Prediction of maximum pitting corrosion depth in oil and gas pipelines
Avoiding failures of corroded steel structures are critical in offshore oil and gas operations.
An accurate prediction of maximum depth of pitting corrosion in oil and gas pipelines has …
An accurate prediction of maximum depth of pitting corrosion in oil and gas pipelines has …
[HTML][HTML] Predictive deep learning for pitting corrosion modeling in buried transmission pipelines
B Akhlaghi, H Mesghali, M Ehteshami… - Process Safety and …, 2023 - Elsevier
Despite significant efforts and investments in the renewable energy sector, fossil fuels
continue to provide the majority of the world's energy supply. Transmission pipelines, which …
continue to provide the majority of the world's energy supply. Transmission pipelines, which …
Spatio-temporal sequence prediction of CO2 flooding and sequestration potential under geological and engineering uncertainties
CO 2 injection for subsurface hydrocarbon development not only enhances oil and gas
recovery but also enables CO 2 sequestration in the subsurface. It is essential to develop …
recovery but also enables CO 2 sequestration in the subsurface. It is essential to develop …
Probabilistic characterization of subsurface stratigraphic configuration with modified random field approach
Accurate and precise characterization of the subsurface stratigraphic configuration
(geological model) at a given site is crucial to geotechnical engineering work. The …
(geological model) at a given site is crucial to geotechnical engineering work. The …
Application of machine learning techniques in mineral classification for scanning electron microscopy-energy dispersive X-ray spectroscopy (SEM-EDS) images
Mineral classification and segmentation is time-consuming in geological image processing.
The development of machine learning methods shows promise as a technique in replacing …
The development of machine learning methods shows promise as a technique in replacing …
Comparative study on supervised learning models for productivity forecasting of shale reservoirs based on a data-driven approach
D Han, J Jung, S Kwon - Applied Sciences, 2020 - mdpi.com
Due to the rapid development of shale gas, a system has been established that can utilize a
considerable amount of data using the database system. As a result, many studies using …
considerable amount of data using the database system. As a result, many studies using …
Efficient assessment of reservoir uncertainty using distance-based clustering: a review
This paper presents a review of 71 research papers related to a distance-based clustering
(DBC) technique for efficiently assessing reservoir uncertainty. The key to DBC is to select a …
(DBC) technique for efficiently assessing reservoir uncertainty. The key to DBC is to select a …
Uncertainty quantification of channel reservoirs assisted by cluster analysis and deep convolutional generative adversarial networks
B Kang, J Choe - Journal of Petroleum Science and Engineering, 2020 - Elsevier
Reservoir characterization is to find reservoir properties of interest by combining available
geological information. In channel reservoirs, flow responses are very sensitive depending …
geological information. In channel reservoirs, flow responses are very sensitive depending …
Characterization of three-dimensional channel reservoirs using ensemble Kalman filter assisted by principal component analysis
Abstracts Ensemble-based analyses are useful to compare equiprobable scenarios of the
reservoir models. However, they require a large suite of reservoir models to cover high …
reservoir models. However, they require a large suite of reservoir models to cover high …
A machine learning approach for predicting the electro-mechanical impedance data of blended RC structures subjected to chloride laden environment
T Bansal, V Talakokula… - Smart Materials and …, 2021 - iopscience.iop.org
The application of the electro-mechanical impedance (EMI) technique using piezo sensors
for structural health monitoring (SHM) is based on baseline/healthy signature data, which …
for structural health monitoring (SHM) is based on baseline/healthy signature data, which …