PhD-Projects in the Geophysics Group

PhD Candidate: Ujjwal Shekhar

Microseismic waveform inversion

PhD Student Kui Xiang, Main Supervisor Morten Jakobsen (UiB) Co-Supervisors Geir Nævdal (IRIS), Kjersti Solberg Eikrem (IRIS)

About the project

Full waveform inversion (FWI) has emerged as the ultimate answer to the Earth imaging and resolution problem. By performing a numerical simulation of the full wavefield including multiply scattered and diffracted waves in addition to diving and refracted waves and iteratively updating the seismic model until the computed waveforms matches the observed waveforms, one can potentially obtain images of the earth’s interior of much higher quality and resolution than when inverting travel times and/or amplitude data only. However, FWI is a very costly method and the iterative inversion results tends to be sensitive to the starting model. 

By using multi-parameter full waveform inversion methods within the context of dynamic reservoir characterization, more accurate images of fluid movements and pressure effects in a reservoir during production can be obtained.  The work of Jakobsen et al. (2015) is based on single parameter (variable velocity) acoustic approximations that we would like to use for the sake of simplicity when processing seismic data. In order to make the workflow more realistic for applications, we shall extend the use of the T-matrix method to complex media such as elastic and anisotropic media and multi-parameter full waveform inversion. In any case, this project represents a natural continuation of the pioneering work of Jakobsen et al. (2015). 

Although we focus on petroleum related applications, the methods developed in this project are also relevant for a related project within medical ultrasound imaging as well as other projects within seismic monitoring of CO₂ sequestration and seismic characterization and monitoring of geothermal reservoirs. 

Figure: Simultaneous inversion of density and compressibility. Left: True model. Middle: Initial model guess. Right: Inverted result. Photo: Kui Xiang, UiB

The main goal of this project is to develop methods for multi-parameter seismic full-waveform inversion using integral equation formulations. Special attention will be given to the development of methods for reducing the computational cost and the sensitivity of the inversion results on the starting model. Both deterministic and Bayesian formulations of the non-linear inverse scattering problems will be used. A further aim is to investigate the use of rock physics model in this context.

Project Partners

IRIS.

People

Project members

Kui XiangPhD Student

Morten JakobsenSupervisor (UiB-GEO)

Geir NævdalCo-Supervisor (IRIS)

Kjersti Solberg EikremCo-Supervisor (IRIS)

My name is Saskia and I am from Germany. I do an Industrial PhD with CGG, a geoscience service company. My main supervisor is Einar Iversen (UiB) and my co-supervisors are Vetle Vinje (CGG) and Jan Erik Lie (Lundin Petroleum).

About the research project

Seismic measurements are done on the sea and on land to investigate and image the subsurface. The measured data can be used to find hydrocarbons and to analyze reservoirs. Seismic modelling tools help us to better understand the measured data. Based on a subsurface model, we can calculate the wave propagation and the signals that would be recorded by receivers. This allows us to perform virtual experiments.

The objective of the project is to apply such a modelling technique, called the reflectivity method, to real-world problems that we encounter when analyzing seismic data of reservoirs. This will allow us to make more sense of the data.

Another topic in the project will be to apply Machine Learning techniques in the context of seismic data processing, analysis and interpretation.

Project Partners

CGG, Lundin Petroleum.

People

Project members

Saskia Tschache - PhD Candidate

Einar Iversen - Supervisor (UiB-GEO)

Vetle Vinje - Co-supervisor (CGG)

Jan Erik Lie - Co-supervisor (Lundin Petroleum)
 

As a PhD-Candidate, I am conducting research on intraplate seismicity in Norway and India. My PhD study is part of the IPSIN project (Intraplate Seismicity in India and Norway: Distribution, properties and causes). The project is a collaborative research project between Norway and India under the INDNOR program administered and funded by the Norwegian Research Council. The main objective of project is to improve our seismotectonic understanding of intraplate earthquakes in Norway and India. To achieve the project goal, I am performing seismic tomography and seismicity analysis of earthquakes in Nordland, northern Norway. I am also collaborating with Indian scientists to perform similar tasks in intraplate regions in India.

About the research project

Here are the descriptions of my current research:

a. Seismic tomography in Nordland, Northern Norway

Nordland has the highest seismicity rate in mainland Norway. Earthquakes occur mostly along the coastal area, and offshore, along the passive margin. In this study, I aim to develop a 3-D seismic velocity model for the crust and the upper mantle of Nordland and use it to explain the effect of crustal lateral heterogeneity on intraplate seismicity in the region. In this study, I discuss the influence of crustal structure on seismicity. Analysis of dominant stress directions from the focal mechanism will also be included. Our interpretation will be linked to geodetical observations. Furthermore, I also plan to perform a gravitational stress modeling using the new 3-D velocity model to study the influence of crustal lateral heterogeneity on the regional stress regime.

b. Hypocenter relocation and source parameters of seismicity in Nordland

Accurate hypocenter location is important to reveal the active fault structures, and the space-time pattern of the seismicity. My plan is to improve the earthquake locations accuracy in Nordland using the newly developed 3-D seismic velocity model. Furthermore, the source mechanisms are going to be examined in more detail, possibly using 3-D waveform modeling for relatively larger earthquakes. Later, I will invert the focal mechanism data to obtain the principal stress components.

I will use the relocated hypocenters, focal mechanism solutions, and principal stress components to investigate the faulting geometry, seismogenic depth, and the seismicity relation to the regional stress. Furthermore, I plan to investigate several clusters of earthquakes along the coastal area, e.g., the crust around Rana where most notable events occurred, e.g., the M 5.9 1819 earthquake, the 1979 seismic swarms and the recent 2015 swarms. In the 2015 swarm cluster, the earthquakes occurred from a very shallow depth (< 2 km) to more than 10 km.

Selected publications:

H. A. Shiddiqi, P. P. Tun, T. L. Kyaw, L. Ottemöller (2018),  Source Study of the 24 August 2016 Mw 6.8 Chauk, Myanmar, Earthquake,  Seismological Research Letters, https://doi.org/10.1785/0220170278 (external link).

• H. A. Shiddiqi, P. P. Tun, L. Ottemöller (2019), Minimum 1D Velocity Model and Local Magnitude Scale for Myanmar, Seismological Research Letters. https://doi.org/10.1785/0220190065 (external link).

• K. Newrkla, H. A. Shiddiqi, A. E. Jerkins, H. Keers, L. Ottemöller (2019), Implications of 3-D Seismic Raytracing on Focal Mechanism Determination, Bulletin of the Seismological Society of America, https://doi.org/10.1785/0120190184 (external link).

• A. E. Jerkins, H. A. Shiddiqi, T. Kværna, S. J. Gibbons, J. Schweitzer, L. Ottemöller, H. Bungum (2020), The 30 June 2017 North Sea Earthquake: Location, Characteristics, and Context, Bulletin of the Seismological Society of America, https://doi.org/10.1785/0120190181 (external link).

Project Partners

IPSIN project (Intraplate Seismicity in India and Norway: Distribution, properties and causes)

People

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Hasbi ASh ShiddiqiPhD Candidate

Funding

The Reserach Council of Norway

I am an industrial Ph.D. student that conducts research in cooperation with CGG Services and the University of Bergen. In CGG Oslo’s R&D group, my colleagues and I work with seismic processing and how to improve it. The main goal of seismic processing is to alter the data to suppress noise and create an accurate image of the subsurface.

About the research project

Personally, I work with seismic processing and machine learning. More specifically using deep neural networks to find new ways of processing seismic data, and hopefully improve the processing results. Deep neural networks are trying to find complicated patterns and connections in the data in a similar way that the neurons in a brain do. Much like a brain, an artificial neural network needs to learn from experience. If a neural network has been trained, it will be able to perform a specific task. It is possible to train an artificial neural network to identify the behavior for different types of seismic noise and to remove it from the data.

In a marine seismic survey an array of air-guns is often used as a seismic source. An air-gun releases over-pressured air into the water. The air expands like a bubble in the water. As the bubble expands, the pressure in the bubble decreases and the expansion will eventually come to a halt and the bubble starts to collapse. As the bubble collapses, the pressure in the bubble increases and the bubble will eventually start to expand again. The bubble produced by all the air-gun will oscillate but is damped with each oscillation. Depth and air-gun volume will change the bubble behavior. In a marine seismic acquisition this differential bubble behavior can be used as an advantage such that the bubble constructively interferes for the first expansion and destructively interfere everywhere else. However, this destructively interference will still leave some energy behind, this is considered noise and is often referred to as the bubble. Figure 1 show a source signature estimated from near-field hydrophones with a sharp peak followed by bubble energy.

A big challenge in marine seismic data is to find a good estimation of the source signature. A source signature estimation is an important step in seismic processing because some of the processing steps are dependent on the source signature (de-ghosting, de-bubble, de-multiple, etc.). The problem is that the source signature can be challenging to estimate and a bad estimation will negatively affect the processing results. My goal is to test an artificial neural network’s ability to do some of these processing steps without using an estimated source signature and to understand how the network operates. I am comparing the results from an artificial neural network to modern industrial processing techniques using synthetic and real data. At the moment I am teaching a network to remove the bubble from the data using a convolutional neural network.

My main supervisor is associate professor Einar Iversen from the University of Bergen. My other supervisor is Vetle Vinje from CGG Services in Oslo. They both have high expertise and competence within the theoretical aspects of seismic wave propagation, seismic inversion and seismic signal processing. 

Photo: Thomas de Jonge

Figure 2: A simple conceptual illustration of how an artificial neural network is trained to remove the bubble from common shot gathers. The input is given to the network that predicts an output. The output is compared to the truth which is used to update the network. This is repeated until the network predicts satisfactory results.

Project Partners

UiB, CGG Services.

People

Project members

Thomas de Jonge - PhD Student

Einar Iversen - Supervisor (UiB-GEO)

Vetle Vinje - Supervisor (CGG Services)

The PhD project aims to explore some challenges related to seismic surveying in arctic environments, with special emphasis on the following subjects:

Duration

To December 2020

About the research project

SUPERVISION

Tor Arne Johansen (UiB), Bent Ole Ruud (UiB), Leiv-Jacob Gelius (UiO)

PROJECT PERIOD

2017 - 2020

AIMS AND OBJECTIVES

The PhD project aims to explore some challenges related to seismic surveying in arctic environments, with special emphasis on the following subjects:

• Impacts of seismic surveying in the Arctic on marine mammals: Concerns about the effects seismic shooting may have on animals life have been raised (e.g. Harris et al. 2001, Gordon et al. 2003, Southall et al. 2007, Hermannsen et al. 2015). We want to study seismic sound levels occurring for various acquisition set-ups, and how these may affect marine mammals in this region.
• Seismic processing of data acquired on floating ice: Separation of wave modes on multi-component seismic data: Seismic data acquired on floating ice contains a suite of various wave modes, including surface waves (flexural ice waves and Scholte waves), longitudinal waves, guided wave modes, along with primary and multiple reflections and refractions from the subsurface, (Press and Ewing 1951, Del Molino et al. 2008, Boiero et al. 2013). To increase the signal-to-noise ratio it is important to be able to separate various wave modes. A method referred to as vector valued deconvolution (Claerbout & Wang 2017) will particularly be implemented and tested for this purpose.
• Rock physics and seismic modelling of thawing ice and frozen sediments: In the Arctic, shallow sediments are usually frozen. Understanding the rock physics properties of these sediments and how they may vary with thawing is important in order to be able to provide a best possible seismic model of the overburden, which is vital for seismic imaging of deeper geological horizons. This is also crucial information for monitoring the state of the permafrozen layers. The viscoelastic properties of melting frozen sediments are not well understood, and one focus will be to study this experimentally and theoretically. 

OBJECTIVES

From the previous paragraphs, it should be clear that work is needed to improve the knowledge about these subjects. The main objectives of the PhD project are to:

1. Study processing techniques for improved separation of wave modes in seismic records acquired on floating ice.
2. Study how the elastic properties of sediments change with degree of freezing, with focus on rock physics modelling. Also, to study the propagation of seismic waves in such sediments by seismic modelling.
3. Study possible impacts of seismic surveying on marine life, especially focusing on seals.

We expect to increase the knowledge about all of these subjects.

Seismic data are obtained when a pressure wave is sent into the Earth and the energy reflected at geological boundaries is recorded at the Earth’s surface. This principle is exactly the same as in medical ultrasound. When 3D seismic data are obtained at the same location at different times, the resulting data are referred to as 4D seismic data. Such data allow us to monitor how properties at a specific target area in the Earth’s subsurface changes with time.

About the research project

Main Supervisor

Morten Jakobsen (UiB)

Co-supervisor

Geir Nævdal (IRIS)

About the project

In the last couple of decades we have witnessed an increased use of 4D seismic data. Traditionally, the result of successful interpretation of 4D seismic data has been a better understanding of the oil saturation in the reservoir, leading to identification of the water-flooded areas and pockets of remaining oil, and an improved understanding of compartmentalization of the reservoir. The interpretation of the reservoir properties and dimensions are crucial when making decisions for drilling new wells.

4D seismic data always contain a certain degree of uncertainty. The quantification of uncertainties in 4D seismic data is, however, not an easy task, since the seismic data are often the result of a complicated seismic processing workflow. This workflow may not be fully consistent for each recurrent seismic data gathering and processing.

One way to better assess the uncertainties involved in a seismic processing chain involves the use of so-called full waveform inversion methods. This method involves several steps. First, a model is created on a computer based on an assumption on what the subsurface geology is like. Next, so-called seismic forward modeling is performed where the computer models what the seismic data should look like when implementing the same survey as was used for the 4D seismic data of interest. The modeled seismic data is then compared quantitatively with the real seismic data, and updates are made iteratively to the computer-generated geological model until the differences between the modeled and real seismic data are minimized. This may then yield a good idea of what the subsurface geology is like.
 

Illustration of full waveform inversion. Top left: True model. Bottom left: Initial model guess. Right: Examples of how full waveform inversion improves the initial model guess so that it approaches the true model. Photo: Xingguo Huang, UiB

The first goal of this project is to develop seismic forward modeling methods for highly complex geology. Another goal of the project is to develop efficient methods for time-lapse full waveform inversion through a number of mathematical and physical methods. This may lead to an improved images of the subsurface and a better understanding of which mathematical and physical approaches are suitable for seismic inversion and imaging, in addition to yielding more accurate images of the subsurface.

People

Project members

Xingguo Huang - PhD Student

Morten Jakobsen - Main Supervisor (UiB)

Geir Nævdal - Co-supervisor (IRIS)

Magma that does not reach the surface but remains in the subsurface is called magmatic intrusions. At the time of the intrusion, the magma may have temperatures above 1000 ° C, while the environment it intrudes has significantly lower temperatures. In a sedimentary basin, such a heat supply will, among other things, accelerate the process of conversion of organic matter (Fig. 1). In my project, I will study the effect of intrusions on temperature history and maturation of hydrocarbons, analyze how sensitive the temperature history is to various factors in structurally complex basins with magmatic intrusions and the impact of intrusions on diagenesis and porosity development.

Duration

To April 2020

About the research project

Articles

The influence of magmatic intrusions on diagenetic processes and stress accumulation

About the PhD-project

Magma that does not reach the surface but remains in the subsurface is called magmatic intrusions. At the time of the intrusion, the magma may have temperatures above 1000 ° C, while the environment it intrudes has significantly lower temperatures. In a sedimentary basin, such a heat supply will, among other things, accelerate the process of conversion of organic matter (Fig. 1). In my project, I will study the effect of intrusions on temperature history and maturation of hydrocarbons, analyze how sensitive the temperature history is to various factors in structurally complex basins with magmatic intrusions and the impact of intrusions on diagenesis and porosity development.

The thickness of the intrusions has a great influence on the temperature effect. Where several magmatic intrusions are relatively dense, the timing of their intrusion relative to each other may have an effect on the overall impact they have on temperature and thus also on the maturation of organic matter.

The structural evolution of sedimentary basins influences the effect of intrusions. The time difference between fault movement and when the magma entered, and the physical properties of the host rock, are crucial for the temperature effect.

Chemical compaction is also a temperature dependent process affected by magmatic intrusions. Results show that the porosity decreases in the surroundings of the intrusions as a consequence of the increase in temperature. However, the diagenesis process also changes the physical properties of the rocks, which can cause the rocks to respond differently to local stress conditions, thus contributing to increased fluid flow in some areas.

People

Project members

Magnhild Sydnes - PhD Student

PhD Candidate: Durra Handri Saputera 

GPU-accelerated integral equation method for 3D modelling of induction logs