基于机器学习方法的油井日产油量预测

Oil production prediction based on a machine learning method

油藏数值模拟是进行油田产量预测最为常用的方法,但其准确性建立在精确的地质模型和较高质量的历史拟合基础之上。为了克服数值模拟计算耗时长、成本高和所需数据资料多等缺点,建立了一种利用机器学习方法,根据现场广泛易得的油藏静态资料和开发动态参数实现油井日产油量的快速准确预测。传统的BP神经网络无法准确描述产量变化在时间维度上的相关性,因而基于长短期记忆神经网络(LSTM),建立能够考虑生产动态数据变化趋势和前后关联性的产量预测模型,是实现油井日产油量预测更为有效的途径。首先根据平均不纯度减少(MDI)方法,分析各个因素对单井产量的影响程度,基于特征参数的重要性进行数据降维,排除不相关的冗余特征,确定影响油井产量的主要因素。结合筛选出的特征参数和日产油量数据对LSTM模型进行训练和优化,建立最终的油井产量预测模型。利用实际油田数据对建立的模型进行验证和应用效果评价,结果表明基于LSTM模型的产量预测值与实际值高度一致,能准确反映产量的动态变化规律,为油井产量预测提供了一种新的方法。

Numerical reservoir simulation is the most common method for oilfield production prediction,but its accuracy is based on exact geological model and quality history matching.In order to overcome the shortcomings of numerical simulation technique(e.g.time consuming,high cost and numerous data),a machine learning based production prediction method was established,which can quickly and accurately predict oil well production based on abundant static reservoir data and dynamic development parameters that can be easily obtained on site.The traditional BP neural network method cannot describe the correlation of the production change in the time dimension,while the production prediction model which is established based on long-short-term memory(LSTM)and takes into account the change trend and correlation of production performance data is a more effective method for predicting oil well production.Firstly,the influential degree of each factor on single-well production was analyzed by means of mean decrease impurity(MDI)method.Then dimension reduction was carried out based on the importance of characteristic parameters,the uncorrelated redundancy characteristics were removed,and the main parameters influencing oil well production were determined.Finally,combined with screened out characteristic parameters and daily oil production,the LSTM model was trained and optimized,and the final oil well production prediction model was established.In addition,this newly established model was verified based on actual oilfield data and its application effect was evaluated.It is shown that the production predicted on the basis of LSTM model is highly accordant with the actual value.It is indicated that this model can reflect the dynamic production change law accurately and provides a new method for predicting oil well production.