Evaluation of brittleness index (BI) is a fundamental principle of a hydraulic fracturing design. A wide variety of BI calculations often baffle field engineers. The traditional value comparison may also not make the best of BI. Moreover, it is often mixed up with the fracability in field applications, thus causing concerns. We, therefore, redefine fracability as the fracturing pressure under certain rock mechanical (mainly brittleness), geological and injecting conditions to clarify the confusion. Then, we propose a data-driven workflow to optimize BIs by controlling the geological and injecting conditions. The machine learning (ML) workflow is employed to predict the fracability (fracturing pressure) based on field measurement. Three representative ML algorithms are applied to average the prediction, aiming to restrict the interference of algorithm performances. The contribution of brittleness on pressure/fracability prediction by error analysis (rather than the traditional method of BI-value comparison) is proposed as the new criterion for optimization. Six classic BI correlations (mineral-, logging- and elastic-based) are evaluated, three of which are optimized for the derivation of a new BI using the backward elimination strategy. The stress ratio (ratio of minimum and maximum horizontal principal stress), representing the geological feature, is introduced into the derived calculation based on the independent variable analysis. The reliability of the new BI is verified by error analyses using data of eight fracturing stages from seven different wells. Approximately 40%–50% of the errors are reduced based on the new BI. The differences among the performances of algorithms are also significantly restrained. The new brittleness index provides a more reliable option for evaluating the brittleness and fracability of the fracturing formation. The machine learning workflow also proposes a promising application scenario of the BI for hydraulic fracturing, which makes more efficient and broader usages of the BI compared with the traditional value comparison.

脆性指数 (BI) 的评估是水力压裂设计的基本原则。各种各样的 BI 计算常常使现场工程师感到困惑。传统的价值比较也可能无法充分利用 BI。此外，它经常与现场应用中的易碎性混为一谈，从而引起关注。因此，我们将可压性重新定义为在一定的岩石力学（主要是脆性）、地质和注入条件下的压裂压力，以澄清混淆。然后，我们提出了一个数据驱动的工作流程，通过控制地质和注入条件来优化 BI。机器学习 (ML) 工作流程用于基于现场测量预测可压性（压裂压力）。应用三种代表性的 ML 算法来平均预测，旨在限制算法性能的干扰。提出了通过误差分析（而不是传统的BI值比较方法）脆性对压力/压裂性预测的贡献作为优化的新标准。评估了六种经典的 BI 相关性（基于矿物、日志和弹性），其中三个针对使用反向消除策略推导新的 BI 进行了优化。在自变量分析的基础上，将代表地质特征的应力比（最小和最大水平主应力之比）引入到推导计算中。新 BI 的可靠性通过使用来自 7 个不同井的 8 个压裂阶段的数据进行误差分析得到验证。基于新的 BI，大约减少了 40%–50% 的错误。算法性能之间的差异也得到了显着抑制。新的脆性指数为评价压裂地层的脆性和脆性提供了更可靠的选择。机器学习工作流程还提出了 BI 用于水力压裂的有前景的应用场景，与传统的价值比较相比，这使得 BI 的使用更加高效和广泛。