Geological Modeling Technology and Application Based on Seismic Interpretation Results under the Background of Artificial Intelligence
| Autor: | Na Li, Ximing Peng, Minglu Li, Yalin Zhu, Hao Dong |
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| Jazyk: | angličtina |
| Rok vydání: | 2021 |
| Předmět: |
Article Subject
Computer Networks and Communications business.industry Computer science Earthquake prediction Information processing Information technology Drilling Unstructured data TK5101-6720 Computer Science Applications Interpretation (model theory) Identification (information) Approximation error Telecommunication Artificial intelligence business |
| Zdroj: | Mobile Information Systems, Vol 2021 (2021) |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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