Microscale crack propagation in shale samples using focused ion beam scanning electron microscopy and three-dimensional numerical modeling

Liu, Xin; Meng, Siwei; Zhao, Liang; Tang, Chun’an; Tao, Jiaping; Tang, Jizhou

doi:10.1016/j.petsci.2022.10.004

Cited by 45 publications

(17 citation statements)

References 69 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Conventional methods are difficult to solve, and artificial intelligence technology still faces both opportunities and challenges in the future. In the future, artificial intelligence technology can be effectively combined with oil and gas field development to achieve the optimal solution of complex problems in intelligent oil and gas reservoir development using machine learning and deep learning methods. , In the future, attention should be paid to the “three modernizations” innovation of standardization of oil and gas development data, intelligence of oil and gas fields, and platform collaboration, in order to achieve leapfrog development and rapid upgrading of artificial intelligence in the oil and gas industry. , …”

Section: Prospects For the Future Development Of Artificial Intellige...mentioning

confidence: 99%

Current Status and Prospects of Artificial Intelligence Technology Application in Oil and Gas Field Development

Wang,

Wei,

Xiong

et al. 2024

ACS Omega

View full text Add to dashboard Cite

Artificial intelligence technology will be increasingly applied in the oil and gas industry. The rapid development of artificial intelligence technology can solve problems such as high environmental sensitivity and complex production processes in the oil and gas industry. In recent years, emerging technologies represented by artificial intelligence have developed rapidly, assisting petroleum enterprises in digital transformation and intelligent upgrading. This article elaborates on the development trend of artificial intelligence technology. Based on the business scenarios and characteristics of the oil and gas industry, the application status of artificial intelligence technology in domestic and foreign petroleum technology service enterprises was summarized and analyzed. The application scenarios of artificial intelligence technology in the fields of dynamic analysis of oil and gas reservoirs, intelligent historical fitting, numerical simulation proxy models, and production plan optimization were analyzed with emphasis. Based on the problems and challenges faced in the development process of oil and gas reservoirs, it is proposed that petroleum enterprises should attach importance to the "three modernizations" innovation of data standardization, oil and gas field intelligence, and platform collaboration, in order to achieve more refined intelligent analysis and management of oil and gas reservoirs and quickly develop more targeted oil and gas reservoir development plans to assist in the intelligent transformation of oil and gas reservoir development. On this basis, prospects for future artificial intelligence technology are proposed, pointing out that the development of artificial intelligence technology will be faster and faster, and there will be higher demand for artificial intelligence technology in the construction of digital oil and gas fields in China in the future. The research results have important reference value for the development of the oil and gas industry.

show abstract

Section: Prospects For the Future Development Of Artificial Intellige...mentioning

confidence: 99%

Current Status and Prospects of Artificial Intelligence Technology Application in Oil and Gas Field Development

Wang,

Wei,

Xiong

et al. 2024

ACS Omega

View full text Add to dashboard Cite

show abstract

“…According to the nature of the basement, geological evolution history, and tectonic features, the Ordos Basin can be divided into six tectonic units: the Yimeng Uplift, the Yishan Slope, the Tianhuan ore-seeking depressions, the Jinshi Belt, the Western Marginal Fold Belt, and the Weibei Uplift. − The Yishan Slope was formed at the end of the early Bailian period. − It is the largest primary tectonic unit in the basin, 250 km wide from east to west and 400 km long from north to south. − The current tectonic feature is a large, gently dipping monocline that dips to the west, with an average dip of about 1° and a dip of less than 1°. , The Ordos Basin was uplifted as a whole at the end of the Ordovician in the Early Paleozoic and underwent hundreds of millions of years of weathering stripping and candle stripping without Silurian, Devonian, and Lower Carboniferous. , During this period, the weathering crustal solution candle stripping zones formed by exposed weathering and buried karst are of great significance for the formation of ancient weathered crustal gas reservoirs in the Lower Paleoproterozoic. − Feng et al studied the geochemical characteristics of natural gas in the Paleozoic era. Zhang et al conducted a systematic study on pore structures such as primary pores, secondary pores, and microcracks.…”

Section: Introductionmentioning

confidence: 99%

Characterization of Lower Paleozoic Water and Gas Distribution in Yan’an Gas Field, Ordos Basin

Gao,

Yuan,

Tan

et al. 2024

ACS Omega

View full text Add to dashboard Cite

The distribution of gas and water in the tight gas reservoirs is complex. This limits exploration and development compared to conventional resources. Elucidating the characteristics that control fluid distribution is critical to unlocking the tight gas potential. This study combines geologic analysis with production data to reveal the water chemistry, gas−water distribution, and factors controlling zoning in the study area. The results show that (a) extracted water includes formation brines, condensate, and residual drilling fluids; (b) formation water dominates production, and the salinity of Lower Paleozoic brines is as high as 169,689 mg/ L; (c) low NaCl and high metamorphic coefficients indicate that the water bodies are disconnected and the hydrocarbons are wellpreserved in several gas−water systems; and (d) paleomorphological features, tectonics, and lithologies control the distribution of gas water. Discrete water bodies are widely distributed.

show abstract

“…Therefore, it is important to investigate which experimental conditions for μ-CT experiments give better experimental results. 12 In this study, the X-ray μ-CT technique was used to characterize the micrometer-scale pore structure of samples of different sizes and different aqueous conditions because the size of the sample and the aqueous condition of the sample are the most frequently changing and controllable factors in μ-CT scanning. Avizo was used to build the pore network and pore network ball-and-stick model (PNM) to quantitatively characterize data on porosity, pore number, pore volume, and pore connectivity to describe and quantify pore throats and to qualitatively characterize the three-dimensional (3D) spatial distribution of pores and fracture.…”

Section: Introductionmentioning

confidence: 99%

“…At the same time, the results of μ-CT scans can be affected by other factors, of which the most direct are the experimental conditions. Therefore, it is important to investigate which experimental conditions for μ-CT experiments give better experimental results …”

Section: Introductionmentioning

confidence: 99%

Quality Enhancement of μ-CT Scanned Shale Images and Optimization of Experimental Conditions

Lin,

Chen,

et al. 2024

Energy Fuels

View full text Add to dashboard Cite

Micrometer CT scanning (μ-CT) is a crucial technique for three-dimensional characterization of pore structures, but both the scanning resolution and scanning outcomes are constrained by several factors. This paper selects shale samples from a section of the Wufeng−Longmaxi Formation in the Luzhou block in southern Sichuan, prepares samples of different sizes and water-bearing conditions, investigates the characteristics of pore development, connectivity, and structural variability of different samples through μ-CT scanning combined with digital image processing technology, and selects the optimal experimental conditions for μ-CT scanning. The results show that the smaller the shale sample size, the higher the accuracy and resolution of the CT image and the more accurate the identification of pore fractures. Moisture in the sample can make the values obtained from CT scanning higher, affecting the identification of pores. Sporadic water at the edge of the pore space and in the pore space will not change the number of pores but will reduce the pore volume; complete water in the pore space will reduce the number of pores and the pore volume; the pore space is separated by the endowment of water into a number of zones; and when the pore space increases, the number of pore spaces and pore volume decrease. Therefore, the smaller the size of the shale samples after drying, the higher the quality of the CT images and the more accurate the identification of the pores. This result can provide more accurate experimental conditions for μ-CT shale porosity research reference.

show abstract

Microscale crack propagation in shale samples using focused ion beam scanning electron microscopy and three-dimensional numerical modeling

Cited by 45 publications

References 69 publications

Current Status and Prospects of Artificial Intelligence Technology Application in Oil and Gas Field Development

Current Status and Prospects of Artificial Intelligence Technology Application in Oil and Gas Field Development

Characterization of Lower Paleozoic Water and Gas Distribution in Yan’an Gas Field, Ordos Basin

Quality Enhancement of μ-CT Scanned Shale Images and Optimization of Experimental Conditions

Contact Info

Product

Resources

About