Carbonate reservoir fracture‐cavity system identification based on the improved YOLOv5s deep learning algorithm DOI Creative Commons

Xiaoyong Feng,

Kai Zhao, Jianguo Zhang

et al.

Energy Science & Engineering, Journal Year: 2024, Volume and Issue: 12(6), P. 2643 - 2660

Published: May 27, 2024

Abstract In carbonate reservoirs characterized by the fracture‐cavity system as storage spaces, drilling process is highly prone to loss of fluid. This not only affects efficiency but can also lead severe accidents, such blowouts. Therefore, it crucial understand distribution pattern these fractures. However, formation rock systems, being controlled various factors, difficult precisely identify. limitation hampers efficient development types oil and gas fields. paper presents a case study M5 5 sub‐section reservoir in Sulige gasfield, proposing an improved You Only Look Once v5s (YOLOv5s) deep learning algorithm. It utilizes enhanced training with conventional logging data identify response characteristics fractures reservoirs. And its identification results have been confirmed be accurate fracture obtained through different means, core samples, cast thin section photographs, imaging data, seismic attributes. method incorporates Ghost convolution module replace Conv backbone network YOLOv5s model, modifies C3 into Bottleneck module, effectively making model more lightweight. Additionally, Convolutional Block Attention Module integrated Neck network, enhancing model's feature extraction capabilities. Finally, employs Efficient Intersection over Union Loss function instead Complete Loss, reducing network's regression loss. The validation using actual demonstrate that this achieves average recognition accuracy 87.3% for system, which 3% improvement baseline (YOLOv5s). enhancement beneficial locating fluid positions

Language: Английский

Reservoir characterization through comprehensive modeling of elastic logs prediction in heterogeneous rocks using unsupervised clustering and class-based ensemble machine learning DOI
Muhammad Ali, Peimin Zhu, Ren Jiang

et al.

Applied Soft Computing, Journal Year: 2023, Volume and Issue: 148, P. 110843 - 110843

Published: Sept. 20, 2023

Language: Английский

Citations

39
Multiscale and diverse spatial heterogeneity analysis of void structures in reef carbonate reservoirs DOI
Dmitriy A. Martyushev, Shadfar Davoodi, Ali Kadkhodaie

et al.

Geoenergy Science and Engineering, Journal Year: 2023, Volume and Issue: 233, P. 212569 - 212569

Published: Dec. 8, 2023

Language: Английский

Citations

24
Subsurface Hydrogen Storage in Limestone Rocks: Evaluation of Geochemical Reactions and Gas Generation Potential DOI
Ahmed Al‐Yaseri, Ahmed Fatah, Bidoor Alsaif

et al.

Energy & Fuels, Journal Year: 2024, Volume and Issue: 38(11), P. 9923 - 9932

Published: May 14, 2024

Underground hydrogen storage (UHS) in carbonate reservoirs is a suitable solution for safe and efficient recovery during the cycling process. The uncertainties associated with potential geochemical reactions between hydrogen, rock, brine may impact long-term containment of produced formations. Despite current interest studying hydrogen-rock reactions, only limited work available literature. In this study, we experimentally evaluate reactivity rocks to address gas generation induced by reactions. Limestone samples are treated under 1500 psi 75 °C temperature duration 6 13 months using simple reaction cells. Scanning electron microscopy (SEM) analysis performed examine dissolution/precipitation hydrogen. contrast, chromatography (GC analyzer) inductively coupled plasma optical emission spectroscopy (ICP-OES) conducted detect ion precipitation. experimental results indicate no significant treatment on surface morphology pore structure even after treatment, suggesting that abiotic unlikely occur first stages UHS. Furthermore, presence brine, there apparent indications occurring calcite, traces any other gases detected treatment. Besides, solutions' pH remains almost unchanged, minor increase calcium (Ca2+) ions solution, which attributed water, not promisingly support utilization storage.

Language: Английский

Citations

11
Identifying payable cluster distributions for improved reservoir characterization: a robust unsupervised ML strategy for rock typing of depositional facies in heterogeneous rocks DOI Creative Commons
Umar Ashraf, Aqsa Anees, Hucai Zhang

et al.

Geomechanics and Geophysics for Geo-Energy and Geo-Resources, Journal Year: 2024, Volume and Issue: 10(1)

Published: Aug. 1, 2024

Abstract The oil and gas industry relies on accurately predicting profitable clusters in subsurface formations for geophysical reservoir analysis. It is challenging to predict payable complicated geological settings like the Lower Indus Basin, Pakistan. In complex, high-dimensional heterogeneous settings, traditional statistical methods seldom provide correct results. Therefore, this paper introduces a robust unsupervised AI strategy designed identify classify zones using self-organizing maps (SOM) K-means clustering techniques. Results of SOM provided potentials six depositional facies types (MBSD, DCSD, MBSMD, SSiCL, SMDFM, MBSh) based cluster distributions. MBSD DCSD exhibited high similarity achieved maximum effective porosity (PHIE) value ≥ 15%, indicating good rock typing (RRT) features. density-based spatial applications with noise (DBSCAN) showed minimum outliers through meta attributes confirmed reliability generated Shapley Additive Explanations (SHAP) model identified PHIE as most significant parameter was beneficial identifying non-payable zones. Additionally, highlights importance managing distribution across various formations, going beyond simple characterization.

Language: Английский

Citations

9
Characteristics and Paleoenvironment of Stromatolites in the Southern North China Craton and Their Implications for Mesoproterozoic Gas Exploration DOI Open Access

Ruize Yuan,

Qiang Yu,

Tao Tian

et al.

Processes, Journal Year: 2025, Volume and Issue: 13(1), P. 129 - 129

Published: Jan. 6, 2025

Stromatolites, distinctive fossil records within Precambrian strata, are essential for investigating the depositional environments of early Earth and geological settings conducive to hydrocarbon formation. The Luonan area is located in Shaanxi Province, China, where a large number stromatolites have been discovered Mesoproterozoic Erathem, providing new perspectives on paleoenvironment reservoir spaces. This study analyzes morphology stromatolites, associated microorganisms, mineralogy, cathodoluminescence from carbonate rocks Jixian System. Carbon oxygen isotope analyses help reconstruct paleosalinity climate, enhancing understanding their petroleum significance. Combining carbon analysis with fine observation description stromatolite can better paleoenvironmental features Era. results indicated narrow range values (δ13C: −5.81‰ −2.43‰; mean: −4.03‰) (δ18O: −9.06‰ −5.64‰). Longjiayuan Formation characterized by high CaO MgO content, low SiO2 minimal terrigenous input, contrast Fengjiawan Formation, which exhibits elevated greater material. display prominent rhythmic laminations, primarily composed dolomite, indicating potential source rocks. Stromatolite morphologies, including layered, columnar, wavy forms, reflect varied microfacies. alternating bright dark laminae, rich CO2 but differing Ca2+ Mg2+ concentrations, signify seasonal growth cycles. These developed warm, humid, stable climatic regime, marine anoxic-to-suboxic setting, typically intertidal or supratidal zones hydrodynamic energy. In southern margin North China Craton, Era extensively exhibit distinct characteristics. Due biogenic alteration porosity rock increased. altered physical properties host some extent, suggesting possibility becoming effective reservoirs. has significant implications deep oil gas exploration, valuable guidance future prospecting efforts.

Language: Английский

Citations

1
Geophysical monitoring of CO2 storage using rock physics template DOI
Javad Sharifi

Geoenergy Science and Engineering, Journal Year: 2025, Volume and Issue: unknown, P. 213808 - 213808

Published: March 1, 2025

Language: Английский

Citations

1
Pore structure analysis of storage rocks during geological hydrogen storage: Investigation of geochemical interactions DOI
Ahmed Al‐Yaseri, Ahmed Fatah, Abdulrauf R. Adebayo

et al.

Fuel, Journal Year: 2023, Volume and Issue: 361, P. 130683 - 130683

Published: Dec. 26, 2023

Language: Английский

Citations

22
Integrated analysis of wireline logs analysis, seismic interpretation, and machine learning for reservoir characterisation: Insights from the late Eocene McKee Formation, onshore Taranaki Basin, New Zealand DOI Creative Commons
John Oluwadamilola Olutoki, Numair Ahmed Siddiqui,

AKM Eahsanul Haque

et al.

Journal of King Saud University - Science, Journal Year: 2024, Volume and Issue: 36(6), P. 103221 - 103221

Published: April 27, 2024

Language: Английский

Citations

7
Geological modeling of diagenetic logs of the Sarvak reservoir in Dezful Embayment, southwestern Iran: implications for geostatistical simulation and reservoir quality assessment DOI Creative Commons
Vali Mehdipour, Ahmad Reza Rabbani, Ali Kadkhodaie

et al.

Journal of Petroleum Exploration and Production Technology, Journal Year: 2023, Volume and Issue: 13(10), P. 2083 - 2107

Published: July 7, 2023

Abstract Reservoir quality in carbonate reservoirs is significantly influenced by diagenetic processes. Although diagenesis studied as a common reservoir damaging/enhancing process many previous studies, literature limited about the spatial modeling of processes using advanced geostatistical algorithms. In current study, 3D models main which affect Sarvak an Iranian oilfield located north Dezful Embayment were constructed geostatistics. According to petrographic total 10 microfacies identified. addition, significant this include dolomitization, cementation, dissolution, and compaction. electrofacies determined “multi-resolution graph clustering” method based on quantitative results studies. The provided average maps used investigate lateral variation those properties their relationship with effective porosity. It shows that trends secondary porosity velocity deviation log (VDL) are generally correlatable confirming impact dissolution enhancement. most observed Lower Sarvak-E2 zone where correlation coefficient 0.75. between VDL some zones high indicating effect it exceeds 0.61 Sarvak-A1 zone. occurrence has dual constructive destructive effects quality. dolomitization Sarvak-E1 Sarvak-F coefficients 0.476 − 0.456, respectively. low developing stylolites, solution seams.

Language: Английский

Citations

14
Application of Deep Learning for Reservoir Porosity Prediction and self Organizing Map for Lithofacies Prediction DOI
Mazahir Hussain, Shuang Liu, Wakeel Hussain

et al.

Journal of Applied Geophysics, Journal Year: 2024, Volume and Issue: 230, P. 105502 - 105502

Published: Aug. 31, 2024

Language: Английский

Citations

6