scholarly journals Integrated Model Development for Tight Oil Sands Reservoir with 2D Fracture Geometry and Reviewed Sensitivity Analysis of Hydraulic Fracturing

2016 ◽  
Vol 12 (4) ◽  
pp. 375-385
Author(s):  
Nguyen Huu Truong ◽  
Wisup Bae ◽  
Hoang Thinh Nhan
Keyword(s):  
Sensitivity Analysis ◽  
Hydraulic Fracturing ◽  
Oil Sands ◽  
Model Development ◽  
Integrated Model ◽  
Tight Oil ◽  
Fracture Geometry

Modeling Pinpoint Multistage Fracturing with 2D Fracture Geometry for Tight Oil Sands

2011 ◽  
Vol 29 (12) ◽  
pp. 1203-1213 ◽  
Author(s):  
M. A. Z. Omar ◽  
M. M. Rahman
Keyword(s):  
Oil Sands ◽  
Tight Oil ◽  
Fracture Geometry

Hydraulic Fracturing of Ultrahigh Stress Tight Oil Reservoir - A Case Study of Yumen Oilfield in China

2016 ◽  
Author(s):  
Peng Yi ◽  
Weng Dingwei ◽  
Xu Yun ◽  
Wang Liwei ◽  
Lu Yongjun ◽  
...  
Keyword(s):  
Hydraulic Fracturing ◽  
Tight Oil ◽  
Oil Reservoir ◽  
Tight Oil Reservoir

Modeling Sorbent Injection for Mercury Control in Baghouse Filters: I—Model Development and Sensitivity Analysis

2003 ◽  
Vol 53 (4) ◽  
pp. 478-488 ◽  
Author(s):  
Joseph R.V. Flora ◽  
Richard A. Hargis ◽  
William J. O’Dowd ◽  
Henry W. Pennline ◽  
Radisav D. Vidic
Keyword(s):  
Sensitivity Analysis ◽  
Model Development ◽  
Mercury Control ◽  
Sorbent Injection

Experimental Investigation on Enhanced-Oil-Recovery Mechanisms of Using Supercritical Carbon Dioxide as Prefracturing Energized Fluid in Tight Oil Reservoir

SPE Journal ◽  
2021 ◽  
pp. 1-16
Author(s):  
Lei Li ◽  
Zheng Chen ◽  
Yu-Liang Su ◽  
Li-Yao Fan ◽  
Mei-Rong Tang ◽  
...  
Keyword(s):  
Carbon Dioxide ◽  
Water Resources ◽  
Supercritical Carbon Dioxide ◽  
Hydraulic Fracturing ◽  
Clay Minerals ◽  
Oil Production ◽  
Tight Oil ◽  
Injection Mode ◽  
Oil Development ◽  
Live Oil

Summary Fracturing is the necessary means of tight oil development, and the most common fracturing fluid is slickwater. However, the Loess Plateau of the Ordos Basin in China is seriously short of water resources. Therefore, the tight oil development in this area by hydraulic fracturing is extremely costly and environmentally unfriendly. In this paper, a new method using supercritical carbon dioxide (CO2) (ScCO2) as the prefracturing energized fluid is applied in hydraulic fracturing. This method can give full play to the dual advantages of ScCO2 characteristics and mixed-water fracturing technology while saving water resources at the same time. On the other hand, this method can reduce reservoir damage, change rock microstructure, and significantly increase oil production, which is a development method with broad application potential. In this work, the main mechanism, the system-energy enhancement, and flowback efficiency of ScCO2 as the prefracturing energized fluid were investigated. First, the microscopic mechanism of ScCO2 was studied, and the effects of ScCO2 on pores and rock minerals were analyzed by nuclear-magnetic-resonance (NMR) test, X-ray-diffraction (XRD) analysis, and scanning-electron-microscope (SEM) experiments. Second, the high-pressurechamber-reaction experiment was conducted to study the interaction mechanism between ScCO2 and live oil under formation conditions, and quantitively describe the change of high-pressure physical properties of live oil after ScCO2 injection. Then, the numerical-simulation method was applied to analyze the distribution and existence state of ScCO2, as well as the changes of live-oil density, viscosity, and composition in different stages during the full-cycle fracturing process. Finally, four injection modes of ScCO2-injection core-laboratory experiments were designed to compare the performance of ScCO2 and slickwater in terms of energy enhancement and flowback efficiency, then optimize the optimal CO2-injection mode and the optimal injection amount of CO2slug. The results show that ScCO2 can dissolve calcite and clay minerals (illite and chlorite) to generate pores with sizes in the range of 0.1 to 10 µm, which is the main reason for the porosity and permeability increases. Besides, the generated secondary clay minerals and dispersion of previously cemented rock particles will block the pores. ScCO2 injection increases the saturation pressure, expansion coefficient, volume coefficient, density, and compressibility of crude oil, which are the main mechanisms of energy increase and oil-production enhancement. After analyzing the four different injection-mode tests, the optimal one is to first inject CO2 and then inject slickwater. The CO2 slug has the optimal value, which is 0.5 pore volume (PV) in this paper. In this paper, the main mechanisms of using ScCO2 as the prefracturing energized fluid are illuminated. Experimental studies have proved the pressure increase, production enhancement, and flowback potential of CO2 prefracturing. The application of this method is of great significance to the protection of water resources and the improvement of the fracturing effect.

scholarly journals Sensitivity Analysis of Seismic Velocity and Attenuation Variations for Longmaxi Shale during Hydraulic Fracturing Testing in Laboratory

Energies ◽  
2017 ◽  
Vol 10 (9) ◽  
pp. 1393 ◽  
Author(s):  
Hongyu Zhai ◽  
Xu Chang ◽  
Yibo Wang ◽  
Ziqiu Xue ◽  
Xinglin Lei ◽  
...  
Keyword(s):  
Sensitivity Analysis ◽  
Hydraulic Fracturing ◽  
Seismic Velocity ◽  
Longmaxi Shale

Modeling Freezing Injury in Alfalfa to Calculate Forage Yield: I. Model Development and Sensitivity Analysis

1998 ◽  
Vol 90 (5) ◽  
pp. 687-697 ◽  
Author(s):  
V. Rao Kanneganti ◽  
C. Alan Rotz ◽  
Richard P. Walgenbach
Keyword(s):  
Sensitivity Analysis ◽  
Model Development ◽  
Forage Yield ◽  
Freezing Injury

scholarly journals Machine learning-based feature importance approach for sensitivity analysis of steel frames

2021 ◽  
Author(s):  
Hyeyoung Koh ◽  
Hannah Beth Blum
Keyword(s):  
Machine Learning ◽  
Sensitivity Analysis ◽  
Feature Selection ◽  
Large Scale ◽  
Failure Modes ◽  
Model Development ◽  
Predictive Performance ◽  
Computational Effort ◽  
Structural Systems ◽  
Feature Importance

This study presents a machine learning-based approach for sensitivity analysis to examine how parameters affect a given structural response while accounting for uncertainty. Reliability-based sensitivity analysis involves repeated evaluations of the performance function incorporating uncertainties to estimate the influence of a model parameter, which can lead to prohibitive computational costs. This challenge is exacerbated for large-scale engineering problems which often carry a large quantity of uncertain parameters. The proposed approach is based on feature selection algorithms that rank feature importance and remove redundant predictors during model development which improve model generality and training performance by focusing only on the significant features. The approach allows performing sensitivity analysis of structural systems by providing feature rankings with reduced computational effort. The proposed approach is demonstrated with two designs of a two-bay, two-story planar steel frame with different failure modes: inelastic instability of a single member and progressive yielding. The feature variables in the data are uncertainties including material yield strength, Young’s modulus, frame sway imperfection, and residual stress. The Monte Carlo sampling method is utilized to generate random realizations of the frames from published distributions of the feature parameters, and the response variable is the frame ultimate strength obtained from finite element analyses. Decision trees are trained to identify important features. Feature rankings are derived by four feature selection techniques including impurity-based, permutation, SHAP, and Spearman's correlation. Predictive performance of the model including the important features are discussed using the evaluation metric for imbalanced datasets, Matthews correlation coefficient. Finally, the results are compared with those from reliability-based sensitivity analysis on the same example frames to show the validity of the feature selection approach. As the proposed machine learning-based approach produces the same results as the reliability-based sensitivity analysis with improved computational efficiency and accuracy, it could be extended to other structural systems.

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