TY - THES

T1 - Prediction of Weight-on-Bit based on Real-Time Surface Measurements

AU - Paulic, Rainer

N1 - embargoed until null

PY - 2007

Y1 - 2007

N2 - High rates of penetration can be achieved when drilling with the right weight-on-bit. Using surface measurements of the hookload, the weight-on-bit can be estimated. Other surface measurements can be used to detect various drilling dysfunctions like stick-slip which influence the transfer of force to the bit. By correlating surface measurements with downhole measurements taken by an ISUB tool, the influence of different drilling dysfunctions on force transfer can be estimated. A spring-mass model is proposed to simulate the force transfer to the bit. Because of the complexity of such a system, neural networks may be the best solution to solve this task in real-time.

AB - High rates of penetration can be achieved when drilling with the right weight-on-bit. Using surface measurements of the hookload, the weight-on-bit can be estimated. Other surface measurements can be used to detect various drilling dysfunctions like stick-slip which influence the transfer of force to the bit. By correlating surface measurements with downhole measurements taken by an ISUB tool, the influence of different drilling dysfunctions on force transfer can be estimated. A spring-mass model is proposed to simulate the force transfer to the bit. Because of the complexity of such a system, neural networks may be the best solution to solve this task in real-time.

KW - axial force transfer weight-on-bit stick-slip neural networks mass-spring model

KW - Kraftübertragung Meissellast Stick-Slip neuronale Netzwerke Feder-Masse Modell

M3 - Master's Thesis

ER -