Investigating the Effects of Mechanical Properties of Rocks on Specific Energy and Penetration Rate of Borehole Drilling

Kolapo, Peter

doi:10.1007/s10706-020-01577-y

Cited by 16 publications

(2 citation statements)

References 20 publications

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“…Predicting the ROP has been thus acknowledged as a major part of drilling operation optimization (Amadi and Iyalla, 2012). In this respect, the correct estimation and forecast of this parameter significantly helps increase efficiency and improve the drilling performance quality, in such a way that it seems to be useful in choosing the most suitable type of drill bit and drill machine, as it provides the right information about rocks and the desired structures, thereby reducing the drilling process (Kolapo, 2021;Kahraman, 1997). The rock uniaxial compressive strength (UCS) has also been the most widely used parameter for predicting the performance of drill bits in drilling operations (Yarali and Soyer, 2013) as well as the uppermost mechanical property of rocks in geological and geotechnical engineering works (Nazir et al 2013).…”

Section: Introductionmentioning

confidence: 99%

Accurate Prediction of Drill Bit Penetration Rate in Rock Using Supervised Machine Learning Techniques Base on Laboratory Test Data

Khosravimanesh,

Esmaeilzadeh,

Akhyani

et al. 2024

MGPB

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Knowing the rate of penetration of a drill bit in rocks is among the most important parameters in their behaviour measurement. However, the direct measurement of ROP in rocks is a high-cost and time-intensive process. Therefore, obtaining the ROP parameter through a method other than direct measurement can be very useful and effective. Predictive machine learning methods are among the strong and precise techniques for the indirect measurement of ROP. To this end, 492 samples were tested under different UCS, µ, WOB, and ω conditions to obtain the corresponding ROP. To achieve an accurate model, three methods of linear regression analysis, lasso regression, and ridge regression were compared in terms of prediction accuracy. These models were compared through performance criteria of the prediction process and error-based charts. The performance criteria were measured using three measures: mean absolute percentage error, D-squared pinball score, and mean Poisson deviance error. For the MAPE index, the Lasso and Ridge models performed the best with values of 0.2557. Concerning the D2PS index, the linear regression model and Ridge performed better with values of 0.4083 and 0.4025, respectively. Finally, for the MPDE index, the Ridge model provided a more accurate performance with a value of 0.0105. For a better comparison, an objective function was created and calculated by combining these three indicators. The results showed the best rank for the Ridge model with an estimated value of 659.475. Finally, it was concluded that the Ridge model is a reliable and accurate model for predicting the ROP.

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Section: Introductionmentioning

confidence: 99%

Accurate Prediction of Drill Bit Penetration Rate in Rock Using Supervised Machine Learning Techniques Base on Laboratory Test Data

Khosravimanesh,

Esmaeilzadeh,

Akhyani

et al. 2024

MGPB

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“…Although, if the mine is designed according to the hydrogeological conditions, it is unlikely for the mine to encounter instability during heavy rainfall. The mechanical properties of the ground plays important role in determining the fracture and aquifer zone that can in uence the stability of the ground (Kolapo, 2020).…”

Section: Introductionmentioning

confidence: 99%

Investigating The Effects of Rainy Season On Open Cast Mining Operation: The Case of Wescoal Khanyisa Colliery

Tlhatlhetji

Kolapo

2021

Preprint

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The aim of this study was to investigate the impact of rainy season in open cast mining and possible factors that could contribute to production loses during these rainy periods. The major issue at Wescoal’s Khanyisa Colliery is the impact that rainfall has on the coal extraction process during the rainy season. The opencast mine operation loses a significant amount of time that is supposed to be used to run the mining value chain by trying to deal with the effects of the rainfall. This study made use of acquired data from Khanyisa Colliery between the period of December 2019 and January 2020 rainy months in South Africa. This data includes, the amount of daily and weekly rainfall, the water pumping rate, frequency of breakdowns and success rate of load and haul. The average weekly rainfall was found to be 69 mm over the 8 weeks of the summer season. However, the adopted method of managing the mine water was not efficient enough as a result of a low capacity pump and their pumping rate in comparison to the volume of water in the pit. This has affected some sections in the mine as productivity dropped due to reduction in loading and hauling activities from the pit to the stockpiles. In order to improve the productivity, the authors made some suggestions than can enhance production during rainy seasons.

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Rock fracture identification with measurement while drilling data in down-the-hole drills

Tian,

Hou,

Ding

et al. 2024

Bull Eng Geol Environ

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Investigating the Effects of Mechanical Properties of Rocks on Specific Energy and Penetration Rate of Borehole Drilling

Cited by 16 publications

References 20 publications

Accurate Prediction of Drill Bit Penetration Rate in Rock Using Supervised Machine Learning Techniques Base on Laboratory Test Data

Accurate Prediction of Drill Bit Penetration Rate in Rock Using Supervised Machine Learning Techniques Base on Laboratory Test Data

Investigating The Effects of Rainy Season On Open Cast Mining Operation: The Case of Wescoal Khanyisa Colliery

Rock fracture identification with measurement while drilling data in down-the-hole drills

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