Rock Physics, Well Logging, and Formation Evaluation in Energy Exploration Systems

A special issue of Processes (ISSN 2227-9717). This special issue belongs to the section "Energy Systems".

Deadline for manuscript submissions: 10 December 2024 | Viewed by 25925

Special Issue Editors


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Guest Editor
School of Geophysics and Information Technology, China University of Geosciences, Beijing 10083, China
Interests: formation evaluation; unconventional reservoirs; well logging; applied nuclear magnetic resonance
College of Geophysics and Petroleum Resources, Yangtze University, Wuhan 430100, China
Interests: digital rock physics; shale oil and gas; formation evaluation

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Guest Editor
Faculty of Mathematics and Natural Sciences, Christian-Albrechts-Universität, 24118 Kiel, Germany
Interests: experimental and theoritical geosciences
Special Issues, Collections and Topics in MDPI journals
Department of Geology, Northwest University, Xi’an 710069, China
Interests: formation evaluation; well logging; unconventional reservoirs; machine learning; CCUS; digital core

Special Issue Information

Dear Colleagues,

Well logging plays a very important role in oil and gas exploration and development since it appeared in 1927. It can help to indicate effective formations, offer reliable formation parameters and identify fluids. In the last decade, as more and more unconventional oil and gas, e.g., shale oil/gas and tight oil/gas, are discovered, common well logging inversion and interpretation techniques face great challenges. Formation evaluation methods also cannot work. For complex reservoir characterization, validity evaluation and “sweet spot” prediction, it is urgent to put forward innovative evaluation methods. In addition, small pore space, poor connectivity and weak fluid response lead to low formation parameter (especially permeability and water saturation) calculation and hydrocarbon-beariong identification accuracy. The emergence of digital rock physics techniques and deep learning methods provides a new direction to solve the problem of complex formation evaluation.

This Special Issue on ‘Rock Physics, Well Logging, and Formation Evaluation in Energy Exploration Systems’ seeks high-quality works focusing on the latest novel advances for conventional and unconventional reservoir evaluation based on rock physics and well logging. Topics include, but are not limited to:

  • Conventional and unconventional reservoir characterization based on well logging techniques;
  • Shale oil/gas, tight oil/gas identification and “sweet spot” prediction;
  • Unconventional reservoir conduction mechanism and parameter evaluation;
  • Application of deep learning methods in formation evaluation;
  • Digital rock physics or NMR techniques for complex formation evaluation.

Prof. Dr. Liang Xiao
Dr. Xin Nie
Prof. Dr. Mehdi Ostadhassan
Dr. Hongyan Yu
Guest Editors

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Keywords

  • formation evaluation
  • pore structure
  • validity characterization
  • deep learning
  • digital rock physics

Published Papers (22 papers)

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Research

14 pages, 4953 KiB  
Article
New Method for Logging Evaluation of Total Organic Carbon Content in Shale Reservoirs Based on Time-Domain Convolutional Neural Network
by Wangwang Yang, Xuan Hu, Caiguang Liu, Guoqing Zheng, Weilin Yan, Jiandong Zheng, Jianhua Zhu, Longchuan Chen, Wenjuan Wang and Yunshuo Wu
Processes 2024, 12(3), 610; https://doi.org/10.3390/pr12030610 - 19 Mar 2024
Viewed by 678
Abstract
Total organic carbon (TOC) content is a key indicator for determining the hydrocarbon content of shale. The current model for calculating the TOC content of shale is relatively simplistic, the modeling process is cumbersome, and the parameters involved are influenced by subjective factors, [...] Read more.
Total organic carbon (TOC) content is a key indicator for determining the hydrocarbon content of shale. The current model for calculating the TOC content of shale is relatively simplistic, the modeling process is cumbersome, and the parameters involved are influenced by subjective factors, which have certain shortcomings. To address this problem, a time-domain convolutional neural network (TCN) model for predicting total organic carbon content based on logging sequence information was established by starting from logging sequence information, conducting logging parameter sensitivity analysis experiments, prioritizing logging-sensitive parameters as model feature vectors, and constructing a TCN network. Meanwhile, to overcome the problem of an insufficient sample size, a five-fold cross-validation method was used to train the TCN model and obtain the weight matrix with the minimum error, and then a shale reservoir TOC content prediction model based on the TCN model was established. The model was applied to evaluate the TOC logging of the Lianggaoshan Formation in the Sichuan Basin, China, and the predicted results were compared with the traditional ΔlogR model. The results indicate that the TCN model predicts the TOC content more accurately than the traditional model, as demonstrated by laboratory tests. This leads to a better application effect. Additionally, the model fully explores the relationship between the logging curve and the total organic carbon content, resulting in improved accuracy of the shale TOC logging evaluation. Full article
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18 pages, 6988 KiB  
Article
Intelligent Classification of Volcanic Rocks Based on Honey Badger Optimization Algorithm Enhanced Extreme Gradient Boosting Tree Model: A Case Study of Hongche Fault Zone in Junggar Basin
by Junkai Chen, Xili Deng, Xin Shan, Ziyan Feng, Lei Zhao, Xianghua Zong and Cheng Feng
Processes 2024, 12(2), 285; https://doi.org/10.3390/pr12020285 - 28 Jan 2024
Cited by 1 | Viewed by 756
Abstract
Lithology identification is the fundamental work of oil and gas reservoir exploration and reservoir evaluation. The lithology of volcanic reservoirs is complex and changeable, the longitudinal lithology changes a great deal, and the log response characteristics are similar. The traditional lithology identification methods [...] Read more.
Lithology identification is the fundamental work of oil and gas reservoir exploration and reservoir evaluation. The lithology of volcanic reservoirs is complex and changeable, the longitudinal lithology changes a great deal, and the log response characteristics are similar. The traditional lithology identification methods face difficulties. Therefore, it is necessary to use machine learning methods to deeply explore the corresponding relationship between the conventional log curve and lithology in order to establish a lithology identification model. In order to accurately identify the dominant lithology of volcanic rock, this paper takes the Carboniferous intermediate basic volcanic reservoir in the Hongche fault zone as the research object. Firstly, the Synthetic Minority Over-Sampling Technique–Edited Nearest Neighbours (SMOTEENN) algorithm is used to solve the problem of the uneven data-scale distribution of different dominant lithologies in the data set. Then, based on the extreme gradient boosting tree model (XGBoost), the honey badger optimization algorithm (HBA) is used to optimize the hyperparameters, and the HBA-XGBoost intelligent model is established to carry out volcanic rock lithology identification research. In order to verify the applicability and efficiency of the proposed model in volcanic reservoir lithology identification, the prediction results of six commonly used machine learning models, XGBoost, K-nearest neighbor (KNN), gradient boosting decision tree model (GBDT), adaptive boosting model (AdaBoost), support vector machine (SVM) and convolutional neural network (CNN), are compared and analyzed. The results show that the HBA-XGBoost model proposed in this paper has higher accuracy, precision, recall rate and F1-score than other models, and can be used as an effective means for the lithology identification of volcanic reservoirs. Full article
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18 pages, 18495 KiB  
Article
Evaluation of Saturation Interpretation Methods for Ultra-Low Permeability Argillaceous Sandstone Gas Reservoirs: A Case Study of the Huangliu Formation in the Dongfang Area
by Bao Wang, Zhonghao Wang, Bo Shen, Di Tang, Yixiong Wu, Bohan Wu, Sen Li and Jinfeng Zhang
Processes 2024, 12(2), 271; https://doi.org/10.3390/pr12020271 - 26 Jan 2024
Viewed by 797
Abstract
Ultra-low permeability argillaceous sandstone reservoirs have become a significant focus for exploration and development. Saturation is a crucial parameter in evaluating such reservoirs. Due to the low porosity, low permeability, complex pore structure, and strong heterogeneity in ultra-low permeability argillaceous sandstone reservoirs, traditional [...] Read more.
Ultra-low permeability argillaceous sandstone reservoirs have become a significant focus for exploration and development. Saturation is a crucial parameter in evaluating such reservoirs. Due to the low porosity, low permeability, complex pore structure, and strong heterogeneity in ultra-low permeability argillaceous sandstone reservoirs, traditional evaluation methods are unable to achieve the required level of interpretation accuracy. To improve the accuracy of gas saturation calculations in ultra-low permeability argillaceous sandstone gas reservoirs, the conductivity characteristics of the ultra-low permeability argillaceous sandstone gas reservoirs in the Huangliu Formation in the Dongfang area, China, were analyzed through rock physics experimental data and geological information. The results revealed the clay content in the study area to range from 6% to 33.4%. Influenced by burial depth and temperature, kaolinite and montmorillonite transform into illite and chlorite, and the cation exchange capacity is not correlated with the clay content. This suggests that the effects of cation-attached conductivity can be ignored. The regional variation in rock electrical parameters is significant. When the lithology coefficient is a = 1, the consolidation index m varies between 1.25 and 1.75. When the lithology coefficient is b = 1, the saturation index n varies between 1.6 and 1.96. Under the influence of high temperature and pressure, the reservoirs in the study area exhibit two distinct characteristics on the capillary pressure curve, fractal dimension, and effective porosity index intersection diagram, (1) In Class I reservoirs, there is a strong correlation between the formation factor and natural gamma, as well as porosity, and a logarithmic relationship between the saturation exponent and the formation factor; and (2) in Class II reservoirs, there is a strong power-law relationship between the formation factor and porosity, as well as a logarithmic relationship between the saturation exponent and formation factor. Based on experimental and logging data, reservoir classification was conducted using core-scale logging and principal component analysis. Additionally, a saturation interpretation model was developed using multivariate regression, based on rock electrical parameters. The actual application results demonstrate that compared to the fixed rock resistivity saturation model, this model has reduced the average absolute error by 5.9%. The calculated values are consistent with the gas saturation analysis of the core at the pure gas section as determined by cable testing sampling. This model meets the requirements of practical production. The study of this interpretation method is of great significance for the formulation of development plans for ultra-low permeability offshore argillaceous sandstone gas reservoirs. Full article
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19 pages, 7993 KiB  
Article
Research on Inversion Log Evaluation Method of Special Mineral in Alkali Lake Shale Oil Reservoir—A Case Study of the Fengcheng Formation in the Mahu Sag, China
by Lei Zhao, Rui Mao, Xili Deng, Ziyan Feng, Junkai Chen, Xianghua Zong and Cheng Feng
Processes 2024, 12(1), 105; https://doi.org/10.3390/pr12010105 - 1 Jan 2024
Viewed by 828
Abstract
The Fengcheng Formation in the Mahu Sag of the Junggar Basin, China, is characterized by alkaline lake deposits, featuring abundant alkaline minerals. The content of alkaline minerals affects the physical properties and oil-bearing properties of the reservoir, and existing mineral inversion methods cannot [...] Read more.
The Fengcheng Formation in the Mahu Sag of the Junggar Basin, China, is characterized by alkaline lake deposits, featuring abundant alkaline minerals. The content of alkaline minerals affects the physical properties and oil-bearing properties of the reservoir, and existing mineral inversion methods cannot calculate the content of alkaline minerals. Based on Litho Scanner Log data, we can calculate the dry weight of elements using the oxide closure model. By improving the rock volume physical model; adding trona, shortite, eitelite, and reedmergnerite to the rock volume physical model; and combining with the least squares method, the mineral content calculation was carried out, using the inversion method of combination models (Shortite Model, Eitelite Model, Reedmergnerite Model, and Trona Model) to achieve mineral inversion of alkali-bearing shale oil reservoirs. Litho Scanner Log is expensive, and its widespread application will increase exploration costs. This article scales the mineral inversion results of Litho Scanner Log into conventional log data, improves the rock volume physical model of conventional log, and uses a combination model to achieve mineral inversion of alkali-bearing shale oil reservoirs in conventional log. Compared with the results of X-ray diffraction experiments, the average absolute error of all minerals except for trona and feldspar is less than 10%, and the inversion results are consistent with the core test results. The research results of this article can provide theoretical and technical support for the log evaluation of alkali-bearing shale oil reservoirs. Full article
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13 pages, 3344 KiB  
Article
Total Organic Carbon Logging Evaluation of Shale Hydrocarbon Source Rocks in the Shan 1 Section of the Sulige Gas Field, Ordos Basin, China
by Tong Wang, Bo Xu, Ting Song, Yatong Chen, Liangguang Deng and Hongmei Du
Processes 2023, 11(11), 3214; https://doi.org/10.3390/pr11113214 - 11 Nov 2023
Viewed by 961
Abstract
The mass fraction of total organic carbon (TOC) is one of the key indicators for evaluating the hydrocarbon generation potential of shale source rocks. Experimental measurements to evaluate the TOC content require significant cost and time. Furthermore, the experimental data are often fragmented [...] Read more.
The mass fraction of total organic carbon (TOC) is one of the key indicators for evaluating the hydrocarbon generation potential of shale source rocks. Experimental measurements to evaluate the TOC content require significant cost and time. Furthermore, the experimental data are often fragmented and may not provide an accurate depiction of the source rocks throughout the entire block. To solve the above problems, this paper proposes to use the combination of conventional logging data and experimental data after an in-depth study of the geophysical characteristics of hydrocarbon source rocks in the Ordos Basin. A quantitative model between logging data and source rocks is established, and then the continuous distribution value of the TOC content in the hydrocarbon source rock interval is calculated. Firstly, the mud shale formation of the Permian–Shanxi Formation in the Upper Paleozoic, located in the Jingbian area of the Ordos Basin, is selected as the research target using the “Jinqiang method”. The model is constructed by selecting appropriate logging curves (acoustic time difference logging, resistivity logging, and density logging) and experimental results based on the response relationship between logging data and TOC data. This method provides more accurate and comprehensive data for source rock studies, combining experimental sampling to contribute to a better evaluation of TOC in source rock. The shale hydrocarbon source rock logging data from 10 wells are selected, and the model is used to realize the full-well section of the logging data to find the hydrocarbon source rock TOC, which is compared with the TOC data from the experimental core tested at a sampling point. The results demonstrate that the model is highly effective and accurate, with a mere 2.7% percentage error observed across 185 sample data points. This method greatly improves the accuracy and completeness of TOC evaluation compared with the results of previous studies and provides a guide for subsequent TOC logging evaluation of source rocks in other areas. With the study in this paper, continuous TOC values of source rocks are obtained, discarding the TOC values representing the whole set of hydrocarbon source rocks with a limited number of sample averages. This method can reflect the contribution of the layers with high and low organic matter abundance, and the calculated reserves are more accurate. By utilizing the measured TOC values of the study area to invert the model to find the parameters, this study contributes to the decision-making of hydrocarbon exploration in domestic and international basins. Full article
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27 pages, 10816 KiB  
Article
Dynamic Monitoring of Low-Yielding Gas Wells by Combining Ultrasonic Sensor and HGWO-SVR Algorithm
by Mingxing Wang, Hongwei Song, Xinlei Shi, Wei Liu, Baojun Wei and Lei Wei
Processes 2023, 11(11), 3177; https://doi.org/10.3390/pr11113177 - 7 Nov 2023
Viewed by 832
Abstract
As gas wells enter the middle and late stages of production, they will become low-yielding gas wells due to fluid loading and insufficient formation pressure. For many years, there has been a lack of effective dynamic monitoring methods for low-yielding gas wells, and [...] Read more.
As gas wells enter the middle and late stages of production, they will become low-yielding gas wells due to fluid loading and insufficient formation pressure. For many years, there has been a lack of effective dynamic monitoring methods for low-yielding gas wells, and it is difficult to determine the production of each phase in each production layer, which makes further development face great uncertainty and a lack of basis for measurement adjustment. In order to solve this problem, this paper proposes an intelligent dynamic monitoring method suitable for low-yielding gas wells, which uses an ultrasonic Doppler logging instrument and machine learning algorithm as the core to obtain the output contribution of each production layer of the gas well. The intelligent dynamic monitoring method is based on the HGWO-SVR algorithm to predict the flow of each phase. The experimental data are selected for empirical analysis, and the effectiveness and accuracy of the method are verified. The research shows that this method has good application prospects and can provide strong technical support for gas reservoir production stability and development adjustment. Full article
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16 pages, 5704 KiB  
Article
A Comparative Study of Oil–Water Two-Phase Flow Pattern Prediction Based on the GA-BP Neural Network and Random Forest Algorithm
by Yongtuo Sun, Haimin Guo, Haoxun Liang, Ao Li, Yiran Zhang and Doujuan Zhang
Processes 2023, 11(11), 3155; https://doi.org/10.3390/pr11113155 - 5 Nov 2023
Cited by 2 | Viewed by 1057
Abstract
As global oil demand continues to increase, in recent years, countries have continued to expand the development of oil reserves, highlighting the importance of oil. In order to adapt to different strata distribution conditions, domestic drilling technology is becoming more and more perfect, [...] Read more.
As global oil demand continues to increase, in recent years, countries have continued to expand the development of oil reserves, highlighting the importance of oil. In order to adapt to different strata distribution conditions, domestic drilling technology is becoming more and more perfect, resulting in a gradual increase in horizontal and inclined wells. Because of the influence of various downhole factors, the flow pattern in the wellbore will be more complex. Accurately identifying the flow pattern of multiphase flow under different well deviation conditions is very important to interpreting the production log output profile accurately. At the same time, in order to keep up with the footsteps of artificial intelligence, big data and artificial intelligence algorithms are applied to the oil industry. This paper uses the GA-BP neural network and random forest algorithm to conduct fluid flow pattern prediction research on the logging data of different water cuts at different inclinations and flow rates. It compares the predicted results with experimental fluid flow patterns. Finally, we can determine the feasibility of these two algorithms for predicting flow patterns. We use the multiphase flow simulation experiment device in the experiment. During the process, the flow patterns are observed and recorded by visual inspection, and the flow pattern is distinguished by referring to the theoretical diagram of the oil-water two-phase flow pattern. The prediction results show that the accuracy of these two algorithms can reach 81.25% and 93.75%, respectively, which verifies the effectiveness of these two algorithms in the prediction of oil–water two-phase flow patterns and provides a new idea for the prediction of oil–water two-phase flow patterns and other phases. Full article
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13 pages, 5776 KiB  
Article
Reservoir Petrofacies Predicted Using Logs Data: A Study of Shale Oil from Seven Members of the Upper Triassic Yanchang Formation, Ordos Basin, China
by Kun Meng, Ming Wang, Shaohua Zhang, Pengye Xu, Yao Ji, Chaoyang Meng, Jie Zhan and Hongyan Yu
Processes 2023, 11(11), 3131; https://doi.org/10.3390/pr11113131 - 1 Nov 2023
Viewed by 844
Abstract
The identification and prediction of petrofacies plays a crucial role in the study of shale oil and gas “sweet spots”. However, the petrofacies identified through core and core test data are not available for all wells. Therefore, it is essential to establish a [...] Read more.
The identification and prediction of petrofacies plays a crucial role in the study of shale oil and gas “sweet spots”. However, the petrofacies identified through core and core test data are not available for all wells. Therefore, it is essential to establish a petrofacies identification model using conventional well logging data. In this study, we determined the petrofacies of shale oil reservoirs in the Upper Triassic Yanchang Formation, Ordos Basin, China, based on scanning electron microscopy, core porosity and total organic carbon (TOC), and brittleness index calculations from X-ray diffraction (XRD) experiments conducted on seven members of the formation. Furthermore, we compared the interpreted logs with the raw well logs data clustered into electrofacies in order to assess their compliance with the petrofacies, using the Multi-Resolution Graph-Based Clustering (MRGC) method. Through an analysis of pore structure type, core porosity, TOC, and brittleness index, we identified four types of lithofacies with varying reservoir quality: PF A > PF B > PF C > PF D. The compliance of the clustered electrofacies with the petrofacies obtained from the interpreted logs was found to be 85.42%. However, the compliance between the clustered electrofacies and the petrofacies obtained from the raw well logs was only 47.92%. Hence, the interpreted logs exhibit a stronger correlation with petrofacies characterization, and their utilization as input data is more beneficial in accurately predicting petrofacies through machine learning algorithms. Full article
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15 pages, 7038 KiB  
Article
Fluid Identification Method of Nuclear Magnetic Resonance and Array Acoustic Logging for Complex Oil and Water Layers in Tight Sandstone Reservoir
by Ze Bai, Maojin Tan, Bo Li, Yujiang Shi, Haitao Zhang and Gaoren Li
Processes 2023, 11(11), 3051; https://doi.org/10.3390/pr11113051 - 24 Oct 2023
Viewed by 839
Abstract
In order to improve the logging interpretation accuracy for complex oil and water layers developed in tight sandstone reservoirs, this study takes the Chang 8 member of the Yanchang Formation in the Huanxian area as the research object, and two new fluid identification [...] Read more.
In order to improve the logging interpretation accuracy for complex oil and water layers developed in tight sandstone reservoirs, this study takes the Chang 8 member of the Yanchang Formation in the Huanxian area as the research object, and two new fluid identification methods were constructed based on nuclear magnetic resonance (NMR) logging and array acoustic logging. Firstly, the reservoir characteristics of physical properties and conductivity were studied in the research area, and the limitations of conventional logging methods in identifying complex oil and water layers were clarified. Then, the sensitive parameters for identifying different pore fluids were established by analyzing the relationship between NMR logging and array acoustic logging with different pore fluids. On this basis, the fluid identification plate, composed of movable fluid apparent diffusion coefficient and effective porosity difference (Daφe) by NMR logging data of D9TWE3 observation mode, and the other fluid identification plate, composed of apparent bulk modulus of pore fluid and elastic parameter sensitive factor (Kf-Fac), were constructed, respectively. Finally, these two fluid identification methods were used for reservoir interpretation of actual logging data. This study shows that the two new fluid identification methods constructed by NMR logging and array acoustic logging can effectively eliminate the interference of rock skeleton on logging interpretation, which make them more effective in identifying complex oil and water layers than the conventional logging method. Additionally, the two methods have their own advantages and disadvantages when used separately for interpreting complex oil and water layers, and the comprehensive interpretation of the two methods provides a technical development direction for further improving the accuracy of logging the interpretation of complex oil and water layers. Full article
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24 pages, 20435 KiB  
Article
Identification of Shale Lithofacies from FMI Images and ECS Logs Using Machine Learning with GLCM Features
by Min Tian, Maojin Tan and Min Wang
Processes 2023, 11(10), 2982; https://doi.org/10.3390/pr11102982 - 14 Oct 2023
Viewed by 1215
Abstract
The identification of sedimentary structures in lithofacies is of great significance to the exploration and development of Paleogene shale in the Boxing Sag. However, due to the scale mismatch between the thickness of laminae and the vertical resolution of conventional wireline logs, the [...] Read more.
The identification of sedimentary structures in lithofacies is of great significance to the exploration and development of Paleogene shale in the Boxing Sag. However, due to the scale mismatch between the thickness of laminae and the vertical resolution of conventional wireline logs, the conventional lithofacies division method fails to realize the accurate classification of sedimentary structures and cannot meet the needs of reservoir research. Therefore, it is necessary to establish a lithofacies identification method with higher precision from advanced logs. In this paper, a method integrating the gray level co-occurrence matrix (GLCM) and random forest (RF) algorithms is proposed to classify shale lithofacies with different sedimentary structures based on formation micro-imager (FMI) imaging logging and elemental capture spectroscopy (ECS) logging. According to the characteristics of shale laminae on FMI images, GLCM, an image texture extraction tool, is utilized to obtain texture features reflecting sedimentary structures from FMI images. It is proven that GLCM can depict shale sedimentary structures efficiently and accurately, and four texture features (contrast, entropy, energy, and homogeneity) are sensitive to shale sedimentary structures. To accommodate the correlation between the four texture features, the random forest algorithm, which has been proven not to be affected by correlated input features, is selected for supervised lithofacies classification. To enhance the model’s ability to differentiate between argillaceous limestone and calcareous mudstone, the carbonate content and clay content calculated from the ECS logs are involved in the input features. Moreover, grid search cross-validation (CV) is implemented to optimize the hyperparameters of the model. The optimized model achieves favorable performance on training data, validation data, and test data, with average accuracies of 0.84, 0.79, and 0.76, respectively. This study also discusses the application of the classification model in lithofacies and production prediction. Full article
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15 pages, 2707 KiB  
Article
Study on the Evaluation Method of the Water Saturation Logging of a Low-Resistance Oil Reservoir in the Guantao Formation, Bohai Basin
by Li Meng, Xinlei Shi, Wangwang Yang, Chong Zhang and Wenbin Xu
Processes 2023, 11(10), 2890; https://doi.org/10.3390/pr11102890 - 30 Sep 2023
Cited by 2 | Viewed by 782
Abstract
As an unconventional and hidden special oil reservoir, a low-resistance oil reservoir has huge exploration potential. The formation of low-resistance oil reservoirs in the study area is affected by multiple factors, and the electrical characteristics of low-resistance oil reservoirs are not obvious, which [...] Read more.
As an unconventional and hidden special oil reservoir, a low-resistance oil reservoir has huge exploration potential. The formation of low-resistance oil reservoirs in the study area is affected by multiple factors, and the electrical characteristics of low-resistance oil reservoirs are not obvious, which makes it difficult to accurately evaluate the oil- and gas-bearing nature of low-resistance reservoirs by traditional saturation evaluation methods. The water saturation evaluation has become the focus and difficulty of the logging interpretation of low-resistance oil reservoirs in the target blocks. Low-resistance oil reservoirs have various reservoir characteristics and genesis mechanisms. Based on this, this paper firstly analyzes the genesis mechanisms of low-resistance oil reservoirs by combining geological, core NMR, X-diffraction, and well logging data. Secondly, on the basis of clarifying the genesis mechanism and the main controlling factors of the low-resistance oil formation, the additional conductivity correction of clay is carried out by combining it with the petroelectricity experiment, and the petroelectricity parameters are determined. Finally, the saturation of the low-resistance oil formation in the study area is quantitatively evaluated using the Simandoux model. The results show that the high immobile water saturation caused by the complex pore structure and the additional electrical conductivity of clay are the main controlling causes of the low-resistance reservoirs in the study area. Compared with the saturation evaluation effect of the W-S model, the evaluation accuracy of the Simandoux model corrected for the additional electrical conductivity of clay is better than that of the W-S model, which is 0.88, the relative error of its computation is 7.2%, and the model constructed can provide quantitative bases for the dredging of the low-resistance reservoirs in the Bohai Basin. Full article
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13 pages, 4302 KiB  
Article
Classification and Evaluation of Tight Sandstone Reservoirs Based on MK-SVM
by Xuefei Lu, Xin Xing, Kelai Hu and Bin Zhou
Processes 2023, 11(9), 2678; https://doi.org/10.3390/pr11092678 - 6 Sep 2023
Viewed by 692
Abstract
It is difficult to determine the main microscopic factors controlling reservoir quality due to the strong microscopic heterogeneity of tight sandstone reservoirs, which also makes it difficult to distinguish dominant reservoirs. At the same time, there are fewer experimental samples available, and data [...] Read more.
It is difficult to determine the main microscopic factors controlling reservoir quality due to the strong microscopic heterogeneity of tight sandstone reservoirs, which also makes it difficult to distinguish dominant reservoirs. At the same time, there are fewer experimental samples available, and data collected from relevant research are thus worth paying attention to. In this study, based on the experimental results of high-pressure mercury injection of 25 rock samples from Chang 6 reservoir in the Wuqi area, Lasso dimensionality reduction was used to reduce the dimensionality of 14 characteristic parameters to 6, which characterize the microscopic pore structure, while a combination of different kernel functions was used to construct the multi-kernel function of the multi-kernel model to be determined. A multi-kernel support vector machine (MK-SVM) model was established for unsupervised learning of microscopic pore structure characteristic parameters that affect reservoir quality. By optimizing the hyperparameters of the model and obtaining the optimal decision function, the tight sandstone reservoirs were classified into classes I, II and III, and the classification results were verified. The results show that the accuracy of the proposed reservoir classification method is as high as 86%, which can effectively reduce the time loss and save labor costs. It provides an effective method for the comprehensive evaluation of tight sandstone reservoirs. Full article
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13 pages, 3855 KiB  
Article
A Model Based on the Random Forest Algorithm That Predicts the Total Oil–Water Two-Phase Flow Rate in Horizontal Shale Oil Wells
by Huimin Zhou, Junfeng Liu, Jiegao Fei and Shoubo Shi
Processes 2023, 11(8), 2346; https://doi.org/10.3390/pr11082346 - 4 Aug 2023
Viewed by 768
Abstract
Due to variables like wellbore deviation variation and flow rate, the local flow velocity in the output wellbore of horizontal shale oil wells varied significantly at various points in the wellbore cross-section, making it challenging to calculate the total single-layer production with accuracy. [...] Read more.
Due to variables like wellbore deviation variation and flow rate, the local flow velocity in the output wellbore of horizontal shale oil wells varied significantly at various points in the wellbore cross-section, making it challenging to calculate the total single-layer production with accuracy. The oil–water two-phase flow rate calculation techniques for horizontal wells developed based on particular flow patterns and array spinners had excellent applicability in their respective niches but suffered from poor generalizability and demanding experience levels for logging interpreters. In this study, we employed five spinners in a triangular walled array instrument to create the multi-decision tree after figuring out how many leaf nodes there were and examining the defining characteristics of the observed values gathered under various experimental setups. The construction of the entire oil–water two-phase flow prediction model was made possible when the random forest regression approach was used with it. The total oil–water flow rate at each perforated layer was predicted using the model in sample wells, and the mean square error with the third party’s interpretation conclusion was 1.42, indicating that the model had an excellent application effect. The approach, which offered a new interpretation method for calculating the oil–water two-phase flow rate of horizontal wells based on multi-location local flow rate, required less interpretation knowledge from the interpreter and had a stronger generalization capacity. Full article
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14 pages, 6387 KiB  
Article
Numerical Simulation of Electrical Properties of Carbonate Reservoirs Using Digital Rocks
by Yuting Hou, Die Liu, Taiping Zhao, Jinyu Zhou, Lili Tian, Xiaopan Kou, Baoding Zhu and Xin Nie
Processes 2023, 11(7), 2214; https://doi.org/10.3390/pr11072214 - 23 Jul 2023
Viewed by 1097
Abstract
Rock electrical experiments are essential means of researching the conductive properties of rocks and are fundamental to interpreting resistivity logging. Carbonate rocks have more complex pore structures than sandstone, which results in more complex conductive properties. However, conducting experiments on representative rock samples [...] Read more.
Rock electrical experiments are essential means of researching the conductive properties of rocks and are fundamental to interpreting resistivity logging. Carbonate rocks have more complex pore structures than sandstone, which results in more complex conductive properties. However, conducting experiments on representative rock samples from carbonate reservoirs is difficult, making it challenging to study the micro factors affecting electrical properties. Therefore, researching the conductive properties of carbonate rocks is difficult. To address this, in this paper, three-dimensional (3D) digital rock models with different porosities are generated, and conductive simulations are carried out on these models using the finite element method (FEM). Firstly, a micro-computed tomography (μ-CT) 3D image of a carbonate rock is obtained. Secondly, mathematical morphology-based methods are used on the μ-CT image to generate cores with varying porosities and fluid distributions. Then, the electrical properties are simulated using the FEM method, and the results are analyzed. The results reveal that the formation factor of the reservoir is mainly influenced by the shape and structure of the pores. The Archie equation is more suitable for carbonate reservoirs with water saturation levels greater than 60%. The wettability of the rock can alter the distribution of fluid in the reservoir space under different water saturation conditions. In pure water-wet rocks, the water phase mainly occupies small pores, while the oil phase occupies larger pores. As a result, compared to pure oil-wet rocks, water-wet rocks have more conductive channels and better conductivity. Therefore, it is important to determine the wettability of the rock when calculating water saturation using the Archie equation. The saturation index value of water-wet carbonate rock is about 2, while that of oil-wet rock is around 3–4. This research lays a foundation for studying the electrical conductivity of carbonate reservoirs using digital rocks. Full article
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21 pages, 5038 KiB  
Article
A Method for Judging the Effectiveness of Complex Tight Gas Reservoirs Based on Geophysical Logging Data and Using the L Block of the Ordos Basin as a Case Study
by Qing Zhao, Jianhong Guo and Zhansong Zhang
Processes 2023, 11(7), 2195; https://doi.org/10.3390/pr11072195 - 21 Jul 2023
Cited by 2 | Viewed by 868
Abstract
As an important unconventional oil and gas resource, the tight gas reservoir faces many technical challenges due to its low porosity, low permeability, and strong heterogeneity. Among them, the accurate definition of effective reservoirs and ineffective reservoirs in tight gas reservoirs directly affects [...] Read more.
As an important unconventional oil and gas resource, the tight gas reservoir faces many technical challenges due to its low porosity, low permeability, and strong heterogeneity. Among them, the accurate definition of effective reservoirs and ineffective reservoirs in tight gas reservoirs directly affects the formulation and adjustment of subsequent development plans. This paper proposes a reservoir effectiveness identification method based on double factors based on conventional geophysical logging data and core experimental data. The double factors considered are based on the logging response and physical parameters of the reservoir. The identification factor F1 is obtained by using the difference in the logging response values of the natural gamma logging curve, compensated density logging curve, and acoustic time difference logging curve in different reservoirs combined with mathematical operation, and the identification factor F2 is calculated by using porosity parameters combined with Archie’s formula. The validity of the reservoir can be judged by the intersection of the above double factors. This method is applied to the Shihezi Formation in the L block, and the applicability of the double factors is compared and analyzed using the traditional method. The results show that the method has strong applicability in tight gas reservoirs and that the accuracy rate reaches 96%. Compared with the direct use of the porosity lower limit method, the accuracy of the judgment is significantly improved, and the calculation is simple, easy to implement, and unaffected by mud invasion. For study areas with different geological backgrounds, the process of this method can also be used to determine the effectiveness of the reservoir. The reservoir effectiveness identification method proposed in this paper has practical engineering significance and lays a solid foundation for subsequent fluid property identification, production calculation, and development plan formulation and adjustment. Full article
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19 pages, 6004 KiB  
Article
Simulation of Rock Electrical Properties in Deep Reservoirs Based on Digital Rock Technology
by Suogui Shang, Qiangyong Gao, Yunjiang Cui, Peichun Wang, Zhang Zhang, Yadong Yuan, Weichao Yan and Peng Chi
Processes 2023, 11(6), 1758; https://doi.org/10.3390/pr11061758 - 9 Jun 2023
Cited by 1 | Viewed by 1431
Abstract
Deep reservoirs are in a high-pressure and high-temperature (HPHT) environment, while the experimental conditions for rock electrical properties that meet the deep reservoir conditions are harsh and costly. Although digital rock technology can simulate the electrical properties of rocks, it is limited to [...] Read more.
Deep reservoirs are in a high-pressure and high-temperature (HPHT) environment, while the experimental conditions for rock electrical properties that meet the deep reservoir conditions are harsh and costly. Although digital rock technology can simulate the electrical properties of rocks, it is limited to electrical simulation studies under normal temperature and pressure conditions (NPT), which limits their ability to capture the electrical characteristics of deep hydrocarbon reservoirs. This limitation affects the accuracy of saturation prediction based on resistivity logging. To simulate the rock electrical properties under HPHT conditions, we proposed a low-cost and high-efficiency HPHT digital rock electrical simulation workflow. Firstly, samples from deep formations were CT-scanned and used to construct multi-component digital rocks that reflect the real microstructure of the samples. Then, mathematical morphology was used to simulate the overburden correction under high-pressure conditions, and the changes in the conductivity of formation water and clay minerals at different temperatures were used to simulate the conductivity changes of rock components under high-temperature conditions. To carry out the electrical simulation of digital rock in deep reservoirs, a numerical simulation condition for HPHT in deep layers was established, and the finite element method (FEM) was used. Finally, based on the equivalent changes in the conductivity of different components, the effects of clay minerals and formation water under HPHT conditions on rock electrical properties were studied and applied to predict the water saturation based on well logging data. We found that considering the influence of temperature, salinity, and clay type, the saturation index (n) of the rock depends on the ratio of the clay conductivity to the formation water conductivity. The larger the ratio is, the smaller the value of n. In addition, the average relative error between the predicted water saturation under HPHT conditions and the sealed coring analysis was 6.8%, which proved the accuracy of the proposed method. Overall, this method can effectively simulate the pressure and temperature environment of deep formations, reveal the electrical conductivity mechanisms of rocks under formation pressure and temperature conditions, and has promising prospects for the study of rock physical properties and reservoir evaluation in deep formations. Full article
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13 pages, 1968 KiB  
Article
A New Method for Mobility Logging Evaluation Based on Flowing Porosity in Shale Oil Reservoirs
by Bo Shen, Yunhe Tao, Gang Wang, Haitao Fan, Xindong Wang and Ke Sun
Processes 2023, 11(5), 1466; https://doi.org/10.3390/pr11051466 - 11 May 2023
Cited by 1 | Viewed by 1398
Abstract
Shale oil reservoirs differ from conventional reservoirs in several aspects, including the sedimentary model, accumulation mechanism, and reservoir characteristics, which pose significant challenges to their exploration and development. Therefore, identifying the location of optimal spots is crucial for the successful exploration and development [...] Read more.
Shale oil reservoirs differ from conventional reservoirs in several aspects, including the sedimentary model, accumulation mechanism, and reservoir characteristics, which pose significant challenges to their exploration and development. Therefore, identifying the location of optimal spots is crucial for the successful exploration and development of shale oil reservoirs. Mobility, particularly in low-permeability shale oil reservoirs with nano-scale pores, is a crucial petrophysical property that determines the development plan. However, two-dimensional nuclear magnetic resonance (2D-NMR) is expensive and has limited applicability, although it can estimate shale oil mobility. Hence, it is of great significance to find a precise method for evaluating shale oil mobility using conventional logging. In this paper, we propose a new method for assessing shale oil mobility based on free oil porosity derived from the difference in flowing porosity detected at different ranges of logging, utilizing the Maxwell conductivity model and conductivity efficiency theory. Our study shows that longitudinal-T2 (T1-T2) NMR logging can accurately evaluate the mobility of shale oil. This is demonstrated by comparing the processing results obtained from our proposed method with those from 2D-NMR and laboratory NMR experiments. The predicted results based on conventional well logs also show good agreement with experimental results, confirming the effectiveness and reliability of our new method. Our proposed method carries reference significance for evaluating shale oil reservoir quality. Full article
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13 pages, 1995 KiB  
Article
A New Method for Calculating Reservoir Core-Bound Water Saturation Using the Cast Thin Section
by Yunjiang Cui, Jun Ming, Xinlei Shi, Wangwang Yang, Zhansong Zhang and Chong Zhang
Processes 2023, 11(5), 1397; https://doi.org/10.3390/pr11051397 - 5 May 2023
Viewed by 1131
Abstract
The rock coring of the reservoir in the Bohai A field is difficult. The cores of the target section in the study area are loose, making it difficult to accurately measure the core-bound water saturation. The purpose of this research was to develop [...] Read more.
The rock coring of the reservoir in the Bohai A field is difficult. The cores of the target section in the study area are loose, making it difficult to accurately measure the core-bound water saturation. The purpose of this research was to develop and validate a method for calculating a reservoir core-bound water saturation ratio using the cast thin section. First, pepper noise denoising and image enhancement were performed on the thin section by median filtering and gamma variation. Based on this, the enhanced sheet images were thresholded for segmentation by the two-dimensional OTSU algorithm, which automatically picked up the thin section pore-specific parameters. Then, the thin section image was equivalent to a capillary cross-section, while the thin film water fused to the pore surface was observed as bound water. For hydrophilic rocks with a strong homogeneity, the area of thin film water in the pore space of the sheet was divided by the total area of the pore space, which produced the bound water saturation. Next, the theoretical relationship between the film water thickness and the critical pore throat radius was derived based on the Young–Laplace equation. The bound water saturation of the rock was calculated by combining the pore perimeter and the area that was automatically picked up from the thin film for a given critical pore throat radius of the rock. Finally, 22 images of thin sections of sparse sandstone from the coring well section of the study area were image processed using the new method proposed in this paper, and the bound water saturation was calculated. The calculated results were compared with 22 NMR-bound water saturations and 11 semi-permeable baffle plate-bound water saturations in the same layer section. The results showed that the bound water saturation values calculated by the three methods produced consistent trends with absolute errors within 5%. The calculated results confirm the reliability of the method proposed in this paper. This method can effectively avoid the problem of the inaccurate results of core experiments due to the easy damage of sparse sandstone and provides a new idea for the accurate determination of the bound water saturation of sparse sandstone. Full article
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17 pages, 10096 KiB  
Article
Theory and Application of Geostatistical Inversion: A Facies-Constrained MCMC Algorithm
by Wenbo Dong, Yonggen Li, Zhixian Gui and Lei Zhou
Processes 2023, 11(5), 1335; https://doi.org/10.3390/pr11051335 - 26 Apr 2023
Cited by 1 | Viewed by 1182
Abstract
To improve the prediction of thin reservoirs with special geophysical responses, a geostatistical inversion technique is proposed based on an in-depth analysis of the theory of geostatistical inversion. This technique is based on the Markov chain Monte Carlo algorithm, to which we added [...] Read more.
To improve the prediction of thin reservoirs with special geophysical responses, a geostatistical inversion technique is proposed based on an in-depth analysis of the theory of geostatistical inversion. This technique is based on the Markov chain Monte Carlo algorithm, to which we added the contents of facies-constrained. The feasibility of the technique and the reliability of the prediction results are demonstrated by a prediction of the sand bodies in the braided river channel bars in the Xiazijie Oilfield in the Junggar Basin. Based on the MCMC algorithm, the results show that leveraging the lateral changes in the seismic waveforms as geologically relevant information to drive the construction of the variogram and the optimization of the statistical sampling can largely overcome the obstacle that prevents traditional geostatistical inversions from accurately delineating the sedimentary characteristics; thereby, the proposed algorithm truly achieves facies-constrained geostatistical inversion. The case study of the Xiazijie Oilfield showed the feasibility and reliability of this technology. The prediction accuracy of the FCMCMC algorithm-based geostatistical inversion is as high as 6 m for thin interbedded reservoirs, and the coincidence rate between the prediction results and the well log data is more than 85%, which confirms the reliability of the technique. The demonstrated performance of the proposed technique provides a preliminary reference for the prediction of the thin interbedded reservoirs formed in terrestrial sedimentary basins and characterized by small thicknesses and rapid lateral changes with special geophysical responses. Full article
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25 pages, 9811 KiB  
Article
Saturation Determination and Fluid Identification in Carbonate Rocks Based on Well Logging Data: A Middle Eastern Case Study
by Jianhong Guo, Zongfa Ling, Xiaori Xu, Yufang Zhao, Chunding Yang, Beilei Wei, Zhansong Zhang, Chong Zhang, Xiao Tang, Tao Chen, Gang Li and Qing Zhao
Processes 2023, 11(4), 1282; https://doi.org/10.3390/pr11041282 - 20 Apr 2023
Cited by 6 | Viewed by 1800
Abstract
In the Middle East, there remain many technical challenges in the water saturation evaluation of carbonate rocks and the effective identification of reservoir fluid properties. The traditional Archie equation is not applicable to carbonate reservoirs with complex pore structures and varying reservoir space [...] Read more.
In the Middle East, there remain many technical challenges in the water saturation evaluation of carbonate rocks and the effective identification of reservoir fluid properties. The traditional Archie equation is not applicable to carbonate reservoirs with complex pore structures and varying reservoir space distribution, as there are obvious “non-Archie” phenomena. In this paper, by analyzing the experimental data on the rock resistivity of the target formation in the study area and analyzing the relationship between stratigraphic factors and porosity, the previous fitting method was modified as a result of using the actual data while avoiding the cementation index as a way to improve Archie’s formula to evaluate the water saturation. Based on the improved Archie formula, the mathematical differential operation of water saturation and porosity was carried out using the formation resistivity. The calculation results of irreducible water saturation were used to calibrate the oil layer, and the water layer was calibrated when the water saturation was 100%, allowing for a novel reservoir fluid property identification method. This total differential method can effectively identify the oil-down-to (ODT) and water-up-to (WUT) levels in an oil–water system and then accurately divide the transition zone of the oil–water layer. When this method was applied, the identification results were in good agreement with production conclusions and test data with an accuracy rate of 89.95%. Although the use of geophysical logging data from open-hole wells combined with the total differential method is only applicable to wells with similar logging time and production time, it is possible to compare geophysical logging data from different periods to construct oil–water profiles to observe the changes in ODT over time to guide development and adjust production plans. The proposed reservoir fluid property identification method and the improved water saturation calculation formula can meet the requirements of water saturation evaluation in the target block with low calculation cost and easy implementation, which provides a new method for water saturation evaluation and rapid identification of reservoir fluid properties. Full article
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19 pages, 8830 KiB  
Article
Analysis of the Influence of Micro-Pore Structure on Oil Occurrence Using Nano-CT Scanning and Nuclear Magnetic Resonance Technology: An Example from Chang 8 Tight Sandstone Reservoir, Jiyuan, Ordos Basin
by Xinglei Song, Hui Gao, Congjun Feng, Ping Yi, Chen Wang and Teng Li
Processes 2023, 11(4), 1127; https://doi.org/10.3390/pr11041127 - 6 Apr 2023
Cited by 2 | Viewed by 1306
Abstract
The micro-pore structure of a tight sandstone reservoir remarkably impacts the occurrence characteristics of the tight oil. The micro-pore structure of the Jiyuan Chang 8 tight sandstone reservoir in the Ordos Basin was examined in this research using a core physical property test, [...] Read more.
The micro-pore structure of a tight sandstone reservoir remarkably impacts the occurrence characteristics of the tight oil. The micro-pore structure of the Jiyuan Chang 8 tight sandstone reservoir in the Ordos Basin was examined in this research using a core physical property test, an environmental scanning electron microscope, thin section identification, and high-pressure mercury intrusion. Using nano-CT scanning and nuclear magnetic resonance technologies, representative core samples were chosen for studies evaluating the tight oil occurrence statically and dynamically. The micro-pore structure effect of a tight sandstone reservoir on the occurrence of tight oil was investigated, and the occurrence of tight oil in the reservoir forming process was discussed. It was significant to the study of tight oil in the reservoir forming process in Ordos Basin. Findings indicated that the Chang 8 reservoir in Jiyuan, Ordos Basin has poor physical properties and exhibits a high degree of heterogeneity. In addition, the oil charging simulation experiment (oil charging) can be separated into the following three stages: fast growth, gradual growth, and stability. In the process of crude oil charging, oil always preferentially entered into medium pores and large pores. These pores were the primary areas of tight oil distribution. Furthermore, the occurrence of tight oil was affected by pore type, pore structure parameters, throat parameters, and combination mode of pore and throat. First, substantially large and medium pores lead to effective pore connectivity and generate a considerable amount of tight oil. The occurrence morphology includes oil film, cluster, porous, and isolated. Second, the greater the degree of intergranular pore growth and soluble feldspar pore development, the thicker the throat, the more developed the effective throat, and the greater the quantity of tight oil. Finally, oil saturation was negatively correlated with median pressure and displacement pressure and positively correlated with sorting factors, median radius, maximum pore throat radius, and efficiency of inverted mercury. Full article
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25 pages, 8437 KiB  
Article
Low Permeability Gas-Bearing Sandstone Reservoirs Characterization from Geophysical Well Logging Data: A Case Study of Pinghu Formation in KQT Region, East China Sea
by Feiming Gao, Liang Xiao, Wei Zhang, Weiping Cui, Zhiqiang Zhang and Erheng Yang
Processes 2023, 11(4), 1030; https://doi.org/10.3390/pr11041030 - 29 Mar 2023
Cited by 4 | Viewed by 1713
Abstract
The Pinghu Formation is a low permeability sandstone reservoir in the KQT Region, East China Sea. Its porosity ranges from 3.6 to 18.0%, and permeability is distributed from 0.5 to 251.19 mD. The relationship between porosity and permeability was poor due to strong [...] Read more.
The Pinghu Formation is a low permeability sandstone reservoir in the KQT Region, East China Sea. Its porosity ranges from 3.6 to 18.0%, and permeability is distributed from 0.5 to 251.19 mD. The relationship between porosity and permeability was poor due to strong heterogeneity. This led to the difficulty of quantitatively evaluating effective reservoirs and identifying pore fluids by using common methods. In this study, to effectively evaluate low permeability sandstones in the Pinghu Formation of KQT Region, pore structure was first characterized from nuclear magnetic resonance (NMR) logging based on piecewise function calibration (PFC) method. Effective formation classification criteria were established to indicate the “sweet spot”. Afterwards, several effective methods were proposed to calculate formation of petrophysical parameters, e.g., porosity, permeability, water saturation (Sw), irreducible water saturation (Swirr). Finally, two techniques, established based on the crossplots of mean value of apparent formation water resistivity (Rwam) versus variance of apparent formation water resistivity (Rwav)—Sw versus Swirr—were adopted to distinguish hydrocarbon-bearing formations from water saturated layers. Field applications in two different regions illustrated that the established methods and techniques were widely applicable. Computed petrophysical parameters matched well with core-derived results, and pore fluids were obviously identified. These methods were valuable in improving low permeability sandstone reservoirs characterization. Full article
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