In this paper, a new assisted history-matching workflow is presented, where rate transient analysis (RTA) results are used to constrain not only the initial discrete fracture network (DFN) models, the interpreted flow regimes are also used to formulate a localization scheme for more efficient updating of the pertinent DFN model parameters. The outcome is an ensemble of DFN realizations that are calibrated to both geologic and dynamic production data.

RTA interpretations and other pertinent geological data are used to infer the prior probability distributions of the unknown fracture parameters, from which an ensemble of initial DFN models is sampled. The DFN models are subjected to numerical multiphase flow simulation. The fracture parameters are adjusted following an indicator-based probability perturbation method, which is capable of minimizing the objective function and reducing the uncertainties in the unknown fracture parameters simultaneously. A key feature is that the flow regimes identified from RTA are used to formulate a localization strategy, where individual segments of the production data are used to tune only a specified subset of the unknown model parameters.

In a case study, the method is applied to characterize the probability distributions of four parameters in a multifractured shale gas well: primary fracture transmissivity, aperture of the secondary fracture, transmissivity of the secondary induced fracture, and global fracture intensity. Their probability distributions are updated following the proposed approach to match the production history. Multiple realizations of the DFN model are sampled.

A key novelty is that the proposed probabilistic approach facilitates the representation of uncertainties in fracture parameters via multiple equally-probable DFN models and their corresponding upscaled flow-simulation models. A more comprehensive and robust approach is presented for integrating specific RTA interpretations and estimations into various steps of the history-matching process.

在本文中，提出了一种新的辅助历史匹配工作流程，其中速率瞬态分析 (RTA) 结果不仅用于约束初始离散裂缝网络 (DFN) 模型，还使用解释的流态来制定定位方案更有效地更新相关的 DFN 模型参数。结果是 DFN 实现的集合，这些实现都针对地质和动态生产数据进行了校准。

RTA 解释和其他相关地质数据用于推断未知裂缝参数的先验概率分布，从中对初始 DFN 模型的集合进行采样。DFN 模型进行数值多相流模拟。裂缝参数的调整采用基于指标的概率微扰方法，能够在最小化目标函数的同时降低未知裂缝参数的不确定性。一个关键特征是从 RTA 识别的流态用于制定定位策略，其中生产数据的各个部分仅用于调整未知模型参数的指定子集。

在一个案例研究中，该方法用于表征多裂缝页岩气井中四个参数的概率分布：原生裂缝导水率、次生裂缝孔径、二次诱导裂缝导水率和整体裂缝强度。它们的概率分布按照建议的方法更新以匹配生产历史。对 DFN 模型的多个实现进行了采样。

一个关键的新颖之处在于，所提出的概率方法有助于通过多个等概率 DFN 模型及其相应的放大流动模拟模型来表示裂缝参数中的不确定性。提出了一种更全面和稳健的方法，用于将特定的 RTA 解释和估计集成到历史匹配过程的各个步骤中。