• Real-Time Prediction for Sonic Slowness Logs from Surface Drilling Data Using Machine Learning Techniques

    This presentation in English is from the 2021 SPE Annual Caspian Technical Conference. Our archived 2020-21 event content is now available free to SPE members!

    • Paper Number: SPE-207000-MS
    • Published: 5 October 2021
    • Authors: Vagif Suleymanov; Hany Gamal; Guenther Glatz; Salaheldin Elkatatny; Abdulazeez Abdulraheem

    Acoustic data obtained from sonic logging tools plays an important role in formation evaluation. Given the associated costs, however, the industry clearly stands to benefit from cheaper technologies to obtain compressional and shear wave slowness data. Therefore, this paper presentation delineates an alternative solution for the prediction of sonic log data by means of Machine Learning.

    This study takes advantage of an adaptive neuro-fuzzy inference system and support vector machine ML techniques to predict compressional and shear wave slowness from drilling data only. The obtained results are promising and supportive of both ANFIS and SVM model as viable alternatives to obtain sonic data without the need for running sonic logs.

    It provides a novel and cost-effective solution to estimate rock compressional and shear-wave slowness solely from readily available drilling parameters.

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  • Real-Time Prediction for Sonic Slowness Logs from Surface Drilling Data Using Machine Learning Techniques