用深度学习挖掘油田开发指标预测模型的知识

Knowledge Mining for Oilfield Development Index Prediction Model Using Deep Learning

油田开发指标变化特征被当作油田开发规划、油田开采状况评价、油田开发方案设计与调整及油田开发风险预测预警等决策管理问题的重要依据。针对至今没有很好解决的建立智慧油田的瓶颈问题之一--油田开发指标智能预测系统的选择预测方法和模型的知识挖掘问题,基于油田开发的海量数据,利用深度学习的卷积神经网络和循环神经网络,提取反映油田开发动态特征和知识。在此基础上,结合已建立的油田开发指标预测的模型库及知识库,利用深度学习的实体和关系的联合提取方法,提出通过油田开发输入信息、油田开发动态特征指标、油田开发指标预测的模型库和知识库挖掘选择油田开发指标最佳预测模型的知识方法。概念设计的模拟实例表明,提出的知识挖掘流程可实现只要输入油田开发的相关信息,就能自主获得恰当的油田开发指标预测模型。

The changing characteristics of oilfield development index are regarded as the important basis of oilfield development planning,oilfield exploitation evaluation,oilfield development scheme design and adjustment,decision management problems of oilfield development risk prediction and early warning,etc.For one of the unsolved bottleneck problem of building intelligent oilfield,i.e.the problem on knowledge mining of selecting prediction method and model of oilfield development indexes intelligent prediction system,based on the massive data of oilfield development,this paper uses the convolutional neural network and cyclic neural network of deep learning to extract the characteristics and knowledge reflecting the development dynamic of oilfield.On this basis,combining the model base and knowledge base of oilfield development index prediction,a knowledge mining method to select the optimal prediction model of oilfield development index is proposed through the input information and dynamic characteristics index of oilfield development,the model base and the knowledge base of oilfield development index prediction by using the joint extraction method of entity and relationship of deep learning.The simulation example of conceptual design shows that the proposed knowledge mining process may realize autonomous obtaining an appropriate prediction model of oilfield development index as long as inputting relevant information of oilfield development.