Abstract and subjects
SUMMARY
Due to international commitments on carbon capture and storage (CCS), an increase in CCS projects is expected in the near future. Saline aquifers and depleted hydrocarbon reservoirs with good seals and located in tectonically stable zones make an excellent storage formation option for geological carbon sequestration. However, stored carbon dioxide (CO2) takes a long time to convert into diagenetically stable form. Hence, ensuring the CO2 does not leak from these reservoirs in this time period is the key to any successful CCS project. Numerous methods are developed over the past couple of decades to identify the leaks which utilizes various types of geophysical, geochemical and engineering data. We demonstrate the automated leakage detection in CCS projects using pressure data obtained from Cranfield reservoir, Mississippi, USA. Our dataset consists of CO2 injection rates and pressure monitoring data obtained from a pressure pulse test. We first demonstrate the differences between the pressure pulse signal in case of a baseline pulse test and a pulse test with an artificially induced leak onsite. We then use machine learning techniques to automatically differentiate between the two tests. The results indicate that even simple deep learning architectures such as multi-layer feedforward network (MFNN) can identify a leak using pressure data and can be used to raise an early warning flag.