About this Research Topic
The effective analysis and efficient information mining of subsurface geoscience big data can improve exploration and development of oil and gas, and support CO2 storage programmes. In recent years, subsurface instrumentation has led to a large amount of data collected, which is also complex and difficult to analyse. The rapid development of big data analysis, deep learning, and machine learning have led many geologists to try to use these emerging computer technologies in subsurface geoscience research, which will become an increasingly important means of subsurface developments in the future.
At present, the application of geological big data, deep learning, and machine learning in petroleum geology is limited due to various types and different amounts of geological data and incomplete data mining technology. Machine learning, deep learning, data mining, and other technologies have shown great potential in diagenetic facies identification using well logging data, geological modeling of sand bodies and other fields. However, the application of deep learning, machine learning and other technologies remains limited in petroleum geology. In addition, the representativeness of training data sets of deep learning, machine learning and other techniques is also hindering more widespread application in petroleum geology. It's worth noting that, some new numerical simulation methods can be used to establish training data sets, which is one of the considered topics.
Therefore, this special issue focuses on “Application of AI and Geological Big Data in Petroleum Geology”. We especially welcome the following content, but are not limited to them:
A. Research on the application of deep learning, machine learning and other emerging technologies and algorithms in petroleum geology
B. How to improve the generalization ability of deep learning, machine learning and other algorithms in petroleum geology;
C. Deep learning, machine learning, and other emerging technologies and algorithms in 3D geological modeling.
D. The application of emerging numerical simulation technology in petroleum geology, and its significance to the application of AI technologies in petroleum geology.
At present, the application of geological big data, deep learning, and machine learning in petroleum geology is limited due to various types and different amounts of geological data and incomplete data mining technology. Machine learning, deep learning, data mining, and other technologies have shown great potential in diagenetic facies identification using well logging data, geological modeling of sand bodies and other fields. However, the application of deep learning, machine learning and other technologies remains limited in petroleum geology. In addition, the representativeness of training data sets of deep learning, machine learning and other techniques is also hindering more widespread application in petroleum geology. It's worth noting that, some new numerical simulation methods can be used to establish training data sets, which is one of the considered topics.
Therefore, this special issue focuses on “Application of AI and Geological Big Data in Petroleum Geology”. We especially welcome the following content, but are not limited to them:
A. Research on the application of deep learning, machine learning and other emerging technologies and algorithms in petroleum geology
B. How to improve the generalization ability of deep learning, machine learning and other algorithms in petroleum geology;
C. Deep learning, machine learning, and other emerging technologies and algorithms in 3D geological modeling.
D. The application of emerging numerical simulation technology in petroleum geology, and its significance to the application of AI technologies in petroleum geology.
Keywords: Geological Big Data, Data Mining, Machine learning, Deep learning, Petroleum Geology
Important Note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.
