A New Fracture Characterization Method Using Petrophysical Model With Inherent Anisotropy and Borehole Data
Yongping Wang
College of Geophysics, China University of Petroleum (Beijing), Beijing, China
State Key Laboratory of Petroleum Resources and Engineering, China University of Petroleum (Beijing), Beijing, China
Search for more papers by this authorCorresponding Author
Jingye Li
College of Geophysics, China University of Petroleum (Beijing), Beijing, China
State Key Laboratory of Petroleum Resources and Engineering, China University of Petroleum (Beijing), Beijing, China
Correspondence: Jingye Li ([email protected])
Search for more papers by this authorWeiheng Geng
Department of Automation, Beijing National Research Center for Information Science and Technology (BNRist), Tsinghua University, Beijing, China
Search for more papers by this authorQiyu Yang
College of Geophysics, China University of Petroleum (Beijing), Beijing, China
State Key Laboratory of Petroleum Resources and Engineering, China University of Petroleum (Beijing), Beijing, China
Search for more papers by this authorLei Han
College of Geophysics, China University of Petroleum (Beijing), Beijing, China
State Key Laboratory of Petroleum Resources and Engineering, China University of Petroleum (Beijing), Beijing, China
Search for more papers by this authorYuning Zhang
College of Geophysics, China University of Petroleum (Beijing), Beijing, China
State Key Laboratory of Petroleum Resources and Engineering, China University of Petroleum (Beijing), Beijing, China
Search for more papers by this authorYongping Wang
College of Geophysics, China University of Petroleum (Beijing), Beijing, China
State Key Laboratory of Petroleum Resources and Engineering, China University of Petroleum (Beijing), Beijing, China
Search for more papers by this authorCorresponding Author
Jingye Li
College of Geophysics, China University of Petroleum (Beijing), Beijing, China
State Key Laboratory of Petroleum Resources and Engineering, China University of Petroleum (Beijing), Beijing, China
Correspondence: Jingye Li ([email protected])
Search for more papers by this authorWeiheng Geng
Department of Automation, Beijing National Research Center for Information Science and Technology (BNRist), Tsinghua University, Beijing, China
Search for more papers by this authorQiyu Yang
College of Geophysics, China University of Petroleum (Beijing), Beijing, China
State Key Laboratory of Petroleum Resources and Engineering, China University of Petroleum (Beijing), Beijing, China
Search for more papers by this authorLei Han
College of Geophysics, China University of Petroleum (Beijing), Beijing, China
State Key Laboratory of Petroleum Resources and Engineering, China University of Petroleum (Beijing), Beijing, China
Search for more papers by this authorYuning Zhang
College of Geophysics, China University of Petroleum (Beijing), Beijing, China
State Key Laboratory of Petroleum Resources and Engineering, China University of Petroleum (Beijing), Beijing, China
Search for more papers by this authorFunding: Financial support was provided by the R&D Department of China National Petroleum Corporation (Investigations on fundamental experiments and advanced theoretical methods in geophysical prospecting applications, 2022DQ0604-04).
ABSTRACT
Fractures represent a critical structural feature in unconventional reservoirs, as they create essential pathways for the migration and accumulation of oil and gas. Therefore, fracture characterization is a fundamental task in the exploration of unconventional hydrocarbon resources. Conventional fracture characterization methods typically do not account for the inherent anisotropy of the formation, which arises from the sedimentary environment and fluid distribution, often leading to inaccurate fracture predictions. To address this challenge, we propose a petrophysical model that incorporates inherent anisotropy, employing rock physics modelling to accurately characterize fracture distribution. Furthermore, to reduce the substantial workload involved in manually calibrating the petrophysical model, we introduce a one-dimensional convolutional neural network combined with an attention mechanism. By leveraging the advanced nonlinear learning capabilities of the convolutional neural network, we aim to fit the petrophysical model and extend its application across all exploration wells and the entire field. The effectiveness and feasibility of the proposed method are demonstrated through experiments using actual borehole data from a fracture-dominated reservoir.
Open Research
Data Availability Statement
The data that support the findings of this study are available from the corresponding author upon reasonable request.
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