Description: High-resolution 2D seismic reflection data during research cruise MSM63 in April/May 2017 onboard RV Maria S. Merian. The seismic profiles were acquired with a two-105/105-in3-GI-Gun-array shot at 210 bar every 5 seconds and a 150 m-long streamer with 96 channels and 1.5625 m channel spacing. The resulting shot point distance is approximately 8.75-12.5 m at 3.5-5 kn ship speed. The frequency range of the two-GI-Gun-array is 15-500 Hz. The processing included geometry and delay corrections, static corrections, binning to 1.5625 m and bandpass filtering with corner frequencies of 25, 45, 420, and 500 Hz. Furthermore, a normal-move-out-correction (with a constant velocity of 1488 m/s calculated from CTD measurements) was applied and the data were stacked and then migrated using a 2D Stolt algorithm (1500 m/s constant velocity model).

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.