Accurate well top picking and reservoir property prediction plays crucial role for petroleum exploration and production. The task is traditionally done manually by geologists, and can be inconsistent and time-consuming. In addition, the work is difficult to normalize and standardize due to human bias. For unconventional resource plays with tens of thousands of wells, constructing geological models and algorithms can be a daunting task. Machine learning is an emerging technology that has been increasingly adopted in the energy industry. It can provide automated and accurate well top picking and reservoir property analysis.
This paper utilizes a case study in the Belly River Formation (BRF) of Western Canada Sedimentary Basin (WCSB) to discuss capabilities enabled by automated well top picking and reservoir property analysis. First, 70993 wells with GR curve covering ~100000 km2 of WCSB were filtered. Among them, 32510 coring wells help to determine boundaries of BRF. Second, several tops were manually picked as seeds for automated picking using Subsequent Dynamic Time Warping approach. After quality control and log normalization, automatic picks were promoted into new seeds for subsequent picking until all pickings were done. Finally, the distribution of the BRF were defined, and combining with logging curves, the variation law of reservoir properties (porosity, permeability, saturation, etc.) was analyzed.
Automated well top picking algorithm natively handles log normalization issues and picks. It completed ~70000s wells top picks in about 100 hours on cross section and map view, which may take over 1000 hours using traditional manual picking methods. Moreover, after automated well top picking, reservoir properties can be predicted as a "one-mouse-click" exercise. What need to do is to ascertain the acquired reservoir properties according to the production practice and to determine the algorithms and formulas according to the regional geological features. This workflow greatly improves efficiencies of the comprehensive reservoir evaluation and reservoir geological modeling of the WCSB by orders of magnitude.
Subsequently, combining automated well top picking and reservoir property analysis results and real-time data of oilfield production, the exploration and production sweet spot prediction of the BRF of the WCSB can be done. In conclusion, this efficient approach based on machine learning has been successfully applied to the potential assessment of petroleum resources in the BRF. The assessment results were used for petroleum reservoir exploration and production, oilfield development plan design, and portfolio management and optimization. Application of the method requires cooperation across different disciplines—data science and earth science. The interdisciplinary nature provides accurate prediction and design optimization for unconventional resources exploration and production.