This document presents a dissertation submitted to the Department of Geophysics at Kurukshetra University in partial fulfillment of the requirements for a Master of Technology degree in Applied Geophysics. The dissertation focuses on AVO (Amplitude Variation with Offset) attribute analysis for the identification of gas-bearing sands. It begins with an introduction to seismic methods for oil exploration, a brief history and basic theory of refraction and reflection seismology. It then describes the dataset and study area used, as well as the geological setting. The document outlines the methodology adopted for AVO attribute analysis and intercept-gradient analysis. It then details the results of the data analysis including well log analysis, well to seismic tie, gradient analysis, creation of
This document describes model-based seismic inversion performed on a 3D seismic dataset from the F3 block in the Dutch sector of the North Sea. The study area has complex geology from the Paleozoic to Cenozoic eras. The authors applied model-based inversion using Hampson-Russell software to determine lithology and fluid distributions in the target reservoir. They imported the 3D seismic cube, identified the target reservoir horizon, performed depth conversion and quality control, extracted the wavelet, built an initial model, ran the inversion, and analyzed the resulting acoustic impedance volume to characterize the subsurface.
Using 3-D Seismic Attributes in Reservoir Characterizationguest05b785
The document discusses using 3D seismic attributes for reservoir characterization. It provides an overview of seismic reflection methods and defines seismic attributes as any measurement derived from seismic data. Common types of attributes are described including time, complex trace, window, Fourier and multi-trace attributes. The document gives examples of attributes like envelope, phase, frequency and coherence that can provide information on lithology, thickness, faults and fractures. Methods of interpreting attribute data from 3D volumes are outlined. The document concludes by providing examples of how attributes can be used for reservoir characterization tasks like fault interpretation and porosity estimation.
Avo ppt (Amplitude Variation with Offset)Haseeb Ahmed
AVO/AVA can physically explain presence of hydrocarbon in the reservoirs and the thickness, porosity, density, velocity, lithology and fluid content of the reservoir of the rock can be estimated.
Application of Low Frequency Passive Seismic Method for Hydrocarbon Detection...Andika Perbawa
Passive seismic survey is a geophysical method that utilizes a spectral frequency from seismicity data to identify subsurface reservoir fluids. Rock pores that contain hydrocarbon fluids show higher low-frequency amplitude between 2-4 Hz compared with those that contain water. This paper shows the feasibility study that has been done in S Field, South Sumatra Basin. Four wells were used to validate the result of the spectral data. This method is also considered as a prospect ranking tool in the vicinity of the S field.
Eighteen measurement points were collected and grouped into 6 clusters. Four clusters are located near S-1, S-2, S-3, and S-4 wells. One cluster is located on prospect K and the other one on prospect G. Standard signal processing flows were conducted such as band-pass filter, FFT, and moving average.
The result shows that the maximum amplitude low-frequency between 2-4 Hz of K and S-1 is less than 0.017. On the other hand, S-2, S-3, S-4 and G show a relatively high amplitude of more than 0.02 which indicates a greater possibility of hydrocarbon accumulation when compared with K and S-1. This result was confirmed by gas production in S-2 and oil production in S-3. S-4 has not been tested yet, but the refined well correlation it indicates that there is a limestone reservoir of about 60 feet above OWC. S-1 shows a low amplitude which indicates low potential. The completion log confirmed that the well did not penetrate the reservoir target. Prospect G which has a high amplitude of low-frequency anomaly is more interesting than prospect K.
To conclude, low-frequency passive seismic method was successful in distinguishing between water or no hydrocarbons. It is feasible to employ this methodology as a tool for hydrocarbon detection and also as a tool to help in prospect ranking.
Well log interpretation involves using well log data to estimate reservoir properties. It has been used since the 1920s to qualitatively identify hydrocarbons and is now a quantitative tool. A key figure was Gustavus Archie who in the 1940s established the field of petrophysics by relating well logs to core data. His work allowed properties like porosity, permeability and fluid saturation to be estimated. A presentation on well log interpretation outlined the workflow including editing logs, estimating properties like shale volume, porosity, permeability and fluid saturation, and presented two case studies analyzing different carbonate reservoirs.
Quantitative and Qualitative Seismic Interpretation of Seismic Data Haseeb Ahmed
This document discusses quantitative and qualitative seismic interpretation techniques used to analyze seismic data and map subsurface geology. It compares traditional qualitative techniques to more modern quantitative techniques. It then focuses on unconventional seismic interpretation techniques used for unconventional reservoirs with low permeability, including AVO analysis, seismic inversion, seismic attributes, and forward seismic modeling. These techniques can help identify tight gas, shale gas, and gas hydrate reservoirs that conventional methods cannot easily detect. The document provides details on how each technique works and its advantages.
Reservoir types and Reservoir characterizations; Styles of Geologic Reservoir Heterogeneity; Classification of Heterogeneity; Scales of Geologic Reservoir Heterogeneity; Factors Causing Reservoir Heterogeneity; Assessing Reservoir Heterogeneity; Diagenetic and Reservoir Quality and Heterogeneity Implications in Deltaic and Marine Sandstones ; Scales of Fluvial Reservoir Heterogeneity; Impact of Bioturbation on Reservoir Heterogeneity; Carbonate Reservoir Heterogeneity
This document provides an overview of basic well logs, including caliper logs, gamma ray logs, and formation density logs. It discusses the tools, principles, and uses of each log. Caliper logs measure borehole diameter and shape using mechanical arms. Gamma ray logs measure natural radiation from formations to indicate lithology. Formation density logs use gamma rays to measure bulk density and derive porosity, helping to identify lithologies when used with neutron logs. The document provides details on how each tool works and the information provided by its logs.
This document describes model-based seismic inversion performed on a 3D seismic dataset from the F3 block in the Dutch sector of the North Sea. The study area has complex geology from the Paleozoic to Cenozoic eras. The authors applied model-based inversion using Hampson-Russell software to determine lithology and fluid distributions in the target reservoir. They imported the 3D seismic cube, identified the target reservoir horizon, performed depth conversion and quality control, extracted the wavelet, built an initial model, ran the inversion, and analyzed the resulting acoustic impedance volume to characterize the subsurface.
Using 3-D Seismic Attributes in Reservoir Characterizationguest05b785
The document discusses using 3D seismic attributes for reservoir characterization. It provides an overview of seismic reflection methods and defines seismic attributes as any measurement derived from seismic data. Common types of attributes are described including time, complex trace, window, Fourier and multi-trace attributes. The document gives examples of attributes like envelope, phase, frequency and coherence that can provide information on lithology, thickness, faults and fractures. Methods of interpreting attribute data from 3D volumes are outlined. The document concludes by providing examples of how attributes can be used for reservoir characterization tasks like fault interpretation and porosity estimation.
Avo ppt (Amplitude Variation with Offset)Haseeb Ahmed
AVO/AVA can physically explain presence of hydrocarbon in the reservoirs and the thickness, porosity, density, velocity, lithology and fluid content of the reservoir of the rock can be estimated.
Application of Low Frequency Passive Seismic Method for Hydrocarbon Detection...Andika Perbawa
Passive seismic survey is a geophysical method that utilizes a spectral frequency from seismicity data to identify subsurface reservoir fluids. Rock pores that contain hydrocarbon fluids show higher low-frequency amplitude between 2-4 Hz compared with those that contain water. This paper shows the feasibility study that has been done in S Field, South Sumatra Basin. Four wells were used to validate the result of the spectral data. This method is also considered as a prospect ranking tool in the vicinity of the S field.
Eighteen measurement points were collected and grouped into 6 clusters. Four clusters are located near S-1, S-2, S-3, and S-4 wells. One cluster is located on prospect K and the other one on prospect G. Standard signal processing flows were conducted such as band-pass filter, FFT, and moving average.
The result shows that the maximum amplitude low-frequency between 2-4 Hz of K and S-1 is less than 0.017. On the other hand, S-2, S-3, S-4 and G show a relatively high amplitude of more than 0.02 which indicates a greater possibility of hydrocarbon accumulation when compared with K and S-1. This result was confirmed by gas production in S-2 and oil production in S-3. S-4 has not been tested yet, but the refined well correlation it indicates that there is a limestone reservoir of about 60 feet above OWC. S-1 shows a low amplitude which indicates low potential. The completion log confirmed that the well did not penetrate the reservoir target. Prospect G which has a high amplitude of low-frequency anomaly is more interesting than prospect K.
To conclude, low-frequency passive seismic method was successful in distinguishing between water or no hydrocarbons. It is feasible to employ this methodology as a tool for hydrocarbon detection and also as a tool to help in prospect ranking.
Well log interpretation involves using well log data to estimate reservoir properties. It has been used since the 1920s to qualitatively identify hydrocarbons and is now a quantitative tool. A key figure was Gustavus Archie who in the 1940s established the field of petrophysics by relating well logs to core data. His work allowed properties like porosity, permeability and fluid saturation to be estimated. A presentation on well log interpretation outlined the workflow including editing logs, estimating properties like shale volume, porosity, permeability and fluid saturation, and presented two case studies analyzing different carbonate reservoirs.
Quantitative and Qualitative Seismic Interpretation of Seismic Data Haseeb Ahmed
This document discusses quantitative and qualitative seismic interpretation techniques used to analyze seismic data and map subsurface geology. It compares traditional qualitative techniques to more modern quantitative techniques. It then focuses on unconventional seismic interpretation techniques used for unconventional reservoirs with low permeability, including AVO analysis, seismic inversion, seismic attributes, and forward seismic modeling. These techniques can help identify tight gas, shale gas, and gas hydrate reservoirs that conventional methods cannot easily detect. The document provides details on how each technique works and its advantages.
Reservoir types and Reservoir characterizations; Styles of Geologic Reservoir Heterogeneity; Classification of Heterogeneity; Scales of Geologic Reservoir Heterogeneity; Factors Causing Reservoir Heterogeneity; Assessing Reservoir Heterogeneity; Diagenetic and Reservoir Quality and Heterogeneity Implications in Deltaic and Marine Sandstones ; Scales of Fluvial Reservoir Heterogeneity; Impact of Bioturbation on Reservoir Heterogeneity; Carbonate Reservoir Heterogeneity
This document provides an overview of basic well logs, including caliper logs, gamma ray logs, and formation density logs. It discusses the tools, principles, and uses of each log. Caliper logs measure borehole diameter and shape using mechanical arms. Gamma ray logs measure natural radiation from formations to indicate lithology. Formation density logs use gamma rays to measure bulk density and derive porosity, helping to identify lithologies when used with neutron logs. The document provides details on how each tool works and the information provided by its logs.
Well Log Interpretation and Petrophysical Analisis in [Autosaved]Ridho Nanda Pratama
PT. Halliburton Logging Service is a branch of Halliburton that provides completion and production services, drilling, and reservoir evaluation to oil companies in Sumatra, Indonesia. Dery Marsan and Ridho Nanda Pratama completed an on-job training program at Halliburton from August to September 2015. Their project involved well log analysis to determine water saturation and the most suitable water resistivity parameters in two formations, with the objectives of identifying water zones, evaluating challenges around determining petrophysical parameters, and analyzing well data. Their analysis identified both water-bearing and possible oil-bearing zones through evaluation of gamma ray, resistivity, neutron-density crossplots, and other well logs.
This document provides instructions for interpreting faults in Petrel. It describes how to manually pick faults on seismic lines, edit fault segments, move and reassign segments between faults, clean faults, and use restrict mode. The exercises section instructs the user to create a new fault interpretation folder, interpret faults on every 20th crossline between lines 400 and 420, highlight and assign fault sticks to individual fault planes, and name the faults. The overall document teaches how to perform a basic fault interpretation project in Petrel.
1. The document discusses spontaneous potential (SP) logging, which measures the electrical potential difference between a downhole electrode and a surface reference electrode. SP logs can be used both qualitatively to detect permeable beds and quantitatively to determine formation water resistivity and shale volume.
2. The key factors that affect the SP response are the ratio between mud filtrate resistivity (Rmf) and formation water resistivity (Rw), as well as bed thickness, resistivity, and porosity. Positive deflections occur when Rmf > Rw and negative deflections when Rmf < Rw. No deflection occurs when Rmf = Rw.
3. Examples are given of how to calculate shale
Well lod ,well Testing and mud logging Ghulam Abbas AbbasiUniversity of Sindh
Well logging records measurements made in boreholes to characterize underground formations. Key logs described include gamma ray, which measures natural radioactivity to identify shale; spontaneous potential, which indicates lithology; caliper, which measures borehole size; resistivity, which distinguishes water and hydrocarbon zones; and neutron, which determines porosity. Mud logging continuously monitors drilling mud and cuttings for gas readings. Well testing evaluates reservoir properties through daily tests and drill stem tests to determine flow rates and commercial potential.
The document provides an overview of principles of seismic data interpretation. It discusses fundamentals of seismic acquisition and processing such as seismic response, phase, polarity, reflections, and resolution. It also covers topics like structural interpretation pitfalls, seismic interpretation workflows involving building databases and time-depth relationships, and structural styles. The document includes sections on depth conversion, subsurface mapping techniques, and different types of velocities.
Role of Seismic Attributes in Petroleum Exploration_30May22.pptxNagaLakshmiVasa
The document discusses seismic attributes which are measurable properties of seismic data computed through mathematical manipulation to highlight geological features. It describes how seismic waves are reflected and refracted and how this seismic response is recorded. The key types of seismic attributes discussed are amplitude, phase, frequency and complex trace attributes. Specific amplitude attributes like RMS amplitude and sweetness are explained. The document also covers applications of seismic attributes like direct hydrocarbon indication and limitations. Spectral decomposition and AVO/AVA analysis are also summarized.
This document outlines a simple seismic data processing workflow. It begins with acquiring field data and updating the geometry. Next steps include trace editing, amplitude recovery, and noise attenuation. Velocity analysis and normal moveout correction are then applied. Deconvolution and multiple attenuation are performed before migration. Post-migration involves stacking, filtering and amplitude scaling to produce the final processed seismic section. The goal of seismic processing is to produce high quality seismic data for geological interpretation and hydrocarbon exploration.
Brief review on Direct hydrocarbon indicators (DHI).
The presentation is a part from Seismic data interpretation course that i teach for undergraduates.
The sources are indicated in the references list.
Contact me via: hatem_refaat95@hotmail.com
1. The document discusses various well logging tools and concepts used in petrophysical interpretation. It describes tools such as the spontaneous potential (SP) log, gamma ray (GR) log, resistivity logs including induction and lateral logs, and porosity logs.
2. Key concepts covered include the logging environment and factors that impact tool measurements like borehole conditions and mud properties. Interpretation techniques for evaluating permeable zones, formation resistivity, water saturation, and porosity are also summarized.
3. The document provides examples of using tools and concepts like the Archie formula to calculate water resistivity, determine hydrocarbon presence, and evaluate clean versus shaly formations. It also discusses corrections that must be applied to well log
The Fullbore Formation MicroImager (FMI) instrument provides high resolution images of bedding and fractures in borehole walls. It uses electrical resistivity contrasts to image features around the borehole at vertical resolutions of 5 mm. FMI data is processed using Schlumberger software to correct speed, equalize histograms, and enhance images. FMI can be used for structural analysis, reservoir characterization of natural fractures and porosity, thin bed detection, and other applications. It images features like dips, fractures, vugs, laminations, and other sedimentological structures.
Introduction Petrel Course (UAB-2014)
This course has been prepared as an introduction of Petrel software (Schlumberger, www.software.slb.com/products/platform/Pages/petrel.aspx), an application which allows the modeling and visualization of reservoirs, since the exploration stage until production, integrating geological and geophysical data, geological modeling (structural and stratigraphic frameworks), well planning, or property modeling ( petrophysical or petrological) among other possibilities.
The course will be focused mainly in the understanding and utilization of workflows aimed to build geological models based on superficial data (at the outcrop scale) but also with seismic data. The course contents have been subdivided in 5 modules each one developed through the combination of short explanations and practical exercises.
The duration of the course covers more or less 10h divided in three sessions. The starting data will be in the first week of December.
This course will be oriented mainly for the PhD and master students ascribed at the Geologic department of the UAB. For logistic reasons the maximum number of places for each torn are 9. The course is free from the Department members but the external interested will have to make a symbolic payment.
Those interested send an e-mail to the Doctor Griera (albert.griera@uab.cat).
The course will be imparted by Marc Diviu (Msc. Geology and Geophysics of reservoirs).
1) Seismic interpretation uses acoustic waves to image the subsurface by measuring the two-way travel time and amplitude of reflections. 2) A seismic source generates wavefronts that travel through the subsurface, reflecting or transmitting at interfaces between rock layers. 3) The amount of reflection depends on the relative difference in physical properties across interfaces, defined by reflection coefficients. Layers thinner than 1/4 the wavelength cannot be resolved individually.
The formation density tool provides a continuous record of a formation's bulk density along the length of a borehole. It works by emitting gamma rays into the formation, which are scattered via Compton scattering. The density measurement is used to derive porosity, with the main advantages being it compensates for mudcake and minor borehole issues. When combined with neutron logs, it provides one of the best ways to identify lithologies in a borehole. The tool has good vertical resolution but can be impacted by borehole quality, drilling mud properties, and shale content.
The document discusses the basics of well logging design. It includes an agenda for a one-day course that covers basic logging theory, interpretation, logging program design, and a workshop. The objectives are to familiarize participants with various log measurements, well evaluation strategies, and approaches to well logging design. Key logging topics covered include definitions, history, measurement principles for resistivity, spontaneous potential, gamma ray, density, neutron, and acoustic logs. Interpretation applications and limitations are also discussed.
Well logging and interpretation techniques asin b000bhl7ouAhmed Raafat
This document provides an introduction to sedimentary rock properties for well log interpretation. It discusses how sedimentary rocks form from the weathering and alteration of existing rocks. Sedimentary rocks are composed mainly of minerals stable under normal surface conditions and may be classified as mechanically or chemically derived. Mechanical rocks include sandstones and conglomerates, while chemical rocks include carbonates and evaporites. Well logs are useful for characterizing sedimentary rocks and pore fluids in order to understand petroleum reservoirs.
The analysis of all of the significant processes that formed a basin and deformed its sedimentary fill from basin-scale processes (e.g., plate tectonics)
to centimeter-scale processes (e.g., fracturing)
The document discusses the classification of well logs. It explains that logs can be classified based on their technology (open hole vs cased hole logs) or their function (lithology, electrical, porosity, nuclear logs). Open hole logs are run before casing while cased hole logs are done after casing through the metal piping. Various logging tools are described, including gamma ray, resistivity, density, neutron, and sonic logs which provide data on formation properties like lithology, porosity, and fluid content. Nuclear logs using gamma rays and neutrons can evaluate formations through casing as well.
This document is a declaration by Ansumana E.M. Dukuly Jr. that the project submitted is entirely his own work. It has not been previously submitted for an academic degree. The project has been prepared under the supervision of James Ecau for partial fulfillment of a Bachelor of Science degree in Petroleum Engineering from the International University of East Africa. The project is dedicated to Dukuly's children and parents for their support. It acknowledges contributions from his assessor, university, and friends who provided support. The abstract indicates the project developed a geological model of the thin-bedded M-Sand reservoir at the Horn Mountain oil field in the Gulf of Mexico to aid production strategies through analysis of core data, pressure
APPLICATION OF REMOTE SENSING AND GIS IN FLOOD HAZARD assesment and mapping.docxMustapheMohMmi
1.0 INTRODUCTION
1.1. Background
Flooding is one of the most devastating hazards worldwide, affecting people’s socio-economic and ecological systems. Flood dangers constantly threaten life and property since they are the most frequent and destructive of all disasters(Ntajal et al., 2017).
Numerous forms of geohazards have been recognized and documented for a very long period in various regions of the world. The majority of these geo-hazards are serious occurrences that cause harm and devastation. The term "geo-hazard" refers to the results of geological, geomorphological, hydro-meteorological, or a mix of these processes. The lives, property, infrastructure, and environment that people value may be dangerously threatened by these processes. Some of these geo-hazards are caused or influenced by man, while the others may be entirely natural. Natural occurrences or processes are referred to as dangers. These natural disasters may result in human casualties, illnesses, property damage, loss of livelihoods and services, social unrest, disruption of the economy, or environmental harm.(Wu, 2023).
Flooding is frequently brought on by excessive rainfall, especially in metropolitan areas with high runoff rates and low soil penetration rates. Until climate change and urban nuisance issues are handled seriously by politicians, flooding and its accompanying threats may continue. Flood damages can be significantly decreased by using maps, a larger audience, preemptive warnings, and preparedness. Any urban planning project should consider mapping to estimate flood extent or potential inundation zones to be an essential step, and it should be included as part of the city blueprint. Flooding incidents are more likely to occur in urban areas constructed next to or along river channels. Urban areas are more susceptible to flooding, which is primarily brought on by inadequate drainage systems, ineffective urban growth regulations, aggressive land use conversion, a lack of appropriate structures or measures for flood protection, and a failure to carry out the initial master plan and physical layout of the city. This is due to the fact that building a reliable flood model requires taking into account a lot of complicated aspects (Van Alphen et al., 2009).
Flood and its associated risks may continue until climate change and urban nuisance issues are handled seriously by politicians, flooding and its accompanying threats may continue. Flooding may be accurately predicted by mapping, increased awareness, early warning, and preparedness, which can significantly lessen its effects. Any urban planning project should consider mapping to estimate flood extent or potential inundation zones to be an essential step, and it should be included as part of the city blueprint.
Well Log Interpretation and Petrophysical Analisis in [Autosaved]Ridho Nanda Pratama
PT. Halliburton Logging Service is a branch of Halliburton that provides completion and production services, drilling, and reservoir evaluation to oil companies in Sumatra, Indonesia. Dery Marsan and Ridho Nanda Pratama completed an on-job training program at Halliburton from August to September 2015. Their project involved well log analysis to determine water saturation and the most suitable water resistivity parameters in two formations, with the objectives of identifying water zones, evaluating challenges around determining petrophysical parameters, and analyzing well data. Their analysis identified both water-bearing and possible oil-bearing zones through evaluation of gamma ray, resistivity, neutron-density crossplots, and other well logs.
This document provides instructions for interpreting faults in Petrel. It describes how to manually pick faults on seismic lines, edit fault segments, move and reassign segments between faults, clean faults, and use restrict mode. The exercises section instructs the user to create a new fault interpretation folder, interpret faults on every 20th crossline between lines 400 and 420, highlight and assign fault sticks to individual fault planes, and name the faults. The overall document teaches how to perform a basic fault interpretation project in Petrel.
1. The document discusses spontaneous potential (SP) logging, which measures the electrical potential difference between a downhole electrode and a surface reference electrode. SP logs can be used both qualitatively to detect permeable beds and quantitatively to determine formation water resistivity and shale volume.
2. The key factors that affect the SP response are the ratio between mud filtrate resistivity (Rmf) and formation water resistivity (Rw), as well as bed thickness, resistivity, and porosity. Positive deflections occur when Rmf > Rw and negative deflections when Rmf < Rw. No deflection occurs when Rmf = Rw.
3. Examples are given of how to calculate shale
Well lod ,well Testing and mud logging Ghulam Abbas AbbasiUniversity of Sindh
Well logging records measurements made in boreholes to characterize underground formations. Key logs described include gamma ray, which measures natural radioactivity to identify shale; spontaneous potential, which indicates lithology; caliper, which measures borehole size; resistivity, which distinguishes water and hydrocarbon zones; and neutron, which determines porosity. Mud logging continuously monitors drilling mud and cuttings for gas readings. Well testing evaluates reservoir properties through daily tests and drill stem tests to determine flow rates and commercial potential.
The document provides an overview of principles of seismic data interpretation. It discusses fundamentals of seismic acquisition and processing such as seismic response, phase, polarity, reflections, and resolution. It also covers topics like structural interpretation pitfalls, seismic interpretation workflows involving building databases and time-depth relationships, and structural styles. The document includes sections on depth conversion, subsurface mapping techniques, and different types of velocities.
Role of Seismic Attributes in Petroleum Exploration_30May22.pptxNagaLakshmiVasa
The document discusses seismic attributes which are measurable properties of seismic data computed through mathematical manipulation to highlight geological features. It describes how seismic waves are reflected and refracted and how this seismic response is recorded. The key types of seismic attributes discussed are amplitude, phase, frequency and complex trace attributes. Specific amplitude attributes like RMS amplitude and sweetness are explained. The document also covers applications of seismic attributes like direct hydrocarbon indication and limitations. Spectral decomposition and AVO/AVA analysis are also summarized.
This document outlines a simple seismic data processing workflow. It begins with acquiring field data and updating the geometry. Next steps include trace editing, amplitude recovery, and noise attenuation. Velocity analysis and normal moveout correction are then applied. Deconvolution and multiple attenuation are performed before migration. Post-migration involves stacking, filtering and amplitude scaling to produce the final processed seismic section. The goal of seismic processing is to produce high quality seismic data for geological interpretation and hydrocarbon exploration.
Brief review on Direct hydrocarbon indicators (DHI).
The presentation is a part from Seismic data interpretation course that i teach for undergraduates.
The sources are indicated in the references list.
Contact me via: hatem_refaat95@hotmail.com
1. The document discusses various well logging tools and concepts used in petrophysical interpretation. It describes tools such as the spontaneous potential (SP) log, gamma ray (GR) log, resistivity logs including induction and lateral logs, and porosity logs.
2. Key concepts covered include the logging environment and factors that impact tool measurements like borehole conditions and mud properties. Interpretation techniques for evaluating permeable zones, formation resistivity, water saturation, and porosity are also summarized.
3. The document provides examples of using tools and concepts like the Archie formula to calculate water resistivity, determine hydrocarbon presence, and evaluate clean versus shaly formations. It also discusses corrections that must be applied to well log
The Fullbore Formation MicroImager (FMI) instrument provides high resolution images of bedding and fractures in borehole walls. It uses electrical resistivity contrasts to image features around the borehole at vertical resolutions of 5 mm. FMI data is processed using Schlumberger software to correct speed, equalize histograms, and enhance images. FMI can be used for structural analysis, reservoir characterization of natural fractures and porosity, thin bed detection, and other applications. It images features like dips, fractures, vugs, laminations, and other sedimentological structures.
Introduction Petrel Course (UAB-2014)
This course has been prepared as an introduction of Petrel software (Schlumberger, www.software.slb.com/products/platform/Pages/petrel.aspx), an application which allows the modeling and visualization of reservoirs, since the exploration stage until production, integrating geological and geophysical data, geological modeling (structural and stratigraphic frameworks), well planning, or property modeling ( petrophysical or petrological) among other possibilities.
The course will be focused mainly in the understanding and utilization of workflows aimed to build geological models based on superficial data (at the outcrop scale) but also with seismic data. The course contents have been subdivided in 5 modules each one developed through the combination of short explanations and practical exercises.
The duration of the course covers more or less 10h divided in three sessions. The starting data will be in the first week of December.
This course will be oriented mainly for the PhD and master students ascribed at the Geologic department of the UAB. For logistic reasons the maximum number of places for each torn are 9. The course is free from the Department members but the external interested will have to make a symbolic payment.
Those interested send an e-mail to the Doctor Griera (albert.griera@uab.cat).
The course will be imparted by Marc Diviu (Msc. Geology and Geophysics of reservoirs).
1) Seismic interpretation uses acoustic waves to image the subsurface by measuring the two-way travel time and amplitude of reflections. 2) A seismic source generates wavefronts that travel through the subsurface, reflecting or transmitting at interfaces between rock layers. 3) The amount of reflection depends on the relative difference in physical properties across interfaces, defined by reflection coefficients. Layers thinner than 1/4 the wavelength cannot be resolved individually.
The formation density tool provides a continuous record of a formation's bulk density along the length of a borehole. It works by emitting gamma rays into the formation, which are scattered via Compton scattering. The density measurement is used to derive porosity, with the main advantages being it compensates for mudcake and minor borehole issues. When combined with neutron logs, it provides one of the best ways to identify lithologies in a borehole. The tool has good vertical resolution but can be impacted by borehole quality, drilling mud properties, and shale content.
The document discusses the basics of well logging design. It includes an agenda for a one-day course that covers basic logging theory, interpretation, logging program design, and a workshop. The objectives are to familiarize participants with various log measurements, well evaluation strategies, and approaches to well logging design. Key logging topics covered include definitions, history, measurement principles for resistivity, spontaneous potential, gamma ray, density, neutron, and acoustic logs. Interpretation applications and limitations are also discussed.
Well logging and interpretation techniques asin b000bhl7ouAhmed Raafat
This document provides an introduction to sedimentary rock properties for well log interpretation. It discusses how sedimentary rocks form from the weathering and alteration of existing rocks. Sedimentary rocks are composed mainly of minerals stable under normal surface conditions and may be classified as mechanically or chemically derived. Mechanical rocks include sandstones and conglomerates, while chemical rocks include carbonates and evaporites. Well logs are useful for characterizing sedimentary rocks and pore fluids in order to understand petroleum reservoirs.
The analysis of all of the significant processes that formed a basin and deformed its sedimentary fill from basin-scale processes (e.g., plate tectonics)
to centimeter-scale processes (e.g., fracturing)
The document discusses the classification of well logs. It explains that logs can be classified based on their technology (open hole vs cased hole logs) or their function (lithology, electrical, porosity, nuclear logs). Open hole logs are run before casing while cased hole logs are done after casing through the metal piping. Various logging tools are described, including gamma ray, resistivity, density, neutron, and sonic logs which provide data on formation properties like lithology, porosity, and fluid content. Nuclear logs using gamma rays and neutrons can evaluate formations through casing as well.
This document is a declaration by Ansumana E.M. Dukuly Jr. that the project submitted is entirely his own work. It has not been previously submitted for an academic degree. The project has been prepared under the supervision of James Ecau for partial fulfillment of a Bachelor of Science degree in Petroleum Engineering from the International University of East Africa. The project is dedicated to Dukuly's children and parents for their support. It acknowledges contributions from his assessor, university, and friends who provided support. The abstract indicates the project developed a geological model of the thin-bedded M-Sand reservoir at the Horn Mountain oil field in the Gulf of Mexico to aid production strategies through analysis of core data, pressure
APPLICATION OF REMOTE SENSING AND GIS IN FLOOD HAZARD assesment and mapping.docxMustapheMohMmi
1.0 INTRODUCTION
1.1. Background
Flooding is one of the most devastating hazards worldwide, affecting people’s socio-economic and ecological systems. Flood dangers constantly threaten life and property since they are the most frequent and destructive of all disasters(Ntajal et al., 2017).
Numerous forms of geohazards have been recognized and documented for a very long period in various regions of the world. The majority of these geo-hazards are serious occurrences that cause harm and devastation. The term "geo-hazard" refers to the results of geological, geomorphological, hydro-meteorological, or a mix of these processes. The lives, property, infrastructure, and environment that people value may be dangerously threatened by these processes. Some of these geo-hazards are caused or influenced by man, while the others may be entirely natural. Natural occurrences or processes are referred to as dangers. These natural disasters may result in human casualties, illnesses, property damage, loss of livelihoods and services, social unrest, disruption of the economy, or environmental harm.(Wu, 2023).
Flooding is frequently brought on by excessive rainfall, especially in metropolitan areas with high runoff rates and low soil penetration rates. Until climate change and urban nuisance issues are handled seriously by politicians, flooding and its accompanying threats may continue. Flood damages can be significantly decreased by using maps, a larger audience, preemptive warnings, and preparedness. Any urban planning project should consider mapping to estimate flood extent or potential inundation zones to be an essential step, and it should be included as part of the city blueprint. Flooding incidents are more likely to occur in urban areas constructed next to or along river channels. Urban areas are more susceptible to flooding, which is primarily brought on by inadequate drainage systems, ineffective urban growth regulations, aggressive land use conversion, a lack of appropriate structures or measures for flood protection, and a failure to carry out the initial master plan and physical layout of the city. This is due to the fact that building a reliable flood model requires taking into account a lot of complicated aspects (Van Alphen et al., 2009).
Flood and its associated risks may continue until climate change and urban nuisance issues are handled seriously by politicians, flooding and its accompanying threats may continue. Flooding may be accurately predicted by mapping, increased awareness, early warning, and preparedness, which can significantly lessen its effects. Any urban planning project should consider mapping to estimate flood extent or potential inundation zones to be an essential step, and it should be included as part of the city blueprint.
Deep Blue: Examining Cerenkov Radiation Through Non-traditional MediaIan Flower
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AVO attribute analysis for the identification of gas bearing sands
1. 1
AVO ATTRIBUTE ANALYSIS FOR THE IDENTIFICATION OF
GAS BEARING SANDS
BY
RASHI
ROLL NO. GP-05
A DISSERTATION SUBMITTED TO DEPARTMENT OF
GEOPHYSICS, KURUKSHETRA UNIVERSITY IN PARTIAL
FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF
MASTER OF TECHNOLOGY
IN
APPLIED GEOPHYSICS
MAY, 2018
SUPERVISED BY: PROF. DINESH KUMAR
AND SHRI ABHIJIT SONOWAL
3. 3
DECLARATION
I, Rashi, Roll. No. GP-05 a bonafide student of Department of Geophysics, Kurukshetra
University hereby declare that the Dissertation entitled “AVO attribute analysis for the
identification of gas bearing sands” submitted by me in partial fulfillment of the requirements
for the award of the Degree of Master of Technology in Applied Geophysics is my original
work.
Place: Kurukshetra
Date: Signature of the Candidate
5. 5
Table of contents
Acknowledgement……………………………………………………………………………...... vii
List of figures……………………………………………………………………………………... viii
List of tables………………………………………………………………………………………. ix
1. Introduction………………………………………………………………..…… 01
1.1 Seismic methods and oil exploration ……………………………………………... …. 01
1.2 AVO (Amplitude variation with offset)………..……………………………………… 10
1.3 Software used………………………………………….………………………………. 14
1.4 Objective of the dissertation………...…………………………………………………. 15
1.5 Outline……………………………………………………………………………… … 15
2. Dataset used & Study area…………………….………………………………..16
2.1 Dataset used………………………………………………………………………… … 16
2.2 Study area…………………………………………………..……….………………. …18
2.3 Geological setup of the study area ……………………………………………………. 20
3. Methodology Adopted…………………………………………………….…….21
3.1 Intercept Gradient analysis………………………………………………………. …22
3.2 Workflow…………………………………………………………..…… ….24
4. Data analysis and Results ….……………………………………….…………. 26
4.1 Well log data analysis………………………………………………………..…… …26
4.2 Well to Seismic tie…………………………………………………………………. … 30
4.3 Gradient analysis……………………………………………………………………. .. 31
4.4 Creation of volume from angle gathers…………………………………………….. …33
4.5 AVO analysis at volume scale………………………………………………………… 37
5. Discussion and Conclusion…………………………………………………….. 39
6. References ……………………………………………………………………… 40
6. 6
ACKNOWLEDGEMENT
I would like to express the deepest appreciation to my dissertation guide Shri Abhijit Sonowal,
Dy. Chief Geophysicist, Oil India Limited for his constant support, guidance during the
dissertation work. I am thankful to Shri G.V.J. Rao, CGM-Geophysics (i/c) for his permission to
use resources available in the Department. I am grateful to Prof. B.S. Chaudhary, Chairman,
Department of Geophysics, Kurukshetra University, for diligently helping me out in all
endeavours.
I am highly indebted to Prof. Dinesh Kumar, my Dissertation supervisor who has the attitude and
substance of a genius; you continually and convincingly conveyed a spirit of adventure in regard
to research and excitement in regard to teaching.
I am and always be thankful to my parents for the kind of environment they gave me while
bringing me up. You have always been my friends rather than parents. I am proud to be your
daughter! Your support has been invaluable. I am thankful to Shri D.S. Manral, DGM-
Geophysics, Oil India Limited for his support & guidance throughout this study. And then there
are my seniors Shri Kartik Sharma and Shri Amrinder Sharma who have always helped me in
various ups and downs during this work. I am thankful to other members of Seismic Imaging &
Modeling Centre for their support. Thank you for being there for me!
Last but not the least, I bow to Maa Sarasvati for what I am today is because of her blessings.
Rashi
7. 7
List of figures
Chapter 1
Figure 1.1 Bats using sound waves to locate their prey................................................................... 3
Figure 1.2 Seismic land survey……………………………………………..…………….............. 4
Figure 1.3 Seismic offshore survey .....…..………………………………………………………. 5
Figure 1.4 Picture (example) of the raw data that is required to be processed for the
subsurface image……………………………………………………………………………….. 5
Figure 1.5 Seismic data translated in to a 3-D picture (example data)………………………… 6
Figure 1.6 Incident, reflected and refracted waves at the interface of two media (viz. 1 and 2)… 9
Figure 1.7 Model CMP gathers contrasting the expected AVO response of a typical wet sand and
gas sand. The increasing amplitudes at far offsets for the gas are anomalous with respect to most
other reflections………………………………………………………………………………..… 11
Figure 1.8 Recording the amplitudes corresponding to a CMP for different angle of incidence. 12
Figure 1.9 Reflection Coefficient variation with the angle of incidence…………...................... 14
Chapter 2
Figure 2.1 Base map of the study area………………………………………………….……….. 17
Figure 2.2 Tectonic map of North-Eastern India……………………………………...………… 18
Figure 2.3 Geology of Upper Assam…………………………………………………….……… 19
Chapter 3
Figure 3.1 Curves showing reservoir top (red) and base (green)………………………….…….. 22
Figure 3.2 Top and base of the reservoir in the gather…………………………………….……. 22
Figure 3.3 Intercept and gradient product plot (data example)…………………………….…… 23
Figure 3.4 Workflow of the methodology adopted…………………………………………...…. 24
8. 8
Chapter 4
Figure 4.1 Zone of investigation along marked on the recorded logs and other basic derived
values………………………………………………………………………………….…………28
Figure 4.2 (a) Cross-plot of the Gamma ray (API) and P-wave impedance……………………..29
Figure 4.2 (b) Display curve of depths corresponding to hydrocarbon saturated zone………….29
Figure 4.3 The well log curves on the first 4 tracks and well tie between the synthetic and
seismic wavelet (7th track) and the seismic gather………………………………………………31
Figure 4.4 (a) AVO analysis corresponding to the event at 1903 ms (well top) and 1922ms (well
base)………………………………………………………………………………….…………..32
Figure 4.4 (b) AVO analysis showing top and base of the gas sand on the basis of intercept and
gradient properties with the help of red and green colored blocks………………………………32
Figure 4.5 Seismic trace is preserving the same character as shown by the synthetic trace…….33
Figure 4.6 (a) Offset gather………………………………………………………………………34
Figure 4.6 (b) Angle gather………………………………………………………………………34
Figure 4.7 Angle gather preserving the same character of amplitude (increasing in a negative
sense)……………………………………………………………………………………………..35
Figure 4.8 Attribute analysis for the synthetic as well as seismic data…………………………..36
Figure 4.9 Crossplot of the intercept and gradient values for both seismic and synthetic
trace……………………………………………………………………………………………....36
Figure 4.10 AVO attribute analysis for the entire volume…….…………………………………37
Figure 4.11 CDP stack overlain on the reservoir top (red) and base (blue)……………………...38
Chapter 5
Figure 5.1 Intercept Gradient product analysis, showing the areal extension of the
hydrocarbon...................................................................................................................................39
9. 9
List of tables
Table 1 Classification of sands………………………………………………………………......13
Table 2 Basic details of seismic data used……………………………………………………….16
10. 10
Chapter 1
Introduction
1.1 SEISMIC METHODS AND OIL EXPLORATION.
1.1.1 A BRIEF HISTORY OF REFRACTION AND REFLECTION METHOD.
The earliest efforts to locate oil-bearing structures by geophysical tools involved gravity
measurements. Shortly before the beginning of the present century, Baron Roland von Eotvos, of
Hungary, completed development of the torsion balance that bears his name. At about the same
time, seismic refraction equipment, very crude by modern standards, was brought from Germany
to look for salt domes in the Gulf Coast. In 1919, Ludger Mintrop (a German researcher) had
applied for a German patent on locating and measuring depths to subsurface features by
refraction profiling (Dobrin, Milton B. and Savit Carl H.)
Both the torsion-balance and refraction campaigns were successful in locating salt domes as
early as 1924. The gravity surveys led to the discovery of the productive Nash domes, and the
seismic shooting was responsible for finding the Orchard dome, both in Texas. These successes
led to more widespread application of the two techniques, and by 1929 virtually all the
piercement-type domes in the Gulf Coast had been discovered.
Early Reflection Work:
The earliest experiments with the seismic reflection method were carried out by J.C. Karcher
from 1919 to 1921(Dobrin, Milton B. and Savit Carl H.). To demonstrate the potential of the
method for oil exploration, he mapped a shallow reflecting bed in central Oklahoma early in
1921. On the 50th anniversary of this event, in April 1971, a monument was dedicated at the site
where these tests had been conducted. It was until 1927; however that reflection method was put
to work for routine exploration. In that year the geophysical research corporation used the
technique to discover the Maud field in Oklahoma. By the early 1930s, reflection became the
most widely used of all geophysical techniques.
11. 11
1.1.2 GENERAL THEORY.
Planet Earth! If you look closer you will see whole of the world exist beneath the surface of land
and sea. Layer after layer, rock structure goes deep under the Earth’s crust and trapped within
these structures along with other liquids you will often find deposits of oil and natural gas; the
world’s two most important sources of energy. These famous fuels are in constant demand
because they make the world go round, day in and day out.
So, how do we find something completely hidden beneath the earth’s surface?
It’s a mystery that people in Oil and Gas industry are always trying to solve and for a very good
reason drilling for hydrocarbon is expensive and before they spend money on equipment and
cruise, exploration and production (E&P) company needs a reliable strategy for pinpointing
where to drill!
Geo-scientists have a secret weapon called as seismic exploration and it involves sending the
acoustic energy which takes the form of wavelet in to the ground to get a sound picture beneath
the surface. It’s complicated.
So, let’s start with the analogy of bats (figure 1.1). Bats can’t see very well. So they send out
little waves of sound that bounces off of objects and then go back to their ears. It’s called
SONAR (Sound Navigation And Ranging). It gives them, what you might call a sound picture of
their world. That’s a good example of how nature already uses a form of seismic acoustic
imaging to locate objects. Doctors also use it for ultrasound imaging.
12. 12
Figure 1.1 Bats using sound waves to locate their prey. (Source: https://askabiologist.asu.edu/echolocation)
Geoscientists use the man made tools to make the sound wavelets listen to them and then record
them when you want to know if oil and gas deposits are in a particular area. Geophysical
companies bring large trucks that have big vibrators on them. Most of the time, this is what
generates the acoustic energy or vibrations. They use geophones to hear the reflected sound, but
sometimes they set off small buried charges. They set many geophones on the ground in a line
and they are attached to a recorder inside the truck (figure 1.2). The vibrator sends thousands of
wavelets down in to all the different layers of the earth. Some of the wavelets bounce off the
boundaries between the rocks below the surface and are reflected back to the geophones that are
waiting to record them. Each geophone along the cable sends the received wavelets to the
recording truck where they are recorded and stored.
13. 13
Figure 1.2 Seismic land survey.( Source: http://www.argas.com/land-data-acquisition/ )
Although the wavelets reach in to the subsurface of the ocean (That’s offshore seismic and it just
require a different device to send out the wavelet and record those that are reflected back out). At
sea, a seismic crew works off a vessel with a specially designed back in. So, it’s easier to lay
floating cables or streamers and all along the length of streamers, hydrophones are attached one
after another. Several of these hydrophone streamers are pulled behind the vessel at once.
Acoustic sources (for example- air gun) are towed behind the vessel in front of the streamers and
release compressed air which creates the wavelets. These wavelets travel through the water and
in to the subsurface below where just like on land they bounce off the rock layers and then return
to the hydrophones to be recorded (figure 1.3).
14. 14
Figure 1.3 Seismic offshore survey. (Source:
https://www.marinelog.com/index.php?option=com_k2&view=item&id=7020:boem-paves-the-way-for-us-
east-coast-seismic&Itemid=230 )
Figure 1.4 Picture (example) of the raw data that is required to be processed for the subsurface image.
(Source: https://www.youtube.com/watch?v=hxJa7EvYoFI)
Here’s what seismic looks like after it’s been recorded (figure 1.4). Basically it’s a bunch of
squiggles. There are still a few more steps to go before it begins to look like an actual picture of
the earth’s interior. Right now, the data is still in it’s raw form. To get a picture that actually
15. 15
looks like the earth beneath us, the data has to be processed. It takes a large supercomputing PC
cluster to process the seismic data. These computers go through all the different traces made by
the wavelet and filter out most of the things we don’t need, such as vibrations made by a tractor
in a field nearby. Using really amazing computer applications and working on state-of-the-art
workstations geo-scientists can see the seismic data translated in to a 3D picture (figure 1.5).
You might be thinking, I don’t see any oil and gas there?
Figure 1.5 Seismic data translated in to a 3-D picture (example data). (Source:
https://www.youtube.com/watch?v=hxJa7EvYoFI)
But believe or not, geo-scientists can look at this processed data with their trained eyes. And
make informed decisions about, whether or not, oil and gas deposits are in the geologic
structures. Seismic data leads to a high percentage of drilling success with less risk to the
environment. And in a world where the demand for oil and gas is increasing faster than the
supply, good seismic information will lead to more affordable energy.
16. 16
1.1.3 BASIC THEORY
Let us briefly study about what is actually happening in the subsurface. The physical properties
of earth materials are not uniform because subsurface variations occur in lithology, porosity,
mineralogy, density, permeability, and pore fluids. To understand wave propagation in these
materials, simplified mathematical models are usually constructed. On such model assumes only
the propagation of compressional or P-wave types and is usually called the acoustic media
model. However, when a P-wave strikes an interface between two solids, at an angle that is
below the critical angle, it generates reflected and transmitted P- and S-waves. Similarly, an
incident SV-wave also generates reflected and transmitted waves of both types. Such a process is
called mode conversion. Models that consider such effects are called elastic-media models and
fully consider the propagation of S-waves and mode-converted waves, in addition to P-waves.
Mathematically, the propagation of such waves can be described by solving the wave equation.
For one-dimensional acoustic-wave propagation (Chopra, S. and Castagna, John P. (2014))
2
2
2
2
2
x
u
V
t
u
(1)
For three-dimensional wave propagation,
2
2
2
2 1
t
u
V
u
(2)
In equations 1 and 2,
2
2
2
2
2
2
2
zyx
(3)
and is also called the Laplacian, u is the seismic wave field, V is the wave velocity in the
medium, and t is time. When a plane wave strikes an interface at normal incidence, a part of the
wave is reflected and the rest is transmitted. The ratio of the reflected wave’s amplitude to the
incident wave’s amplitude is called the reflection coefficient, and is determined by the
impedance contrast between the two layers, impedance being the product of velocity and
density of the medium. The amplitude of the reflected wave is given by multiplying the
amplitude of the incident wave by the reflection coefficient. Thus, for a plane wave reflected at
17. 17
normal incidence, the reflection coefficient R is given as (Chopra, S. and Castagna, John P.
(2014))
1122
1122
VV
VV
R
(4)
where V and ρ are the velocity and density, respectively, for the two media (with appropriate
indices) on either side of the interface (figure1.6), and with medium 2 being below and medium
1 above the interface. Because the product of the velocity and the density is the impedance (I) of
the medium, we can write
12
12
II
II
R
(5)
The greater the difference is between the impedances of the media on either side of the
interface, the greater the percentage of energy is that will be reflected. The numerator in
equation 5 determines the sign (sometimes referred to as the polarity) of the reflection. If the
impedance of the lower layer is higher than the impedance of the upper layer, the reflection
coefficient for the interface is positive, and vice versa. Thus, the reflection coefficient is a
numerical measure of the amplitude and polarity of a wave reflected from an interface, with
respect to those values for the incident wave. Similarly, the amplitude of the transmitted wave is
given by multiplying the amplitude of the incident wave by the transmission coefficient.
Because the sum of the amplitudes of the reflected and transmitted waves is equal to the
amplitude of the incident wave (by the law of conservation of energy at an interface, and
because there are no sources at an interface), the transmission coefficient can be calculated by
subtracting the reflection coefficient R from 1 (Chopra, S. and Castagna, John P. (2014))
12
12
1
II
I
RT
(6)
when a plane wave strikes a rock interface at an oblique angle of incidence, as we commonly
observe in reflection seismic recordings, a more complicated situation arises. The discontinuity
in the elastic parameters that the obliquely incident P-waves encounter at the interface results in
compressive and shear stresses. This leads to partitioning of the incident energy at the interface,
so that, in addition to the reflection and refraction of the incident P-wave, there is P- to S-mode
18. 18
energy conversion. Thus, below the critical angle, an incoming P-wave gives rise to a reflected
P-wave, a transmitted P-wave, a reflected S-wave, and a transmitted S-wave (Figure 1.6). In
such a case, equation 4 is no longer applicable in a practical sense for angles of incidence
greater than 10°or 15°, and these angles may be smaller for large reflection coefficients. The
angular relationships among the different wave components follow Snell’s law, which is given
as (Chopra, S. and Castagna, John P. (2014))
,
sinsinsinsin
2121 s
t
s
r
p
t
p
r
VVVV
(7)
Figure 1.6 Incident, reflected and refracted waves at the interface of two media (viz. 1 and 2). (Source: AVO5
)
where VP and VS are the P-wave velocity and S-wave velocity, respectively, for the two media
(as indicated by their indices) on either side of the interface. Angle Өi is the angle that the
incident ray makes with the normal and is called the angle of incidence. Similarly, Өr and Өt are
the angle of reflection and the angle of transmission, respectively, for the P-waves, and φr and φt
are the angle of reflection and the angle of transmission, respectively, for the S-waves. It is
important to remember that the transverse waves generated by the incident P-waves at plane
19. 19
interfaces are of the SV type; that is, the vibrations are parallel to the plane of incidence. The
partitioning of incident-wave energy into the different components depends largely on the angle
of incidence as well as on the physical properties of the two media. The physical properties we
refer to here are the P-wave velocity, the S-wave velocity, and the densities of those two media.
A fundamental principle of direct hydrocarbon detection using AVO analysis is the idea that
anomalous contrasts in these parameters — especially in the values for VP/VS or Poisson’s ratio
on either side of an interface — result in anomalous partitioning of energy as a function of angle
of incidence.
Knowing the basics we have seen there are many seismic attributes including p-wave velocity, s-
wave velocity, poisson’s ratio and combination of these. But while doing AVO attribute analysis
we will focus on just two. These are intercept and gradient, and together are called as AVO
attributes.
1.2 AVO (AMPLITUDE VARIATION WITH OFFSET)
1.2.1 HOW AVO CAME IN TO PICTURE (DISCOVERY OF AVO)
Earlier bright-spot analysis and direct hydrocarbon detection were developed in the 1970s, and
during that time they met with considerable success (Chopra, S. and Castagna, John P. (2014).
High-amplitude seismic events were being drilled, and the success rate for exploration wells was
excellent in the Cretaceous sands of the Sacramento Valley in California. However, not all bright
spots were associated with hydrocarbons. During the bright-spot era, the challenge for
geophysicists was to be able to distinguish, on conventional, stacked seismic sections, true gas-
sand signatures from those of non gaseous or abnormally high- or low velocity layers. In the fall
of 1974, Chevron drilled a well on a very high-amplitude event in the Fallon Basin of Nevada. It
turned out to be a high-velocity basalt layer rather than hydrocarbons. Thus, Ostrander suggested
that under suitable geologic conditions, gas sands display a distinct increase in amplitude with an
increase in offset, whereas their amplitude under other conditions decreases or remains flat with
increasing offset. Such an examination of reflection amplitudes from varying source receiver
offsets has been termed ―AVO analysis.‖
20. 20
1.2.2 INTRODUCTION TO AVO
Amplitude variation with offset (AVO) is the offset dependent variation of P wave reflection
coefficients to estimate anomalous contrasts in shear wave velocities and densities across an
interface (Chopra, S. and Castagna, John P. (2014)). Although the conventional p-wave
reflection coefficient at normal incidence is, in itself, a hydrocarbon indicator, AVO goes beyond
the P-wave normal incidence by producing a second attribute that is related to the contrast in
Poisson’s ratio.
Most of the time, the gas sands that produce these amplitude anomalies have lower impedance
than the encasing shales and have reflections that increase in magnitude with offset. The theory
behind AVO exploration for gas in clastic rocks is straightforward. Gas within the pore space of
a clastic rock lowers the compressional wave velocity of the rock substantially, but leaves the
shear wave velocity relatively unaffected. The change in the ratio of P-wave velocity to S-wave
velocity causes the partitioning of an incident wave to differ for the case of a gas-sand /shale or
gas-sand/wet sand reflector from that of most other reflectors. For some reservoirs the reflections
associated with gas bearing rocks increase in amplitude with offset relative to other reflections
(Figure-1.7). Such an increase with offset is uncommon in seismic data; most reflections
decrease in amplitude with offset. In this sense, AVO analysis is a search for such an anomalous
seismic response. The input to the AVO analysis is a common midpoint gather which is a set of
traces sampling the same subsurface point at varying offsets. The use of AVO as a direct
hydrocarbon indicator in clastic rocks is based on differences in the response of the P-wave
Figure 1.7 Model CMP gathers contrasting the expected AVO response of a typical wet sand
and gas sand. The increasing amplitudes at far offsets for the gas are anomalous with respect
to most other reflections. After (Mujiburrahmam, 2018).
21. 21
velocity (vp) and S-wave velocity (vs) of a reservoir rock to the introduction of gas in the pore
spaces. P-waves are sensitive to the changes in the pore fluids. The introduction of only a small
amount of air or gas into the pore spaces of the rock can reduce the P-wave velocity of the rock
drastically. In contrast, S-waves do not see the pore spaces of the rock and have a velocity that
depends mainly on the rock framework. Therefore, the decrease in the vp/vs ratio of a reservoir
rock upon the introduction of gas in the pore spaces changes the relative amplitude of reflection
from the top and base of the reservoir as a function of angle at which a wave strikes the
boundary. The study of relative amplitudes of the traces within a CMP gather is known as
amplitude variation with offset analysis. Amplitude variation with angle (AVA) denotes the
examination of traces sampling the same midpoint at increasing angles of incidence (figure 1.8).
Figure 1.8 Recording the amplitudes corresponding to a CMP for different angle of incidence. After (CGG)
Seismic reflections from gas sands exhibit a wide range of amplitude-versus-offset (AVO)
characteristics. The two factors that most strongly determine the AVO behavior of a gas-
sand reflection are the normal incidence reflection coefficient Ro and the contrast in
Poisson's ratio at the reflector. Based on their AVO characteristics, gas-sand reflectors can
be grouped into three classes defined in terms of Ro at the top of the gas sand. Class I gas
sands have higher impedance than the encasing shale with relativity large positive values
for Ro. Class 2 gas sands have nearly the same impedance as the encasing shale and are
22. 22
characterized by values of Ro near Zero. Class 3 sands have lower impedance than the encasing
shale with negative large magnitude values for Ro. Each of these sand classes has a distinct AVO
characteristic.
Shuey’s approximation to Zoeppritz Equation is given by (Yilmaz, Oz., 2008)
)sin(tan
2
1
sin]
)1(
[)( 222
2000
pppp RARR (8)
As shown in figure 1.8, the traces in a seismic gather reflect from the subsurface at increasing
angles of incidence . The first order approximation to the reflection coefficients equation as a
function of angle is given by (Yilmaz, Oz., 2008)
(9)
B is a gradient term which produces the AVO effect. It is dependent on changes in density, ρ, P-
wave velocity, Vp, and S-wave velocity, Vs. The AVO classes are represented in the table 1
below.
Table 1: Classification of sands..
AVO Class Characteristics
Class 1:
High impedance sand with decreasing AVO. The layer has higher
impedance than the surrounding shales.
Class 2:
Near-zero impedance contrast between the sand and surrounding
shales.
Class 2p: Near-zero impedance contrast with polarity reversal.
Class 3:
Low impedance sand with decreasing AVO, compared to surrounding
shales.
Class 4: Low impedance sand with increasing AVO.
2
0 sin)( BRR
23. 23
1.3 SOFTWARE USED
The software used for the AVO analysis is Hampson Russell Solutions, commonly known as
HRS. It was first launched in 1987. Hampson Russell is reservoir characterization software,
having features for attribute extraction and prediction along horizontal wells, as well as geo-
statistical mapping capabilities. Hampson Russell also provides it’s users workflows with data
conditioning, inversion and map prediction features. Key features include data conditioning
processes, residual Normal Moveout (NMO) correction, and FXY deconvolution for noise
attenuation and spectral balancing. Inversion can now output relative impedances for both pre-
and post-stack data and extract attributes along horizontal well paths. This new information helps
Figure 1.9 Reflection Coefficient variation with the angle of incidence. (After Chopra, S. and
Castagna, John P. (2014))
24. 24
interpret data from derived attributes resulting in more accurate reservoir model. The MapPredict
application is fully integrated, easy-to-use, map-based geo-statistical software that integrates
well, seismic and attribute data into accurate, detailed maps. MapPredict encompasses the
functionality of Hampson Russell’s former ISMap application and has evolved even further to
include the ability to handle horizontal wells. MapPredict is especially suited to finding
relationships between multiple seismic attribute slices and properties derived from well
information such as hydrocarbon production. GeoSoftware delivers innovative reservoir
characterization and advanced seismic interpretation and analysis software that offers expanded
capabilities for improved productivity (After CGG).
1.4 OBJECTIVE OF THE DISSERTATION
The objective of this dissertation is to delineate the extension of hydrocarbon saturated zones on
the basis of AVO analysis. The area under study is located in Upper Assam Basin in OIL’s
operational area. The area is covered by 3D seismic data and drilled wells established
hydrocarbon in Miocene age formations. Amplitude versus offset (AVO) technique was
therefore used to model subsurface synthetic response from well logs and applied as a tool to
identify and delineate the extension of hydrocarbon reservoir within the area.
1.5 OUTLINE
As of now we have known about the history of seismic refraction and reflection methods, their
role in oil exploration, seismic attributes and the very background of AVO that we require for
this dissertation work. Then proceeding towards the introduction of the software that has been
used for the ―AVO attribute analysis for the identification of gas bearing sands”, we are now
aware of the objective of this dissertation work too. Summing up all of this in Chapter 1, we will
study about the dataset used and the study area in the Chapter 2. Chapter 3 will contain the
methodology adopted. Then the data analysis and results will be covered in Chapter 4. Chapter 5
will contain the discussion and conclusion. Chapter 6 will contain the references that have helped
writing this text.
25. 25
Chapter 2
Dataset used and study area
2.1 DATASET USED
We require two types of dataset for performing an AVO attribute analysis. They are:
1. Seismic data
2. Well log data
The available seismic data within the study area is processed in an amplitude preserved manner
which is a pre-requisite for AVO analysis. Whereas, in case of well logs sonic (p-wave and s-
wave) along with density logs are required.
The base map below (see figure 2.1) shows the seismic coverage and drilled well position in the
study area. Details about the seismic dataset used are given in table 2.
Table 2: Basic details of seismic dataset used
Inline Number range 1050 to 1125
Cross-line Number range 2000 to 2400
Inline Interval 50m
Cross-line Interval 25m
Sampling Interval 2ms
Record Length 06 seconds
27. 27
2.2 STUDY AREA
The study area includes OIL’s operational area in the Upper Assam Basin (See figure 2.2). The
Upper Assam Basin is a foreland Basin located at the boundary of two convergent plates viz.
Indian and Eurasian. The formation of the basin comprises of alternate sand and shale bed from
Eocene to Recent Age. Hydrocarbon production in these areas primarily comes from Eocene,
Oligocene-Miocene age formations. In the present study, AVO analysis has been carried out in a
Miocene reservoir to identify hydrocarbon proven sands and investigate its possible areal
extension in and around the well location.
Figure 2.2 Tectonic map of North-Eastern India. (After Ishwar, N.B. and Bhardwaj, A,2013)
29. 29
2.3 GEOLOGICAL SETUP OF THE STUDY AREA:
The Upper Assam Basin is one of the petroliferous basins of India and encompasses parts of the
Indo-Burma range and shelf areas to the west. The Indo-Burma range is a geologically complex
tectonic belt which extends in north-south direction along the geographical boundary of India
and Burma (presently Myanmar). It is characterized by association of a number of
thrust/overthrust, ophiolitic rocks, high degree of metamorphism, pelagic sediments etc. On the
other hand, the shelf area is comparatively free from the thrust tectonics and is characterized by
occurrences of normal faults down to basement. Sediments ranging in thickness from 3500 m to
more than 7000 m was deposited over granitic basement. The age of sediments ranges from
Upper Cretaceous through Paleogene to Neogene times. One important structural feature is the
area known as ―Belt of Shuppen‖ which is a series of thrusts and overthrusts trending in the
northeast-southwest direction and flanks the eastern part of the shelf area of the basin. The
thickness of sediments increases towards the eastern thrust belts as well as to the northeast.
Presence of commercial hydrocarbon has been established in clastic sediments of both Paleogene
and Neogene age.
The study area is in eastern part of Upper Assam Basin. Major formations (see figure 2.3) of the
basin are viz. Sylhet group( Eocene), Kopili (Late Eocene –Oligocene), Barail (Oligocene-
Miocene), Tipam (Miocene), Girujan (Miocene), Namsang (Pliocene) and Siwalik/Dhekiajuli
(Recent). The formations are primarily of clastic sediments. The thickness of these formations
varies in N-S direction (i.e. across the basin) whereas the thickness variation is less in NE-SW
direction (i.e. basinal strike direction).
30. 30
CHAPTER 3
Methodology Adopted
In this study, AVO (amplitude variation with offset) forward modeling and analysis was done in
a well in the gas charged reservoir zone. Synthetic gathers were generated using Aki-Richard’s
equation and subsequently, AVO attributes, intercepts and gradient were calculated based on
Aki-Richard’s two term equation. The available seismic data was processed in AVO friendly
manner where relative amplitudes were preserved. Well to seismic tie was performed to match
the synthetic event with seismic and a good correlation has been observed.
Intercept and gradient analysis is the common and popular AVO analysis method. The method is
to plot the amplitude of the signal for a reflector (i.e., horizon) against the offset of the trace or
the calculated angle that the corresponding sound wave would make when it met the reflector.
This plot yields the "Intercept", where the trend of the amplitude measurements meets the zero-
offset line (so it would be equivalent to a geophone directly next to the source, and a 90° angle to
the reflector). It also yields the "Gradient", which is the slope of the curve made by the plot
points which in our case has been done by the software and we used the direct values.
Intercept and gradient plots on angle gather for a particular horizon or a reservoir top and base
would be responses as shown in figure 3.1. These type of curves plot would infer reservoir sand
properties with respect to overburden and underlying shale layer.
31. 31
Figure 3.1 Curves showing reservoir top (red) and base (green).
3.1 INTERCEPT GRADIENT ANALYSIS:
Figure 3.2 Top and base of the reservoir in the gather. After (Rebecca Goffey,2012)
In this case, both the intercept (A) and the gradient (B) are large numbers or ―bright‖. Also, they
have the same sign. This is an example of a Class 3 anomaly. Forming the product of A and B,
we get:
Top
Base
32. 32
Top of sand: (-A)*(-B) = +AB
Base of sand: (+A)*(+B) = +AB
This gives a positive ―bright‖ response at both top and base being consecutive cycle (see figure
3.2). This is Class III type AVO response. When this intercept and gradient product plot in the
color scale is plotted, anomaly along the reservoir top and base would show positive response for
class-III sand represented in Figure 2.4. This type of AVO response is very easy to detect in the
section.
Figure 3.3 Intercept and gradient product plot (data example). After (Mujiburrahmam, 2018)
33. 33
3.2 WORK FLOW:
Fig 3.4 Workflow of the methodology adopted
Figure 3.4 explains part of the workflow adopted in this study in order to perform AVO analysis.
In this study, well log data from sonic log and density log has been utilized in order to obtain
values of density and compressional velocity which finally gives the acoustic impedance.
Thereafter, synthetic trace is generated on the basis of convolution between reflectivity series
and wavelet extracted from the seismic gathers. As the well log data is in the depth domain and
Well Log Data
P-sonic
(Vp)
Density
(ρ)
Compute Acoustic
Impedance
(ρ*v)
Generate
reflectivity
series
Seismic gathers
Extract
wavelet
Convolution
(*)
Generate
synthetic trace
Well tie &
event
correlation
34. 34
seismic data is in the time domain, we utilize the time depth relationship (TDR) and events
correlation so as to tie the synthetic trace with the seismic gathers.
35. 35
Chapter 4:
Data analysis and Results
4.1 WELL LOG DATA ANALYSIS:
Recorded logs contain caliper, gamma ray, resistivity, neutron porosity, full wave sonic (P, S),
density and other basic logs (figure 4.1). Log data interpretation and drilled well information
confirm two gas bearing zones at depth 2320-2340m and 2370-2390m (Measured depth)
respectively.
Gamma ray log generally gives higher value for the shale lithology (due to the presence of
radioactive grains in shales) and lesser for the sandstone. So, gamma log give us an indication
about the lithology of the subsurface.
In resistivity logs, generally three logs, namely, MSFL (Micro Spherical Focused Logs), LLS
(Laterolog Short) and LLD (Laterolog Deep) are recorded. MSFL and LLS logs are of lesser
value in AVO analysis as their depth of investigation is restricted to the invaded zone and the
transition zone. LLD records resistivity values of un-invaded zone, hence, is required for AVO
analysis.
36. 36
Table 2
Log / Derived property used Property
recorded/Used
Observations
Gamma ray log Gamma ray index
(API units)
High values for shale lithology and
low values for sandstone lithology
LLD (Resistivity log) Resistivity(ohm-m)
High values corresponding to
sandstone region and low values
for the shale medium
Neutron log Porosity(fraction)
Under-estimation of the porosity in
the presence of hydrocarbon
saturated gas-sand
Density log Bulk density(g/cm3
)
Low value in the presence of the
hydrocarbon saturated gas sand
reservoir.
Sonic log Transit time (μs/ft)
Decrease in p wave velocity in the
presence of the hydrocarbon
saturated zone ( due to decrease in
the value of bulk modulus as bulk
modulus is the direct measure of
the resistance a material towards
the application of the stress)
Poisson’s Ratio Vp/Vs
Sharp decrease in the Poisson’s
ratio as vp shows a decrease and vs
increases in the presence of
hydrocarbon saturated zone
37. 37
On the basis of observations made using the various logs and derived properties, the zone of
investigations to perform AVO analysis has been chosen to be from 2320-2340m and 2370-
2390m (Measured depth).
As we know there is a probability of finding a reservoir corresponding to low values of p-wave
impedance and gamma ray. Fig. 4.2(a) shows a selected region that looks favorable to the
presence of the hydrocarbon bearing zone.
Figure 4.2(b) is display curve corresponding to this selected region, which will display the depth
values corresponding to hydrocarbon saturated zone. The red color region depicts the different
depths corresponding to the selected area of hydrocarbon saturation.
Figure 4.1 Zone of investigation along marked on the recorded logs and other basic derived values.
Zone of
investigation
38. 38
(a) (b)
Zone of
interest
Figure 4.2 (a) Cross-plot of the Gamma ray (API) and P-wave impedance. (b) Display curve of depths corresponding to
hydrocarbon saturated zone
39. 39
4.2 WELL TO SEISMIC TIE:
Well-seismic tie allows well data (measured in units of depth) to be correlated with seismic data
(measured in units of time). For any geo-scientific study, which uses seismic & well data, both
dataset needs to be converted in one domain. Here, Well-to-seismic tie aims to convert depth unit
of wells into time units. A Well-seismic tie is a four step process, which includes:
Select the seismic data in periphery of the well location; extract statistical wavelet from
the target zone using appropriate wavelength.
Synthetic seismic is created by convolving well derived impedances and statistical
wavelet, and synthetic is correlated with observed seismic near well. First, check
shot/VSP correction are required for sonic calibration, however, in case check shot/VSP
is not available, logs can be shifted by matching the major sequence boundaries in logs &
seismic. After that, minor stretch/squeeze operation on logs is performed (well-log
correlation) for optimum correlation between synthetic and observed seismic.
Once a reasonable T-D curve is established, deterministic wavelet is extracted by
correlating the Synthetic and Observed Seismic near well.
On correlating the deterministic wavelet derived synthetic and seismic near well, minor
corrections are done for optimum correlation, and in this process T-D curve is further
refined.
Using seismic data we have extracted a statistical wavelet. The figure 4.3 is showing a good
correlation between synthetic and seismic traces.
40. 40
Figure 4.3 The well log curves on the first 4 tracks and well tie between the synthetic and seismic wavelet (7th
track) and the seismic gather.
4.3 GRADIENT ANALYSIS
Also we can see that seismic traces are also giving the same result as the well log data. The
trough in seismic wavelet corresponding to the well top shows a negative intercept and negative
gradient while the peak (crest) corresponding to the well base shows a positive intercept and
positive gradient. These are visible as mirror images of each other (fig 4.4(a)).
41. 41
Fig. 4.4(a)AVO analysis corresponding to the event at 1903 ms(well top) and 1922ms(well base) and (b) AVO
analysis showing top and base of the gas sand on the basis of intercept and gradient properties with the help
of red and green colored blocks.
If we see the cross plot of AVO attributes i.e. intercept and gradient, an interesting deviation
from the background trend is observed. Looking at all those values having different colors, the
red color block in the third quadrant corresponds to the well top and green color block in the first
quadrant corresponds to the well base. This is very beautifully shown in the figure 4.4(b). After
the AVO analysis we can see if we take the product of these attributes, the product will always
come out to be a positive value which confirms that the sand present in the reservoir is of class
III. If we see the trend in synthetic traces and the seismic traces, we find them talking to each
other (fig. 4.5)
(a) (b)
42. 42
Fig. 4.5 Seismic trace is preserving the same character as shown by the synthetic trace.
4.4 CREATION OF VOLUME FROM ANGLE GATHERS
Offset gathers (see figure 4.6(a)) were converted in to angle gathers (see figure 4.6(b)) for AVO
analysis which is completely dependent on the angle of incidence and it was observed that the
amplitude is increasing in negative sense (i.e. the value was increasing in negative sense with
increase in incidence angle) and gradient behavior is also negative which is shown in figure 4.7.
44. 44
Figure 4.7 Angle gather preserving the same character of amplitude (increasing in a negative sense).
Now working on the AVO attributes and doing their analysis we proceeded towards the study of
same for two events corresponding to the synthetic and seismic traces for 1903 ms and 1922ms.
In Figure 4.8, panel 1 shows seismic events and panel 2 synthetic gather for the same events.
This makes us confident about our observations using well log information. Analysis of the
attributes on their cross-plot (figure 4.9),we see that red and navy blue colored blocks lie in the
third quadrant and the green and sky blue colored blocks lie in the first quadrant, affirming us
that their product will be always be positive (intercept*gradient) which delineates the class III
type of the gas-sand present in the reservoir from the background.
45. 45
Figure 4.8 Attribute analysis for the synthetic as well as seismic data.
Figure 4.9 Cross-plot of the intercept and gradient values for both seismic and synthetic trace.
46. 46
4.5 AVO ANALYSIS AT VOLUME SCALE
After performing AVO analysis for a single CDP gather, the AVO analysis study was extended
to the complete volume by determination of intercept (A) and gradient (B) for the whole volume.
We selected two regions (one red and another blue) depicting our class-III type of sand in the
reservoir (figure 4.10).
Figure 4.10 AVO attribute analysis for the entire volume.
On analyzing the volumetric version, CDP stack section was overlain on the reservoir top (red)
and base (blue) zones derived from the cross-plot, as shown in the figure 4.11, it was observed
that there was good correlation between the two. Further, it was observed that highlighted
47. 47
portion in figure 4.10 corresponding to the positive value of the Intercept* gradient product
shows the extension of the reservoir (from N to S) (figure4.11). For Class-III gas sand in our
study area Intercept* gradient product is helpful to identify the anomaly from background trend.
Figure 4.11 CDP stack overlain on the reservoir top (red) and base (blue).
48. 48
Chapter 5
Discussion and Conclusion
On the basis of AVO analysis performed on the seismic gathers, it has been inferred that there is
a presence of Class III type gas sand in the reservoir. Cross-plotting of rock properties indicate
that reservoir sandstone is of low impedance with high impedance encased lithology (shale).
Drop in Poisson’s ratio has been observed for gas charged reservoir sand allow utilization of
AVO technique to characterize the reservoir. AVO anomaly in the target reservoir within the
study area has been classified as class III, with real seismic PSTM gathers which showed large
amplitude at far offset for the gas charged sand.
The extent of gas charged sand can be determined using the intercept- gradient attribute as
shown in Figure 5.1. The extension of the hydrocarbon saturated zone was prominent in the In-
line direction where as in the cross-line direction its extent was limited spatially. Thus, within the
study area AVO analysis on seismic data can help us delineate hydrocarbon bearing zones from
non-hydrocarbon bearing zones.
Figure 5.1 Intercept Gradient product analysis, showing the areal extension of the hydrocarbon.
49. 49
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