube voltage to increase the accuracy and efficiency of an intelligent X-ray radiation-based two-phase flow meter. The detection system consists of an industrial X-ray tube and one detector located on either side of a steel pipe. Tube voltages in the range of 125–300 kV with a step of 25 kV were investigated. For each tube voltage, different gas volume percentages (GVPs) in the range of 10–90% with a step of 5% were modeled. A feature extraction method was performed on the output signals of the detector in every case, and the obtained matrixes were applied to the designed radial basis function neural networks (RBFNNs). The desired output of the networks was GVP. The precision of the networks in every voltage and every number of neurons in the hidden layer were obtained. The results showed that 225 kV tube voltage is the optimum voltage for this purpose. The obtained mean absolute error (MAE) for this case is less than 0.05, which demonstrates the very high precision of the metering system with an optimum X-ray tube voltage.

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An analysis of the effectiveness of hydraulic fracturing at YS1 of the Northern field

Cited by 13 publications

References 1 publication

Extraction of Time-Domain Characteristics and Selection of Effective Features Using Correlation Analysis to Increase the Accuracy of Petroleum Fluid Monitoring Systems

Extraction of Time-Domain Characteristics and Selection of Effective Features Using Correlation Analysis to Increase the Accuracy of Petroleum Fluid Monitoring Systems

Improving the Efficiency of Oil and Gas Wells Complicated by the Formation of Asphalt–Resin–Paraffin Deposits

Optimization of X-ray Tube Voltage to Improve the Precision of Two Phase Flow Meters Used in Petroleum Industry

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