Productivity evaluation model for multi-cluster fractured wells based on volumetric source method

Lu, Yu; Li, Haitao; Wang, Junchao; Liu, Tao; Wu, Keliu

doi:10.1016/j.egyr.2022.06.013

Cited by 6 publications

(3 citation statements)

References 25 publications

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“…However, for methyl acetylene, the productivity loss is evident in concentrations below 1 ppm, which indicates that there is a greater risk of affecting production with traces of methyl acetylene than with traces of acetylene. The percentage loss of productivity was calculated using Equation (1) [ 21 , 22 ]. % productivity loss = (1 − real produced/estimated production) × 100 …”

Section: Resultsmentioning

confidence: 99%

Experimental Study of the Impact of Trace Amounts of Acetylene and Methylacetylene on the Synthesis, Mechanical and Thermal Properties of Polypropylene

Hernández‐Fernández

Vivas‐Reyes

Toloza

2022

IJMS

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During the production of polymer-grade propylene, different processes are used to purify this compound and ensure that it is of the highest quality. However, some impurities such as acetylene and methyl acetylene are difficult to remove, and some of these impurities may be present in the propylene used to obtain polypropylene, which may have repercussions on the process. This study evaluates the impact of these acetylene and methyl acetylene impurities on the productivity of the polypropylene synthesis process and on the mechanical and thermal properties of the material obtained through the synthesis of eight samples with different concentrations of acetylene and eight samples with different concentrations of acetylene. We discovered that for the first concentrations of both acetylene (2 and 3 ppm) and methyl acetylene (0.03 and 0.1), the MFI, thermal recording, and mechanical properties of the resin were unaffected by the variation of the fluidity index, thermal degradation by TGA, and mechanical properties such as resistance to tension, bending, and impact. However, when the concentration exceeded 14 ppm for methyl acetylene and 12 ppm for acetylene, the resistance of this resin began to decrease linearly. Regarding production, this was affected by the first traces of acetylene and methyl acetylene progressively decreasing.

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Section: Resultsmentioning

confidence: 99%

Experimental Study of the Impact of Trace Amounts of Acetylene and Methylacetylene on the Synthesis, Mechanical and Thermal Properties of Polypropylene

Hernández‐Fernández

Vivas‐Reyes

Toloza

2022

IJMS

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“…To address this issue, semi-analytical models have been used as an alternative. While these models overcome the complexity of numerical simulation in terms of modeling and solving difficulties, they may still have limitations in their application [8][9][10].…”

Section: Introduction 1traditional Methodsmentioning

confidence: 99%

Using a Deep Neural Network with Small Datasets to Predict the Initial Production of Tight Oil Horizontal Wells

Yang,

Tan,

Cheng

et al. 2023

Electronics

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Due to its abundant reserves, tight oil has emerged as a significant substitute for conventional petroleum resources. It has become one of the focal points of exploration and research, and a new hot spot in global unconventional oil and gas exploration and development. This has led to a significant increase in the demand for forecasting the production capacity of tight oil horizontal wells. The deep neural network (DNN), as a mature model, has demonstrated significant advantages in many fields. However, due to the confidentiality and uniqueness of oilfield data, acquiring large datasets has become a challenge. Traditional methods using small datasets for training DNN models result in low accuracy and overfitting issues, which hinders the development of neural networks in the petroleum industry. This study aims to predict the initial production capacity of tight oil horizontal wells by using a small dataset of 650 data points through a DNN model. The research results indicate that pre-trained and fine-tuned DNNs outperform shallow neural networks, supporting vector machines, and DNN trained with traditional methods in terms of better generalization performance. Their accuracy reached 91.3%, demonstrating that it is reasonable to use a small dataset with pre-trained and fine-tuned DNN models.

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“…Liehui et al [25] introduced conformal transformation to derive the productivity model of horizontal wells in low-permeability gas reservoirs by simultaneously considering the impact of starting pressure gradient and high-speed non-Darcy effect. Lu et al [26] established a new productivity evaluation model for multicluster fractured wells based on volumetric source method. Hailong et al [27] adopted the effective stress function of the body rock, considered the change of medium porosity, and applied variable substitution and variable separation methods to derive the productivity model of horizontal wells in low-permeability gas permeable reservoirs which considered both the stress sensitivity effect and the influence of starting pressure gradient.…”

Section: Introductionmentioning

confidence: 99%

Productivity Formula of Horizontal Well in Low-Permeability Gas Reservoir considering Multiple Factors

Feng

Qiao

et al. 2023

Geofluids

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For horizontal well in low-permeability gas reservoir, the effects of threshold pressure gradient, stress sensitivity, and gas slippage have significant impacts on the well productivity. At present, there are few productivity formulas for horizontal gas well considering all these factors. In this paper, based on the flow analysis of horizontal well in low-permeability gas reservoir, the whole flow field was divided into two parts, namely, far wellbore region and near wellbore region, among which the far wellbore region is composed of plane linear flow region and plane radial flow region and the near wellbore region is composed of vertical plane radial flow region and spherical plane central flow region. Then, a new productivity formula is established based on the steady-state seepage theory and the equivalent seepage resistance method, with the consideration of threshold pressure gradient, stress sensitivity, and gas slippage effect. The accuracy of the formula in this paper is verified through comparing with other classical models, and the influence of various factors on the well productivity is analyzed. The analysis results show that stress sensitivity has the most significant effect on horizontal gas well production, followed by threshold pressure gradient, and the gas slippage has the least effect. With the consideration of all influencing factors, the higher the formation pressure and reservoir thickness, the higher the productivity, and the increase of productivity increases with the increase of flow pressure difference. The increasing trend of gas productivity index per meter with the increase of reservoir permeability is first fast and then slow. When the reservoir permeability is greater than 1.2 mD, the increment of gas productivity index per meter (MGPI) decreases. When the length of horizontal well is greater than 1400 m, the increment of gas productivity index per meter decreases with the increase of gas reservoir thickness. Therefore, it is recommended to control the horizontal well length within 1400 m in low-permeability gas reservoir. In addition, the absolute open flow charts corresponding to reservoir thickness and horizontal well length under different reservoir permeability conditions were also given, which can provide theoretical guidance for the selection of horizontal well length during the development of low-permeability gas reservoirs.

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Productivity evaluation model for multi-cluster fractured wells based on volumetric source method

Cited by 6 publications

References 25 publications

Experimental Study of the Impact of Trace Amounts of Acetylene and Methylacetylene on the Synthesis, Mechanical and Thermal Properties of Polypropylene

Experimental Study of the Impact of Trace Amounts of Acetylene and Methylacetylene on the Synthesis, Mechanical and Thermal Properties of Polypropylene

Using a Deep Neural Network with Small Datasets to Predict the Initial Production of Tight Oil Horizontal Wells

Productivity Formula of Horizontal Well in Low-Permeability Gas Reservoir considering Multiple Factors

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