Application of machine learning to quantification of mineral composition on gas hydrate-bearing sediments, Ulleung Basin, Korea

SY Park, BK Son, J Choi, H Jin, K Lee - Journal of Petroleum Science and …, 2022 - Elsevier
Mineral quantification is essential to evaluate gas hydrate (GH) resources because the
mineral composition is closely related to the origin of sediment, the reservoir properties, and …

An insight into the prediction of scale precipitation in harsh conditions using different machine learning algorithms

R Yousefzadeh, A Bemani, A Kazemi… - SPE Production & …, 2023 - onepetro.org
Scale precipitation in petroleum equipment is known as an important problem that causes
damages in injection and production wells. Scale precipitation causes equipment corrosion …

[HTML][HTML] Hydrate Blockage in Subsea Oil/Gas Pipelines: Characterization, Detection, and Engineering Solutions

Y Meng, B Han, J Wang, J Chu, H Yao, J Zhao… - Engineering, 2024 - Elsevier
With the development of offshore oil and gas resources, hydrates pose a significant
challenge to flow assurance. Hydrates can form, accumulate, and settle in pipelines, causing …

Enabling site-specific well leakage risk estimation during geologic carbon sequestration using a modular deep-learning-based wellbore leakage model

S Baek, DH Bacon, NJ Huerta - International Journal of Greenhouse Gas …, 2023 - Elsevier
Amid growing climate concerns, geologic carbon sequestration (GCS) is a promising
technology for mitigating net carbon emissions by storing CO 2 in reservoirs. Oil and gas …

Machine learning models for fast selection of amino acids as green thermodynamic inhibitors for natural gas hydrate

G Wu, F Coulon, JC Feng, Z Yang, Y Jiang… - Journal of Molecular …, 2023 - Elsevier
Natural amino acids are non-toxic thermodynamic hydrate inhibitors without negative
environmental impact, but it is difficult to accurately select the appropriate amino acid as a …

[HTML][HTML] Synergistic enhancement of productivity prediction using machine learning and integrated data from six shale basins of the USA

S Kim, KH Kim, JT Lim - Geoenergy Science and Engineering, 2023 - Elsevier
This study aimed to validate the synergistic enhancement of the machine learning model
random forest (RF) to predict the oil and gas estimated ultimate recovery (EUR) by …

Toward Field Soil Surveys: Identifying and Delineating Soil Diagnostic Horizons Based on Deep Learning and RGB Image

R Yang, J Chen, J Wang, S Liu - Agronomy, 2022 - mdpi.com
The diagnostic horizon in a soil is reflective of the environment in which it developed and the
inherent characteristics of the material, therefore quantitative approaches to horizon …

Geochemical Biodegraded Oil Classification Using a Machine Learning Approach

S Bispo-Silva, CJF de Oliveira, G de Alemar Barberes - Geosciences, 2023 - mdpi.com
Chromatographic oil analysis is an important step for the identification of biodegraded
petroleum via peak visualization and interpretation of phenomena that explain the oil …

[HTML][HTML] Improved prediction of shale gas productivity in the Marcellus shale using geostatistically generated well-log data and ensemble machine learning

S Kim, Y Hong, JT Lim, KH Kim - Computers & Geosciences, 2023 - Elsevier
This study proposes the application of geostatistically generated well-log data to predict well
productivity in Marcellus shale reservoirs using ensemble machine learning (ESM). ESM …

[HTML][HTML] Spatiotemporal interpretation of three-phase saturation behaviors in gas hydrate formation and dissociation through deep learning modeling

S Kim, K Lee, M Lee, J Lee, T Ahn, JT Lim - Geoenergy Science and …, 2023 - Elsevier
This study provides an interpretation of the three-phase saturation (water, gas, gas hydrate
(GH); SW, SG, S GH) in the GH cores during GH formation and depressurization …