The Experts below are selected from a list of 510 Experts worldwide ranked by ideXlab platform
Mohammad Ataei - One of the best experts on this subject based on the ideXlab platform.
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Prediction of rotary drilling penetration rate in iron ore oxides using rock engineering system
'Elsevier BV', 2018Co-Authors: Hossein Inanloo Arabi Shad, Mohammad Ataei, Farhang Sereshki, Mohammad KaramoozianAbstract:Prediction of the drilling penetration rate is one of the important parameters in mining operations. This parameter has a direct impact on the mine planning and cost of mining operations. Generally, effective parameters on the penetration rate is divided into two classes: rock mass properties and specifications of the machine. The chemical components of intact rock have a direct effect in determining rock mechanical properties. Theses parameters usually have not been investigated in any research on the rock Drillability. In this study, physical and mechanical properties of iron ore were studied based on the amount of magnetite percent. According to the results of the tests, the effective parameters on the penetration rate of the rotary drilling machines were divided into three classes: specifications of the machines, rock mass properties and chemical component of intact rock. Then, the rock Drillability was studied using rock engineering systems. The results showed that feed, rotation, rock mass index and iron oxide percent have important effect on penetration rate. Then a quadratic equation with 0.896 determination coefficient has been obtained. Also, the results showed that chemical components can be described as new parameters in rotary drill penetration rate. Keywords: Penetration rate, Rotary drill, Rock engineering system, Chemical component
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A fuzzy logic based classification for assessing of rock mass Drillability
International Journal of Mining and Mineral Engineering, 2016Co-Authors: Reza Khalokakaie, R. Mikaiel, Mohammad Ataei, Seyed Hadi HoseinieAbstract:This paper describes a fuzzy classification system for evaluating of rock mass Drillability. Six parameters; Uniaxial Compressive Strength (UCS), joints dipping, Mohs hardness, joints aperture, joints spacing and grain size have been used. In this fuzzy system, each rock mass is classified into five modes from very poor to excellent condition. As a case study, 15 rock masses in two mines in Iran have been studied and classified using fuzzy system and classic classification. The comparison of the results shows that the fuzzy classification produces clearer results than classic system especially in rock masses with boundary condition.
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drilling rate prediction of an open pit mine using the rock mass Drillability index
International Journal of Rock Mechanics and Mining Sciences, 2015Co-Authors: Mohammad Ataei, Reza Kakaie, Mehdi Ghavidel, Omid SaeidiAbstract:Abstract Besides intact rock properties, structural parameters of rock mass have strong effect on drilling rate. In this research, 11 different zones of an open pit iron mine were studied precisely to classify the area based on rock Drillability point of view. Laboratory tests were conducted on the rock samples to determine strength parameters. Geological mapping of the rock facies was carried out and rock mass structural parameters as joint inclination, spacing, aperture and filling were recorded along with net drilling times of drill holes. Using these data, an empirical relation was developed to predict drilling rate ( DR ) using the rock mass Drillability index ( RDi ) and also a relation that can predict uniaxial compressive strength ( UCS ) of rocks in terms of Schmidt hammer rebound values at this mine. In conclusion, all 66 zones of the mine area were classified according to the RDi . It was observed that RDi can reasonably predict drilling rate of rock masses. A new penetration rate model is defined based on the measured data and then compared with previous model of penetration rate from literature. Since the new model which involves not only intact rock mechanical properties but also structural properties of rock masses could attain better predictions in relation to the previous model.
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Development of a New Index to Assess the Rock Mass Drillability
Geotechnical and Geological Engineering, 2013Co-Authors: Omid Saeidi, Seyed Rahman Torabi, Mohammad AtaeiAbstract:Knowledge of Drillability of rock masses in engineering projects is very important in determining drilling costs. In drilling operations, so many parameters such as the properties of rock and the drilling equipment affect the drilling performance. In this study, after discussing the rock mass Drillability process and identifying all the effective parameters, interaction matrixes based on the rock engineering systems, that analyze the interrelationship between the parameters affecting rock engineering activities, is introduced to study the rock mass Drillability tribosystem. Given that interaction matrix codes are not unique numbers, and then possible interactive intensities are calculated for each matrix and a group decision-making method, Fuzzy–Delphi–AHP technique has been used to obtain appropriate weights. As a result, rock mass Drillability index (RMDI) is presented to classify the rock mass Drillability. The results indicate the ability of this method to analyze rock mass Drillability procedure. Drilling data along with laboratory rock properties from Sungun copper mine were collected and were ranked according to the new classification system. Fifteen zones at the mine site were ranked based upon the new index RMDI and a reasonable correlation was obtained between measured drilling rate at the zones and RMDI data.
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Comparison of Some Rock Hardness Scales Applied in Drillability Studies
Arabian Journal for Science and Engineering, 2012Co-Authors: Seyed Hadi Hoseinie, Mohammad Ataei, R. MikaielAbstract:In this paper, the influence of hardness of rock material on drilling rate has been studied. During the research, eight various rock types were subjected to drilling and hardness tests such as; Mohs hardness, Indentation Hardness Index (IHI) and L-type Schmidt hammer. Mean Mohs hardness of each rock was calculated based on the hardness of contained minerals and other two scales are carried out based on ISRM standards. For drilling studies, rock samples have been drilled using actual pneumatics top hammer drilling machine with three inches diameter cross type bit. Regression analyses between mean Mohs hardness and the drilling rate reveal that in soft rocks, with increase in hardness, drilling rate decreases logarithmically but in hard rocks, with increase in hardness, drilling rate decreases linearly. In total, with increase in Mohs hardness, drilling rate decreases exponentially. Also, with increase in Indentation Hardness Index and Schmidt hammer value, drilling rate decreases logarithmically. The regression analyses showed that Indentation Hardness Index has the best and stronger relationship with the rate of percussive drilling.
Kangzhi Liu - One of the best experts on this subject based on the ideXlab platform.
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a new spatial modeling method for 3d formation Drillability field using fuzzy c means clustering and random forest
Journal of Petroleum Science and Engineering, 2021Co-Authors: Chao Gan, Weihua Cao, Kangzhi LiuAbstract:Abstract A spatial model of the 3D formation Drillability field is crucial for drilling optimization in the petroleum area. In this paper, a new spatial modeling method is proposed for the 3D formation Drillability field, which has two stages. In the first stage, the number of formation modes is determined according to the formation characteristics and these modes are identified by the fuzzy c-means clustering algorithm. In the second stage, random forest models are built separately for all formation modes. X, Y ground coordinates and depth coordinate are selected as the model inputs while the model output is the formation Drillability. After that, these spatial 3D formation models are combined into one spatial 3D formation model for the whole Drillability field. The proposed method and four compared methods (Random forest, ScatteredInterpolant, Support vector regression, and Kriging) are cross-validated and tested by using the data from eight drilling wells in the Xujiaweizi area, Northeast China. The results indicate the effectiveness of proposed method in spatial 3D formation Drillability modeling.
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spatial estimation for 3d formation Drillability field a new modeling framework
Journal of Natural Gas Science and Engineering, 2020Co-Authors: Chao Gan, Weihua Cao, Kangzhi LiuAbstract:Abstract Spatial 3D formation Drillability distribution is crucial for the drilling trajectory planning, collision detection, and drilling optimization in the natural gas and petroleum fields. Conventional geostatistical methods are usually used to establish the 3D formation Drillability field model. However, few or no machine learning methods, which have a powerful fitting capability, are used to build that model. This paper proposes a new modeling framework for establishing the spatial 3D formation Drillability model. Four methods, one geostatistical (Kriging), one non-stochastic (ScatteredInterpolant), and two machine learning methods (random forest and support vector regression) are analyzed in this modeling framework. Well logging data such as acoustic and formation density are introduced as the input parameters. Moreover, the mutual information analysis is introduced to measure the correlations between the 3D coordinates and formation Drillability. Finally, comparisons are explored in the 10-fold cross-validation, 3D modeling, and final test experiments using data from Xujiaweizi area, Northeast China. The results indicate that SI has the best 10-fold cross-validation performance while RF achieves the best prediction accuracy in the final test among the four compared methods. The proposed new modeling framework for 3D formation Drillability model provides a platform and is applicable to other modeling methods and data.
Chao Gan - One of the best experts on this subject based on the ideXlab platform.
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a new spatial modeling method for 3d formation Drillability field using fuzzy c means clustering and random forest
Journal of Petroleum Science and Engineering, 2021Co-Authors: Chao Gan, Weihua Cao, Kangzhi LiuAbstract:Abstract A spatial model of the 3D formation Drillability field is crucial for drilling optimization in the petroleum area. In this paper, a new spatial modeling method is proposed for the 3D formation Drillability field, which has two stages. In the first stage, the number of formation modes is determined according to the formation characteristics and these modes are identified by the fuzzy c-means clustering algorithm. In the second stage, random forest models are built separately for all formation modes. X, Y ground coordinates and depth coordinate are selected as the model inputs while the model output is the formation Drillability. After that, these spatial 3D formation models are combined into one spatial 3D formation model for the whole Drillability field. The proposed method and four compared methods (Random forest, ScatteredInterpolant, Support vector regression, and Kriging) are cross-validated and tested by using the data from eight drilling wells in the Xujiaweizi area, Northeast China. The results indicate the effectiveness of proposed method in spatial 3D formation Drillability modeling.
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spatial estimation for 3d formation Drillability field a new modeling framework
Journal of Natural Gas Science and Engineering, 2020Co-Authors: Chao Gan, Weihua Cao, Kangzhi LiuAbstract:Abstract Spatial 3D formation Drillability distribution is crucial for the drilling trajectory planning, collision detection, and drilling optimization in the natural gas and petroleum fields. Conventional geostatistical methods are usually used to establish the 3D formation Drillability field model. However, few or no machine learning methods, which have a powerful fitting capability, are used to build that model. This paper proposes a new modeling framework for establishing the spatial 3D formation Drillability model. Four methods, one geostatistical (Kriging), one non-stochastic (ScatteredInterpolant), and two machine learning methods (random forest and support vector regression) are analyzed in this modeling framework. Well logging data such as acoustic and formation density are introduced as the input parameters. Moreover, the mutual information analysis is introduced to measure the correlations between the 3D coordinates and formation Drillability. Finally, comparisons are explored in the 10-fold cross-validation, 3D modeling, and final test experiments using data from Xujiaweizi area, Northeast China. The results indicate that SI has the best 10-fold cross-validation performance while RF achieves the best prediction accuracy in the final test among the four compared methods. The proposed new modeling framework for 3D formation Drillability model provides a platform and is applicable to other modeling methods and data.
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an online modeling method for formation Drillability based on os nadaboost elm algorithm in deep drilling process
IFAC-PapersOnLine, 2017Co-Authors: Chao Gan, Xin Chen, Fulong Ning, Weihua Cao, Guojun Wen, Hui Gao, Huafeng DingAbstract:Abstract To achieve safety, high quality, and efficiency in deep drilling, it is necessary to get formation Drillability around the borehole during drilling-trajectory planning and intelligent drilling control. Since the drilling data have the characteristics of low value density and noise in the process of deep drilling, it is difficult to model formation Drillability in deep drilling. In this paper, a new online modeling method for formation Drillability based on online sequential nadaboost extreme learning machine (OS-Nadaboost-ELM) algorithm has been proposed. Firstly, the well logging parameters are chosen as the inputs of the model, whose output is formation Drillability. Then, several ELM models are established and the outputs of these models are as weak learners. Then the weak learners are combined by Nadaboost algorithm in order to get a strong learner. Finally, the recursive least squares algorithm is used to adjust the model. The numerical test results show that, in both prediction accuracy and training efficiency aspects, the proposed method is better than other prediction methods such as multiple regression, gray method, back-propagation neural networks, Nadaboost extreme learning machine and online sequential extreme learning machine. Thus the prediction model serves as the online geological model to develop intelligent drilling systems.
Amund Bruland - One of the best experts on this subject based on the ideXlab platform.
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applications of ntnu sintef Drillability indices in hard rock tunneling
Rock Mechanics and Rock Engineering, 2013Co-Authors: Shokrollah Zare, Amund BrulandAbstract:Drillability indices, i.e., the Drilling Rate Index™ (DRI), Bit Wear Index™ (BWI), Cutter Life Index™ (CLI), and Vickers Hardness Number Rock (VHNR), are indirect measures of rock Drillability. These indices are recognized as providing practical characterization of rock properties used in the Norwegian University of Science and Technology (NTNU) time and cost prediction models available for hard rock tunneling and surface excavation. The tests form the foundation of various hard rock equipment capacity and performance prediction methods. In this paper, application of the tests for tunnel boring machine (TBM) and drill and blast (D&B) tunneling is investigated and the impact of the indices on excavation time and costs is presented.
Jafar Khademi Hamidi - One of the best experts on this subject based on the ideXlab platform.
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the assessment of rock brittleness effect on their Drillability
Advanced Applied Geology, 2020Co-Authors: Seyed Sajjad Karrari, Jafar Khademi Hamidi, Ebrahim Sharifi TashniziAbstract:The brittleness is an important rock property and effective in rock excavation. Recognition of the relationship between Drillability and brittleness will increase performance the rock excavations. In this study, the number of 16 samples has been studied rock mechanical properties of granite, granodiorite, dolomite, hornfels and marble in the area of Glass water conveyance tunnel project in the margin of Naghadeh city. The number of 16 brittleness indices were calculated with stress-strain curve, modulus and rocks strength properties. In addition, Sievers’ J-miniature drill test and the brittleness test are carried out and DRI values were calculated. The statistical relationships between the brittleness indices and the drilling rate index are showed a strong correlation between B3 brittleness index (R2= 0.76 and RMSE= 1.02 coefficients) and B4 brittleness index (R2= 0.79 and RMSE= 0.95 respectively) with rocks drilling rate index. The statistical relationships are compared with study former between the brittleness indices and the drilling rate index of rocks. The Precision and accuracy of relationships are confirmed with relationships former. In addition, a model was presented with multiple regression analysis. It can use for the assessment of rock Drillability.
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an estimation of the penetration rate of rotary drills using the specific rock mass Drillability index
International journal of mining science and technology, 2012Co-Authors: Alireza Cheniany, Khoshrou Seyed Hasan, Kourosh Shahriar, Jafar Khademi HamidiAbstract:Abstract The main purpose of the present study was to provide a practical, convenient Drillability prediction model based on rock mass characteristics, geological sampling from blast holes, and drill operational factors. Empirical equations that predict drill penetration rate have been developed using statistical analyses of data from the Sarcheshmeh Copper Mine. Seven parameters of the rock or rock mass, including uniaxial compressive strength (UCS) of the rock, Schmidt hammer hardness value, quartz content, fragment size (d80), alteration, and joint dip, are included in the model along with two operational parameters of the rotary drill, bit rotational speed and thrust. These parameters were used to predict values of the newly developed Specific Rock Mass Drillability (SRMD) index. Comparing measured SRMD values to those predicted by the multi-parameter linear, or nonlinear, regression models showed good agreement. The correlation coefficients were 0.82 and 0.81, respectively.
