Autoscan shift: A new core-rock data integration technique to overcome the shortcomings of conventional regression

For integrating rock mechanical data from cores and well logs, a new, two-stage technique is presented. The first stage consists of a less known criterion for filtering spurious data (outliers). It was developed and applied in astrophysics by Chauvenet [Chauvenet, W., 1863. Theory and Use of Astronomical Instruments: Method of Least Squares, pp. 558-566] but not applied in the E&P industry. The second stage is a new calibration method devised by us. The new calibration method has a unique characteristic: it preserves the shape of the depth vs. parameter profile of well logs. The conventional calibration technique uses the well-known Least Squares-based regression technique. This usually results in some distortion of the shape of the log-based data profile. The distortion is more serious whenever the coefficient of correlation is low, and can entirely mask the true variability of the measured parameter as obtained from the well log. In addition, the presence of any spurious data in the core data itself can render the calibration process meaningless. Calibrating the continuous log-based parameters (e.g., rock-mechanical elastic moduli) with the help of a limited number of core-based data is a routine job in designing mud-weight window for a stable borehole, ensuring sand-free hydrocarbon production, and productivity enhancement by hydraulic fracturing. The proposed technique will help make more accurate designs of these important exploration and production operations. The proposed technique can be commercialized as an independent package or by embedding it in an existing petroleum engineering software package. A preliminary version has already been delivered to Saudi Aramco to help design hydraulic fracturing that is currently being performed in the Hawiya/Haradh Gas Initiative.