Abstract: A method for detecting a borehole caving based on cuttings and elements logging data includes: integrating real-time elements logging data and real-time cuttings return data of a target well, and historical elements logging data and stratum evaluation data of an adjacent well; calculating a root mean square deviation (RMSD) ? of a relative content of each element of the target well and the adjacent well and a real-time cuttings return ratio; setting a threshold ? of the RMSD ? of the relative content of each element and a threshold range (a,b) of the real-time cuttings return ratio; and establishing an intelligent stratum identification model based on a support vector machine (SVM) for real-time determination of a horizon from which current cuttings are returned. The method can achieve effective borehole caving detection, such that on-site personnel can deal with borehole caving in time and prevent it from developing into a complicated drilling accident.

Abstract: A method for detecting a borehole caving based on cuttings and elements logging data includes: integrating real-time elements logging data and real-time cuttings return data of a target well, and historical elements logging data and stratum evaluation data of an adjacent well; calculating a root mean square deviation (RMSD) ? of a relative content of each element of the target well and the adjacent well and a real-time cuttings return ratio; setting a threshold ? of the RMSD ? of the relative content of each element and a threshold range (a,b) of the real-time cuttings return ratio; and establishing an intelligent stratum identification model based on a support vector machine (SVM) for real-time determination of a horizon from which current cuttings are returned. The method can achieve effective borehole caving detection, such that on-site personnel can deal with borehole caving in time and prevent it from developing into a complicated drilling accident.