ALBERT

Description: Raw data of pCO2 measurements during Celtic Explorer cruise CE12010 at station 44ROV12 (Gas Release Experiment). The gas discharge of the gas release experiment was observed in situ during a 4 hour dive with GEOMAR's remotely operated vehicle ROV Kiel 6000 equipped with HD camera/video device and a sonar system. The spread of the dissolved CO2 plume was monitored geochemically using the commercial HydroC pCO2 sensor (S/N 0412-006, Kongsberg Maritime Contros GmbH) mounted to the front porch of the ROV. The sensor was calibrated for pCO2 signals up to 3,000 µatm (accuracy ~1% of reading resolution; resolution: 〈30 µatm) and was programmed to measure in 60 s intervals, which is equal to the sensor's response time.

Description: Video showing the gas release experiment, which has been conducted at ~80 m water depth in the vicinity of the Sleipner CO2 storage site in the North Sea. The gas discharge was observed in situ during a 4 hour dive with GEOMAR's remotely operated vehicle ROV Kiel 6000 equipped with HD camera/video device.

Description: Source data of the North Sea well inventory: United Kingdom (UK)- Oil and Gas Authority (Dec. 2018) - https://data-ogauthority.opendata.arcgis.com/datasets/oga-wells-ed50 Contains information provided by the OGA. Wells are extracted for the area of the PGS data set PGS Mega Survey Plus. We measured the distance between all wells of the test group (n = 43) and all those who are within the seismic data set (n = 1,792; presented here) and their closest bright spot with polarity reversal. Furthermore, we calculated the mean RMS amplitudes and RMS amplitude standard deviation for a buffer radius of 300 m around the well paths for all wells inside the seismic data set and the visited wells as 300 m is the distance below which all of the visited wells of the test group showed gas release in form of flares from the seafloor. We test, if the propensity of a well to leak can be identified by using a logistic regression, which includes regressors such as well activity data and/or derived parameters such as mean RMS amplitude and mean RMS amplitude standard deviation, the distance towards the most proximal bright spot with polarity reversal and age (spud date). In order to identify the most suitable regressor combination best subset selection is employed. The main selection criterion chosen was the prediction accuracy from randomly and repeatedly splitting the visited wells into a training and a test set and then using the fitted logistic regression to predict the test data. The most suitable subset turns out to only employ the distance to polarity reversal, producing a prediction accuracy of 89% and the following logistic regression results: In order to obtain confidence intervals using the normal distribution the distance to bright spot with polarity reversal has to be normally distributed, which it is not. Yet it can be transformed to normality by adding 100 meters to the original distance and then taking the natural logarithm: Logistic regression fit for leakage of all visited wells using distance to bright spot with polarity reversal in meters as a regressor. Please find further information on the applied statistical analyses in the supplementary material. EstimateStd. Errorz valuePr(〉|z|)Significance Intercept4,853.9461,735.1282.7970.005150.01 Distance−0.0073610.002700−2.7260.006400.01 The transformed logistic regression model is then used to predict the probabilities of leakage for the wells within our seismic data set in the Central North Sea (here presented data). In order to obtain confidence bands this logistic regression is performed subtracting and adding two standard deviations from the calculated probability. The point estimate predicts leakage from 926 of the 1,792 wells, where the 95% confidence interval ranges from 719 to 1,058.