Brittleness index predictions from Lower Barnett Shale well-log data applying an optimized data matching algorithm at various sampling densities - Wood, David A. (Di xue qian yuan., 2021)
The capability of accurately predicting mineralogical brittleness index (BI) from basic suites of well logs is desirable as it provides a useful indicator of the fracability of tight formations. Measuring mineralogical components in rocks is expensive and time consuming. However, the basic well log curves are not well correlated with BI so correlation-based, machine-learning methods are not able to derive highly accurate BI predictions using such data. A correlation-free, optimized data-matching algorithm is configured to predict BI on a supervised basis from well log and core data available from two published wells in the Lower Barnett Shale Formation (Texas). This transparent open box (TOB) algorithm matches data records by calculating the sum of squared errors between their variables and selecting the best matches as those with the minimum squared errors. It then applies optimizers to adjust weights applied to individual variable errors to minimize the root mean square error (RMSE) between calculated and predicted (BI). The prediction accuracy achieved by TOB using just five well logs (Gr, ρb, Ns, Rs, Dt) to predict BI is dependent on the density of data records sampled. At a sampling density of about one sample per 0.5 ft BI is predicted with RMSE ~ 0.056 and R2 ~ 0.790. At a sampling density of about one sample per 0.1 ft BI is predicted with RMSE ~ 0.008 and R2 ~ 0.995. Adding a stratigraphic height index as an additional (sixth) input variable method improves BI prediction accuracy to RMSE ~ 0.003 and R2 ~ 0.999 for the two wells with only 1 record in 10,000 yielding a BI prediction error of > ± 0.1. The model has the potential to be applied in an unsupervised basis to predict BI from basic well log data in surrounding wells lacking mineralogical measurements but with similar lithofacies and burial histories. The method could also be extended to predict elastic rock properties in and seismic attributes from wells and seismic data to improve the precision of brittleness index and fracability mapping spatially. [Display omitted] •Brittleness index (BI) prediction from basic well log data avoiding correlations.•Optimized data matching and supervised learning at varied sampling densities.•Higher data-sampling density of input logs leads to higher BI prediction accuracy.