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Exploring Hydrocarbons in Carbonate Reservoirs
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Exploring Hydrocarbons in Carbonate Reservoirs

A special issue of Energies (ISSN 1996-1073). This special issue belongs to the section "H1: Petroleum Engineering".

Deadline for manuscript submissions: closed (31 October 2023) | Viewed by 7683

Special Issue Editors

Dr. Weichao Yan

E-Mail Website
Guest Editor
Frontiers Science Center for Deep Ocean Multispheres and Earth System, Key Lab of Submarine Geosciences and Prospecting Techniques, College of Marine Geosciences, Ocean University of China, Qingdao 266100, China
Interests: well logging methods and interoretation; rock physics; digital rock technology; multi-scale; multi-phase and multi-field coupling supercomputing theory
Special Issues, Collections and Topics in MDPI journals
Dr. Huaimin Dong

E-Mail Website
Guest Editor
School of Geological Engineering and Geomatics, Chang'an University, Xi'an 710054, China
Interests: petrophysics experiment; digital rock technology; gas hydrates; well logging interpretation and evaluation
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Carbonate reservoirs are one of the obstacles in exploration and development due to their complex geological conditions, strong heterogeneity, and diverse types. In recent years, with the development of geological theories, rock physical technologies, well-logging evaluation methods, forward modelling, fracture prediction, and fluid detection, many new advances have been made in carbonate reservoir exploration technologies, evaluation methods, and field applications.

This Special Issue will compile research results on petrophysics, numerical simulation, well-logging evaluation, reservoir prediction, and other oil and gas exploration methods and technologies of carbonates, as well as practical application studies. Topics of interest for publication include, but are not limited to:

  • Theory, experiments, and application of rock physics.
  • Construction method and numerical simulation of carbonate digital rock models.
  • Logging identification and parameter evaluation of carbonate reservoirs.
  • Prediction of fluid distribution and geophysical response in carbonate reservoirs.
  • Application of hydrogeology in carbonate geomorphology and key zones.

Dr. Weichao Yan
Dr. Huaimin Dong
Guest Editors

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Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • carbonates
  • petrophysics
  • digital rock
  • well logging
  • seismic exploration

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Published Papers (4 papers)

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Research

15 pages, 1960 KiB  
Article
Permeability Prediction of Carbonate Reservoir Based on Nuclear Magnetic Resonance (NMR) Logging and Machine Learning
by Jianpeng Zhao, Qi Wang, Wei Rong, Jingbo Zeng, Yawen Ren and Hui Chen
Energies 2024, 17(6), 1458; https://doi.org/10.3390/en17061458 - 18 Mar 2024
Cited by 1 | Viewed by 1324
Abstract
Reservoir permeability is an important parameter for reservoir characterization and the estimation of current and future production from hydrocarbon reservoirs. Logging data is an important means of evaluating the continuous permeability curve of the whole well section. Nuclear magnetic resonance logging measurement results [...] Read more.
Reservoir permeability is an important parameter for reservoir characterization and the estimation of current and future production from hydrocarbon reservoirs. Logging data is an important means of evaluating the continuous permeability curve of the whole well section. Nuclear magnetic resonance logging measurement results are less affected by lithology and have obvious advantages in interpreting permeability. The Coates model, SDR model, and other complex mathematical equations used in NMR logging may achieve a precise approximation of the permeability values. However, the empirical parameters in those models often need to be determined according to the nuclear magnetic resonance experiment, which is time-consuming and expensive. Machine learning, as an efficient data mining method, has been increasingly applied to logging interpretation. XGBoost algorithm is applied to the permeability interpretation of carbonate reservoirs. Based on the actual logging interpretation data, with the proportion of different pore components and the logarithmic mean value of T2 in the NMR logging interpretation results as the input variables, a regression prediction model is established through XGBoost algorithm to predict the permeability curve, and the optimization of various parameters in XGBoost algorithm is discussed. The determination coefficient is utilized to check the overall fitting between measured permeability versus predicted ones. It is found that XGBoost algorithm achieved overall better performance than the traditional models. Full article
(This article belongs to the Special Issue Exploring Hydrocarbons in Carbonate Reservoirs)
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13 pages, 10519 KiB  
Article
Lithofacies Characteristics and Methodology to Identify Lacustrine Carbonate Rocks via Log Data: A Case Study in the Yingxi Area, Qaidam Basin
by Mingzhi Tian, Zhanguo Liu, Chao Zhu, Kunyu Wu, Yanqing Wang, Guangyong Song, Zhiyuan Xia and Senming Li
Energies 2023, 16(16), 6041; https://doi.org/10.3390/en16166041 - 18 Aug 2023
Viewed by 1020
Abstract
Lacustrine carbonate reservoirs, extensively distributed in China, have extensive oil and gas exploration potential. However, such reservoirs are characterized by high content of terrigenous debris and complex lithofacies, and the resultant high difficulty in lithofacies identification severely restrains exploration expansion and efficient development, [...] Read more.
Lacustrine carbonate reservoirs, extensively distributed in China, have extensive oil and gas exploration potential. However, such reservoirs are characterized by high content of terrigenous debris and complex lithofacies, and the resultant high difficulty in lithofacies identification severely restrains exploration expansion and efficient development, especially for the Upper Member of the Paleogene Lower Ganchaigou Formation (E32) of the Yingxi area in the Qaidam Basin, with burial depths generally greater than 4000 m. This research targets this area and develops a methodology for detailed lithofacies identification, after systematically investigating the characteristics of lithofacies and well log responses of lacustrine carbonate rocks, on the basis of a massive volume of data of cores, thin sections, and experiments of the study area. The analysis identified lithofacies in the Upper Member of the Paleogene Lower Ganchaigou Formation of the Yingxi area, namely, pack-wackestone, mudstone, laminated carbonate, muddy gypsum, and limy claystone. The analysis of well log response characteristics suggested that natural gamma ray, matrix density, and bulk density were sensitive to lithofacies. Then, for the first time, the rock fabric factor (RFF) method was proposed, and the lithofacies identification plot was based on the calculated RFF and high-definition spectroscopy log. The presented methodology was applied to 55 wells in the study area. The average accuracy of lithofacies interpretation in 14 cored wells reached 82.4%, indicating good application performance. This method improves the lithofacies identification accuracy of lacustrine carbonate rocks, which is of great significance for investigating the reservoir distribution law and guiding exploration and development. Full article
(This article belongs to the Special Issue Exploring Hydrocarbons in Carbonate Reservoirs)
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21 pages, 9492 KiB  
Article
A Numerical Model for Pressure Analysis of a Well in Unconventional Fractured Reservoirs
by Jiwei He, Qin Li, Guodong Jin, Sihai Li, Kunpeng Shi and Huilin Xing
Energies 2023, 16(5), 2505; https://doi.org/10.3390/en16052505 - 6 Mar 2023
Viewed by 2119
Abstract
Fractured reservoirs are highly heterogeneous in both matrix and fracture properties, which results in significant variations in well production. Assessing and quantifying the influence of fractures on fluid flow is essential for developing unconventional reservoirs. The complicated effects of fractures in unconventional fractured [...] Read more.
Fractured reservoirs are highly heterogeneous in both matrix and fracture properties, which results in significant variations in well production. Assessing and quantifying the influence of fractures on fluid flow is essential for developing unconventional reservoirs. The complicated effects of fractures in unconventional fractured reservoirs on fluid flow highly depend on fracture geometry, fracture distribution, and fracture properties, which can be reflected in pressure transient testing. The biggest challenge lies in delineating the pre-existing natural fracture distribution pattern, density, azimuth, and connectivity. Using the advanced finite element method, this paper builds a finely characterized near-wellbore model to numerically simulate the pressure transient testing process in naturally fractured reservoirs and further evaluates fracture-related effects to obtain a more accurate solution. First, the numerical program is benchmarked by the analytical solutions and numerical results of Eclipse. Next, different fracture models with single fractures or fracture networks are set up to investigate the effects of fracture parameters numerically (e.g., fracture location, fracture dip angle, fracture spacing, the ratio of fracture permeability to matrix permeability, fracture network orientation, horizontal fracture distribution, etc.) on pressure transient behaviors in naturally fractured reservoirs. Velocity and pressure profiles are presented to visualize and analyze their effects, and new features in the flow regimes of the derivative plots of the bottom-hole pressure are identified and discussed. Finally, based on geological and geophysical data, including image logs, core descriptions, wireline logs, and seismic and well test data, a practical fractured model of the Dalwogan 2 well in the Surat basin is built, analyzed, and compared with homogenous and measured data. The results show significance in characterizing the complex fracture networks in near-wellbore models of unconventional fractured reservoirs. Full article
(This article belongs to the Special Issue Exploring Hydrocarbons in Carbonate Reservoirs)
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26 pages, 14769 KiB  
Article
Identification of Karst Cavities from 2D Seismic Wave Impedance Images Based on Gradient-Boosting Decision Trees Algorithms (GBDT): Case of Ordovician Fracture-Vuggy Carbonate Reservoir, Tahe Oilfield, Tarim Basin, China
by Allou Koffi Franck Kouassi, Lin Pan, Xiao Wang, Zhangheng Wang, Alvin K. Mulashani, Faulo James, Mbarouk Shaame, Altaf Hussain, Hadi Hussain and Edwin E. Nyakilla
Energies 2023, 16(2), 643; https://doi.org/10.3390/en16020643 - 5 Jan 2023
Cited by 4 | Viewed by 2059
Abstract
The precise characterization of geological bodies in fracture-vuggy carbonates is challenging due to their high complexity and heterogeneous distribution. This study aims to present the hybrid of Visual Geometry Group 16 (VGG-16) pre-trained by Gradient-Boosting Decision Tree (GBDT) models as a novel approach [...] Read more.
The precise characterization of geological bodies in fracture-vuggy carbonates is challenging due to their high complexity and heterogeneous distribution. This study aims to present the hybrid of Visual Geometry Group 16 (VGG-16) pre-trained by Gradient-Boosting Decision Tree (GBDT) models as a novel approach for predicting and generating karst cavities with high accuracy on various scales based on uncertainty assessment from a small dataset. Seismic wave impedance images were used as input data. Their manual interpretation was used to build GBDT classifiers for Light Gradient-Boosting Machine (LightGBM) and Unbiased Boosting with Categorical Features (CatBoost) for predicting the karst cavities and unconformities. The results show that the LightGBM was the best GBDT classifier, which performed excellently in karst cavity interpretation, giving an F1-score between 0.87 and 0.94 and a micro-G-Mean ranging from 0.92 to 0.96. Furthermore, the LightGBM performed better in cave prediction than Linear Regression (LR) and Multilayer Perceptron (MLP). The prediction of karst cavities according to the LightGBM model was performed well according to the uncertainty quantification. Therefore, the hybrid VGG16 and GBDT algorithms can be implemented as an improved approach for efficiently identifying geological features within similar reservoirs worldwide. Full article
(This article belongs to the Special Issue Exploring Hydrocarbons in Carbonate Reservoirs)
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