Methane (CH₄) is a key greenhouse gas for which accurate source estimation is crucial for climate action. This study evaluates divergence-based (DIV) methods: physics-based, training-free techniques that estimate pixel-wise methane emissions based wind and methane concentration fields. We conducted a systematic search and evaluation into parametrisation and method variations across idealized and simulated cases to find which combination yields the most accurate emission estimations when using observations from the TROPOMI instrument aboard Sentinel-5P. This study confirmed the choice of a pressure-based background correction combined with monthly flux-averaging on synthetic cases. This was followed by a case study of the Permian basin using TROPOMI data which was consistent with studies using divergence and inversion methods on the 2018-2021 period. Finally, an increase of 154% was revealed over the sparsely studied 2022-2024 period which may be attributed to the development of the oil and gas industry in the region.