Quantifying non-steady state natural gas leakage from the pipelines using an innovative sensor network and model for subsurface emissions - InSENSE.

Detecting and quantifying subsurface leaks remains a challenge due to the complex nature and extent of belowground leak scenarios. To address these scenarios, monitoring and evaluating changes in gas leakage behavior over space and time are crucial for ensuring safe and efficient responses to known or potential gas leaks. This study demonstrates the capability of linking environmental and gas concentration data obtained using a low-cost, near real-time methane (CH4) detector network and an inverse gas migration model to capture and quantify non-steady state belowground natural gas (NG) leaks. The Estimating Surface Concentration Above Pipeline Emission (ESCAPE) model was modified to incorporate the impact of soil properties on gas migration. Field-scale controlled NG experiments with leakage rates ranging from 37 to 121 g/h indicate that elevated belowground near-surface (BNS) gas concentrations persist long before elevated surface concentrations are observed. On average, BNS CH4 concentrations were 20%-486% higher than surface CH4 concentrations within the monitoring radius of 4 m from the leak location. An increase in the BNS CH4 concentration was observed within 3 h as the leak rate increased from 37 to 89 g/h. However, due to the atmospheric fluctuations, any changes in surface CH4 concentrations could not be confirmed within this period. The plume area of the BNS CH4 extended approximately two times farther than that of the surface CH4 as the gas leak rate increased from 37 to 121 g/h. The estimated NG leak rates by the modified ESCAPE model agreed well with the experimental NG leak rates (m = 0.99 and R2 = 0.77), demonstrating that including soil characteristics and BNS CH4 measurements can advance estimations of non-steady NG leak rates in low and moderate NG leak rate scenarios. The CH4 detector network and model show potential as an innovative tool to improve operators' risk assessment and NG leakage response.

Investigating detection probability of mobile survey solutions for natural gas pipeline leaks under different atmospheric conditions.

The 2015 Paris agreement aims to cut greenhouse gas emissions and keep global temperature rise below 2 °C above pre-industrial levels. Reducing CH4 emissions from leaking pipelines presents a relatively achievable objective. While walking and driving surveys are commonly used to detect leaks, the detection probability (DP) is poorly characterized. This study aims to investigate how leak rates, survey distance and speed, and atmospheric conditions affect the DP in controlled belowground conditions with release rates of 0.5-8.5 g min-1. Results show that DP is highly influenced by survey speed, atmospheric stability, and wind speed. The average DP in Pasquill-Gifford stability (PG) class A is 85% at a low survey speed (2-11 mph) and decreases to 68%, 63%, 65%, and 60% in PGSC B/C, D, E/F, and G respectively. It is generally less than 25% at a high survey speed (22-34 mph), regardless of stability conditions and leak rates. Using the measurement data, a validated DP model was further constructed and showed good performance (R2: 0.76). The options of modeled favorable weather conditions (i.e., PG stability class and wind speed) to have a high DP (e.g., >50%) are rapidly decreased with the increase in survey speed. Walking survey is applicable over a wider range of weather conditions, including PG stability class A to E/F and calm to medium winds (0-5 m s-1). A driving survey at a low speed (11 mph) can only be conducted under calm to low wind speed conditions (0-3 m s-1) to have an equivalent DP to a walking survey. Only calm wind conditions in PG A (0-1 m s-1) are appropriate for a high driving speed (34 mph). These findings showed that driving survey providers need to optimize the survey schemes to achieve a DP equivalence to the traditional walking survey.

Estimating natural gas emissions from underground pipelines using surface concentration measurements☆.

Rapid response to underground natural gas leaks could mitigate methane emissions and reduce risks to the environment, human health and safety. Identification of large, potentially hazardous leaks could have environmental and safety benefits, including improved prioritization of response efforts and enhanced understanding of relative climate impacts of emission point sources. However, quantitative estimation of underground leakage rates remains challenging, considering the complex nature of methane transport processes. We demonstrate a novel method for estimating underground leak rates based on controlled underground natural gas release experiments at the field scale. The proposed method is based on incorporation of easily measurable field parameters into a dimensionless concentration number, ε, which considers soil and fluid characteristics. A series of field experiments was conducted to evaluate the relationship between the underground leakage rate and surface methane concentration data over varying soil and pipeline conditions. Peak surface methane concentrations increased with leakage rate, while surface concentrations consistently decreased exponentially with distance from the source. Deviations between the estimated and actual leakage rates ranged from 9% to 33%. A numerical modeling study was carried out by the TOUGH3 simulator to further evaluate how leak rate and subsurface methane transport processes affect the resulting methane surface profile. These findings show that the proposed leak rate estimation method may be useful for prioritizing leak repair, and warrant broader field-scale method validation studies. A method was developed to estimate fugitive emission rates from underground natural gas pipeline leaks. The method could be applied across a range of soil and surface covering conditions.