Abstract
Unconfined compressive strength (UCS) of rock is a key parameter in drilling and stimulation of oil and gas wells such as wellbore stability and fracturing operations. Better estimates of UCS could increase the efficiency of drilling and stimulation operations. Current techniques for UCS determination either rely on laboratory measurements or empirical relationships using well logs. The laboratory measurements, although more reliable, are expensive and time-consuming. On the other hand, the empirical relations derived from core data and well logs are very unique because they are developed for a specific rock type; hence has limited applications. This paper presents a data-driven model for UCS prediction from petrophysical properties and elemental spectroscopy using artificial intelligent technique, namely support-vector regression (SVR).
Elemental spectroscopy, density, porosity, and UCS data presented in this paper are based on various geological formations. UCS is determined by the uniaxial compression test in the laboratory, while elemental spectroscopy was obtained from X-ray fluorescence (XRF) analysis. We first use SVR to establish a correlation between elemental spectroscopy, density, and porosity with UCS. We separate these data into two categories: training and testing data. Training data is used to train SVR and establishes the UCS prediction model. The model will generate UCS prediction using testing data and compared with the laboratory-measured UCS.
In total, 21 cases were run with different combination of input parameters. Good agreement was observed between the SVR-predicted UCS and the laboratory measurement. Two quantitative measures for estimation accuracy are calculated and examined including the coefficient of determination and the mean absolute percentage error. Considering limited number of available data used in this study, the SVR-predicted UCS produces very good coefficient of determination and small error. The results also demonstrate the significant influence of elemental spectroscopy on the UCS prediction because elements determine grain density, which contributes to the rock strength. This emphasizes the advantage of incorporating elemental spectroscopy, together with other petrophysical properties, for UCS prediction. The favorable results in this study demonstrate the promising capability of SVR to build a UCS-prediction model based on elemental spectroscopy and petrophysical properties. Further application of SVR can be adapted to predict UCS directly from mineralogy logs and conventional well logs.
