Analysis of Gas Well Classifications and Productivity Influencing Factors in Kela-2 Gas Field

Liu, Zhao-long; ZHANG, Yongzhong; LIU, Hualin; Lv, Z.; Huang, Weigang

doi:10.29047/01225383.382

Cited by 2 publications

(1 citation statement)

References 13 publications

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“…The Kuqa Depression in the Tarim Basin is a sedimentary system that includes alluvial fan, fan delta or braided river delta, and shoreshallow lake (Gu et al, 2001;Zhang et al, 2008;Pan et al, 2013;Lai et al, 2018;Liu et al, 2019;Liu et al, 2021b;. The subfacies are mainly subaqueous distributary channels and mouth bars of the fan delta or braided river delta.…”

Section: Methodsmentioning

confidence: 99%

3D geological modeling of deep fractured low porosity sandstone gas reservoir in the Kuqa Depression, Tarim Basin

et al. 2023

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The fractured reservoir is one of the significant petroleum reservoir types in China, representing over one-third of total reserves. The Kuqa Depression in the Tarim Basin is dominated by fractured low-porosity sandstone gas reservoirs with characteristic tight matrix, developed fractures, and edge and bottom water. However, the continued development of these reservoirs has led to various problems, including strong reservoir heterogeneity, low well control, complex gas-water relationships, and early water invasion. Addressing these issues requires a detailed understanding of the reservoir’s geological characteristics. One method for achieving a fine reservoir description is through the use of 3D geological modeling. This high-level, comprehensive characterization technique is widely used throughout the entire life cycle of oil and gas field development. A 3D geological model can accurately predict the actual underground reservoir characteristics and provide a geological basis for later numerical simulation work. Based on a study of the geological characteristics of the Kuqa Depression in the Tarim Basin, a 3D geological modeling technique was developed, which includes structural modeling, facies modeling, petrophysical modeling, and fracture modeling. This technology has been successfully applied to many deep gas reservoirs in the Kuqa Depression of the Tarim Basin, leading to enhanced gas recovery.

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Section: Methodsmentioning

confidence: 99%

3D geological modeling of deep fractured low porosity sandstone gas reservoir in the Kuqa Depression, Tarim Basin

et al. 2023

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Deep-learning-based natural fracture identification method through seismic multi-attribute data: a case study from the Bozi-Dabei area of the Kuqa Basin, China

Tang,

Chen,

Deng

et al. 2024

Front. Earth Sci.

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Fractures play a crucial role in tight sandstone gas reservoirs with low permeability and low effective porosity. If open, they not only significantly increase the permeability of the reservoir but also serve as channels connecting the storage space. Among numerous fracture identification methods, seismic data provide unique advantages for fracture identification owing to the provision of three-dimensional information between wells. How to accurately identify the development of fractures in geological bodies between wells using seismic data is a major challenge. In this study, a tight sandstone gas reservoir in the Kuqa Basin (China) was used as an example for identifying reservoir fractures using deep-learning-based method. First, a feasibility analysis is necessary. Intersection analysis between the fracture density and seismic attributes (the characteristics of frequency, amplitude, phase, and other aspects of seismic signals) indicates that there is a correlation between the two when the fracture density exceeds a certain degree. The development of fractures is closely related to the lithology and structure, indirectly affecting differences in seismic attributes. This indicates that the use of seismic attributes for fracture identification is feasible and reasonable. Subsequently, the effective fracture density data obtained from imaging logging were used as label data, and the optimized seismic attribute near the well data were used as feature data to construct a fracture identification sample dataset. Based on a feed-forward neural network algorithm combined with natural fracture density and effectiveness control factor constraints, a trained identification model was obtained. The identification model was applied to seismic multi-attribute data for the entire work area. Finally, the accuracy of the results from the training, testing, and validation datasets were used to determine the effectiveness of the method. The relationship between the fracture identification results and the location of the fractures in the target reservoir was used to determine the reasonableness of the results. The results indicate that there is a certain relationship between multiple seismic attributes and fracture development, which can be established using deep learning models. Furthermore, the deep-learning-based seismic data fracture identification method can effectively identify fractures in the three-dimensional space of reservoirs.

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Analysis of Gas Well Classifications and Productivity Influencing Factors in Kela-2 Gas Field

Cited by 2 publications

References 13 publications

3D geological modeling of deep fractured low porosity sandstone gas reservoir in the Kuqa Depression, Tarim Basin

3D geological modeling of deep fractured low porosity sandstone gas reservoir in the Kuqa Depression, Tarim Basin

Deep-learning-based natural fracture identification method through seismic multi-attribute data: a case study from the Bozi-Dabei area of the Kuqa Basin, China

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