基于可解释机器学习的水平井产能预测方法

An Interpretable Machine Learning Approach to Prediction Horizontal Well Productivity

准确预测致密气藏分段压裂水平井产能是压裂效果评价和优化设计的关键环节。现有的产能预测方法,引入了过多的假设和简化,很难全面反映致密储层流体多尺度的运移机理和复杂物理过程,导致产能预测误差较大。提出一种基于机器学习的致密气藏分段压裂水平井产能预测方法,该方法综合利用已收集的地质、压裂水平井产能及钻完井等多类型数据,通过机器学习算法直接挖掘数据内部规律,建立产能预测模型。此外,为解决常规机器学习模型的“黑盒子”问题,还利用SHAP(SHapley Additive exPlanations)方法对建立的机器学习模型进行全局和局部解释,分析影响产能的主要因素,增加了模型的可信性和透明度。以苏里格气田苏东示范区为例,验证了该方法的有效性和实用性。与油气藏数值方法相比,该方法不仅提高了产能预测的精度,而且缩短了建模周期,加快了计算速度。

It is essential to predict multistage fractured horizontal well(MFHW)productivity of tight sand gas reservoirs for evaluation of hydraulic fracturing performance and optimization of hydraulic fracturing design.However,most of the current predictive methods introduce multiple assumptions and simplifications.Therefore,these methods cannot fully account for multi-scale fluid flow mechanisms in the tight formations of well productivity.A machine learning approach for predicting MFHW productivity is proposed.A well productivity model is built by machine learning algorithms to uncover hidden patterns in a data set including geological,fractured well productivity,drilling and completion data.In addition,to solve the“black box”issue of conventional machine learning modelling,the SHAP(SHapley Additive exPlanations)method is used to explain the built ML model globally and locally.The efficiency and practicality of the proposed method is demonstrated by the application to the Eastern Sulige Gas Field.Compared with petroleum reservoir simulation,the method not only improves the prediction performance of the well productivity,but also reduces modelling cycle and improve computational speed.