Addressing Random Variations in MWD Penetration Rate with the DPM Algorithm

Siyuan Wu, Xuefan Wang and Zhongqi Quentin Yue ()
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Siyuan Wu: Department of Civil Engineering, The University of Hong Kong, Hong Kong, China
Xuefan Wang: Department of Civil Engineering, The University of Hong Kong, Hong Kong, China
Zhongqi Quentin Yue: Department of Civil Engineering, The University of Hong Kong, Hong Kong, China

Sustainability, 2022, vol. 14, issue 20, 1-15

Abstract: Measurement While Drilling (MWD) is an in situ technique for the assessment of ground conditions. It records the drilling parameters of a drilling machine when its drill bit is penetrating into new geomaterials below the bottom of a drill hole. Its penetration rate along drill hole depth, however, has various random variations. Such random variations are inconsistent with the fact that the geomaterials in the ground have piece-wise homogeneity. Many methods have been adopted to filter and normalize the random variations in penetration rate for ground characterization. However, they have not completely resolved the issue and obtained the piece-wise homogeneous distribution of geomaterials in the ground. This paper uses the time-series algorithm of the Drilling Process Monitoring (DPM) technique to remove the random variations in MWD penetration rate. It further obtains the piece-wise constant variations in the newly addressed DPM penetration rate along the drill hole depth. With this algorithm, the DPM penetration rate is consistent with the fact that ground geomaterials have the property of piece-wise homogeneous distribution. Consequently, the piece-wise constant variations in DPM penetration rate can be used to characterize the strength variations in geomaterials along the drill hole depth to substantially upgrade the MWD technique for ground investigation.

Keywords: Measurement While Drilling (MWD); Drilling Process Monitoring (DPM); penetration rate; ground investigation (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2022
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