机器学习预测注水和注汽共同驱动的成熟陆上油田产油量

L. Kubota, Danilo Reinert
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引用次数: 8

摘要

在本文中,我们利用油藏工程师工具箱中相对较新的技术来解决一个老问题——产量预测。当前的问题包括预测一个成熟的陆上油田同时注水和注汽的产油量。然而,我们没有采用传统的方法,而是采用了机器学习算法,该算法将以大量的历史数据为基础,提取隐藏的模式和潜在的关系,以预测石油产量。不需要地质模型和/或数值油藏模拟器,只需要3组时间序列:注入历史、生产历史和生产商数量。使用了两种机器学习算法:线性回归和循环神经网络。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Machine Learning Forecasts Oil Rate in Mature Onshore Field Jointly Driven by Water and Steam Injection
In this paper, we tackle an old problem – production forecast - using techniques that are relatively new to the reservoir engineer toolbox. The problem at hand consists of forecasting oil production in a mature onshore field simultaneously driven by water and steam injection. However, instead of turning to traditional methods, we deploy machine-learning algorithms which will feed on a plethora of historical data to extract hidden patterns and underlying relationships with a view to forecasting oil rate. No geological model and/or numerical reservoir simulators will be needed, only 3 sets of time-series: injection history, production history and number of producers. Two Machine-Learning algorithms are used: linear-regression and recurrent neural networks.
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