Handbook of Petroleum Geoscience E-Book

• Herausgeber: John Wiley & Sons
• Kategorie: Wissenschaft und neue Technologien
• Sprache: Englisch

Seitenzahl: 778

Veröffentlichungsjahr: 2022

Beschreibung

HANDBOOK OF PETROLEUM GEOSCIENCE This reference brings together the latest industrial updates and research advances in regional tectonics and geomechanics. Each chapter is based upon an in-depth case study from a particular region, highlighting core concepts and themes as well as regional variations. Key topics discussed in the book are: * Drilling solutions from the Kutch offshore basin * Geophysical studies from a gas field in Bangladesh * Exploring Himalayan terrain in India * Tectonics and exploration of the Persian Gulf basin * Unconventional gas reservoirs in the Bohemian Massif This book is an invaluable industry resource for professionals and academics working in and studying the fields of petroleum geoscience and tectonics.

Leseprobe

Handbook of Petroleum Geoscience

Exploration, Characterization, and Exploitation of Hydrocarbon Reservoirs

Edited by

Soumyajit Mukherjee

Indian Institute of Technology Bombay

Powai, Mumbai, India

Swagato Dasgupta

Indian Institute of Technology (ISM) Dhanbad

Dhanbad, India

Chandan Majumdar

Schlumberger Ltd.

Shiravane, Nerui Navi Mumbai

Mumbai, India

Subhandip Mandal

Integrated Reservoir Solutions

Houston

TX, USA

Troyee Dasgupta

Indian Institute of Technology Bombay

Powai, Mumbai, India

This edition first published 2023© John Wiley & Sons Ltd

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The right of Soumyajit Mukherjee, Swagato Dasgupta, Chandan Majumdar, Subhandip Mandal and Troyee Dasgupta to be identified as the authors of the editorial material in this work has been asserted in accordance with law.

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Library of Congress Cataloging‐in‐Publication DataNames: Mukherjee, Soumyajit, editor. | Dasgupta, Swagato, editor. | Majumdar, Chandan, editor. | Mandal, Subhadip, editor. | Dasgupta, Troyee, editor.Title: Handbook of petroleum geoscience : exploration, characterization, and exploitation of hydrocarbon reservoirs / edited by Soumyajit Mukherjee, Swagato Dasgupta, Chandan Majumdar, Subhadip Mandal, Troyee Dasgupta.Description: Hoboken, NJ : Wiley, 2023. | Includes bibliographical references.Identifiers: LCCN 2022026417 (print) | LCCN 2022026418 (ebook) | ISBN 9781119680031 (hardback) | ISBN 9781119680086 (adobe pdf) | ISBN 9781119680109 (epub)Subjects: LCSH: Petroleum–Geology. | Hydrocarbon reservoirs.Classification: LCC TN870.5 .H358 2023 (print) | LCC TN870.5 (ebook) | DDC 622/.1828–dc23/eng/20220713LC record available at https://lccn.loc.gov/2022026417LC ebook record available at https://lccn.loc.gov/2022026418

Cover Design: WileyCover Image: © P.V.R.Murty/Shutterstock

S. Mukherjee dedicates this book to Dr. A. K. Dubey (Senior Scientist retired from Wadia Institute of Himalayan Geology, Dehradun), and to Prof. Dr. Wolf‐Christian Dullo (retired from GEOMAR, Germany, former Chief Editor in the International Journal of Earth Sciences).

List of Contributors

Tuli BakshiComputational and Experimental Geomechanics LaboratoryDepartment of Earth SciencesIndian Institute of Technology BombayMumbai, Maharashtra, IndiaandClean Coal Technology LaboratoryDepartment of Mining EngineeringIndian Institute of Technology KharagpurKharagpur, West Bengal, India

Devojit BezbaruahDepartment of Applied GeologyDibrugarh UniversityDibrugarh, Assam, India

Abhijeet BhardwajDepartment of Industrial and Systems EngineeringUniversity of Wisconsin‐MadisonMadison, WI, USA

Soumi ChakiDivision of Signals and SystemsDepartment of Electrical EngineeringUppsala UniversityUppsala, Sweden

Chandreyi ChatterjeeSchlumbergerNavi Mumbai, Maharashtra, India

Juan Carlos Rabanal ChavezSchlumbergerQuito, Pichincha, Ecuador

Guilherme Furlan ChinelattoGeology InstituteUniversidade Estadual de Campinas (UNICAMP)São Paulo, Brazil

Nigel CleggGlobal Subject Matter Expert – GeosteeringHalliburton, Norway

Renzo D'souzaDepartment of Earth SciencesIndian Institute of Technology Bombay (IIT Bombay)Mumbai, Maharashtra, India

Mohuli DasDepartment of Earth SciencesIndian Institute of Technology Bombay (IIT Bombay)Mumbai, Maharashtra, India

Sudipta DasguptaDepartment of Earth SciencesIndian Institute of Technology Bombay (IIT Bombay)Mumbai, Maharashtra, India

Swagato DasguptaDepartment of Applied GeophysicsIndian Institute of Technology (ISM)Dhanbad, Jharkhand, India

Troyee DasguptaDepartment of Earth SciencesIndian Institute of Technology BombayMumbai, Maharashtra, India

Bronwyn DjefelEpirocKewdale, Western Australia, Australia

Deepali GadkariDepartment of GeographyUniversity of MumbaiKalina, Santacruz (E)Mumbai, Maharashtra, India

Tapos Kumar GoswamiDepartment of Applied GeologyDibrugarh UniversityDibrugarh, Assam, India

Eduardo GuareschiGeology Graduate ProgramUniversidade do Vale do Rio Dos Sinos (UNISINOS)São Leopoldo, Brazil

Lenin Mora GuerreroSchlumbergerBogota, Colombia

S.L. HappyHP PPS India Operations Pvt. Ltd.Bengaluru, Karnataka, India

Bodhisatwa HazraCSIR‐Central Institute of Mining and Fuel ResearchDhanbad, Jharkhand, IndiaandAcademy of Scientific and Innovative Research (AcSIR)Ghaziabad, Uttar Pradesh, India

Rodrigo Scalise HorodyskiGeology Graduate ProgramUniversidade do Vale do Rio Dos Sinos (UNISINOS)São Leopoldo, Brazil

Delwar HossainDepartment of Geological SciencesJahangirnagar UniversityDhaka, Bangladesh

Qinhong HuDepartment of Earth and Environmental SciencesThe University of Texas at ArlingtonArlington, TX, USA

M.S. KalpanaCentre for Earth, Ocean and Atmospheric SciencesUniversity of HyderabadHyderabad, Telangana, India

KamruzzamanDepartment of Geological SciencesJahangirnagar UniversityDhaka, Bangladesh

Nihar Ranjan KarCentre for Earth, Ocean and Atmospheric SciencesUniversity of HyderabadHyderabad, Telangana, India

Mahmuda KhatunandDepartment of Geological SciencesJahangirnagar UniversityDhaka, Bangladesh

Md Golam KibriaDepartment of Earth and Environmental SciencesThe University of Texas at ArlingtonArlington, TX, USA

Marcos Antonio KlunkDepartment of Mechanical EngineeringUniversity of Vale do Rio dos SinosSão Leopoldo, Brazil

Chandan MajumdarSchlumbergerNavi Mumbai, Maharashtra, India

Subhadip MandalFreeport‐McMoRanMorenci OperationMorenci, AZ, USA

Devleena ManiCentre for Earth, Ocean and Atmospheric SciencesUniversity of HyderabadHyderabad, Telangana, India

Subhobroto MazumderRemote Sensing and GeomaticsKDM Institute of Petroleum ExplorationOil and Natural Gas CorporationDehradun, Uttarakhand, India

D.S. MitraRemote Sensing and GeomaticsGeology Group, KDM Institute of Petroleum ExplorationOil and Natural Gas CorporationDehradun, Uttarakhand, India

William K. MohantyDepartment of Geology and GeophysicsIndian Institute of Technology KharagpurKharagpur, West Bengal, India

Biplab Kumar MukherjeeLTIMind Space SEZ (Serene Properties)Navi Mumbai, Maharashtra, India

Soumyajit MukherjeeDepartment of Earth SciencesIndian Institute of Technology BombayMumbai, Maharashtra, India

Rajesh PandeyOil and Natural Gas CorporationDehradun, Uttarakhand, India

K.K.S. PangteyRemote Sensing and GeomaticsGeology Group, KDM Institute of Petroleum ExplorationOil and Natural Gas CorporationDehradun, Uttarakhand, India

Khanindra PathakClean Coal Technology LaboratoryDepartment of Mining EngineeringIndian Institute of Technology KharagpurKharagpur, West Bengal, India

Ravi PrakashRemote Sensing and GeomaticsKDM Institute of Petroleum ExplorationOil and Natural Gas CorporationDehradun, Uttarakhand, India

Deependra Pratap SinghCSIR‐Central Institute of Mining and Fuel ResearchDhanbad, Jharkhand, IndiaandAcademy of Scientific and Innovative Research (AcSIR)Ghaziabad, Uttar Pradesh, India

B.K. PrustyClean Coal Technology LaboratoryDepartment of Mining EngineeringIndian Institute of Technology KharagpurKharagpur, West Bengal, India

Karol Riofrio RodriguezGeosteering Focal PointHalliburtonNorway

Aurobinda RoutrayDepartment of Electrical EngineeringIndian Institute of Technology KharagpurKharagpur, West Bengal, India

Somali RoyDepartment of Earth SciencesIndian Institute of Technology BombayMumbai, Maharashtra, India

Sankhajit SahaComputational and Experimental Geomechanics LaboratoryDepartment of Earth SciencesIndian Institute of Technology BombayMumbai, Maharashtra, India

Mizanur Rahman SarkerDepartment of Geological SciencesJahangirnagar UniversityDhaka, Bangladesh

Hugo Schmidt‐NetoGeology Graduate ProgramUniversidade do Vale do Rio Dos Sinos (UNISINOS)São Leopoldo, Brazil

Md. Upal ShahriarDepartment of Geological SciencesJahangirnagar UniversityDhaka, Bangladesh

Ranjan Kumar SarmahDepartment of Applied GeologyDibrugarh UniversityDibrugarh, Assam, India

Pradeep K. SinghCSIR‐Central Institute of Mining and Fuel ResearchDhanbad, Jharkhand, India

Shubham SinghMcCombs School of BusinessThe University of Texas at AustinAustin, TX, USA

Vivek SinghCSIR‐Central Institute of Mining and Fuel ResearchDhanbad, Jharkhand, India

Soyane Juceli Siqueira XavierDepartment of GeologyUniversity of Vale do Rio dos SinosSão Leopoldo, Brazil

Kuppili Meenakshi SundaramPetrophysics ConsultantMumbai, Maharastra, India

Blecy TepRemote Sensing and GeomaticsGeology Group, KDM Institute of Petroleum ExplorationOil and Natural Gas CorporationDehradun, Uttarakhand, India

Venkata Yasaswy TurlapatiClean Coal Technology LaboratoryDepartment of Mining EngineeringIndian Institute of Technology KharagpurKharagpur, West Bengal, India

Vikram VishalComputational and Experimental Geomechanics LaboratoryDepartment of Earth SciencesIndian Institute of Technology BombayMumbai, Maharashtra, India

Paulo Roberto WanderDepartment of Mechanical EngineeringUniversity of Vale do Rio dos SinosSão Leopoldo, Brazil

Preface

This edited book provides recent updates in hydrocarbon geology and economic issues. Several chapters present scientific issues and provide reviews of a few specific disciplines of petroleum geology.

Refer to this book as:Mukherjee S, Dasgupta S, Majumdar C, Mandal S, Dasgupta T. 2022. Handbook of Petroleum Geoscience: Exploration, Characterization, and Exploitation of Hydrocarbon Reservoirs. John Wiley & Sons, Hoboken, ISBN 978-1-119-68003-1.

Refer to an individual chapter as:Singh S, Bhardwaj A. 2022. Petrophysical Predictions Using Regression and Advanced Machine Learning Algorithm. In: Mukherjee S, Dasgupta S, Majumdar C, Mandal S, Dasgupta T (Eds) Handbook of Petroleum Geoscience: Exploration, Characterization, and Exploitation of Hydrocarbon Reservoirs. Wiley-Blackwell. John Wiley & Sons, Hoboken, ISBN 978-1-119-68003-1, pp. 21–49.

Soumyajit Mukherjee, Swagato Dasgupta, Chandan Majumdar, Subhadip Mandal, Troyee Dasgupta (Editors).

Acknowledgments

We thank the book proposal reviewers for making us cautious in the phase of planning this book. We acknowledge the authors for contributing the chapters, and the reviewers for improving the articles. Merryl Le Roux, Frank Otmar Weinreich, Andrew Harrison, and the proofreading team (Wiley Blackwell) are thanked for their assistance. We are additionally thankful to the authors and the publisher for patiently waiting during the COVID pandemic time, in giving us significant extra time, when all of us were struggling to fit with the new normal way of life and faced uncertainty/drastic changes in the job (responsibilities). SM acknowledges Subhobroto Mazumder (Oil and Natural Gas Corporation Limited, Dehradun), Sukanta Goswami (Atomic Mineral Directorate for Exploration and Research, Hyderabad), Mohit Kumar Puniya (Survey of India, Dehradun), Bikramaditya Mondal, and Md. Haroon Shaikh (IIT Bombay) for assistance.

Soumyajit Mukherjee, Swagato Dasgupta, Chandan Majumdar, Subhadip Mandal, and Troyee Dasgupta (Editors)

Introduction to “Handbook of Petroleum Geoscience: Exploration, Characterization, and Exploitation of Hydrocarbon Reservoirs”

Soumyajit Mukherjee1,*, Swagato Dasgupta2, Chandan Majumdar3, Subhadip Mandal4, and Troyee Dasgupta5

1 Department of Earth Sciences, Indian Institute of Technology Bombay, Mumbai, Maharashtra, India

2 Department of Applied Geophysics, Indian Institute of Technology (ISM), Dhanbad, Jharkhand, India

3 Geological Services, Schlumberger Asia Services Limited, P21, Navi Mumbai, Maharashtra, India

4 Freeport‐McMoRan, Morenci Operation, Morenci, AZ, USA

5 Department of Earth Sciences, Indian Institute of Technology Bombay, Mumbai, Maharashtra, India

* Author for correspondence: [email protected], [email protected]

This edited book consists of 22 main chapters. Chaki et al. (2022a) present how signal processing and machine learning algorithms are applied to characterize hydrocarbon reservoirs. This review article includes algorithms for preprocessing, prediction, and postprocessing for such characterization. Singh and Bharadwaj (2022) present how regression and advanced machine learning algorithms can make petrophysical predictions. The predicted results are then verified using test wells. Chaki et al. (2022b) present an altered guided filter capable of denoising seismic data. Based on real data sets from a hydrocarbon field, performance evaluators using a real hydrocarbon field dataset have been assessed. Chatterjee et al. (2022) present few case studies of challenges faced in geomechanics. They cover drilling and hydraulic fracturing optimization from the Assam‐Arakan Basin, production optimization from the basement reservoir in the Kutch Saurashtra Basin (India), and sanding prediction from a reservoir in Ecuador. Sundaram (2022) summarizes theoretical aspects of in situ stress estimation from log measurements. The article covers several inversion processes of geophysical parameters and also discusses the utility of such studies. Clegg and Rodriguez (2022) emphasize the need of 3D inversion and modeling of reservoirs. They elaborate this issue with the help of Azimuthal resistivity images. Djefel (2022) presents the key skill requirements for a geosteering engineer in oil industry. The article will be important for the geoscientist about to join a for geosteering research. Djefel and Clegg (2022) deal with a more specific issue – how the company Halliburton has been handling geosteering‐related activities. These authors state that although several sophisticated geosteering techniques are available presently, gamma geosteering continues to be a the simple yet useful method. Saha and Vishal (2022) present how to undertake local calibration for the unconfined compressive strength on a carbonate rock sample. The work elaborates how to test and calibrate rock cores. Roy et al. (2021) present rapid method of documenting lithologic boundaries using the gamma ray log. Amplitude spectrum of the seismic data was tied with the well‐derived output.

Shale has been a very important topic of study in petroleum geosciences in last few decades (e.g. Mukherjee and Kumar (2018)). Singh et al. (2022) review organic porosity in shale reservoirs. Thermal maturity is considered the most crucial factor for the development of nano‐porosity in shales. Kibria and Hu (2022) study experimentally reservoir nanopore structures and wettability characters of the USA shale. Hydrocarbon recovery seems to significantly control the connectivity of the matrix pores and the network of fractures. Bakshi et al. (2022) analyze pore characters of the Indian shales. Different kinds of pores are distinguished using scanning electron microscopy. Greater nacropore volume percentages are manifested in terms of lower tightness and better connectivity. Mesoporous samples are tighter.

Klunk et al. (2022) study diagenetic changes in a turbidite through a software‐based model. The key point of study has been how fluid flows through the reservoir and non‐reservoir sediments. Mazumder et al. (2022a) present an interesting work of using the plate fit model to predict hydrocarbon‐bearing rock in the Indian mainland. Their key finding has been that the premised rift sediment can plausibly be targeted for exploration in the Cauvery and the Krishna‐Godavari basins. The work is in line with the first author’s previous work: Mazumder et al. (2017). Tectonics of the eastern Himalayas have been of great international attention (e.g. Goswami et al. 2018, 2020, 2022). In this context, Bezbaruah et al. (2022) discuss the origin of the Upper Assam shelf area in terms of tectonics amongst Indian, Eurasian, and Burmese plates. Being a petroliferous basin, the study has important implications in hydrocarbon geosciences. Mazumder et al. (2022b) present the basement tectonics of the Assam shelf through compiled geological and geophysical studies. In the authors’ language, the work is important since “Structural highs associated with such fractured areas form promising zones of basement exploration”. Horodyski et al. (2022) discuss how taphofacies can be important in petroleum geoscience. They provide examples from a few Brazilian terrains. Pandey (2022) presents a literature review‐cum‐original work on the interpretation of seismic images from the petroliferous Jaisalmer Basin from western Rajasthan, India (also see Biswas et al. 2022). He comes up with a fine phase evolution of the basin. Field structural data from this basin has been sparse. Therefore, this work will be invaluable for the next geologist to work on this terrain. Kamruzzaman et al. (2022) present gravity and magnetic studies from the Habiganj gas field (Bangladesh) and evaluated the petrophysical parameters for a specific well from that field. The authors finally postulated two hydrocarbon‐bearing zones. Gadkari and Mukherjee (2022) study the b‐value in relation to the seismicity from Nepal, Sumatra, Japan, and Chile. In these studied cases the b‐value dropped before major earthquakes. Continuous monitoring of b‐values is recommended in seismic regions.

Acknowledgments

We thank the authors for contributing the chapters, and the reviewers for improving the articles. Merryl Le Roux, Frank Otmar Weinreich, Joss Everett, Andrew Harrison, and the proofreading team (Wiley Blackwell) are thanked for their assistance. CPDA grant (IIT Bombay) supported SM.

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Handbook of Petroleum Geoscience: Exploration, Characterization, and Exploitation of Hydrocarbon Reservoirs

(ed. S. Mukherjee, S. Dasgupta, C. Majumdar, et al.). John Wiley & Sons, Hoboken, ISBN 978-1-119-68003-1.

1Application of Machine Learning Algorithms for Petroleum Reservoir Characterization

Soumi Chaki1,*, Aurobinda Routray2, and William K. Mohanty3

1 Division of Signals and Systems, Department of Electrical Engineering, Uppsala University, Uppsala, Sweden

2 Department of Electrical Engineering, Indian Institute of Technology Kharagpur, West Bengal, India

3 Department of Geology and Geophysics, Indian Institute of Technology Kharagpur, West Bengal, India

* Author for Correspondence: [email protected]

Abstract

The aim of reservoir characterization (RC) is to find different petrophysical properties and facies information from integrated dataset of different domains, especially seismic attributes and well logs. The nonlinear and diverse subsurface properties pose several challenges to accurately estimating petrophysical properties. In order to rise above those challenges, RC has evolved as an interdisciplinary research domain relating to computational science, geostatistics, signal processing, and geophysics over the years.

This article provides an overall summary of the applications of signal processing and machine learning (ML) algorithms for the petroleum RC. We provide an account of various state‐of‐the‐art algorithms essential for preprocessing, prediction, and, finally, “post‐processing” stages of an RC framework. It is a highly data‐driven research area. Experts decipher about the subsurface properties from integrated dataset of a large amount of seismic and well logs to identify the existence of a potential reservoir. The signal‐processing tools are primarily applied for information matching, removing noise and artifacts, and post‐processing for eliminating outliers in the predicted data; whereas, the ML tools are required to map the seismic data to well logs. This chapter offers a comprehensive account of the recent advances in RC involving seismic volumes and well logs.

Keywords Artificial neural network (ANN); deep learning; machine learning (ML); prediction; pre‐processing; post‐processing; regularization; reservoir characterization (RC); seismic attributes; signal processing; well‐logs

1.1 Introduction

3D seismic survey is carried out at a prospective field before taking decisions regarding where to start drilling initial wells for oil exploration. Acoustic energy is sent to the subsurface by explosions or seismic vibrators and the reflected or refracted energy from different layers is recorded by an array of geophones strategically placed at various locations over the study area. The seismic volume resulting from the 3D survey characterizes the subsurface more efficiently compared to its 2D counterpart (Vyas and Sharma 2016). Different attributes like impedance, instantaneous frequency, instantaneous phase, sweetness, etc., are estimated from the seismic amplitude cube (Schlumberger Oilfield Glossary n.d.). Most of the processed seismic variables are smooth lacking detailed information along the depth. Instead, the well logs, carrying high vertical resolution, are acquired at few numbers of well locations over a study area. Experienced scientists derive multiple lithological properties such as sand fraction, shale fraction, porosity, permeability, or facies from well logs. Otherwise, these logs are determined from core samples under a laboratory environment. These logs can indicate prospective hydrocarbon‐enriched zones in a field (Chaki et al. 2015a). The sections are categorized in dry, water‐bearing, and hydrocarbon‐containing layers from the distributions of lithological properties to identify the prospective zones. As these properties are only available at the borehole locations, seismic attributes and existing well logs can be used to approximate them away from the boreholes (Bosch et al. 2010; Chaki et al. 2015a). However, there is no definitive approach to carrying out this task. Therefore, the calibration of the relationship between these data is an open challenge to the researchers. Literature survey shows that reservoir characterization (RC) has evolved as an interdisciplinary research domain relating to computational science, geostatistics, signal processing, and geophysics. In summary, the aim of RC is to find different petrophysical properties and facies information from an integrated dataset of different domains, especially seismic attributes and well logs (Chaki et al. 2018a, 2018b, 2019, 2022; Vallabhaneni et al. 2022). The nonlinear and heterogeneous subsurface characteristics are the major challenge in accurately estimating the reservoir properties. An overview of the integration of well logs and seismic data for RC from an ML and signal‐processing perspective was presented in Chaki et al. (2018b). ML has also been used for proxy flow modelling (Chaki et al. 2021) and domain conversion (Roy et al. 2021).

In the present work, we have added additional details on the application of ML models, especially deep learning techniques for petroleum RC that were carried out mostly in the last two years. The application of different deep learning techniques is coming out rapidly in this field due to the availability of a huge amount of data as well as powerful computation systems.

1.2 The Importance of Preprocessing Stage

In petroleum RC, any framework starts with preprocessing of the dataset involving several stages, viz. integration, normalization, filtering, feature selection, etc. The seismic data is available over the reservoir area, whereas well logs are sparsely available at a limited number of well locations only. Due to the necessity of having an estimation of different reservoir properties away from the boreholes, seismic datasets are used as guidance to predict those properties over the study area. Figure 1.1 presents a petroleum reservoir where the seismic amplitude is collected over the complete field along inlines, xlines, and time‐wise. A few number of wells marked with the red line are placed over the field.

Figure 1.1 The distributions of seismic amplitude and a limited number of wells (12) in a petroleum reservoir.

1.2.1 Integration

The contrasts between the characteristic of seismic attributes and well logs are listed inside Figure 1.2. Due to the difference between the information content between these two sources, a regularization step is introduced to match high‐information‐carrying well logs to the low‐information‐carrying seismic attributes (Chaki et al. 2015a, 2018a, 2019). First, seismic attributes are extracted along well trajectories and well logs are converted from the depth domain to the time domain using velocity profiles provided at well locations (Chaki et al. 2015a). Different research groups have identified different solutions to address the disparity in resolution, frequency content, and sampling interval of these datasets during integration (Stright et al. 2009, Chaki et al. 2015a