Developing the efficiency-modeling framework to explore the potential of CO2 storage capacity of S3 reservoir, Tahe oilfield, China

Carbon capture and utilisation is a viable method of reducing greenhouse gas emissions. As a result, carbon dioxide (CO2) injection in oil formations is recognised as a promising solution for improving oil recovery factor whilst storing carbon in target sites. To achieve this goal, this study developed a novel efficiency workflow model to produce a reasonable geological model for exploring the potential CO2 storage capacity in the S3 reservoir of Tahe oilfield in China. The petrophysical properties of a well were initially predicted by artificial neural network. Then, object-based modelling technique was utilised to construct a lithofacies model. Afterwards, SGS and co-kriging techniques were employed to distribute petrophysical properties in a 3D geological model. Subsequently, 100 geological realisations were generated to assess the uncertainty of the pore volume. Thereafter, three ranked realisations (P10, P50 and P90) were utilised for uncertainty asessment of potential CO2 storage. Moreover, the CO2 storage capacity of brownfield was estimated to be in the range of 5.25-78.3 x106 tons. Ultimately, this paper has clearly improved our understanding of potential for carbon storage and boost oil recovery in the S3 reservoir of Tahe oilfield.

Automated summary

This study developed a novel workflow model to explore the potential CO2 storage capacity in the S3 reservoir of Tahe oilfield in China and proposed a viable solution for oil recovery. By using artificial neural network and modelling technique, researchers obtained a reasonable geological model and assessed the uncertainty of CO2 storage.