Textural Properties Of Rock For Penetration Rate Prediction

Abstract

The relationship between textural properties of selected Nigeria rocks and penetration rate of top-hole hammer drill was investigated. These rock samples were tested in the laboratory for mineral composition, silica content and porosity. Also, average grain size and packing density were determined from photomicrograph of the samples using empirical equations proposed by researchers. Penetration rate for each rock samples obtained in the field were correlated with the textural properties to establish their relationships.The results show that all textural character and penetration rate have high coefficient of correlation for all the samples. The highest penetration rate was experience on biotite hornblende-granite having mean packing density of 92.58% and the silica content vary for 82. 60 -82.72%. This has revealed that penetration rate of rock drill bit in quarries is related to textural rock properties and this will be necessary to have overview of time of drilling as well as rock response to mechanical loading.

Research on the Rate of Penetration Prediction Method Based on Stacking Ensemble Learning

“…After each cross-validation, the trained learner predicts the test set and the validation set. (3) The predicted values of each validation set obtained by a single primary learner after K cross-validation are integrated into matrix M, and matrix Q is obtained by averaging the K predicted values of the test set in rows. (4) The output feature matrix (M 1 , M 2 , ⋯, M n ) of the training set can be obtained after the training of the primary learner in the first part, and this matrix is used as the training set of the metalearner in the second part.…”

“…Test dataset t i = {(x 1 ,y 1 ),...,(x j ,y j )} for i = 1,2,...,m for n = 1,2,...,N )} Figure 3: Operating principle of the stacking model. 3 Geofluids of different sizes of drill bits is divided into three grades: low ROP, medium ROP, and high ROP. The ROP classification makes the value of ROP independent of the size of the drill bit and transforms the modeling task from regression problem to classification problem, which is beneficial to improve the accuracy of model prediction.…”

“…For example, Bahari and Seyed [2] established the W.C. Maaurer equation, but the consideration factors need to be more comprehensive. Adebayo and Akande [3] established an empirical formula between rock characteristics, physical properties and ROP, but did not consider the influence of drilling parameters on ROP. Changsheng [4] improved the B-Y ROP equation based on the multiple regression method so that the coefficients of different formations could be changed, but this equation only applied to mud drilling.…”

Brittleness and Packing Density Effects on Blast-hole Cuttings Yield of Selected Rocks

“…They concluded that textural characteristics will go a long way to determine the mechanical behavior of rocks. Adebayo and Akande [14] correlated selected textural properties (quartz proportion, silica content, average grain size, porosity and packing density) with penetration and found that these have strong relation with penetration rate. Rock is one of the material where brittle failure dominates [15].…”

Effect of Rock Properties on Excavation-Loading Operation in Selected Quarries

“…Granite is one of the most abundant plutonic rocks. Rock properties and other characteristics varied widely [2]. Granite is an important structural stone because of its good appearances, hardness and resistance to weathering (except when crystals of mica are large and weathered leaving voids which on the finished surface).…”