Seismic property prediction using deep learning in LN area, Tarim Basin, China
Year: 2024
Type: article
Abstract: Stratigraphy and faults provide the framework for subsurface sequences and structural information, forming the basis for high-precision reservoir parameter prediction. However, reservoir parameter prediction often relies on sparse stratigraphic interpretations based on existing manual interpretations. There is a certain connection between stratigraphic and fault interpretations and reservoir parameter predictions. Therefore, this paper proposes a fully automated workflow from stratigraphic and fault interpretation to reservoir modeling. First, using known sparse expert stratigraphic interpretation results as constraints, three-dimensional implicit model construction is achieved through deep learning networks in the working area. That model incorporates all stratigraphic and fault information, thereby enabling detailed stratigraphic framework interpretation. Second, based on this implicit model, an initial reservoir parameter model is built by integrating well-logging information. Finally, using this model and seismic data as input, with target reservoir parameters as output, a two-dimensional deep learning training network is established to achieve the three-dimensional characterization of reservoir parameters. Applying this method in the LN area of Tarim Basin, based on automatically interpreting fault and stratigraphic horizons results, the predicted results of reservoir parameters such as gamma and impedance guided by this stratigraphic construction model are generally consistent at blind wells. This study demonstrates that the automated workflow can achieve results consistent with geological understanding in practical applications.
DOI: 10.1093/jge/gxae099
Authors: Jianjun Li, Haining Zhang, Yongfu Liu, Jingping Zhou, Xiaoming Sun
Institution: University of Science and Technology of China
Language: English
Subfield: Geochemistry and Petrology
Field: Earth and Planetary Sciences
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $1,600
OpenAlex ID: W4402854207