Two episodes of structural fractures: Numerical simulation of Yanchang Oilfield in the Ordos basin, northern China

Li, Yaohua; Song, Yan; Jiang, Zhenxue; Yin, Li; Luo, Qun; Ge, Yunjin; Liú, Dan

doi:10.1016/j.marpetgeo.2018.06.031

Cited by 8 publications

(8 citation statements)

References 93 publications

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“…Energies 2020, 13, x FOR PEER REVIEW 4 of 20 this way, we can calculate the boundary stress state by combining the regional tectonic stress, tectonic direction, and rock density. The detailed modification of the Newberry formula and the calculative process of the boundary stress state can also be found in our previous study [17].…”

Section: Stress Balance Along the Laminamentioning

confidence: 92%

“…In this study, the principle of the finite element approach is composed of the following three relationships: (1) The strain-displacement relationship; (2) strain-stress relationship; and (3) stress-external force relationship. The principle and its detailed explanations can be found in our previous study [17].…”

Section: Finite Element Simulationmentioning

confidence: 99%

“…Moreover, the lamina-induced fractures also correspond to the km-scale regional bedding fractures, which are induced by the formation beddings. These fractures have been the focus of conventional and unconventional oil and gas reservoir characterization including shale, tight oil [14][15][16][17][18][19][20][21][22][23], and Carboniferous rocks [24]. The fractures have also played an important role in methane gas emissions from coal seams [25][26][27].…”

Section: Introductionmentioning

confidence: 99%

“…The fractures have also played an important role in methane gas emissions from coal seams [25][26][27]. It has been found that the open state of bedding fractures in complicated tectonic zones could play an essential role in oil and gas diffusion, emission, migration, and accumulation [14,17,[28][29][30][31][32][33]. In addition, the lamina or bedding in the tight reservoirs coupled with the hydraulic fractures could induce a more complicated in-situ fracture network [34].…”

Section: Introductionmentioning

confidence: 99%

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Finite Element Simulation of Multi-Scale Bedding Fractures in Tight Sandstone Oil Reservoir

Wang

Yang

et al. 2019

Energies

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Multi-scale bedding fractures, i.e., km-scale regional bedding fractures and cm-scale lamina-induced fractures, have been the focus of unconventional oil and gas exploration and play an important role in resource exploration and drilling practice for tight oil and gas. It is challenging to conduct numerical simulations of bedding fractures due to the strong heterogeneity without a proper mechanical criterion to predict failure behaviors. This research modified the Tien–Kuo (T–K) criterion by using four critical parameters (i.e., the maximum principal stress (σ1), minimum principal stress (σ3), lamina angle (θ), and lamina friction coefficient (μlamina)). The modified criterion was compared to other bedding failure criteria to make a rational finite element simulation constrained by the four variables. This work conducted triaxial compression tests of 18 column samples with different lamina angles to verify the modified rock failure criterion, which contributes to the simulation work on the multi-scale bedding fractures in the statics module of the ANSYS workbench. The cm-scale laminated rock samples and the km-scale Yanchang Formation in the Ordos Basin were included in the multi-scale geo-models. The simulated results indicate that stress is prone to concentrate on lamina when the lamina angle is in an effective range. The low-angle lamina always induces fractures in an open state with bigger failure apertures, while the medium-angle lamina tends to induce fractures in a shear sliding trend. In addition, the regional bedding fractures of the Yanchang Formation in the Himalayan tectonic period tend to propagate under the conditions of lower maximum principal stress, higher minimum principal stress, and larger stratigraphic dip.

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Section: Stress Balance Along the Laminamentioning

confidence: 92%

Section: Finite Element Simulationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Finite Element Simulation of Multi-Scale Bedding Fractures in Tight Sandstone Oil Reservoir

Wang

Yang

et al. 2019

Energies

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“…This Basin is a typical low-permeability formation with an average permeability of less than 1 mD. Except for the Chang 6 reservoir with developed horizontal bedding [25,26], the horizontal stress in most areas of the basin is greater than the vertical stress, which means that the fractures generated by hydraulic fracturing are mainly vertical fractures [27][28][29][30].…”

Section: Introductionmentioning

confidence: 99%

An Automatic Classification Method of Well Testing Plot Based on Convolutional Neural Network (CNN)

Chu

Dong

et al. 2019

Energies

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The precondition of well testing interpretation is to determine the appropriate well testing model. In numerous attempts in the past, automatic classification and identification of well testing plots have been limited to fully connected neural networks (FCNN). Compared with FCNN, the convolutional neural network (CNN) has a better performance in the domain of image recognition. Utilizing the newly proposed CNN, we develop a new automatic identification approach to evaluate the type of well testing curves. The field data in tight reservoirs such as the Ordos Basin exhibit various well test models. With those models, the corresponding well test curves are chosen as training samples. One-hot encoding, Xavier normal initialization, regularization technique, and Adam algorithm are combined to optimize the established model. The evaluation results show that the CNN has a better result when the ReLU function is used. For the learning rate and dropout rate, the optimized values respectively are 0.005 and 0.4. Meanwhile, when the number of training samples was greater than 2000, the performance of the established CNN tended to be stable. Compared with the FCNN of similar structure, the CNN is more suitable for classification of well testing plots. What is more, the practical application shows that the CNN can successfully classify 21 of the 25 cases.

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Origin of calcite cements and their impact on reservoir heterogeneity in the Triassic Yanchang Formation, Ordos Basin, China: A combined petrological and geochemical study

Xie

Yao

Cao

et al. 2020

Marine and Petroleum Geology

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Two episodes of structural fractures: Numerical simulation of Yanchang Oilfield in the Ordos basin, northern China

Cited by 8 publications

References 93 publications

Finite Element Simulation of Multi-Scale Bedding Fractures in Tight Sandstone Oil Reservoir

Finite Element Simulation of Multi-Scale Bedding Fractures in Tight Sandstone Oil Reservoir

An Automatic Classification Method of Well Testing Plot Based on Convolutional Neural Network (CNN)

Origin of calcite cements and their impact on reservoir heterogeneity in the Triassic Yanchang Formation, Ordos Basin, China: A combined petrological and geochemical study

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