Abstract
The development of seismic technology has made seismic data to be widely used in the interpretation of stratigraphic sequence frames, reservoir identification, fluid detection, and other research fields involved in reservoir description. The 3D technology reservoirs have always been the focus, as well as difficulty, of research. With the rapid development of information technology and the continuous improvement of seismic exploration level, people have put forward higher requirements for the accuracy of seismic data interpretation results. Aiming at the large number of structural and unstructured data in seismic, logging, geology, and other disciplines involved in seismic interpretation, how to effectively organize and coordinate analysis to discover the hidden reservoir structure and oil and gas distribution information has always been a geological and important topic for information processing technicians. This thesis is aimed at the current high-water-phase development of Shengtuo Oilfield reservoir and the problems existing in geological research. Based on seismic structural interpretation and attribute analysis, this paper analyzes the reservoir structural characteristics, sedimentary characteristics, and reservoir physical parameter characteristics based on geology, logging interpretation, core analysis, drilling, and seismic interpretation. Using the kriging method with external drift can cooperate with seismic variables to establish a reservoir geological model to study the Shengtuo Oilfield reservoir. We combine artificial intelligence technology with geological modeling technology of seismic interpretation results to explore the best method for predicting earthquakes. The research results in this paper show that the relative error of the model established by the kriging method in the article is relatively small for thinning wells, mainly concentrated around 1%. Examination of the thinning wells of 45 wells shows that the model established is basically good and the example has high accuracy. The research results in this paper have a guiding study of distribution and tapping potentials in the study area, formulating reasonable development and adjustment plans and improving oil recovery.
Highlights
With the development and application of database technology, large amounts of data are stored in databases or data warehouses. ese data are often multidimensional
Growing technology has made seismic data widely used in multiple research fields involved in the interpretation of stratigraphic sequence frames, reservoir identification, and fluid detection; using seismic interpretation of isochronous tectonic plates and faults, combined with well contrast, stratification and stratigraphic development characteristics, the application of high-precision 3D area, and the improvement of seismic reservoir interpretation levels have made the reliability of seismic description reservoirs increasingly greater [1, 2]. e difference between complex fault block reservoirs and conventional reservoirs is that reservoirs are often complicated by faults
With the development of petroleum geology, many new technologies and methods require the comprehensive use of multidisciplinary materials. e purpose of this article is to propose effective analysis methods for the multidimensional data involved in seismic oil and gas prediction and seismic and sonic logging matching process to improve the accuracy of prediction and matching
Summary
With the development and application of database technology, large amounts of data are stored in databases or data warehouses. ese data are often multidimensional. E purpose of this article is to propose effective analysis methods for the multidimensional data involved in seismic oil and gas prediction and seismic and sonic logging matching process to improve the accuracy of prediction and matching. Erefore, it is of great significance to study fine oil and gas prediction methods and seismic logging data correlation matching methods. Is subject is aimed at the research of information fusion and multidimensional data analysis based on the oil and gas prediction problems based on geophysical exploration data such as seismic and well logging. We perform the correlation analysis and processing between the seismic logging data, divide the two into a unit domain through the well-seismic matching method, and organize the multisource influencing factor data in the oil and gas prediction process with a multidimensional data model. We perform the correlation analysis and processing between the seismic logging data, divide the two into a unit domain through the well-seismic matching method, and organize the multisource influencing factor data in the oil and gas prediction process with a multidimensional data model. rough analysis and processing, corresponding multidimensional analysis method is proposed, and the method of combining support vector machine and information fusion is used for oil and gas prediction [13, 14]
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