An intelligent approach to brittleness index estimation in gas shale reservoirs: A case study from a western Iranian basin

Kivi, Iman Rahimzadeh; Reisabadi, Mohammadreza Zare; Saemi, Mohsen; Zamani, Ziba

doi:10.1016/j.jngse.2017.04.016

Cited by 40 publications

(5 citation statements)

References 37 publications

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“…Due to long-term exposure to a water-changing environment, the strength properties of the rock mass are comprehensively influenced by a DWC [7][8][9][10]. For instance, in the construction and operation of reservoir bank protection engineering, the bank slope constantly experiences periodic rises and falls in the RWL and periodic rainfall [11,12]. This experience is generally related to the DWC weathering of the rock mass in the bank slope, which comprehensively affects the stability of the engineering.…”

Section: Introductionmentioning

confidence: 99%

Damage Evolution and Failure Mechanism of Red-Bed Rock under Drying–Wetting Cycles

Wen

Wang

Tang

et al. 2023

Water

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The rock mass on the bank slope of the Three Gorges Reservoir (TGR) area often suffers from a drying–wetting cycle (DWC). How the DWCs significantly affect the mechanical properties and the stability of the rock mass is worth comprehensively investigating. In this study, the influence of the DWC on the mechanical properties of red-bed rock, mainly purplish red argillaceous siltstone, is explored in detail. Triaxial compression tests were conducted on siltstones that were initially subjected to different DWCs. The results show that DWCs lead to a decrease in mechanical properties such as peak stress, residual stress, and elastic modulus, while an increase in confining pressure (CP) levels leads to an increase in these mechanical properties. Significant correlations are found between the energy parameters and the DWC or the CP. Notably, the total absorption energy (TAE) demonstrates a positive correlation with the CP, but the capability of siltstones to absorb energy shows a negative correlation with DWC. Moreover, the study also examines the damage evolution laws of rocks under different DWCs by proposing a damage variable (DV). Results demonstrate that the effect of the CP on the DV is more pronounced than that of DWCs. A novel brittleness index (BI) was also proposed for estimating rock brittleness through damage strain rate analysis. The effectiveness of the proposed BI is validated by evaluating the effects of DWCs and CP on rock brittleness. Finally, the failure mechanism of the rocks under water–rock interaction is revealed. The weakening of mechanical properties occurs due to the formation of microcracks in response to DWCs. These findings provide valuable guidance for the long-term stability assessment of bank slope engineering projects under DWCs.

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Section: Introductionmentioning

confidence: 99%

Damage Evolution and Failure Mechanism of Red-Bed Rock under Drying–Wetting Cycles

Wen

Wang

Tang

et al. 2023

Water

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“…The evaluation of rock brittleness holds significant research significance in the field of academic studies [1,2]. Understanding the brittleness characteristics of rocks is crucial for various applications, such as geotechnical engineering, rock mechanics, and petroleum reservoir characterization [3,4]. Evaluating rock brittleness helps in predicting rock failure behavior, designing safe and stable structures, optimizing drilling and hydraulic fracturing operations, and assessing the potential for induced seismicity [5][6][7].…”

Section: Introductionmentioning

confidence: 99%

“…Currently, numerous studies have been conducted to investigate rock brittleness evaluation methods [2]. These studies have proposed various approaches, including empirical indexes, laboratory testing, and numerical modeling techniques [2,3,8]. Empirical indexes often rely on simple parameters, such as the ratio of compressive to tensile strength or the strength ratio index, to estimate rock brittleness [9,10].…”

Section: Introductionmentioning

confidence: 99%

“…Numerical modeling techniques utilize advanced computational methods, such as finite element analysis or discrete element modeling, to simulate rock behavior and predict brittleness. Brittleness indexes based on mineral composition vary with the classification criteria of brittle minerals and do not consider the influence of rock diagenesis and strength components on rock brittleness [1,3,14]. Although brittleness indexes based on mechanical properties take into account the influence of rock mass strength characteristics, they are not suitable for evaluating the brittleness characteristics of different types of rocks and complex stress conditions in the rock mass [5,6].…”

Section: Introductionmentioning

confidence: 99%

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Evaluation Method for Determining Rock Brittleness in Consideration of Plastic Deformation in Pre-Peak and Failure Energy in Post-Peak

Yue,

Wen,

Gao

et al. 2023

Applied Sciences

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The assessment of rock brittleness holds significant importance for understanding and predicting the mechanical properties and engineering behavior of rocks. Due to the lack of a unified definition of rock brittleness, numerous evaluation methods for brittleness indexes have been proposed by scholars both domestically and internationally in recent decades, resulting in diverse evaluation outcomes. In this study, we first summarize the existing rock brittleness evaluation methods and highlight their respective advantages and disadvantages. Subsequently, considering the pre-peak plastic deformation of the rock mass, the pre-peak brittleness index factor is introduced. Furthermore, taking into account the total energy consumed by the rock mass for failure after the peak, the post-peak brittleness index factor is proposed. These two components of the brittleness index describe the characteristics of different stages of the stress-strain curve, leading to the development of a novel brittleness index. The proposed method is then applied to evaluate the brittleness of both red-bed sandstone and cyan sandstone, revealing the variation of rock brittleness under different working conditions. Finally, three existing evaluation methods are selected to validate the rationality of the proposed method. The results demonstrate that for red-bed sandstone, the proposed brittleness index exhibits maximum values under natural conditions at all confining pressures. The four brittleness indexes consistently characterize the brittleness of red-bed sandstone under natural conditions. Under saturated conditions, the brittleness indexes exhibit different patterns of variation. For cyan sandstone, the three brittleness indexes—B7, B9, and Bnew—exhibit a similar trend in characterizing the brittleness of cyan sandstone under natural conditions and freezing-thawing conditions, while the trend of B17 is essentially opposite to that of the previous three indexes. The research findings provide guidance for the assessment of sandstone brittleness.

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“…Compressional (V p ) and shear (V s ) sonic wave velocities, routinely obtained from seismic surveys and wireline logging, play a first-order role in reservoir evaluation under in-situ conditions. Sonic velocity measurements provide significant insights into formation pore pressure 1 , rock physical properties, including porosity, pore geometry, pore fluid, and mineralogical content 2 – 4 , as well as rock stiffness, strength, and brittleness of target strata 5 , with a wide range of applications from reservoir management and development 6 to a variety of geomechanical, geotechnical and geophysical studies 7 , 8 . Therefore, in-situ measurements of compressional and shear velocities, frequently using full-waveform recordings, for example, Schlumberger Dipole Sonic Imaging tool (DSI), should be incorporated into the standard practice for reservoir evaluation.…”

Section: Introductionmentioning

confidence: 99%

An advanced computational intelligent framework to predict shear sonic velocity with application to mechanical rock classification

Safaei-Farouji

Hasannezhad

Kivi

et al. 2022

Sci Rep

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Shear sonic wave velocity (Vs) has a wide variety of implications, from reservoir management and development to geomechanical and geophysical studies. In the current study, two approaches were adopted to predict shear sonic wave velocities (Vs) from several petrophysical well logs, including gamma ray (GR), density (RHOB), neutron (NPHI), and compressional sonic wave velocity (Vp). For this purpose, five intelligent models of random forest (RF), extra tree (ET), Gaussian process regression (GPR), and the integration of adaptive neuro fuzzy inference system (ANFIS) with differential evolution (DE) and imperialist competitive algorithm (ICA) optimizers were implemented. In the first approach, the target was estimated based only on Vp, and the second scenario predicted Vs from the integration of Vp, GR, RHOB, and NPHI inputs. In each scenario, 8061 data points belonging to an oilfield located in the southwest of Iran were investigated. The ET model showed a lower average absolute percent relative error (AAPRE) compared to other models for both approaches. Considering the first approach in which the Vp was the only input, the obtained AAPRE values for RF, ET, GPR, ANFIS + DE, and ANFIS + ICA models are 1.54%, 1.34%, 1.54%, 1.56%, and 1.57%, respectively. In the second scenario, the achieved AAPRE values for RF, ET, GPR, ANFIS + DE, and ANFIS + ICA models are 1.25%, 1.03%, 1.16%, 1.63%, and 1.49%, respectively. The Williams plot proved the validity of both one-input and four-inputs ET model. Regarding the ET model constructed based on only one variable,Williams plot interestingly showed that all 8061 data points are valid data. Also, the outcome of the Leverage approach for the ET model designed with four inputs highlighted that there are only 240 “out of leverage” data sets. In addition, only 169 data are suspected. Also, the sensitivity analysis results typified that the Vp has a higher effect on the target parameter (Vs) than other implemented inputs. Overall, the second scenario demonstrated more satisfactory Vs predictions due to the lower obtained errors of its developed models. Finally, the two ET models with the linear regression model, which is of high interest to the industry, were applied to diagnose candidate layers along the formation for hydraulic fracturing. While the linear regression model fails to accurately trace variations of rock properties, the intelligent models successfully detect brittle intervals consistent with field measurements.

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An intelligent approach to brittleness index estimation in gas shale reservoirs: A case study from a western Iranian basin

Cited by 40 publications

References 37 publications

Damage Evolution and Failure Mechanism of Red-Bed Rock under Drying–Wetting Cycles

Damage Evolution and Failure Mechanism of Red-Bed Rock under Drying–Wetting Cycles

Evaluation Method for Determining Rock Brittleness in Consideration of Plastic Deformation in Pre-Peak and Failure Energy in Post-Peak

An advanced computational intelligent framework to predict shear sonic velocity with application to mechanical rock classification

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