This study uses a commercial reservoir simulator to generate production rate data for shale wells with systematic variations in fracture treatment design parameters using a plausible range of reservoir properties. The synthetic well data is then used to generate diagnostic log-rate log-time plots, which allow for a distinction of four types of flow regimes. Such a distinction of flow regimes is of practical relevance, because of the inverse link with reservoir properties and fracture treatment design parameters. Each flow regime has a characteristic slope, which traditionally has been attributed to typical flow conditions in the reservoir. The synthetic production data from the reservoir simulator are used to constrain b-sigmoid patterns for narrow discrete time increments of production (1 month) throughout the various flow regimes occurring during the economic life of each synthetic well model. Nearly a hundred different models were generated as a basis for our analysis. From the analysis of the DCA parameters for time series, using the difference in inverse of decline rates (loss ratio) for two adjacent time steps, it appears possible to recognize systematic shifts in the instantaneous decline rates and temporal b-values. In particular, the generated b-value patterns (b-sigmoids) appear diagnostic for flow regime changes recognized on diagnostic log-rate log-time plots. The results demonstrate that it is possible, based on well design and reservoir parameters, to establish correlation trends between b-sigmoids and flow regime changes using time series of the DCA parameters. The results are subsequently used to improve the accuracy of a modified 3-segment DCA method based on Arps equation. The proposed method is applied to synthetic production data, generated based on an Eagle Ford shale oil well (with known reservoir and fracture properties), to demonstrate the practical value for production forecasting and reserves estimation. This research paper paves the way for wider, future application of the methods described.

这项研究使用商业储层模拟器来生成页岩井的生产率数据，并利用合理范围的储层性质对裂缝处理设计参数进行系统性的改变。然后，将合成井数据用于生成诊断测井速率测井时间曲线，从而可以区分四种流态。由于与储层特性和裂缝处理设计参数成反比关系，所以这种流态的区别具有实际意义。每种流态都有一个特征斜率，该斜率传统上归因于储层中的典型流态。来自储层模拟器的综合生产数据用于约束b-S型模式，用于在每个合成井模型的经济寿命期间发生的各种流态中，以窄的离散时间增量生产（1个月）。生成了近一百种不同的模型作为我们分析的基础。通过对时间序列的DCA参数进行分析，使用两个相邻时间步的下降率倒数（损耗比）的倒数，可以识别瞬时下降率和时间b值的系统变化。特别是，生成的b值模式（b-sigmoids）可以诊断在诊断对数速率对数时间图上识别出的流动状态变化。结果表明，基于油井设计和储层参数，可以使用DCA参数的时间序列在b型乙状结肠和流态变化之间建立相关趋势。结果随后被用于提高基于Arps方程的改进的3段DCA方法的准确性。该方法应用于基于Eagle Ford页岩油井（具有已知储层和压裂特性）的综合生产数据，以证明其对产量预测和储量估算的实用价值。该研究论文为所述方法的更广泛的未来应用铺平了道路。