基于人工神经网络的钻井事故预测诊断方法

发布时间：2018-05-26 19:32

本文选题：预测方法 + 井下复杂情况　；参考：《中国石油大学(华东)》2015年硕士论文

【摘要】：井下复杂情况直接关系到钻井的成败,消除钻井过程中的井下复杂问题是安全钻完井的最重要任务之一。提前预测钻井井下复杂情况、采取适当措施能确保钻井施工安全,同时可以节约钻进时间和成本。人工神经网络方法具有解决需要复杂模式识别钻井井下复杂问题预测的巨大潜力,本文基于人工神经网络方法理论,开展钻井井下复杂情况预测方法研究。本文首先归纳总结了钻井过程中的各类井下复杂情况,重点分析了各类钻井井下复杂情况的影响因素,从而准确地为人工神经网络预测方法选择合适的参数。引用人工神经网络方法,对比选用一种新型高效数学算法,基于C++程序设计语言,开发钻井井下复杂情况预测诊断应用程序,形成了钻井井下复杂情况人工神经网络预测诊断方法。结合油田井下复杂情况实例数据,验证人工神经网络算法和钻井井下复杂情况预测诊断方法的可靠性。预测结果和实际结果对比分析表明,该人工神经网络算法和钻井井下复杂情况预测诊断方法具有较高的精度和准确性,建立的钻井井下复杂情况预测诊断方法可行、结果可靠,能应用于钻井实际来预测和确认钻井中可能出现的井下复杂问题。这种基于人工神经网络的钻井井下复杂情况及其应用计算程序预测诊断精确度高,具有很大应用潜力,对钻井井下复杂问题诊断和预测具有重要的意义。
[Abstract]:The downhole complex situation is directly related to the success or failure of drilling. It is one of the most important tasks for safe drilling and completion to eliminate the downhole complex problem during drilling. The complex condition of drilling well can be predicted ahead of time, and appropriate measures can be taken to ensure the safety of drilling operation, and at the same time, the drilling time and cost can be saved. The artificial neural network (Ann) method has great potential to solve the complex problems in drilling wells which need complex pattern recognition. Based on the theory of artificial neural network (Ann), the prediction method of drilling downhole complex situation is studied in this paper. In this paper, we first summarize the various downhole complex conditions in drilling process, and analyze the influencing factors of various drilling downhole complex conditions, so as to accurately select the appropriate parameters for the artificial neural network prediction method. By using artificial neural network method, a new and efficient mathematical algorithm is used to develop a prediction and diagnosis program for complex conditions in drilling wells based on C programming language. The artificial neural network prediction and diagnosis method for complex conditions in drilling well is formed. The reliability of artificial neural network algorithm and drilling downhole complex condition prediction and diagnosis method is verified by combining with the data of oilfield downhole complex case. The comparison and analysis between the prediction results and the actual results show that the artificial neural network algorithm and the drilling downhole complex situation prediction and diagnosis method have high accuracy and accuracy, the established prediction and diagnosis method for the drilling downhole complex situation is feasible and the results are reliable. It can be used in drilling practice to predict and confirm complex downhole problems that may occur in drilling. This kind of artificial neural network based drilling downhole complex situation and its application calculation program has high accuracy of prediction and diagnosis, and has great application potential, which is of great significance to the diagnosis and prediction of drilling downhole complex problems.
【学位授予单位】：中国石油大学(华东)
【学位级别】：硕士
【学位授予年份】：2015
【分类号】：TP183;TE28

【相似文献】