Applications of AI in oil and gas projects towards sustainable development: a systematic literature review

A Waqar, I Othman, N Shafiq, MS Mansoor - Artificial Intelligence Review, 2023 - Springer
Oil and gas construction projects are critical for meeting global demand for fossil fuels, but
they also present unique risks and challenges that require innovative construction …

The Fourier transform infrared spectroscopy (FTIR) analysis for the clay mineralogy studies in a clastic reservoir

G Jozanikohan, MN Abarghooei - Journal of Petroleum Exploration and …, 2022 - Springer
The complete characteristics knowledge of clay minerals is necessary in the evaluation
studies of hydrocarbon reservoirs. Ten samples taken from two wells in a heterogeneous …

Reservoir quality prediction of gas-bearing carbonate sediments in the Qadirpur field: Insights from advanced machine learning approaches of SOM and cluster …

M Rashid, M Luo, U Ashraf, W Hussain, N Ali… - Minerals, 2022 - mdpi.com
The detailed reservoir characterization was examined for the Central Indus Basin (CIB),
Pakistan, across Qadirpur Field Eocene rock units. Various petrophysical parameters were …

Reservoir characterization through comprehensive modeling of elastic logs prediction in heterogeneous rocks using unsupervised clustering and class-based …

M Ali, P Zhu, R Jiang, M Huolin, M Ehsan… - Applied Soft …, 2023 - Elsevier
Geophysical reservoir characterization is a significant task in the oil and gas industry and
elastic logs prediction of subsurface formations is a fundamental aspect of this process …

A core logging, machine learning and geostatistical modeling interactive approach for subsurface imaging of lenticular geobodies in a clastic depositional system, SE …

U Ashraf, H Zhang, A Anees, HN Mangi, M Ali… - Natural Resources …, 2021 - Springer
Facies models are essential tools for imaging subsurface geobodies and for reducing
exploration and development risks efficiently. The Lower Goru Formation is one of the …

Machine learning-A novel approach of well logs similarity based on synchronization measures to predict shear sonic logs

M Ali, R Jiang, H Ma, H Pan, K Abbas, U Ashraf… - Journal of Petroleum …, 2021 - Elsevier
This study proposes a novel approach to predict missing shear sonic log responses more
precisely and accurately using similarity patterns of various wells with similar geophysical …

A robust strategy of geophysical logging for predicting payable lithofacies to forecast sweet spots using digital intelligence paradigms in a heterogeneous gas field

U Ashraf, H Zhang, HV Thanh, A Anees, M Ali… - Natural Resources …, 2024 - Springer
The most crucial elements in the oil and gas sector are predicting subsurface lithofacies
utilizing geophysical logs for reservoir characterization and sweet spot assessment …

[HTML][HTML] Classification of reservoir quality using unsupervised machine learning and cluster analysis: Example from Kadanwari gas field, SE Pakistan

N Ali, J Chen, X Fu, W Hussain, M Ali, SM Iqbal… - Geosystems and …, 2023 - Elsevier
Understanding geological variance in a proved reservoir requires accurate as well as exact
characterization of lithological facies. In the Kadanwari gas field, machine learning (ML) …

An ensemble-based strategy for robust predictive volcanic rock ty** efficiency on a global-scale: a novel workflow driven by big data analytics

U Ashraf, H Zhang, A Anees, M Ali, HN Mangi… - Science of the Total …, 2024 - Elsevier
Laboratory measurements, paleontological data, and well-logs are often used to conduct
mineralogical and chemical analyses to classify rock samples. Employing digital intelligence …

[HTML][HTML] Application of machine learning for lithofacies prediction and cluster analysis approach to identify rock type

M Hussain, S Liu, U Ashraf, M Ali, W Hussain, N Ali… - Energies, 2022 - mdpi.com
Nowadays, there are significant issues in the classification of lithofacies and the
identification of rock types in particular. Zamzama gas field demonstrates the complex nature …