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A special issue of Energies (ISSN 1996-1073). This special issue belongs to the section "H: Geo-Energy".

Deadline for manuscript submissions: closed (17 August 2021) | Viewed by 26709

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Prof. Dr. Jadwiga A. Jarzyna

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Faculty of Geology Geophysics and Environmental Protection, AGH University of Science and Technology, 30-059 Krakow, Poland
Interests: petrophysics; well logging; rock modeling; machine learning
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Dr. Paulina Krakowska-Madejska

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Faculty of Geology Geophysics and Environmental Protection,AGH University of Science and Technology, 30-059 Krakow, Poland
Interests: well logging; petrophysics

Special Issue Information

Dear Colleagues,

Well logging belongs to the group of geophysical methods which are carried out in boreholes and provide detailed information on petrophysical parameters of rocks. There is a great variety of logs/methods due to their different physical bases. Well logging methods and their outcomes are in the middle between laboratory experiments and their point results in micro scale in one side and surface of parameters, for instance, seismic projects with 3D cubes, in macro scale on the other. Working with data in mezzo scale, well log analysts and petrophysicists closely cooperate with seismic, geoelectric, magnetotelluric, and other specialists to scale and calibrate information and combine data. Well logging has been around since the 1920s and has been developed for almost 100 years in terms of measurement techniques, processing procedures, and interpretation systems. Today, we have at our disposal sophisticated devices generating data of amazing resolution in situ, in real time, from difficult rock formations. There is also a huge amount of archive data which can be included in the reinterpretation. It is a great challenge to skillfully use these data together to save money and the environment and to not plan more new wells than necessary. In this Special Issue, we invite papers dealing with current problems of measurements, processing, and interpretation of well logs used in various projects in prospection for conventional and unconventional hydrocarbons (shale gas and oil, tight gas), table and geothermal water, and logs in mining to recognize methane resources in coal seams and different raw materials. We specifically invite authors specializing in technological novelties in well logging (solutions for vertical and horizontal wells) and those who would like to present the results of modeling, show statistical methodologies for processing big data sets, and use old data and the newest results together for reinterpretation.

This Special Issue calls for theoretical and empirical papers focusing on the following topics:

Results of well logging interpretation in various projects, interesting case studies;
Well logging in the construction of digital models of rocks;
Results of modeling to improve the inverse problem solution in well logging;
Novelty in devices, measurement methods, and interpretation methodologies;
Calibrating and scaling problems using well logging for other geophysical methods;
Well logging data mining using machine learning and artificial intelligence.

Prof. Dr. Jadwiga A. Jarzyna
Dr. Paulina Krakowska-Madejska
Guest Editors

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Keywords

Results of well logging interpretation in various projects, interesting case studies
Well logging in the construction of digital models of rocks
Results of modeling to improve the inverse problem solution in well logging
Novelty in devices, measurement methods, and interpretation methodologies
Calibrating and scaling problems using well logging for other geophysical methods
Well logging data mining using machine learning and artificial intelligence

Published Papers (11 papers)

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Research

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21 pages, 7358 KiB

Open AccessArticle

Hydraulic Flow Unit Classification and Prediction Using Machine Learning Techniques: A Case Study from the Nam Con Son Basin, Offshore Vietnam

by Ha Quang Man, Doan Huy Hien, Kieu Duy Thong, Bui Viet Dung, Nguyen Minh Hoa, Truong Khac Hoa, Nguyen Van Kieu and Pham Quy Ngoc

Energies 2021, 14(22), 7714; https://doi.org/10.3390/en14227714 - 18 Nov 2021

Cited by 7 | Viewed by 3530

Abstract

The test study area is the Miocene reservoir of Nam Con Son Basin, offshore Vietnam. In the study we used unsupervised learning to automatically cluster hydraulic flow units (HU) based on flow zone indicators (FZI) in a core plug dataset. Then we applied supervised learning to predict HU by combining core and well log data. We tested several machine learning algorithms. In the first phase, we derived hydraulic flow unit clustering of porosity and permeability of core data using unsupervised machine learning methods such as Ward’s, K mean, Self-Organize Map (SOM) and Fuzzy C mean (FCM). Then we applied supervised machine learning methods including Artificial Neural Networks (ANN), Support Vector Machines (SVM), Boosted Tree (BT) and Random Forest (RF). We combined both core and log data to predict HU logs for the full well section of the wells without core data. We used four wells with six logs (GR, DT, NPHI, LLD, LSS and RHOB) and 578 cores from the Miocene reservoir to train, validate and test the data. Our goal was to show that the correct combination of cores and well logs data would provide reservoir engineers with a tool for HU classification and estimation of permeability in a continuous geological profile. Our research showed that machine learning effectively boosts the prediction of permeability, reduces uncertainty in reservoir modeling, and improves project economics. Full article

(This article belongs to the Special Issue Well Logging Applications)

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29 pages, 18680 KiB

Open AccessArticle

Characterization of Pliocene Biogenic Gas Reservoirs from the Western Black Sea Shelf (Romanian Offshore) by Integration of Well Logs and Core Data

by Bogdan Mihai Niculescu and Victor Mocanu

Energies 2021, 14(20), 6629; https://doi.org/10.3390/en14206629 - 14 Oct 2021

Cited by 1 | Viewed by 2361

Abstract

The successful interpretation of open-hole well logging data relies on jointly using all available petrophysical and geological information. This paper presents relevant case studies related to the integration of well logs with core measurements for exploration wells drilled in the Romanian continental shelf area of the Western Black Sea basin. The analyzed wells targeted gas-bearing sands and silts complexes of Early Pliocene (Dacian) age, developed in a deltaic to shallow marine sedimentary environment in two distinct fields. The wireline logging programs included conventional formation evaluation logs, pressure surveys, nuclear magnetic resonance, and borehole electrical imaging logs. The core dataset comprised routine and special measurements (porosity, grain density, permeability, water saturation, and Archie parameters) carried out at quasi-reservoir confining pressure. The wireline logging suites were interpreted via a deterministic workflow, including core-derived interpretation parameters. Other core-derived parameters were used for constraining and validating the log interpretations. The results show that a problem related to the ambiguity of formation water resistivity can be overcome through resistivity–porosity dependencies constructed to include potential aquifer zones in the proximity of the Dacian gas-bearing reservoirs. This study also revealed and quantified uncertainties regarding the estimation of gas–water contacts from formation pressure surveys, which can be mitigated by the confirmation or correction of pressure-derived fluid contacts via the well log interpretation results. Lastly, we identified a probable resistivity logs suppression effect related both to high contents of capillary-bound water and also to the limited resolution of electrical logging tools in the presence of sand-shale thin bedding or laminations. Full article

(This article belongs to the Special Issue Well Logging Applications)

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33 pages, 9841 KiB

Open AccessArticle

Prediction and Early Detection of Karsts—An Overview of Methods and Technologies for Safer Drilling in Carbonates

by Danil Maksimov, Alexey Pavlov and Sigbjørn Sangesland

Energies 2021, 14(20), 6517; https://doi.org/10.3390/en14206517 - 11 Oct 2021

Cited by 3 | Viewed by 2104

Abstract

The nature of carbonate deposition as well as diagenetic processes can cause the development of unique geological features such as cavities, vugs and fractures. These are called karsts. Encountering karsts while drilling can lead to serious consequences such as severe mud losses, drops of bottom hole assembly and gas kicks. To improve drilling safety in intervals of karstification, it is important to detect karsts as early as possible, preferably in advance. In this paper, we review methods and technologies that can be used for the prediction and early detection of karsts. In particular, we consider acoustic, resistivity, seismic and drilling-data methods. In addition to the inventions and technologies developed and published over the past 40 years, this paper identifies the advantages, limitations and gaps of these existing technologies and discusses the most promising methods for karst detection and prediction. Full article

(This article belongs to the Special Issue Well Logging Applications)

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27 pages, 17053 KiB

Open AccessArticle

Multiple Regression and Modified Faust Equation on Well Logging Data in Application to Seismic Procedures: Polish Outer Carpathians Case Study

by Kamila Wawrzyniak-Guz, Jadwiga A. Jarzyna, Krzysztof Pieniądz and Krzysztof Starzec

Energies 2021, 14(19), 6300; https://doi.org/10.3390/en14196300 - 02 Oct 2021

Cited by 1 | Viewed by 1943

Abstract

An appropriate velocity model from well logs is a key issue in the processing and interpretation of seismic data. In a deep borehole located in the central part of the Polish Outer Carpathians, the sonic measurements were inadequate for seismic purposes due to the poor quality of data and gaps in the logging. Multiple regression (MR) and a modified Faust equation were proposed to model the velocity log. MR estimated the P-wave slowness as a dependent variable on the basis of sets of various logs as independent variables. The solutions were verified by the interval velocity from Check Shots (CS) and by the convergence of synthetic seismograms and the real seismic traces. MR proved to be an effective method when a set of other logs was available. The modified Faust method allowed computation of P-wave velocity based on the shallow resistivity logs, depth, and compaction factor. Faust coefficients were determined according to the lithology and stratigraphy divisions and were calibrated with the use of the velocity previously determined in the MR analysis. The modified Faust equation may be applied in nearby old wells with limited logging data, particularly with no sonic logs, where MR could not be successfully applied. Full article

(This article belongs to the Special Issue Well Logging Applications)

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18 pages, 9496 KiB

Open AccessArticle

Electrofacies as a Tool for the Prediction of True Resistivity Using Advanced Statistical Methods—Case Study

by Stanisław Baudzis, Joanna Karłowska-Pik and Edyta Puskarczyk

Energies 2021, 14(19), 6228; https://doi.org/10.3390/en14196228 - 30 Sep 2021

Cited by 1 | Viewed by 2133

Abstract

Statistical analysis methods have been widely used in all industries. In well logs analyses, they have been used from the very beginning to predict petrophysical parameters such as permeability and porosity or to generate synthetic curves such as density or sonic logs. Initially, logs were generated as simple functions of other measurements. Then, as a result of the popularisation of algorithms such as the k-nearest neighbours (k-NN) or artificial neural networks (ANN), logs were created based on other logs. In this study, various industry and general scientific programmes were used for statistical data analysis, treating the well logs data as individual data sets, obtaining very convergent results. The methods developed for processing well logs data, such as Multi-Resolution Graph-Based Clustering (MRGBC), as well as algorithms commonly used in statistical analysis such as Kohonen self-organising maps (SOM), k-NN, and ANN were applied. The use of the aforementioned statis-tical methods allows for the electrofacies determination and prediction of an Rt log based on the other recorded well logs. Correct determination of Rt in resistivity measurements made with the Dual Laterolog tool in the conditions of the Groningen effect is often problematic. The applied calculation methods allow for the correct estimation of Rt in the tested well. Full article

(This article belongs to the Special Issue Well Logging Applications)

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16 pages, 3728 KiB

Open AccessArticle

Vitrinite Equivalent Reflectance Estimation from Improved Maturity Indicator and Well Logs Based on Statistical Methods

by Sebastian Waszkiewicz and Paulina I. Krakowska-Madejska

Energies 2021, 14(19), 6182; https://doi.org/10.3390/en14196182 - 28 Sep 2021

Cited by 3 | Viewed by 1527

Abstract

Estimation and correct determination of vitrinite equivalent reflectance in rock is crucial for the assessment of the source rock in both conventional and unconventional hydrocarbon deposits. These parameters can be determined in laboratories on rock samples. Laboratory measurements provide only point information. However, the use of well logs could overcome discontinuities in the data and provide parameters throughout a study interval. Attention has been paid to the estimation of TOC based on well logs. Vitrinite equivalent reflectance estimation is less well discussed and most papers reported cases with high TOC content in analyzed deposits. In this paper, the estimation of improved R_o is presented using a calculated maturity indicator with well logs. As the organic matter content is not high, additional steps were required for the calculation. To improve the quality of the fit and to find similar intervals, the data were grouped using cluster and neural network analysis. The next step was to use the resistivity log to improve the obtained maturity indicator. Due to the changing properties of kerogen with the type and degree of thermal maturity, this approach turned out to be reliable. The use of resistivity significantly increased the correlation coefficient and reduced errors. The method was tested on two wells with different type and maturity of kerogen. The obtained results are satisfactory, which makes it possible to use the method even in formations with a low organic matter content. Full article

(This article belongs to the Special Issue Well Logging Applications)

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24 pages, 9644 KiB

Open AccessArticle

Characterization of the Carbonate Formation Fracture System Based on Well Logging Data and Results of Laboratory Measurements

by Marek Stadtműller, Paulina I. Krakowska-Madejska, Grzegorz Leśniak and Jadwiga A. Jarzyna

Energies 2021, 14(19), 6034; https://doi.org/10.3390/en14196034 - 22 Sep 2021

Cited by 5 | Viewed by 1789

Abstract

This article presents a novel methodology for data integration including laboratory data, the results of standard well logging measurements and interpretation and the interpretation of XRMI imager data for determination of the porosity and permeability of the fracture system in carbonate rock. An example of the results of the micro computed tomography applied for carbonate rock is included. Data were obtained on the area of the Polish Lowland Zechstein Main Dolomite formation. The input set of data included the results of mercury injection porosimetry (MICP), thin section and polished section analysis, well logging measurements and comprehensive interpretation and micro computed tomography. The methodology of the macrofractures’ analysis based on borehole wall imagery as well as estimation of their aperture was described in detail. The petrophysical characteristics of the fracture systems were analyzed as an element of standard interpretation of well logging data along a carbonate formation. The results of permeability determination, with micro-, mezzo- and macrofractures’ presence in the rock taken into consideration, were compared with outcomes of the drill stem tests (DSTs). Full article

(This article belongs to the Special Issue Well Logging Applications)

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25 pages, 17427 KiB

Open AccessArticle

The Use of Well-Log Data in the Geomechanical Characterization of Middle Cambrian Tight Sandstone Formation: A Case Study from Eastern Pomerania, Poland

by Małgorzata Słota-Valim and Anita Lis-Śledziona

Energies 2021, 14(19), 6022; https://doi.org/10.3390/en14196022 - 22 Sep 2021

Viewed by 2446

Abstract

Geomechanical characterization plays a key role in optimizing the stimulation treatment of tight reservoir formations. Petrophysical models help classify the reservoir rock as the conventional or unconventional type and determine hydrocarbon-saturated zones. Geomechanical and petrophysical models are fundamentally based on well-log data that provide reliable and high-resolution information, and are used to determine various relationships between measured borehole parameters and modeled physical rock properties in 3D space, with the support of seismic data. This paper presents the geomechanical characterization of the Middle Cambrian (Cm2) sediments from Eastern Pomerania, north Poland. To achieve the aim of this study, 1D well-log-based and 3D models based on seismic data of the rocks’ petrophysical, elastic, and strength properties, as well as numerical methods, were used. The analysis of the Middle Cambrian deposits revealed vertical and horizontal heterogeneity in brittleness, the direction of horizontal stresses, and the fracturing pressure required to initiate hydraulic fractures. The most prone to fracturing is the gas-saturated tight sandstones belonging to the Paradoxides Paradoxissimus formation of Cm2, exhibiting the highest brittleness and highest fracturing pressure necessary to stimulate this unconventional reservoir formation. Full article

(This article belongs to the Special Issue Well Logging Applications)

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17 pages, 3372 KiB

Open AccessArticle

Factor Analysis of Well Logs for Total Organic Carbon Estimation in Unconventional Reservoirs

by Norbert P. Szabó, Rafael Valadez-Vergara, Sabuhi Tapdigli, Aja Ugochukwu, István Szabó and Mihály Dobróka

Energies 2021, 14(18), 5978; https://doi.org/10.3390/en14185978 - 20 Sep 2021

Cited by 3 | Viewed by 2558

Abstract

Several approaches have been applied for the evaluation of formation organic content. For further developments in the interpretation of organic richness, this research proposes a multivariate statistical method for exploring the interdependencies between the well logs and model parameters. A factor analysis-based approach is presented for the quantitative determination of total organic content of shale formations. Uncorrelated factors are extracted from well logging data using Jöreskog’s algorithm, and then the factor logs are correlated with estimated petrophysical properties. Whereas the first factor holds information on the amount of shaliness, the second is identified as an organic factor. The estimation method is applied both to synthetic and real datasets from different reservoir types and geologic basins, i.e., Derecske Trough in East Hungary (tight gas); Kingak formation in North Slope Alaska, United States of America (shale gas); and shale source rock formations in the Norwegian continental shelf. The estimated total organic content logs are verified by core data and/or results from other indirect estimation methods such as interval inversion, artificial neural networks and cluster analysis. The presented statistical method used for the interpretation of wireline logs offers an effective tool for the evaluation of organic matter content in unconventional reservoirs. Full article

(This article belongs to the Special Issue Well Logging Applications)

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21 pages, 6094 KiB

Open AccessArticle

Estimation of Shallow Sulphur Deposit Resources Based on Reflection Seismic Studies and Well Logging

by Kamil Cichostępski and Jerzy Dec

Energies 2021, 14(17), 5323; https://doi.org/10.3390/en14175323 - 27 Aug 2021

Cited by 3 | Viewed by 1444

Abstract

In this article we present a novel method for the estimation of sulphur deposit resources based on high-resolution shallow reflection seismic survey and well logging. The study area was sited in the northern part of the Carpathian Foredeep (SE Poland), where sulphur ore occurs in carbonate rocks at a depth of about 120 m, with a thickness of approximately 25 m. The results of many years of seismic monitoring performed in the area of the sulphur deposit allowed us to determine the quantitative relationships between the amplitude of the seismic signal reflected from the top of the deposit and its petrophysical parameters such as porosity and sulphur content. The method of evaluating sulphur deposit is based on extensive statistics concerning the reservoir properties obtained from borehole data. We also discuss a methodology for conducting field acquisition and processing of seismic data in the aspect of mapping the actual amplitudes of the signal reflected from the top of a deposit. The results of estimating the abundance of carbonate sulphur deposits are presented based on the example of a seismic cross-section from the Osiek sulphur mine. Obtained results allow indicating the most prospective zones suitable for exploitation. Full article

(This article belongs to the Special Issue Well Logging Applications)

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Review

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31 pages, 6970 KiB

Open AccessReview

Estimation of Petrophysical Parameters of Carbonates Based on Well Logs and Laboratory Measurements, a Review

by Marek Stadtműller and Jadwiga A. Jarzyna

Energies 2023, 16(10), 4215; https://doi.org/10.3390/en16104215 - 20 May 2023

Cited by 3 | Viewed by 1857

Abstract

The purpose of this review paper is to show the possibilities of carbonate reservoir characterization using well logging and laboratory measurements. Attention was focused on standard and new methods of well logging acquisition and interpretation including laboratory experiments to show a part of the history of carbonate rock investigations as hydrocarbon or water reservoirs. Brief information on the geology, mineralogy and petrography of carbonate rocks was delivered. Reservoir properties, i.e., porosity (including fracturing), permeability, and saturation, were defined to emphasize the specific features of carbonates, such as fractures, and vugs. Examples of methodologies were selected from the commonly used laboratory techniques (thin sections examination, mercury and helium porosimetry, X-ray diffraction—XRD) combined with the standard well logs (bulk density—RHOB, neutron porosity—NPHI, sonic slowness—DT, and deep resistivity—Rd) to show the methods that have been used since the very beginning of the scientific and engineering studies of carbonates. Novelty in well logging, i.e., resistivity and acoustic imaging, nuclear magnetic resonance–NMR, dipole shear sonic imaging–DSI, and a spectral neutron-gamma log-geochemical device–GLT combined with modern laboratory investigations (NMR laboratory experiments, scanning electron microscopy SEM), showed how continuous information on mineral composition, porosity and saturation could be obtained and juxtaposed with very detailed laboratory data. Computed X-ray tomography (CT) enabling the 2D and 3D analyses of pores and fractures was presented as a quantitative methodology, effective in pore space characterization, revealing rock filtration abilities. Deep learning and artificial intelligence were used for joining various types of data. It was shown that thanks to new computational technologies original data from very small samples (micro scale), extensively describing the flow ability of the reservoir, could be extended to mezzo scale (core samples) and macro scale (well log images). Selected examples from the published papers illustrated the review. References cited in the text, together with the issues included in them, were the rich source of the practical knowledge processed These were checked by the authors and could be used in other projects. Full article

(This article belongs to the Special Issue Well Logging Applications)

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