TY  - CONF
PB  - Institute of Physics Publishing
AV  - none
VL  - 495
N2  - Appropriate selection of bits for different bore-hole sections is the key to achieve superior drilling performance. This is done with the intention to maximize the rate of penetration while maintaining bit integrity and drilling safety, which plays an important role in maintaining well economies. An accurate selection of drilling bit is dependent on the physical characteristics of formation and the compressive strength of rocks. The acquisition of rock strength along the wellbore can be obtained from various sources such as logs, cutting and rock mechanical test or drilling data. This paper posed a trial to obtain compressive strength profile of oilfield's formation from a sonic log. According to the results, the formations have been divided into several groups from very soft to very hard formation to optimize bit selection. The acquisition of rock strength information in different conditions is made possible by the generation of similar rock strength logs by different sources. Nevertheless, the best prediction will be given by meter-by-meter based logs from different references. Hence, log based or drilling based methods remains the most preferred methods used to obtain rock strength logs. In this paper, it is desired to predict the compressive strength of wellbore by using empirical correlation based on well logging data and then investigate the confidence of results by data obtained from drilling data. Later, this method is used to predict uniaxial compressive strength in the entire of oilfield. © Published under licence by IOP Publishing Ltd.
SN  - 17578981
Y1  - 2019///
KW  - Acoustic logging; Boreholes; Compressive strength; Forecasting; Infill drilling; Oil field equipment; Oil fields; Oil wells; Rocks
KW  -  Drilling performance; Empirical correlations; Hard formation; Physical characteristics; Rate of penetration; Rock strength; Uniaxial compressive strength; Well logging data
KW  -  Oil well logging
TI  - Oil well compressive strength analysis from sonic log; A case study
N1  - cited By 3; Conference of 11th Curtin University Technology, Science and Engineering International Conference, CUTSE 2018 ; Conference Date: 26 November 2018 Through 28 November 2018; Conference Code:148783
UR  - https://www.scopus.com/inward/record.uri?eid=2-s2.0-85067835169&doi=10.1088%2f1757-899X%2f495%2f1%2f012077&partnerID=40&md5=0c5725a86e5df1c8b701d35a26171ce0
A1  - Hamdi, Z.
A1  - Momeni, M.S.
A1  - Meyghani, B.
A1  - Zivar, D.
A1  - Chung, B.Y.
A1  - Bataee, M.
A1  - Asadian, M.A.
ID  - scholars12135
ER  -