This study validates the top-view method for contact angle measurement, based on the spherical cap assumption, as a practical alternative to conventional side-view techniques. It is particularly useful when side-view imaging is challenging, such as on rough, deformable, irregular, or confined areas where baseline visibility, meridian clarity, or drop symmetry is compromised. To address these challenges, this study details the simultaneous acquisition of top and side-view images of sessile drops along with precise drop volume via drop mass measurements. The contact angles were then deduced from drop volume and contact area based on the spherical cap assumption. Contact angles of water and formamide (1-40 μL) on poly(methyl methacrylate) (PMMA) and Teflon surfaces, covering both wetting (θ_s < 90°) and nonwetting (θ_s > 90°) regimes, were measured. The results demonstrate that the top-view method yields statistically comparable contact angle values to side-view methods for small drops (1-10 μL). The study also introduces a drop projection index (DPI), a dimensionless parameter analogous to the bond number, to quantify gravity-induced effects in top-view imaging. The results showed that for θ_s < 90°, DPI was ∼0.15 for both formamide and water on PMMA, while for θ_s > 90°, DPI ranged from ∼0.2 (formamide on Teflon) to ∼0.3 (water on Teflon). Findings in this study provide a foundation for refining the applicability of the top-view method based on the spherical cap assumption and adapting it to a wide range of systems.

The Panoma Field in the Hugoton Embayment, Kansas, has produced significant gas resources from thousands of wells perforating the Permian Chase and Council Grove Groups. Variability in gas production from these formations is controlled by facies-influenced petrophysical properties. The use of geological facies data in numerical modeling is often limited to delineating regions of interest without intrinsic use in estimating petrophysical properties. Machine learning provides opportunities to integrate facies data into the numerical model-building process. In this study, we employ facies data in optimizing a numerical model permeability matrix scaling parameter using Monte Carlo Simulation of Markov Switching Dynamic Regression and machine learning. Realizations of the scaling parameter are included in a machine learning facies prediction workflow to identify the parameter that maximizes facies prediction accuracy, with test accuracy as high as 83%. A 3D numerical model was constructed to represent the interlayered carbonate, shale, and non-marine sandstones facies typical of the Council Grove intervals. Multiple field development and completion scenarios were evaluated to maximize cumulative gas recovery and assess the role of facies distribution on reservoir performance. History matching results of historical gas production demonstrate strong coupling between facies distribution and the optimized permeability, emphasizing the importance of facies data integration in reservoir property modeling and gas production estimation in Permian reservoirs. This implies that probabilistically constrained permeability scaling using the Monte Carlo and machine learning workflow produces more realistic modeling compared to traditional approaches.