Towards an automated process for assessing reservoir rock quality from seismic imaging
Lead Research Organisation:
Royal Holloway University of London
Department Name: Earth Sciences
Abstract
This project will lay the basis of a training model for automated evaluation of georesources using the vast knowledge that can be acquired studying the global catalogue of subsurface datasets held by geo-energy companies.
The current global energy climate is characterised by readily available primary fuels at a relatively low cost. This has impacted the geo-energy business with a number of companies currently downsizing with reduction of their workforce. Yet, advances in computational power in the last 20 years has allowed acquisition and releasing of extensive subsurface datasets which provide a deeper understating on where georesources might be found. This means that automated processes will be increasingly important to efficiently analyse and tap the full potential of extensive subsurface datasets now available.
This project will focus on the analysis of reservoir units. These deposits represent a very valuable subsurface asset as they have elevated porous space, hence have the potential to contain hydrocarbons, water as well as to absorb carbon dioxide in 'Carbon Capture and Storage' operations. The amount of porous space in reservoir units, hence their efficiency to hold fluids and gas, is a function of a number of physical properties of such deposits. These same physical properties are also thought to control the overall shape and the size of the deposits.
With this project, we will analyse the morphology and physical properties of a number of reservoirs so to understand how they are interrelated. In doing so, we would be able to predict the reservoir quality by looking at seismic data, which is a method used in the industry to provide an image of the subsurface. The project will deliver a series of case studies that document how specific reservoir morphologies would indicate good or bad reservoir quality. This would provide the basics to construct, in the future, a training model for automated processes that will be able to efficiently scan large seismic dataset in search of rock with the right shape that indicates a high-quality reservoir.
The current global energy climate is characterised by readily available primary fuels at a relatively low cost. This has impacted the geo-energy business with a number of companies currently downsizing with reduction of their workforce. Yet, advances in computational power in the last 20 years has allowed acquisition and releasing of extensive subsurface datasets which provide a deeper understating on where georesources might be found. This means that automated processes will be increasingly important to efficiently analyse and tap the full potential of extensive subsurface datasets now available.
This project will focus on the analysis of reservoir units. These deposits represent a very valuable subsurface asset as they have elevated porous space, hence have the potential to contain hydrocarbons, water as well as to absorb carbon dioxide in 'Carbon Capture and Storage' operations. The amount of porous space in reservoir units, hence their efficiency to hold fluids and gas, is a function of a number of physical properties of such deposits. These same physical properties are also thought to control the overall shape and the size of the deposits.
With this project, we will analyse the morphology and physical properties of a number of reservoirs so to understand how they are interrelated. In doing so, we would be able to predict the reservoir quality by looking at seismic data, which is a method used in the industry to provide an image of the subsurface. The project will deliver a series of case studies that document how specific reservoir morphologies would indicate good or bad reservoir quality. This would provide the basics to construct, in the future, a training model for automated processes that will be able to efficiently scan large seismic dataset in search of rock with the right shape that indicates a high-quality reservoir.
Publications
Scarselli N
(2022)
Exploring the predictive power of seismic geomorphology to assess sedimentary characteristics of gravity-flow deposits: examples from offshore East and West Africa reservoirs
in Geological Society, London, Special Publications
| Description | The research has demonstrated that the use of the seismic reflection method is useful in providing a framework for predicting type of rocks in the subsurface. However, we have found out that a number of factors make difficult to establish a straight link between overall morphology and lithological characteristic, hence reservoir properties, of rocks - in primis the shape of substrate at the time sediments are deposited. Further complications arise from a non-unique relationship between the shape of a sedimentary body and the processes that have created it, meaning that sedimentary bodies with similar overall depositional geometries may have completely different sedimentary characteristics. Complexities are also related to the fact that external factors such as burial, compaction and cementation, have a major control on the reservoir quality regardless of the internal characteristics of a sedimentary body. These findings highlight potential hurdle in developing reliable automatic processes of seismic interpretation able to 'flag' reservoirs from seismic only. We have, however, found some evidence of deposits probably sedimented from viscous and high yield strength flows to form elongated bodies in planform. This initial finding would provide some support that a link between internal and external characteristic of sedimentary bodies exists. Despite this encouraging finding, we did not have access to enough key examples we could analyse to test this initial hypothesis rigorously. This is something which future on this topic research should focus on. |
| Exploitation Route | There is a large drive in the software and geo-energy industry to develop automated software solutions for the analysis of seismic data. The findings of this research can be taken forward by developers and geoscientists that are looking into enhancing automation of seismic data interpretation in order to assess reservoir presence and quality in the subsurface. The findings of the research has highlighted the main issues and limitations that would affect such processes of automated assessment. Researchers that would take forward the findings of this research will have to address these key issues. |
| Sectors | Digital/Communication/Information Technologies (including Software) Education Energy |
| Description | Collaboration with Ophir Energy, London |
| Organisation | Ophir Energy plc |
| Country | United Kingdom |
| Sector | Private |
| PI Contribution | During the project, I collaborated closely with the subsurface team at Ophir Energy in London. The team had offered invaluable data to carry out my project research. The team also contributed to the scientific discussion of the results during a number of review sessions. |
| Collaborator Contribution | They provided research data as well as they reviewed the results of the research. |
| Impact | The outputs are those listed in the publications related to this award. |
| Start Year | 2018 |
| Description | Social media campaign - announce of initiation of research project |
| Form Of Engagement Activity | Engagement focused website, blog or social media channel |
| Part Of Official Scheme? | No |
| Geographic Reach | International |
| Primary Audience | Industry/Business |
| Results and Impact | I have published a series of blogpost to publicise the research and engage with a wider audience. The posts have been seen by an international audience of 10000+ people. A substantial amount of comments (100+ per entry) to the posts with requests of further information and request of participation are a clear measure of the impact of this engagement initiative. |
| Year(s) Of Engagement Activity | 2008 |
| URL | https://www.linkedin.com/feed/update/urn:li:activity:6329328846797623296 |
| Description | Social media campaign - announcement of dissemination of results at international conference |
| Form Of Engagement Activity | Engagement focused website, blog or social media channel |
| Part Of Official Scheme? | No |
| Geographic Reach | International |
| Primary Audience | Industry/Business |
| Results and Impact | I have published a series of blogpost to publicise the research and engage with a wider audience. The posts have been seen by an international audience of 10000+ people. A substantial amount of comments (100+ per entry) to the posts with requests of further information and request of participation are a clear measure of the impact of this engagement initiative. |
| Year(s) Of Engagement Activity | 2018 |
| URL | https://www.linkedin.com/feed/update/urn:li:activity:6458970291895889920 |