机器学习预测油气产量现状

doi:10.13809/j.cnki.cn32-1825/te.2021.04.018

Abstract

Abstract:

The machine learning is not only an important tool for oil and gas big data analysis, but also a general data-driven analysis method. As an important field with a long history and a large data base, oil and gas exploration and development has a great potential for data mining. The use of big data analysis technology for oil and gas field can help decision makers to conduct investment analysis, risk assessment and production optimization, which brings significant economic benefits. The machine learning method has been tried by the researchers applying to the researches on oil and gas. Nowadays, many application scenarios have been proposed with the development of machine learning algorithms, but general solutions for specific scenario are still divided. So that, we introduces the procedure of a machine learning modeling upon the most basic principles, and summarizes the development history of the main three kinds of machine learning methods that can be applied to oil and gas big data analysis. And then based on the characteristics of oil and gas field big data, the core contents, goals and advantages of oil and gas field big data analysis and utilization are discussed, the main application scenarios of machine learning in oil and gas field are analyzed, and the existing problems and countermeasures in typical oil and gas production prediction are summarized.

Key words: big data of oil and gas field, data-drive model, production forecast, machine learning, intelligent oilfield

CLC Number:

TE328

HUANG Jiachen,ZHANG Jinchuan. Overview of oil and gas production forecasting by machine learning[J].Petroleum Reservoir Evaluation and Development, 2021, 11(4): 613-620.

Figures/Tables 3

Fig. 1

Table 1

Characteristics of big data in oil and gas field and key points of data analysis and utilization"

数据特点	互联网大数据	油气田大数据	油气田大数据分析利用核心内容
4V特征（数据量Volume、数据类型Variety、数据价值Value、产生速度Velocity）	数据量大;类型繁多;价值密度低;产生快、失效快	数据量大;类型繁多;价值密度高;产生快、失效慢	尽快尽早地挖掘数据之间的关系,如：地质和开发条件与产量的关系
数据产生基础	被动无意识产生,需要数据挖掘才能找到数据的价值	主动设计产生,所有测量都与油气生产有关	根据应用场景和所选分析方法,进一步进行原始数据采集
数据之间的相关性	事先未知,需要进行统计分析,统计意义通常不明确	知道定性关系,但有时难以准确定量地描述	挖掘内在机理与观测数据的本质关系而非统计关系
处理技术	使用通用的大数据处理方法,这些机器学习算法通常是基于特定问题而被发明的	带专业约束关系的处理。需要建立适合油气田大数据分析的改进过的机器学习新模型	结合专业知识选择合适的数据驱动模型,使用数学模型对数据驱动模型进行约束和优化
数据结构化特征	可用数据较为完整;对于图像、自然语言等非结构化数据,有较为成熟的预处理算法和相应机器学习模型	历史数据通常不完整,特别是数据关系缺少;一些主导产量的信息难以结构化,如：构造特征、沉积环境等	如何进行数据预处理（包括数据筛选、特征工程等）。数据预处理工作量最大,并且对最终分析效果影响最大
数据预测的准确性	信息通常完备,特征中包含预测目标的全部信息,数据量足够大时弱学习器等价于强学习器,预测准确性高	信息通常不完备,很多重要信息不能被获取或特征化,数据噪声较大,预测效果存在理论上限,预测准确性低	在样本和可选特征有限、数据随机性成分较多的情况下,选择最适合预测场景模型,尽量提高预测准确性和稳定性

Table 1

Table 2

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模型	优势	劣势
传统解析模型	遵从实际机理,结果易于解释,更容易用于指导开发设计; 解析模型对计算机算力要求低,数值模拟对已知信息使用充分; 用于新的目标区时不需要先验的认识	模型应用条件苛刻,实际应用中必须先获得模型中所有参数; 无法处理一些非结构化的数据,如：井的层位信息等
数据驱动模型	普遍适用性高,稳定性好,需要后期人工调整的部分较少; 基于模糊逻辑,对专业知识依赖少; 可以使用通用的数据特征处理方法,容易处理非结构化数据	基于统计学原理,不易解释机理; 需要很多先验的训练样本才能得到可靠的模型,并且模型不能推广到其他研究区
数据驱动建模工作要点	充分利用已知信息,结合专业知识进行数据特征工程; 根据已知数据的特点,建立对目标区最适用的、通用的预测模型,最终实现动态生产监测。样本中对其标签产生影响的、但未作为机器学习或统计学建模特征的若干属性要尽量一致,排除非确定性信息的影响

Overview of oil and gas production forecasting by machine learning

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