Article
Geosciences, Multidisciplinary
Gabriel C. Unomah, Manika Prasad, Michael A. Oladunjoye, Idowu A. Olayinka
Summary: This study investigates the geological properties of the Lokpanta Shale in the Anambra Basin and identifies its potential as an unconventional hydrocarbon resource. The study findings suggest that the shale contains organic matter and clay minerals, with wide pore distribution and high fluid storage, transport, and adsorption capacity.
MARINE AND PETROLEUM GEOLOGY
(2022)
Article
Geosciences, Multidisciplinary
Humayun Khalil Khan, Muhsan Ehsan, Abid Ali, Muhammad Attique Amer, Haroon Aziz, Abdullah Khan, Yasir Bashir, Tamer Abu-Alam, Mohamed Abioui
Summary: This research estimated the organic carbon content of Talhar Shale in the Southern Indus Basin, Pakistan through various methods, and concluded that the Multivariate Fitting method is the optimized method for TOC estimation, particularly when well cuttings/core data are not available. The study correlated the TOC estimations with well cuttings data and found strong correlation values in both wells for Talhar Shale SIB, Pakistan.
FRONTIERS IN EARTH SCIENCE
(2022)
Article
Energy & Fuels
Lucas Abreu Blanes de Oliveira, Cleyton de Carvalho Carneiro
Summary: Geochemical logs are essential for hydrocarbon reservoir characterization, but in reduced logging operations in Brazilian pre-salt carbonate reservoirs, the geochemical tool is no longer acquired for cost reduction. This research develops synthetic geochemical logs using machine learning algorithms that can substitute actual logs with high confidence, providing quality data for formation evaluation. Robust models are trained for Al, Ca, Fe, Mg, Si, S, Ti, and Na, with R-2 above 0.70, and RMSE between 10(-2) to 10(-4), showing good agreement with acquired logs and general trends of the pre-salt formations.
JOURNAL OF PETROLEUM SCIENCE AND ENGINEERING
(2021)
Article
Geochemistry & Geophysics
Wen Pan, Carlos Torres-Verdin, Ian J. Duncan, Michael J. Pyrcz
Summary: Well-log interpretation is crucial for estimating rock properties and supporting reservoir development. However, measurement errors and variability can affect the accuracy of estimations. To improve multiwell rock-property estimation, we propose discriminative adversarial (DA) and linear constraint models for well-log normalization and estimation. The DA model considers the joint distribution of well logs and rock properties, while the linear constraint model uses an ensemble of linear predictions to constrain normalization and estimation. The results show that the DA model outperforms other models by reducing the mean-squared error of permeability prediction by 20%-50%.
Article
Energy & Fuels
Qiulei Guo, Songqi Pan, Feng Yang, Yue Yao, He Zheng
Summary: The study provides quantitative and visual qualitative analyses of shale oil occurrence using petrophysical and geochemical approaches. It found that soluble organic matter in oil-bearing shales is mainly controlled by clay minerals, and that solvent extraction can significantly enhance the pore volume and surface area of shales. Light and heavy hydrocarbons were found to reside in pores with different scales in the extracted soluble organic matter.
Article
Energy & Fuels
Partha Pratim Mandal, Reza Rezaee, Irina Emelyanova
Summary: This study proposes a workflow for predicting continuous TOC profiles in complex and heterogeneous shale reservoirs using ensemble learning regression models. The results show that careful data preparation, feature selection, reconstruction of corrupted logs, and the use of ensemble learning methods significantly improve the accuracy of TOC prediction.
Article
Energy & Fuels
Majid Safaei-Farouji, Ali Kadkhodaie
Summary: This study utilized various machine learning techniques to estimate kerogen type from petrophysical well logs in the Perth Basin, Western Australia. Optimization algorithms were applied to improve the accuracy of hydrogen index estimation, and the weighted averaging committee machine was proposed as the optimal solution.
JOURNAL OF PETROLEUM SCIENCE AND ENGINEERING
(2022)
Article
Geochemistry & Geophysics
Naihao Liu, Teng Huang, Jinghuai Gao, Zongben Xu, Daxing Wang, Fangyu Li
Summary: Lithology interpretation plays a crucial role in understanding subsurface properties. Automatic well log interpretation tools based on machine learning and deep learning show fine performance, but face challenges with generalization. By leveraging parameterized quantum circuits in a deep-learning model, the proposed QEDL model demonstrates improved generalization power for interpreting thin and thick lithology layers, with significantly fewer model parameters compared to LSTM and CNN models.
IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING
(2022)
Article
Geosciences, Multidisciplinary
Bruno Valle, Patrick Fuhr Dal, Helisson Nascimento dos Santos, Gilberto Raitz, Pedro Coelho, Michele Arena, Jeferson Santos, Julia Favoreto, Carolina Ribeiro, Leonardo Borghi
Summary: This study proposes an algorithm to identify zones with magnesium clay, and confirms the distribution of magnesium clays in the Santos Basin through well logs and core samples analysis. The algorithms proved to be reliable methods for both qualitative and quantitative detection of magnesium clay-rich zones, contributing to the improvement of petrophysical interpretations and reducing exploratory and development risks.
MARINE AND PETROLEUM GEOLOGY
(2023)
Article
Multidisciplinary Sciences
Alireza Rostami, Ali Kordavani, Shahin Parchekhari, Abdolhossein Hemmati-Sarapardeh, Abbas Helalizadeh
Summary: In this study, intelligent mathematical methods and optimization algorithms were used to calculate permeability in a low-porosity carbonate reservoir in Southwest Iran. The CMIS method based on acoustic logging was found to be more accurate than NMR techniques and can easily identify different types of fractures, making it highly valuable for reservoir simulation and well completion work.
SCIENTIFIC REPORTS
(2022)
Article
Energy & Fuels
Souvik Sen, Mohamed Abioui, Shib Sankar Ganguli, Ahmed Elsheikh, Akash Debnath, Mohammed Benssaou, Ahmed Awad Abdelhady
Summary: Capturing petrophysical heterogeneities within a reservoir is crucial for reservoir deliverability and field development programs. This study evaluated the Alamein dolomite reservoir in the Western Desert of Egypt, identifying a wide range of porosity and permeability. A permeability prediction model based on Random Forest regression provided more confident predictions compared to conventional methods, and six distinct petrofacies associations were identified based on petrophysical attributes.
Article
Energy & Fuels
L. Goliatt, C. M. Saporetti, E. Pereira
Summary: Determining the total organic carbon (TOC) content is crucial for risk assessment in oil exploration, but it can be costly and demanding. Therefore, computational methods and machine learning models, such as stacking models, emerge as an option to overcome these challenges. This paper presents a super learner strategy based on stacking approaches, which outperforms standalone machine learning models and other stacking models in TOC modeling.
Review
Energy & Fuels
Marek Stadtmuller, Jadwiga A. Jarzyna
Summary: The purpose of this review paper is to demonstrate the potential of well logging and laboratory measurements for characterization of carbonate reservoirs. Various standard and new methods of well logging acquisition and interpretation were examined, along with laboratory experiments to highlight the historical aspects of carbonate rock investigations. The paper also provided a brief overview of the geology, mineralogy, and petrography of carbonate rocks. Reservoir properties such as porosity, permeability, and saturation were discussed, with special emphasis on the unique features of carbonates such as fractures and vugs. Examples were presented to illustrate the methodologies used in both laboratory techniques and standard well logs. The paper also discussed the application of novel well logging techniques and modern laboratory investigations, as well as the use of computational technologies for data integration.
Article
Chemistry, Multidisciplinary
Naveed Ahmad, Sikandar Khan, Abdullatif Al-Shuhail
Summary: The study area in the Punjab Territory of Pakistan was analyzed using 2D seismic and well log data to interpret the subsurface structure and reservoir characteristics of the Kabirwala area Tola (01) well. Formation evaluation for hydrocarbon potential and petrophysical analysis were conducted to identify possible hydrocarbon-bearing zones, with the presence of the Tola-01 well providing confirmation for potential hydrocarbon entrapment in the high zone of interest.
APPLIED SCIENCES-BASEL
(2021)
Article
Energy & Fuels
Waleed Osman, Mohamed Kassab, Ahmed ElGibaly, Hisham Samir
Summary: This study evaluates the Kharita gas reservoir to enhance production by accurately determining pore throats, pores connectivity, and fluid distribution. Integration of core and logging data responses helps draw inferences about lithology, depositional sequences, facies, and fluid content, based on petrophysical models. The study also utilized a new unconventional petrophysical evaluation approach, resulting in doubling the volumes in the Upper Kharita reservoir.
JOURNAL OF PETROLEUM EXPLORATION AND PRODUCTION TECHNOLOGY
(2021)
Article
Geochemistry & Geophysics
Jimmy Xuekai Li, Reza Rezaee, Tobias M. Muller, Mahyar Madadi, Rupeng Ma, Mohammad Sarmadivaleh
Summary: Understanding seismic wave propagation in granular porous media is crucial for subsurface characterization. This study investigates the influence of wettability conditions on wave propagation in (partially) saturated granular porous media through laboratory experiments. The results reveal that the presence of liquid bridges in water-wetting conditions reinforces force chains and increases the P-wave velocity, leading to incoherent scattering. In contrast, gas-wetting conditions prevent the formation of liquid bridges, resulting in negligible incoherent scattering.
Article
Polymer Science
Farzad Pashapouryeganeh, Ghasem Zargar, Ahmad Rabiee, Ali Kadkhodaie, Mohammad Ali Takassi
Summary: Synthetic water-soluble polymers, such as partially hydrolyzed polyacrylamide (HPAM), are commonly used in enhanced oil recovery and drilling fluid operations. However, they have low thermal stability and mechanical resistance. This research focuses on synthesizing a nanocomposite by adding nano-alumina particles to improve the properties of the polymer. The results show that the nanocomposite with 0.2 wt% nano-alumina has high rheological behavior.
Article
Environmental Sciences
Aziz Abdolahi, Ali Chehrazi, Ali Kadkhodaie, Seyedmohsen Seyedali
Summary: This study examines the Ghar Member, the most productive zone in the Oligo-Miocene-aged Asmari Formation in Iran's Hendijan Field, through petrophysical and seismic analyses, flow unit analysis, and facies modeling. It identifies the Ghar sand as the best interval within the Asmari Formation, equivalent to HFU-2 and HFU-4 in terms of reservoir quality.
MODELING EARTH SYSTEMS AND ENVIRONMENT
(2023)
Article
Geology
Mohammad Derafshi, Ali Kadkhodaie, Hossain Rahimpour-Bonab, Rahim Kadkhodaie-Ilkhchi, Hamid Moslman-Nejad, Amir Ahmadi
Summary: Detecting and understanding pore types and geometries in porous media is essential for mapping reservoir properties. This study used velocity deviation logs and capillary pressure curves to estimate pore system properties of the Fahliyan Formation. Petrographic studies and velocity log calculations identified different pore types and their distribution in the reservoir. The results indicate that diagenesis plays a significant role in developing complex pore types and determining reservoir quality.
CARBONATES AND EVAPORITES
(2023)
Article
Engineering, Manufacturing
Amin Abdollahzadeh, Behrouz Bagheri, Ali Shamsipur
Summary: A joint of aluminum to copper sheets was successfully achieved by friction stir spot welding with a nanoparticle interlayer. The introduction of nanoparticles improved the microstructure and interface characteristics of the joints. Moreover, the use of nanoparticles decreased the thickness of the intermetallic compound layer in the bond interface and resulted in higher mechanical properties of the welded samples.
MATERIALS AND MANUFACTURING PROCESSES
(2023)
Article
Energy & Fuels
Sirous Hosseinzadeh, Ali Kadkhodaie, David A. Wood, Reza Rezaee, Rahim Kadkhodaie
Summary: Understanding the fracture patterns in hydrocarbon reservoirs influenced by plate collision is crucial in the Zagros area of Iran. In this study, an integrated workflow was used to assess the impact of various fracture sets on heterogeneous carbonate reservoir rocks. The results indicate that secondary permeability significantly affects well productivity.
JOURNAL OF PETROLEUM EXPLORATION AND PRODUCTION TECHNOLOGY
(2023)
Article
Energy & Fuels
Afzal Mir, Muhammad Rustam Khan, Ali Wahid, Muhammad Atif Iqbal, Reza Rezaee, Syed Haroon Ali, Yucel Deniz Erdal
Summary: This study analyzed the petroleum system of the Bannu Basin in Pakistan, a foreland basin of the Himalayan fold and thrust belts. The study divided the area into three zones and integrated seismic and well log data. The results showed higher sedimentation levels and abrupt sedimentation due to Himalayan orogeny. Potential source rocks and local faults were identified as potential traps for hydrocarbon preservation. Specific formations were identified as potential reservoir rocks in different zones. Post-Miocene sedimentation was found to be a significant event for hydrocarbon generation and accumulation.
Article
Environmental Sciences
Asghar Asghari Moghaddam, Soraya Nouri Sangarab, Ali Kadkhodaie Ilkhchi
Summary: By modifying the DRASTIC method and integrating the fuzzy knowledge, the DRASTICL model was developed to map vulnerable areas and pollution index, providing a basis for preventing groundwater pollution.
ENVIRONMENTAL MONITORING AND ASSESSMENT
(2023)
Article
Geosciences, Multidisciplinary
Mostafa Sabouhi, Reza Moussavi-Harami, Ali Kadkhodaie, Payman Rezaee, Mahmoud Jalali, David A. Wood
Summary: This study provides a detailed analysis of the relationships between stratigraphic features and heterogeneities in carbonate reservoirs, emphasizing their importance for petroleum geologists. The findings highlight the impact of these stratigraphic features on reservoir characteristics, emphasizing their significance at regional and local scales for heterogeneity.
JOURNAL OF ASIAN EARTH SCIENCES
(2023)
Article
Geosciences, Multidisciplinary
Aziz Abdolahi, Mohammad Bahrehvar, Ali Chehrazi, Ali Kadkhodaie, David A. Wood
Summary: This study divides the Asmari Formation into six depositional sequences and develops 3D reservoir-property models. The high-permeability zones are associated with AS-4 and AS-5, which consist of grain-supported lithologies related to shoal facies. The lower-quality reservoir is seen in the supratidal, intertidal, and lagoonal facies of AS-2.
MARINE AND PETROLEUM GEOLOGY
(2023)
Article
Geosciences, Multidisciplinary
Hassan Valinasab, Behzad Soltani, Hamid Hassanzadeh, Ali Kadkhodaie, Maziyar Nazemi, Ebrahim Abdolahi
Summary: This study integrates well data, seismic data, and literature to establish a seismic stratigraphic-based sedimentary conceptual model of the Berriasian-Early Valanginian Lower Fahliyan Formation in the northwestern Persian Gulf. Three depositional sequences were identified based on facies changes, depositional geometries, and fossil assemblages. Tectonic activities and sea-level changes controlled the sedimentation of the depositional sequences. It was concluded that the Lower Fahliyan Formation was deposited in the inner to outer settings of a carbonate shelf.
MARINE AND PETROLEUM GEOLOGY
(2023)
Article
Energy & Fuels
Amir Mazdarani, Ali Kadkhodaie, David A. Wood, Zohreh Soluki
Summary: This study focuses on the role of natural fractures in reservoir quality using image logs and conventional well logs. The interpretation of image logs reveals various natural structures and their relationships with reservoir characteristics. The relationship between fracture frequency and permeability shows that high permeability zones are correlated with high frequencies of open and vuggy fractures, which is important for optimizing reservoir development and resource recovery.
JOURNAL OF PETROLEUM EXPLORATION AND PRODUCTION TECHNOLOGY
(2023)
Article
Energy & Fuels
Faramarz Talaie, Ali Kadkhodaie, Mehran Arian, Mohsen Aleali
Summary: This study analyzes the geochemical characteristics and interrelationships of the Cenomanian Sarvak oil reservoirs in the Persian Gulf basin. The research classifies the Sarvak oil samples and investigates the causes of genetic differences in oil families. The findings show that the oils in the Persian Gulf basin originate from different source rocks, and the thermal maturity and source rock locations vary among the oil families.
JOURNAL OF PETROLEUM EXPLORATION AND PRODUCTION TECHNOLOGY
(2023)
Article
Energy & Fuels
Ahmad Azadivash, Hosseinali Soleymani, Ali Kadkhodaie, Farshid Yahyaee, Ahmad Reza Rabbani
Summary: This study utilized machine learning techniques to predict key parameters such as Organic Oxygen Index (OI), Hydrogen Index (HI), and kerogen type using petrophysical log data of a well in the Perth Basin. Six machine learning algorithms were used to predict OI and HI, with the Support Vector Machines method performing the best. Additionally, six classifiers were employed to determine kerogen types, and the Gradient Boosting method achieved the highest accuracy. The study concludes that machine learning methodologies, in combination with petrophysical logs, offer an efficient and accurate alternative for characterizing kerogen type and evaluating source rock.
JOURNAL OF PETROLEUM EXPLORATION AND PRODUCTION TECHNOLOGY
(2023)
Article
Engineering, Multidisciplinary
Zahra Sadeqi-Arani, Ali Kadkhodaie
Summary: With the emergence of Artificial Intelligence and Machine Learning, the petroleum industry has made significant progress in optimizing decision making, time, and costs. This research analyzes bibliometric studies to provide an overview of the application of AI and machine learning in the upstream sector of the petroleum industry. The results show exponential growth in this field, with China, Iran, and the US being the leading countries in terms of publications. The most influential journal in this field is the 'Journal of Petroleum Science and Engineering', and SALAHELDIN ELKATATNY is the most productive author. The focus of AI and machine learning applications is on predicting and optimizing porosity, well logs, and permeability.
RESULTS IN ENGINEERING
(2023)
Article
Computer Science, Interdisciplinary Applications
Yapo Abole Serge Innocent Oboue, Yunfeng Chen, Sergey Fomel, Wei Zhong, Yangkang Chen
Summary: Strong noise can disrupt the recorded seismic waves and negatively impact subsequent seismological processes. To improve the signal-to-noise ratio (S/N) of seismological data, we introduce MATamf, an open-source MATLAB code package based on an advanced median filter (AMF) that simultaneously attenuates various types of noise and improves S/N. Experimental results demonstrate the usefulness and advantages of the proposed AMF workflow in enhancing the S/N of a wide range of seismological applications.
COMPUTERS & GEOSCIENCES
(2024)
Article
Computer Science, Interdisciplinary Applications
Upkar Singh, P. N. Vinayachandran, Vijay Natarajan
Summary: The Bay of Bengal maintains its salinity distribution due to the cyclic flow of high salinity water and the mixing with freshwater. This paper introduces an advection-based feature definition and algorithms to track the movement of high salinity water, validated through comparison with observed data.
COMPUTERS & GEOSCIENCES
(2024)
Article
Computer Science, Interdisciplinary Applications
Bijal Chudasama, Nikolas Ovaskainen, Jonne Tamminen, Nicklas Nordback, Jon Engstro, Ismo Aaltonen
Summary: This contribution presents a novel U-Net convolutional neural network (CNN)-based workflow for automated mapping of bedrock fracture traces from aerial photographs acquired by unmanned aerial vehicles (UAV). The workflow includes training a U-Net CNN using a small subset of photographs with manually traced fractures, semantic segmentation of input images, pixel-wise identification of fracture traces, ridge detection algorithm and vectorization. The results show the effectiveness and accuracy of the workflow in automated mapping of bedrock fracture traces.
COMPUTERS & GEOSCIENCES
(2024)
Article
Computer Science, Interdisciplinary Applications
Ruizhen Wang, Siyang Wan, Weitao Chen, Xuwen Qin, Guo Zhang, Lizhe Wang
Summary: This paper proposes a novel framework to generate a finer soil strength map based on RCI, which uses ensemble learning models to obtain USCS soil classification and predict soil moisture, in order to improve the resolution and reliability of existing soil strength maps.
COMPUTERS & GEOSCIENCES
(2024)
Article
Computer Science, Interdisciplinary Applications
Zhanlong Chen, Xiaochuan Ma, Houpu Li, Xuwei Xu, Xiaoyi Han
Summary: Simulated terrains are important for landform and terrain research, disaster prediction, rescue and disaster relief, and national security. This study proposes a deep learning method, IGPN, that integrates global information and pattern features of the local terrain to generate accurate simulated terrains quickly.
COMPUTERS & GEOSCIENCES
(2024)
Article
Computer Science, Interdisciplinary Applications
Daniele Secci, Vanessa A. Godoy, J. Jaime Gomez-Hernandez
Summary: Neural networks excel in various machine learning applications, but lack physical interpretability and constraints, limiting their accuracy and reliability in predicting complex physical systems' behavior. Physics-Informed Neural Networks (PINNs) integrate neural networks with physical laws, providing an effective tool for solving physical problems. This article explores recent developments in PINNs, emphasizing their application in solving unconfined groundwater flow, and discusses challenges and opportunities in this field.
COMPUTERS & GEOSCIENCES
(2024)
Article
Computer Science, Interdisciplinary Applications
Renguang Zuo, Ying Xu
Summary: This study proposes a hybrid deep learning model consisting of a one-dimensional convolutional neural network (1DCNN) and a graph convolutional network (GCN) to extract joint spectrum-spatial features from geochemical survey data for mineral exploration. The physically constrained hybrid model performs better in geochemical anomaly recognition compared to other models.
COMPUTERS & GEOSCIENCES
(2024)