ALBERT

Description: California hosts ∼124,000 abandoned and plugged (AP) oil and gas wells, ∼38,000 idle wells, and ∼63,000 active wells, whose methane (CH4) emissions remain largely unquantified at levels below ∼2 kg CH4 h–1. We sampled 121 wells using two methods: a rapid mobile plume integration method (detection ∼0.5 g CH4 h–1) and a more sensitive static flux chamber (detection ∼1 × 10–6 g CH4 h–1). We measured small but detectable methane emissions from 34 of 97 AP wells (mean emission: 0.286 g CH4 h–1). In contrast, we found emissions from 11 of 17 idle wells—which are not currently producing (mean: 35.4 g CH4 h–1)—4 of 6 active wells (mean: 189.7 g CH4 h–1), and one unplugged well—an open casing with no infrastructure present (10.9 g CH4 h–1). Our results support previous findings that emissions from plugged wells are low but are more substantial from idle wells. In addition, our smaller sample of active wells suggests that their reported emissions are consistent with previous studies and deserve further attention. Due to limited access, we could not measure wells in most major active oil and gas fields in California; therefore, we recommend additional data collection from all types of wells but especially active and idle wells.

Description: Highlights • Gas release from wells may counteract efforts to mitigate greenhouse gas emissions. • An approach for assessing methane release from marine decommissioned wells. • This gas release largely depends on the presence of shallow gas accumulations. • Methane release from hydrocarbon wells represents a major source in the North Sea. Abstract Hydrocarbon gas emissions from with decommissioned wells are an underreported source of greenhouse gas emissions in oil and gas provinces. The associated emissions may partly counteract efforts to mitigate greenhouse gas emissions from fossil fuel infrastructure. We have developed an approach for assessing methane leakage from marine decommissioned wells based on a combination of existing regional industrial seismic and newly acquired hydroacoustic water column imaging data from the Central North Sea. Here, we present hydroacoustic data which show that 28 out of 43 investigated wells release gas from the seafloor into the water column. This gas release largely depends on the presence of shallow gas accumulations and their distance to the wells. The released gas is likely primarily biogenic methane from shallow sources. In the upper 1,000 m below the seabed, gas migration is likely focused along drilling-induced fractures around the borehole or through non-sealing barriers. Combining available direct measurements for methane release from marine decommissioned wells with our leakage analysis suggests that gas release from investigated decommissioned hydrocarbon wells is a major source of methane in the North Sea (0.9-3.7 [95% confidence interval = 0.7-4.2] kt yr−1 of CH4 for 1,792 wells in the UK sector of the Central North Sea). This means hydrocarbon gas emissions associated with marine hydrocarbon wells are not significant for the global greenhouse gas budget, but have to be considered when compiling regional methane budgets.

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.