基于机器学习的石油多峰模型研究及应用

油田在实际开发过程中,受新区块投产、开发方案调整和三采措施等因素的影响,年产量数据会呈现多峰形态。针对经典的Hubbert、HCZ等模型不能直接拟合多峰数据序列的问题,开展了基于机器学习的油田产量多峰预测模型研究。基于Hubbert模型,对多峰数据序列进行分段最小二乘拟合,在拟合误差函数中引入控制分段个数的罚分项,采用动态规划算法,自动求得最优分段的多峰预测模型,该模型运用在实际的油田产量数据上,预测结果达到预期目的。提出了一种通过自动最优分段的线性回归学习来建立油田产量多峰预测模型的方法,在实际应用中具有建模简单、自适应性强的优点。

Research and Application of Oil Multi-peak Model Based on Machine Learning

In the actual process of oilfield development, affected by the production of new blocks, the adjustment of development plans and the t three mining measures, the annual output data will show multi-peak form. As the classical Hubbert, HCZ and other models cannot directly fit the multi-peak data sequence, the multi-peak prediction model of oilfield production based on machine learning is studied. Based on the Hubbert model, the piecewise least squares fitting is performed for multi-peak data sequence, the penalty term controlling the number of segments is introduced into the fitting error function. Using dynamic programming algorithm, the multi-peak Hubbert prediction model for the optimal segment is automatically obtained. The model is applied to the actual oilfield production data, and the prediction results achieve the expected purpose. This paper presents a method to build a multi-peak prediction model of oilfield production through automatic optimal segmental linear regression learning. In practical application, it has the advantages of simple modeling and strong adaptability.