Fracture Characterization Via AI‐Assisted Analysis of Temperature Logs

• Published in: Journal of geophysical research. Machine learning and computation Vol. 1; no. 3
• Main Authors: Yang, Xiaoyu, Horne, Roland N., Tartakovsky, Daniel M.
• Format: Journal Article
• Language: English
• Published: 01.09.2024
• Subjects: fracture; temperature log; well testing
• ISSN: 2993-5210; 2993-5210
• DOI: 10.1029/2024JH000263

Fractures control fluid flow, mass transport, and heat transfer in a geothermal reservoir. This makes accurate characterization of fracture networks a prerequisite for optimal design and control of a reservoir's exploitation. We develop a deep‐learning procedure to identify fracture locations via interpretation of temporally and spatially continuous downhole temperature measurements. A long short‐term memory fully convolutional network (LSTM‐FCN) is used both to capture long‐term dependencies in sequential temperature data and to distill local features around fractures. A wellbore and fractured‐reservoir thermal model is established to generate temperature data for network training. The trained LSTM‐FCN exhibits a unique ability to detect multiple fractures intersecting a borehole. We use the LSTM‐FCN algorithm to evaluate the effectiveness of different‐stage wellbore temperature measurements on fracture detection in a complex fractured system. Our experiments reveal that the use of various‐stage temperature information as an input feature set improves the robustness of fracture detection to noise interference. This study indicates the practical feasibility of obtaining accurate fracture‐network reconstructions from temperature signals, at reasonable computational cost. Plain Language Summary Fractures control fluid flow, mass transport, and heat transfer in a geothermal reservoir. This makes accurate characterization of fracture networks a prerequisite for optimal design and control of a reservoir's exploitation. We develop a deep‐learning procedure to identify fracture locations via interpretation of temporally and spatially continuous downhole temperature measurements. Our method exhibits a unique ability to detect multiple fractures intersecting a borehole in a complex fractured system. This study indicates the practical feasibility of obtaining accurate fracture‐network reconstructions from temperature signals, at reasonable computational cost. Key Points Deep learning is used to automate fracture identification from temperature logs A long short‐term memory fully convolutional network identifies fracture locations Fusion of noisy various‐stage temperature data improves the fracture detection