A novel score system to evaluate carbonate reservoir combining microscale and macroscale parameters DOI Creative Commons
Huilin Xu, Guanqun Wang, Wei Xu

et al.

Journal of Petroleum Exploration and Production Technology, Journal Year: 2024, Volume and Issue: 14(5), P. 1101 - 1112

Published: Feb. 22, 2024

Abstract The central Sichuan Basin, located in western China, holds great significance terms of hydrocarbon production, especially relation to complex carbonate reservoirs, notably the Qixia Formation Middle Permian epoch. However, comprehensive evaluation this geological formation presents considerable challenges due lithology, limited availability reservoir property data at various scales, inadequacies integration, and absence a reliable ranking system for development decision making. Previous studies primarily relying on conventional level, such as well logs information, have proven insufficient accurately characterizing reservoir. This is evident without precise lithological information detailed knowledge microscale properties, which are crucial effective evaluation. To address these challenges, study integrates advanced technologies like X-ray diffraction, micro-CT scanning electron microscope (SEM) techniques digital drill cutting analysis microscale. A novel scoring has been developed using prominent component (PCA) approach an expert system, incorporates existing log analysis. validated actual production data, thus establishing robust methodology assessing exploration potential optimizing strategies gas reservoirs Formation. innovative approach, parameters both micro- macroscales, promising facilitating future efforts.

Language: Английский

A Robust Strategy of Geophysical Logging for Predicting Payable Lithofacies to Forecast Sweet Spots Using Digital Intelligence Paradigms in a Heterogeneous Gas Field DOI
Umar Ashraf, Hucai Zhang, Hung Vo Thanh

et al.

Natural Resources Research, Journal Year: 2024, Volume and Issue: 33(4), P. 1741 - 1762

Published: May 14, 2024

Language: Английский

Citations

22
Reservoir rock typing assessment in a coal-tight sand based heterogeneous geological formation through advanced AI methods DOI Creative Commons
Umar Ashraf, Wanzhong Shi, Hucai Zhang

et al.

Scientific Reports, Journal Year: 2024, Volume and Issue: 14(1)

Published: March 7, 2024

Abstract Geoscientists now identify coal layers using conventional well logs. Coal layer identification is the main technical difficulty in coalbed methane exploration and development. This research uses advanced quantile–quantile plot, self-organizing maps (SOM), k-means clustering, t-distributed stochastic neighbor embedding (t-SNE) qualitative log curve assessment through three wells (X4, X5, X6) complex geological formation to distinguish from tight sand shale. Also, we reservoir rock typing (RRT), gas-bearing non-gas bearing potential zones. Results showed gamma-ray resistivity logs are not reliable tools for identification. Further, highlighted high acoustic (AC) neutron porosity (CNL), low density (DEN), photoelectric, values as compared While, 5–10% values. The SOM clustering provided evidence of good-quality RRT facies, whereas other clusters related shale poor-quality RRT. A t-SNE algorithm accurately distinguished was used make CNL DEN plot that presence low-rank bituminous rank study area. presented strategy shall provide help comprehend coal-tight lithofacies units future mining.

Language: Английский

Citations

19
Artificial intelligence-driven assessment of salt caverns for underground hydrogen storage in Poland DOI Creative Commons
Reza Derakhshani, Leszek Lankof, Amin GhasemiNejad

et al.

Scientific Reports, Journal Year: 2024, Volume and Issue: 14(1)

Published: June 20, 2024

Abstract This study explores the feasibility of utilizing bedded salt deposits as sites for underground hydrogen storage. We introduce an innovative artificial intelligence framework that applies multi-criteria decision-making and spatial data analysis to identify most suitable locations storing in caverns. Our approach integrates a unified platform with eight distinct machine-learning algorithms—KNN, SVM, LightGBM, XGBoost, MLP, CatBoost, GBR, MLR—creating rock deposit suitability maps The performance these algorithms was evaluated using various metrics, including Mean Squared Error (MSE), Absolute (MAE), Percentage (MAPE), Root Square (RMSE), Correlation Coefficient (R 2 ), compared against actual dataset. CatBoost model demonstrated exceptional performance, achieving R 0.88, MSE 0.0816, MAE 0.1994, RMSE 0.2833, MAPE 0.0163. novel methodology, leveraging advanced machine learning techniques, offers unique perspective assessing potential is valuable asset stakeholders, government bodies, geological services, renewable energy facilities, chemical/petrochemical industry, aiding them identifying optimal

Language: Английский

Citations

12
Leveraging Automated Deep Learning (AutoDL) in Geosciences DOI
Nandito Davy, Umair bin Waheed, Ardiansyah Koeshidayatullah

et al.

Computers & Geosciences, Journal Year: 2024, Volume and Issue: 188, P. 105600 - 105600

Published: April 28, 2024

Language: Английский

Citations

6
Data-driven total organic carbon prediction using feature selection methods incorporated in an automated machine learning framework DOI Creative Commons
Bruno da Silva Macêdo, Dennis Delali Kwesi Wayo, Deivid Campos

et al.

Scientific Reports, Journal Year: 2025, Volume and Issue: 15(1)

Published: March 27, 2025

An accurate assessment of shale gas resources is highly important for the sustainable development these energy resources. Total organic carbon (TOC) analysis thus becomes fundamental understanding distribution and quality hydrocarbon source rocks within a reservoir. The elevation TOC often associated with presence rocks, indicating potential oil production. performed using laboratory methods, which can be time-consuming costly. Data-driven models have been successfully applied to model relationship between other constituents predict content. However, methods depend on extensive parameter adjustments that must carefully conducted in different sedimentary environments. In this context, Automated Machine Learning (AutoML) an alternative accurately predicting TOCs, saving fine-tuning steps development. This study aims develop AutoML strategy estimating well log data. procedure automatically preprocesses search best method parameters, reducing execution time. Among evaluated, Extremely Randomized Trees (XT) (R = 0.8632, MSE 0.1806) test set. proposed provides powerful data-driven method, allows real-world use assist data subsequent decision-making.

Language: Английский

Citations

0
Optimizing Permeability and Porosity Prediction with Advanced Machine Learning: A Case Study Unlocking the Complexities of Late Cretaceous Reservoirs, Gulf of Suez, Egypt. DOI
Amer A. Shehata, Mohamed Ahmed,

Ahmed A. Kassem

et al.

Journal of African Earth Sciences, Journal Year: 2025, Volume and Issue: unknown, P. 105670 - 105670

Published: April 1, 2025

Language: Английский

Citations

0
Advancing shale geochemistry: Predicting major oxides and trace elements using machine learning in well-log analysis of the Horn River Group shales DOI
Ammar Abdlmutalib, Korhan Ayrancı, Umair bin Waheed

et al.

International Journal of Coal Geology, Journal Year: 2025, Volume and Issue: unknown, P. 104767 - 104767

Published: April 1, 2025

Language: Английский

Citations

0
Reservoir Property Prediction in the North Sea Using Machine Learning DOI Creative Commons
Abdulrahman Al‐Fakih, SanLinn I. Kaka, Ardiansyah Koeshidayatullah

et al.

IEEE Access, Journal Year: 2023, Volume and Issue: 11, P. 140148 - 140160

Published: Jan. 1, 2023

The North Sea sedimentary basin is characterized by geological complexity, encompassing a wide range of rock types and structures, including multiple reservoirs (carbonates siliciclastic) with variations in reservoir quality heterogeneity. These phenomena pose significant challenges for accurately predicting properties using traditional well log analysis. Moreover, these are further compounded complex conditions scarcity available data. Hence, the aim this study was to address applying advanced machine learning techniques within basin. This delves into both supervised unsupervised approaches forecast essential petrophysical that crucial assessing quality. encompass total porosity, effective shale volume, all derived from data originating models were trained four wells consisting 32,215 points (80% training, 10% testing, validation). Furthermore, our introduced pioneering data-driven preprocessing workflow, exploratory analysis, missing imputation, outlier detection improve performance models. ANN RF achieved best results among algorithms evaluated, an average MAE 0.01, RMSE R-squared 0.95 volume shale, respectively. metrics demonstrate model can predict challenging basin, even limited availability, enabling characteristics field development optimization, particularly areas where core scarce.

Language: Английский

Citations

8
Machine Learning-Based Prediction of Pore Types in Carbonate Rocks Using Elastic Properties DOI
Ammar Abdlmutalib,

Abdallah Abdelkarim

Arabian Journal for Science and Engineering, Journal Year: 2024, Volume and Issue: unknown

Published: Aug. 13, 2024

Language: Английский

Citations

1
A novel score system to evaluate carbonate reservoir combining microscale and macroscale parameters DOI Creative Commons
Huilin Xu, Guanqun Wang, Wei Xu

et al.

Journal of Petroleum Exploration and Production Technology, Journal Year: 2024, Volume and Issue: 14(5), P. 1101 - 1112

Published: Feb. 22, 2024

Abstract The central Sichuan Basin, located in western China, holds great significance terms of hydrocarbon production, especially relation to complex carbonate reservoirs, notably the Qixia Formation Middle Permian epoch. However, comprehensive evaluation this geological formation presents considerable challenges due lithology, limited availability reservoir property data at various scales, inadequacies integration, and absence a reliable ranking system for development decision making. Previous studies primarily relying on conventional level, such as well logs information, have proven insufficient accurately characterizing reservoir. This is evident without precise lithological information detailed knowledge microscale properties, which are crucial effective evaluation. To address these challenges, study integrates advanced technologies like X-ray diffraction, micro-CT scanning electron microscope (SEM) techniques digital drill cutting analysis microscale. A novel scoring has been developed using prominent component (PCA) approach an expert system, incorporates existing log analysis. validated actual production data, thus establishing robust methodology assessing exploration potential optimizing strategies gas reservoirs Formation. innovative approach, parameters both micro- macroscales, promising facilitating future efforts.

Language: Английский

Citations

0