Evaluation of engineering characteristics and estimation of static properties of clay-bearing rocks

Rastegarnia, Ahmad; Lashkaripour, Gholam Reza; Teshnizi, Ebrahim Sharifi; Ghafoori, Mohammad

doi:10.1007/s12665-021-09914-x

Cited by 20 publications

(13 citation statements)

References 58 publications

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“…To reach the accurate number of hidden layer neurons, different MFF-ANN with different numbers of neurons were designed. The achieved optimum MFF-ANN consists of two neurons in a hidden layer with 5 inputs such as Vp, point load index (PLI), porosity (P), Training, validation, and test groups were chosen to fix the weights, stop the overfitting procedure, and test the MFF-ANN in predicting new data, respectively [48]. Purelin and Sigmoid were used for neuron transfer functions in the middle layer and output layer, respectively.…”

Section: The Mff-ann Resultsmentioning

confidence: 99%

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Prediction of Rock Tensile Strength Using Soft Computing and Statistical Methods

Zheng

Shen

Motahari

et al. 2023

Period. Polytech. Civil Eng.

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The tensile strength of the rocks is one of the effective factors in the rupture of structure foundations and underground spaces, the stability of rocky slopes, and the ability to drill and explode in rocks. This research was conducted to estimate tensile strength using methods such as simple regression (SR), multivariate linear regression (MVLR), support vector regression (SVR) with radial basis kernel function, multilayer feed-forward artificial neural network (MFF-ANN), Gaussian process regression (GPR) using squared exponential kernel (SEK) function, and adaptive neuro-fuzzy inference system (ANFIS) based on Gaussian membership function. For this purpose, petrography, and engineering features of the limestone, sandstone, and argillaceous limestone samples in the south of Iran, were assessed. The results obtained from this study were compared with those of previous research, revealing a strong correlation (R2=0.95 to 1.00) between our findings and the published works. To estimate Brazilian tensile strength (BTS), the index properties including water absorption by weight, point load index (PLI), porosity%, P-wave velocity (Vp), and density were considered as inputs. Methods were compared using various criteria. The SVR precision (R=0.96) was higher than MFF-ANN (R=0.92), ANFIS (R=0.95), GPR (R=0.945), and MVLR (R=0.89) to estimate the tensile strength. The average BTS measured in the laboratory and predicted by all 5 methods is 6.62 and 6.71 MPa, respectively, which shows the very high precision of the investigated methods. Analysis of model criteria using statistical analysis for developed relationships revealed that there is sufficient accuracy to use the empirical equations.

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Section: The Mff-ann Resultsmentioning

confidence: 99%

“…Characteristics of sedimentary rocks are more influenced by texture [48]. The studied limestones as a Guri section of Mishan formation are composed of medium to thick layer limestones with some fossils.…”

Section: Texture Effect On Sample Characteristicsmentioning

confidence: 99%

Prediction of Rock Tensile Strength Using Soft Computing and Statistical Methods

Zheng

Shen

Motahari

et al. 2023

Period. Polytech. Civil Eng.

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“…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]. However, there is no general standard test method available concerning rock brittleness measurements.…”

Section: Introductionmentioning

confidence: 99%

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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“…However, obtaining samples with the appropriate geometry and quality from jointed rocks is a challenging task, making it difficult to prepare suitable samples. Therefore, prediction models have been developed based on physical characteristics and wave velocity [1,2]. Numerous studies have been performed by various researchers to estimate the geo-mechanical properties in dry and saturated conditions [3][4][5].…”

Section: Introductionmentioning

confidence: 99%

Prediction of Rock's Brittleness and Dynamic Properties Utilizing Effective Artificial Intelligence Approaches

Xie,

Wang,

et al. 2024

Period. Polytech. Civil Eng.

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This research aims to determine the brittleness index (BI) and engineering properties of limestone specimens. In addition, this study evaluates the effect of moisture on the developed models to predict the BI and shear wave velocity (Vs), based on the point load index (Is50), dry and saturated tensile strength (Ts-d and Ts-s), and porosity. Gaussian process regression (GPR), multilayer feed-forward neural network (MFFNN), and multiple linear regression (MLR) predictive models were utilized. Microscopic examination of the limestone specimens revealed that calcite is the predominant mineral. It was observed that samples with higher calcite content exhibited greater brittleness and strength properties while having lower porosity. The results obtained from the MLR analysis demonstrated that it is possible to accurately forecast the brittleness index (BI), as well as the dry and saturated shear wave velocities (Vs-d and Vs-s) at the specific sites under investigation. The moisture effect on developed models showed that Vs prediction in dry conditions (Vs-d) was less accurate compared to the saturated conditions (Vs-s). Conversely, the relationships developed for estimating the BI in dry conditions exhibited higher accuracy. The analysis of all model assumptions using MLR indicated that the models could be reliably utilized. However, the MFFNN and GPR methods were found to be more conservative in estimating these properties. Moreover, the study identified the best transfer function and training algorithm for predicting Vs and BI. The evaluation metrics, such as R2 and RMSE revealed that GPR demonstrated higher precision compared to MFFNN and MLR.

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Evaluation of engineering characteristics and estimation of static properties of clay-bearing rocks

Cited by 20 publications

References 58 publications

Prediction of Rock Tensile Strength Using Soft Computing and Statistical Methods

Prediction of Rock Tensile Strength Using Soft Computing and Statistical Methods

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

Prediction of Rock's Brittleness and Dynamic Properties Utilizing Effective Artificial Intelligence Approaches

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