Research And Application Of Drilling Engineering Optimization Based On Knowledge Discovery

"Optimization"is the aim that every product designer pursues. Drilling is a complex system engineering, which produces plenty of uncertained random information. Optimizatioin of Dilling engineering is important to improve the efficiency and economic benefit. Due to the complexity and uncertainty of oil and gas drilling engineering, the optimization turns to be a multi-functional problem which has complex influence factors. New scientific optimization method and theory need to be designed to solve the problem.To solve the optimization problem in engineering field and get over the limitatioin of conventional method, an optimization method based on knowledge discovery is provided in this paper. It is a mining process from data and information to knowledge discovery. It is also a knowledge learning model specified in simulating human brain work process. Even without the mathematic model and standard measurement, knowledge discovery theory could help us to find the inherent law and build up the knowledge model with the analysis and simplification of the achieved experience, facts, and measuring data.Through systemic research and application, the content of the thesis and the creative serearch solution include:(1) Research on the uncertainty problem in drilling process. Analyse the orginal data obtained from drilling process using uncertainty theory. Build up the drilling data information analysis method.(2) Theoretical research on the drilling engineering information database. Based on the database information uncertainty, data processing model and structure of database system to build basic database is set up, which could process data information and establish a foundation of various arithmetic and knowledge discovery method.(3) Select drill bit on artificial neural network. Adaptive resonance theory is used in neural network learning model to optimize drill bit selection in deep well of Daqing Oilfield based on bit database. The knowledge discovery model of bit data processing is set up.(4) Drilling tool failure analysis based on support vector machine. Based on the drilling tool failure database and support vector machine study model, analyse Daqing, Hailaer oil field deep well drilling tool failure data and give a conclusion to its characteristic and reason, therefore a preventation measure could be established to direct the field work.(5) Visualization theory and application research on wellpath trajectory. Based on path survey database, a program was designed to implement wellpath trajectory and design visualizatioin using Delphi and OpenGL. The multi-obstacle path design model is set up.It provides the evidence to wellpath trajectory design, wellpath control and three dimensional wellpath trajectory descriptions.This paper unites data, information, knowledge, wisdom together in Knowledge Diskovery system. From original drilling data to uncertain information analysis, discovery knowledge based on information is used in field application. The feature of this paper is combining knowledge discovery with information property. It could contribute to wellpath trajectory desing and visualization theory, which shows the great importance and potential of knowledge discovery theory in drilling engineering.