Estimation of punch strength index and static properties of sedimentary rocks using neural networks in south west of Iran

Rastegarnia, Ahmad; Teshnizi, Ebrahim Sharifi; Hosseini, Seyyed Mohammad; Shamsi, Husain; Etemadifar, Mahin

doi:10.1016/j.measurement.2018.05.080

Cited by 22 publications

(6 citation statements)

References 42 publications

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“…Apart from BI-8 (R 2 = 0.50), indices BI-5 to BI-10 produced good to strong correlations with the HTS according to their calculated R 2 values. The strongest correlations were obtained for indices BI-6, BI-10, and BI-7 (i.e., Equations ( 6), ( 7) and (10), with R 2 values of 0.94, 0.87, and 0.86, respectively. Hence, the authors concluded that these three equations may be appropriate for obtaining preliminary approximations of the HTS parameter for the investigated carbonate-dolomite formation.…”

Section: Discussionmentioning

confidence: 98%

“…This section investigates the strength of the correlations between the ten existing strengthbased BIs listed in Table 1 and the measured HTS for each of the eight GUs. Using the measured UCS, BTS, bulk density, unit weight, and/or Young's modulus values as inputs, the values of the ten BI parameters were calculated for each GU using Equations ( 1)- (10), as listed in Table 6.…”

Section: Hts Correlation With Bismentioning

confidence: 99%

“…Accordingly, the rock brittleness is a significant parameter in the selection process for identifying suitable candidate reservoirs for performing HF operations [8]. In other words, knowledge of the rock brittleness can provide valuable information for optimizing the gaps created in the reservoir during the HF treatment [9,10]. High values of rock brittleness are indicators of: (i) effective initiation and propagation of hydraulic fractures in reservoir stimulation; (ii) increasing fracture complexity; and (iii) resistance to proppant embedment [11][12][13].…”

Section: Introductionmentioning

confidence: 99%

“…However, there is no general standard test method available concerning rock brittleness measurements. Nonetheless, the rock brittleness can be related to other parameters, such as rock lithology, texture, effective stress, and strength [10,14]. Some researchers indicated that stiffer rocks (i.e., of high Young's modulus, E) are more suitable in low permeability zones to heighten the productivity of multistage HF operations [15,16]; high stiffness and rock brittleness are broadly related to one another [17].…”

Section: Introductionmentioning

confidence: 99%

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Laboratory Hydraulic Tensile Strength Correlation with Strength-Based Brittleness Indices for Carbonate Reservoirs

Ezazi,

Ghorbani,

Shafiei

et al. 2024

Geosciences

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Hydraulic fracturing (HF) is the primary choice for stimulating petroleum reservoirs. Rock tensile strength and brittleness are crucial parameters required for screening candidate reservoirs and in designing successful HF operations. However, in situ and laboratory determinations of the hydraulic tensile strength (HTS) of rock can prove problematic. Alternatively, the HTS could be estimated from the rock brittleness once a reliable relationship has been established between them. Accordingly, this paper investigates the correlations between the HTS, as measured using laboratory hydraulic fracture tests, and ten strength-based brittleness indices (BIs) selected from the research literature. The primary inputs for computing these BIs are uniaxial compressive strength (UCS) and the Brazilian tensile strength (BTS), which are typically measured for most projects using standard laboratory rock mechanics tests or obtained from log data. For the purposes of this experimental investigation, intact rock core samples were obtained from a carbonate–dolomite formation in Iran, comprising eight distinct geomechanical units, with measured values of UCS, BTS, and HTS ranging 29.7–162.2, 1.93–12.23, and 7.20–20.63 MPa, respectively. The measured HTS was found to directly correlate with the UCS, BTS, and Young’s modulus, and inversely correlated with the rock porosity. Seven of the ten investigated BIs correlated with the measured HTS over 69% (R2 ≥ 0.69). In particular, the BI expressions developed by Yagiz and Gokceoglu, Ghadernejad et al., and Khandelwal et al. exhibited relatively strong correlations with the measured HTS (producing R2 values of 0.94, 0.87, and 0.86, respectively), suggesting that these three HTS–BI correlations could be used to provide preliminary HTS estimates for the investigated carbonate–dolomite formation in Iran. This work adds to a database that can be expanded to include other geographical regions for providing useful information about the selection of a suitable site or reserve for conducting HF operations.

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Section: Discussionmentioning

confidence: 98%

Section: Hts Correlation With Bismentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Laboratory Hydraulic Tensile Strength Correlation with Strength-Based Brittleness Indices for Carbonate Reservoirs

Ezazi,

Ghorbani,

Shafiei

et al. 2024

Geosciences

Self Cite

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“…Yang et al (2019) used a Bayesian method to predict intact granite's E, and the model produced suitable predictions. Rastegarnia et al (2018) predicted the mechanical characteristics of sedimentary rocks, especially UCS and E, using ANN with R 2 of 0.99 and 0.97, respectively. Singh et al (2017) assessed a range of geomechanical parameters, with a specific focus on the parameter E using a combination of MRVA and ANFIS methods.…”

Section: Related Workmentioning

confidence: 99%

Hybrid PSO with tree-based models for predicting uniaxial compressive strength and elastic modulus of rock samples

Shahani,

Xiaowei,

Wei

et al. 2024

Front. Earth Sci.

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The mechanical characteristics of rocks, specifically uniaxial compressive strength (UCS) and elastic modulus (E), serve as crucial factors in ensuring the integrity and stability of relevant projects in mining and civil engineering. This study proposes a novel hybrid PSO (particle swarm optimization) with tree-based models, such as gradient boosting regressor (GBR), light gradient boosting machine (LightGBM), random forest (RF), and extreme gradient boosting (XGBoost) for predicting UCS and E of rock samples from Block IX of the Thar Coalfield in Pakistan. A total of 122 datasets were divided into training and testing sets, with an 80:20 ratio, respectively, to develop the predictive models. Key performance metrics, including the coefficient of determination (R2), mean absolute error (MAE), and root mean square error (RMSE), were employed to assess the model’s predictive performance. The results indicate that the PSO-XGBoost model demonstrated the highest accuracy in predicting UCS and E, outperforming the other models, which exhibited inferior predictive performance. Furthermore, this study utilized the SHAP (Shapley Additive exPlanations) machine learning method to enhance our understanding of how each input feature variable influences the output values of UCS and E. In conclusion, the proposed framework offers significant advantages in evaluating the strength and deformation of rocks at Thar Coalfield, with promising applications in the field of mining and rock engineering.

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Machine Learning Approach to Model Rock Strength: Prediction and Variable Selection with Aid of Log Data

et al. 2020

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Estimation of punch strength index and static properties of sedimentary rocks using neural networks in south west of Iran

Cited by 22 publications

References 42 publications

Laboratory Hydraulic Tensile Strength Correlation with Strength-Based Brittleness Indices for Carbonate Reservoirs

Laboratory Hydraulic Tensile Strength Correlation with Strength-Based Brittleness Indices for Carbonate Reservoirs

Hybrid PSO with tree-based models for predicting uniaxial compressive strength and elastic modulus of rock samples

Machine Learning Approach to Model Rock Strength: Prediction and Variable Selection with Aid of Log Data

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