非常规资源中的预测分析

发布时间：2021-05-27 11:20

　　油气行业对数据并不陌生。数字、智能化领域在非常规油气生产上游行业运用中的迅速发展促进了千兆字节、兆字节和1015字节中产生的数据量（大数据）在石油行业中的应用的急剧增加。与传统油气领域的发展和生产不同的是,常规油气中的多相流基本规律并不适用于非常规油气。此外,为了提高单井自然产能,非常规油气开采需要采用多段压裂和整体压裂来提高经济效益。根据非常规油气生产过程中的大量数据建立一个生产模型,利用大数据进行分析预测和管理。这就强调了数据分析对石油和天然气工业生产的重要性。因此,本文提出了机器学习算法（预测分析）来分析致密气的生产数据,建立一个分析模型,可以通过改变储层/流体性质以及气井的水力压裂参数来预测气井产能。利用K-均值聚类理论,根据气井原始生产速率,将压裂井分为两类。第一类分为5个等级（极好、很好、好、平均和差）,第二类分为3个等级（好、平均和差）。每个类别中的各组代表井的性能。利用人工神经网络理论来建立预测模型,并与线性模型进行比较分析,利用均方差确定拟合程度衡量模型的预测准确性,并优选出预测模型,然后利用蒙特卡洛模拟进行动态分析气井的生产效果并重新对气井的生产等级进行划分。最后对... 

【文章来源】：西安石油大学陕西省

【文章页数】：129 页

【学位级别】：硕士

【文章目录】：
Abstract
摘要
NOMENCLATURE
CHAPTER 1 INTRODUCTION
    1.1 Unconventional Resources and Predictive Analytics
    1.2 Unconventional Resources
    1.3 Hydraulic Fracturing
    1.4 Definition of Analytics
    1.5 Objective of Study
CHAPTER 2 BACKGROUND AND LITERATURE REVIEW
    2.1 Big Data
    2.2 Big Data Analytics
        2.2.1 Types of Analytics
            2.2.1.1 Descriptive Analytics
            2.2.1.2 Predictive Analytics
            2.2.1.3 Prescriptive Analytics
        2.2.2 Big Data Analytics Platform
    2.3 Data Warehouse/Cloud Computing
        2.3.1 Benefits of Cloud Computing
    2.4 Data Mining
        2.4.1 Types of Data
        2.4.2 Overfiting and Underfitting
        2.4.3 Noise and Attribute Importance
    2.5 Predictive Analytics in the Oil and Gas Industry
        2.5.1 Drilling and Optimization
        2.5.2 Production Optimization
        2.5.3 Reservoir and Asset Management
        2.5.4 Asset Maintenance Business Management
    2.6 K-means Clustering Algorithm
    2.7 Artificial Neural Network (ANN)
        2.7.1 ANN Transfer Function
        2.7.2 ANN Activation Function
        2.7.3 Types of Artificial Neural Network
        2.7.4 Neural Network Algorithms
            2.7.4.1 Forward Propagation
            2.7.4.2 Backpropagation
            2.7.4.3 Adaptative Learning Algorithms (Resilient Backpropagation (RPROP))
    2.8 Generalized Linear Model
    2.9 Measuring the Quality of Fit
CHAPTER 3 RESEARCH METHODOLOGY
    3.1 Data Mining
    3.2 Data Exploration
    3.3 Clustering
    3.4 Predictive Modeling
        3.4.1 Measure of Quality of Fit
        3.4.2 Key Performance Index (KPI)
        3.4.3 The Sensitivity Analysis of the Model
    3.5 The Look-back Modeling
    3.6 Software Used for the Research
CHAPTER 4 PREDICTIVE ANALYTICAL MODELS
    4.1 Exploratory Data Analysis
        4.1.1 Data Set
        4.1.2 Correlation Analysis
    4.2 K-Means Clustering Analysis
    4.3 Predictive Model Analysis
        4.3.1 Artificial Neural Network Model
            4.3.1.1 Sensitivity Analysis
            4.3.1.2 Explanation of the Sensitivity Analysis
        4.3.2 GLM Model
        4.3.3 Key Performance Index (KPI)
    4.4 Look-back Analysis
        4.4.1 Monte Carlo Simulation
CHAPTER 5 CONCLUSION AND RECOMMENDATION
    5.1 Conclusion
    5.2 Recommendation
ACKNOWLEDGEMENT
REFERENCES



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