Fluctuation of fracturing curves indicates in-situ brittleness and reservoir fracturing characteristics in unconventional energy exploitation

Sun, Fuqiang; Du, Shanshan; Zhao, Ya‐Pu

doi:10.1016/j.energy.2022.124043

Cited by 14 publications

(4 citation statements)

References 44 publications

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“…Researchers have developed various prediction models based on ML methods to evaluate the impact of oil well properties, reservoir characteristics, and well production behaviors. With the help of ML simulation and modeling, the exploitation characteristics of shale oil/gas reservoirs are quickly analyzed, saving the exploration cost. − Due to the shale oil/gas production characteristics of rapid decay and gradual recovery, various parameters such as oil/gas well location, geological conditions, petrophysics, etc., must be considered comprehensively . Hence, oil/gas production is challenging to be predicted even with ML-based methods .…”

Section: Reconstruction Methods Of Kerogen Modelmentioning

confidence: 99%

“…146−148 Due to the shale oil/gas production characteristics of rapid decay and gradual recovery, various parameters such as oil/gas well location, geological conditions, petrophysics, etc., must be considered comprehensively. 149 Hence, oil/gas production is challenging to be predicted even with ML-based methods. 150 In addition, the training of ML models requires massive data from shale oil/gas reservoirs.…”

Section: Kerogen Molecular Model Reconstruction By ML Methodsmentioning

confidence: 99%

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Perspectives of Machine Learning Development on Kerogen Molecular Model Reconstruction and Shale Oil/Gas Exploitation

Kang

Zhao

2022

Energy Fuels

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The shale revolution has provided abundant shale oil/gas resources for the world, but the efficient, sustainable, and environmentally friendly exploitation of shale oil/gas is still challenging. Kerogen is the primary hydrocarbon source of shale oil/gas. The research on the kerogen chemo-mechanical properties significantly influences the development of shale oil/gas extraction technology. Rapid reconstruction of the kerogen molecular models is the most effective way to study the generation mechanism of shale oil/gas from the bottom-up molecular level. However, due to the combinatorial explosion problem, the reconstruction complexity of kerogen increases sharply because of the kerogen’s characteristics of complex origin, large molecular weight, and diverse functional groups. The traditional kerogen molecular reconstruction methods require professionals to comprehensively analyze various experimental information to approximate the actual kerogen molecular models through trial-and-error. So, the traditional methods are time and material-consuming and extremely inefficient. These shortcomings make researchers spend too much strength on the reconstruction of kerogen molecular models and cannot focus on the study of kerogen chemo-mechanical properties. For the past few years, state-of-the-art machine learning (ML) methods have been applied to intelligently reconstruct the kerogen molecular models through high-throughput and predict shale oil/gas production mechanisms. Although the current work is still in the infancy stage, ML methods are believed to be the most promising way to solve the drawbacks of traditional methods and reconstruct kerogen in reliable and large molecular weight. Hence, mechano-energetics is proposed to study the efficient development and utilization of energy based on mechanics and ML. This paper briefly reviews the development history of kerogen molecular model reconstruction methods and the research of ML in the fields of kerogen reconstruction and shale oil/gas exploitation. Some recommendations for further ML-based work are also suggested. We are convinced that the ML methods will accelerate the research of kerogen and promote the significant development of unconventional oil/gas exploitation technologies.

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Section: Reconstruction Methods Of Kerogen Modelmentioning

confidence: 99%

Section: Kerogen Molecular Model Reconstruction By ML Methodsmentioning

confidence: 99%

Perspectives of Machine Learning Development on Kerogen Molecular Model Reconstruction and Shale Oil/Gas Exploitation

Kang

Zhao

2022

Energy Fuels

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“…Moreover, it is highly important to obtain the in-situ mechanical properties of rocks; however, in-situ mechanical tests are often hard to conduct 23 . Owing to the difficulty in sampling natural joint surfaces, it can be difficult to guarantee the consistency of each sample during testing.…”

Section: Introductionmentioning

confidence: 99%

Shear strength deterioration effect of rock mass joint surface under cyclic shear load

Zhang

Dong

et al. 2022

Sci Rep

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Understanding the shear strength degradation mechanism of a rock mass joint surface under cyclic shear load and determining a corresponding analytical model is an important foundation for accurately evaluating the safety of rock mass engineering under seismic loads. It is worth noting that, to date, there has been a dearth of studies on the strength characteristics of joint surfaces that consider the number of loading cycles, normal load, and initial undulant angle of the structural plane. In this study, focused on the behaviour of sandstone, the particle flow code (PFC) modelling framework was used to simulate a joint surface cyclic shear test considering first- and second-order undulations. Based on the experimental results, the comprehensive effects of the number of cyclic shear cycles, the normal stress, first- and second-order undulation and the dilatancy angle on shear stress during cyclic shear were analysed. Formulas for the joint surface shear basic friction angle and dilatancy angle under cyclic shear were proposed, and a method for calculating the joint surface peak shear strength under cyclic shear considering the deterioration of the dilatancy angle and basic friction angle was established. The peak shear strength of a sample after five cycles of shearing was calculated using the proposed formula and compared with the results of numerical simulations, the Barton method, and the Homand method. The results showed that the calculated values have good consistency with the results of the numerical simulations, demonstrating the effectiveness and accuracy of the proposed formula. However, under a low normal stress, there could be errors in estimating the cyclic shear strength of the joint surface.

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“…Therefore, it is crucial to establish an accurate method for evaluating the fracturability of shale. Shale fracturability is influenced by various factors due to its comprehensive reflection of geological and reservoir characteristics [19,20]. Scholars worldwide have conducted research on evaluating the fracturability of shale reservoirs.…”

mentioning

confidence: 99%

Shale Fracturability Graphic Template Based on Mixed Analytic Hierar-chy Process and Mutation Theory

Li,

Liu,

Cheng

et al. 2024

ENERGY

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Due to the depletion of conventional energy reserves, there has been a global shift towards non-conventional energy sources. Shale oil and gas have emerged as key alternatives. These resources have dense and heterogeneous reservoirs, which require hydraulic fracturing to extract. This process depends on identifying optimal fracturing layers, also known as 'sweet spots' . However, there is currently no uniform standard for locating these sweet spots. This paper presents a new model for evaluating fracturability that aims to address the current gap in the field. The model utilizes a hierarchical analysis approach and a mutation model, and is distinct in its use of original logging data to generate a fracturability evaluation map. Using this paper's shale fracturing sweet spot evaluation method based on a two-step mutation model, four wells in different blocks of Fuling and Nanchuan Districts in China were validated, and the results showed that the proportion of high-yielding wells on the sweet spot line could reach 97.6%, while the proportion of low-producing wells was only 78.67%. Meanwhile, the evaluation results of the model were compared with the microseismic data, and the matching results were consistent.

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Fluctuation of fracturing curves indicates in-situ brittleness and reservoir fracturing characteristics in unconventional energy exploitation

Cited by 14 publications

References 44 publications

Perspectives of Machine Learning Development on Kerogen Molecular Model Reconstruction and Shale Oil/Gas Exploitation

Perspectives of Machine Learning Development on Kerogen Molecular Model Reconstruction and Shale Oil/Gas Exploitation

Shear strength deterioration effect of rock mass joint surface under cyclic shear load

Shale Fracturability Graphic Template Based on Mixed Analytic Hierar-chy Process and Mutation Theory

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