Abstract
Polycrystalline diamond compact (PDC) bits experience a serious wear problem in drilling tight gravel layers. To achieve efficient drilling and prolong the bit service life, a simplified model of a PDC bit with double cutting teeth was established by using finite-element numerical simulation technology, and the rock-breaking process of PDC bit cutting teeth was simulated using the Archard wear principle. The numerical simulation results of the wear loss of the PDC bit cutting teeth, such as the caster angle, temperature, linear velocity, and bit pressure, as well as previous experimental research results, were combined into a training dataset. Then, machine learning methods for equal-probability gene expression programming (EP-GEP) were used. Based on the accuracy of the training set, the effectiveness of this method in predicting the wear of PDC bits was demonstrated by verifying the dataset. Finally, a prediction dataset was established by a Latin hypercube experiment and finite-element numerical simulation. Through comparison with the EP-GEP prediction results, it was verified that the prediction accuracy of this method meets actual engineering needs. The results of the sensitivity analysis method for the gray correlation degree show that the degree of influence of bit wear is in the order of temperature, back dip angle of the PDC cutter, linear speed, and bit pressure. These results demonstrate that when an actual PDC bit is drilling hard strata such as a conglomerate layer, after the local high temperature is generated in the formation cut by the bit, appropriate cooling measures should be taken to increase the bit pressure and reduce the rotating speed appropriately. Doing so can effectively reduce the wear of the bit and prolong its service life. This study provides guidance for predicting the wear of a PDC bit when drilling in conglomerate, adjusting drilling parameters reasonably, and prolonging the service life of the bit.
Highlights
With increasing oil and gas exploration in China, many glutenite reservoirs have been discovered, among which a representative oilfield is the Mahu oilfield in the Xinjiang oil region
Fifty-four groups of data from 80 groups were used for the equal-probability gene expression programming (EP-Gene expression programming (GEP)) time series training
This shows that the model established by the EP-GEP method can accurately fit the bit wear
Summary
With increasing oil and gas exploration in China, many glutenite reservoirs have been discovered, among which a representative oilfield is the Mahu oilfield in the Xinjiang oil region. At the bottom of the Badaowan Formation in the oilfield, the gravels are well developed, with a thickness of 100– 350 m, and the ability to drill is poor. The hard gravel makes it difficult to drill [1, 2]. Polycrystalline diamond compact (PDC) bits have the advantages of high rock-breaking efficiency, strong wear resistance, and a long service life, when drilling in a conglomerate formation, the wear rate of the cutting teeth increases sharply, leading to bit failure. To enhance the rock-breaking ability, accurately predicting the bit wear and reducing the adverse effects is important. In research on the PDC bit wear law in conglomerate layers, experimental methods and numerical simulation
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