the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 13 Mar 2023
Advanced seismic characterization of a geothermal carbonate reservoir – Insight into the structure and diagenesis of a reservoir in the German Molasse Basin
Sonja H. Wadas
Johanna F. Bauer
Vladimir Shipilin
Michael Krumbholz
David C. Tanner
Hermann Buness
Abstract. The quality of geothermal carbonate reservoirs is controlled by numerous factors and processes, such as the depositional environment, lithology, diagenesis, karstification, fracture networks, and tectonic deformation. Carbonatic rock formations are thus often extremely heterogeneous, and reservoir parameters and their spatial distribution are therefore difficult to predict. For the example of a 3D seismic dataset combined with well data from Munich, Germany, we demonstrate, how an advanced analysis can deliver an improved reservoir model concept and help to identify possible exploitation targets within the Upper Jurassic carbonates. To identify possible reservoir sections and to understand their above-mentioned controlling factors, we analyse different seismic single- and multi-attributes. Some of the seismic attributes, together with lithology logs from wells, are then used to identify parameter correlations between the seismic attributes and the different carbonate lithologies to obtain a supervised neural network based 3D lithology model of the geothermal reservoir. Furthermore, we compare the fracture orientations measured in seismic (ant-tracking analysis) and well scale (image log analysis), to address scalability. Our results show that, for example, acoustic impedance is well suited to identify reefs and karst-related dolines. Areas with strong karstification or fault- and fracture-related deformation, which are both associated with high permeabilities, are also indicated by e.g. strong frequency attenuation, variance anomalies, and/or morphological features like bowl-shaped structures derived from the shape index. Furthermore, by using sweetness we can reconstruct the reef development of two exemplary reefs, and regarding the lithology distribution, we show that the upper part of the reservoir is dominated by limestone and dolomitic limestone rather than dolomite. In addition, we observe spatial trends in the degree of dolomitization. With respect to the fracture orientations on seismic- and well scales, we point out that a general scalability is not possible due to a combination of methodological limitations and geological reasons. Nonetheless, we argue that the combination of both methods provides an improved overall impression of the fracture systems, and therefore possible fluid pathways. By taking all the results into account, we are able to improve and adapt recent reservoir concepts to outline the different phases of its structural and diagenetic evolution. Furthermore, our results help to identify high quality reservoir zones in the Munich area.
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Notice on discussion status
The requested preprint has a corresponding peer-reviewed final revised paper. You are encouraged to refer to the final revised version.
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Preprint
(54388 KB)
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The requested preprint has a corresponding peer-reviewed final revised paper. You are encouraged to refer to the final revised version.
- Preprint
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- BibTeX
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- Final revised paper
Journal article(s) based on this preprint
Sonja H. Wadas et al.
Interactive discussion
Status: closed
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RC1: 'Comment on egusphere-2023-275', Anonymous Referee #1, 03 Apr 2023
1. In figure 2d, what is the gray color in Th3?
2. For the supervised neural network, what is the network structure used in the study?
3. In the confusion matrix, people have to check every element in the matrix to analyze the prediction accuracy. It is better to provide an overall/single index for an easy analysis.
4. In the analysis of the seismic cube for internal structure of the GRAME area, different subareas have been chosen in different technologies (Figs. 4-11, such as a,b,c,d,e). Could you focus on the same area and apply the different seismic data analytic methods for a consistent evaluation?
5. For the lithology prediction, what are the inputs for the classification?
6. Please rewrite the discussion section, as some new figures and equations are introduced. And also the discussion should be more focused on the main findings and limitation of the current research, as well as some suggestions for future researches.
7. It is also for the conclusion which is too long. Please shorten it.
Citation: https://doi.org/10.5194/egusphere-2023-275-RC1 -
AC1: 'Reply on RC1', Sonja Wadas, 25 May 2023
Author response to reviewer and editor comments
We would like to thank the two anonymous reviewers for their constructive comments that helped to improve the manuscript. We have tried to answer all comments and questions as best as we could and incorporated them into the manuscript accordingly.
A detailed tabular overview of the comments by the referees, sorted by chapter, and our corresponding answers can be found in the attached ZIP-folder in the PDF file ‘Table of Reviewer Comments & Answers’.
The highlighted changes (tracked changes) can be found in the attached ZIP-folder in the PDF file ‘LatexDiff_ Advanced seismic characterization_tracked changes’.
Please also note the name change of the co-author, Johanna Bauer. She now has the name of Johanna Krumbholz.
With best regards,
Sonja Halina Wadas - on behalf of all co-authors
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AC1: 'Reply on RC1', Sonja Wadas, 25 May 2023
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RC2: 'Comment on egusphere-2023-275', Anonymous Referee #2, 26 Apr 2023
The authors describe a case study for an advanced geothermal carbonate reservoir characterization
based on 3D seismic attribute analysis, lithological classification and structural imaging.
The approach is demonstrated for a region with considerable geothermal potential and undergoing
development for geothermal utilization. The study site is located within the Molasse basin
in southern Germany, covering the city of Munich and surrounding area.The paper is well written and well-structured. The work is based on data of high quality.
Data analysis is carried out with existing state-of-the-art methods. Figures are of excellent quality.
The results are described and discussed regarding both methodological aspects and geological interpretation.The motivation for the study is reasonably explained by the very complex situation
and specific challenges of carbonate reservoirs for geothermal exploration.The underlying data are described at the beginning of the Methods.
The authors could think about a separate chapter to describe the Data.
But the presented version is also clear enough with given references
for the data.The advanced data analysis is nicely categorized into four seperate approaches:
+ single attribute analysis
+ multi-attribute analysis
+ neural network-based lithology classification
+ fracture orientation analyisI suggest to extend a bit the explanation of the neural network-based clasification.
My assumption at this point is, that a few readers would like to know more technical
details such as
+ type of neural network
+ architecture
+ specifications of network (e.g. learning rule, internal functions)
+ software used / implementationCommendable for me in this manuscript is the critical discussion of seismic attributes
as given in the discussion chapter.
Maybe it would be worth to mention that seismic attributes are signal properties, and
not inherent rock physical properties.
As an example, frequency based attributes could be influenced by different factors
including inherent seismic attenuation, complex geological structures
such as thin layers or gradient structures with potential shifts of signal frequencies,
data processing or acquisition footprints.
At least from my perspective the formulation "reservoir control factors
that may affect the physical properties of the seismic signal" (line 550)
is suggesting that the signal properties of the seismic reflection waveform directly
represent subsurface rock physical properties. As descibed above, the causality is
more complicated in my opinion.
But this might be an overcritical comment.The important support for the interpretation of results in this paper is given
by the presented empirical correlations between attributes and borehole data.
The compehensive attribute analysis and their interpretation is a very nice
contribution to improve the characterization of geothermal carbonate reservoirs.Citation: https://doi.org/10.5194/egusphere-2023-275-RC2 -
AC2: 'Reply on RC2', Sonja Wadas, 25 May 2023
Author response to reviewer and editor comments
We would like to thank the two anonymous reviewers for their constructive comments that helped to improve the manuscript. We have tried to answer all comments and questions as best as we could and incorporated them into the manuscript accordingly.
A detailed tabular overview of the comments by the referees, sorted by chapter, and our corresponding answers can be found in the attached ZIP-folder in the PDF file ‘Table of Reviewer Comments & Answers’.
The highlighted changes (tracked changes) can be found in the attached ZIP-folder in the PDF file ‘LatexDiff_ Advanced seismic characterization_tracked changes’.
Please also note the name change of the co-author, Johanna Bauer. She now has the name of Johanna Krumbholz.
With best regards,
Sonja Halina Wadas - on behalf of all co-authors
-
AC2: 'Reply on RC2', Sonja Wadas, 25 May 2023
Interactive discussion
Status: closed
-
RC1: 'Comment on egusphere-2023-275', Anonymous Referee #1, 03 Apr 2023
1. In figure 2d, what is the gray color in Th3?
2. For the supervised neural network, what is the network structure used in the study?
3. In the confusion matrix, people have to check every element in the matrix to analyze the prediction accuracy. It is better to provide an overall/single index for an easy analysis.
4. In the analysis of the seismic cube for internal structure of the GRAME area, different subareas have been chosen in different technologies (Figs. 4-11, such as a,b,c,d,e). Could you focus on the same area and apply the different seismic data analytic methods for a consistent evaluation?
5. For the lithology prediction, what are the inputs for the classification?
6. Please rewrite the discussion section, as some new figures and equations are introduced. And also the discussion should be more focused on the main findings and limitation of the current research, as well as some suggestions for future researches.
7. It is also for the conclusion which is too long. Please shorten it.
Citation: https://doi.org/10.5194/egusphere-2023-275-RC1 -
AC1: 'Reply on RC1', Sonja Wadas, 25 May 2023
Author response to reviewer and editor comments
We would like to thank the two anonymous reviewers for their constructive comments that helped to improve the manuscript. We have tried to answer all comments and questions as best as we could and incorporated them into the manuscript accordingly.
A detailed tabular overview of the comments by the referees, sorted by chapter, and our corresponding answers can be found in the attached ZIP-folder in the PDF file ‘Table of Reviewer Comments & Answers’.
The highlighted changes (tracked changes) can be found in the attached ZIP-folder in the PDF file ‘LatexDiff_ Advanced seismic characterization_tracked changes’.
Please also note the name change of the co-author, Johanna Bauer. She now has the name of Johanna Krumbholz.
With best regards,
Sonja Halina Wadas - on behalf of all co-authors
-
AC1: 'Reply on RC1', Sonja Wadas, 25 May 2023
-
RC2: 'Comment on egusphere-2023-275', Anonymous Referee #2, 26 Apr 2023
The authors describe a case study for an advanced geothermal carbonate reservoir characterization
based on 3D seismic attribute analysis, lithological classification and structural imaging.
The approach is demonstrated for a region with considerable geothermal potential and undergoing
development for geothermal utilization. The study site is located within the Molasse basin
in southern Germany, covering the city of Munich and surrounding area.The paper is well written and well-structured. The work is based on data of high quality.
Data analysis is carried out with existing state-of-the-art methods. Figures are of excellent quality.
The results are described and discussed regarding both methodological aspects and geological interpretation.The motivation for the study is reasonably explained by the very complex situation
and specific challenges of carbonate reservoirs for geothermal exploration.The underlying data are described at the beginning of the Methods.
The authors could think about a separate chapter to describe the Data.
But the presented version is also clear enough with given references
for the data.The advanced data analysis is nicely categorized into four seperate approaches:
+ single attribute analysis
+ multi-attribute analysis
+ neural network-based lithology classification
+ fracture orientation analyisI suggest to extend a bit the explanation of the neural network-based clasification.
My assumption at this point is, that a few readers would like to know more technical
details such as
+ type of neural network
+ architecture
+ specifications of network (e.g. learning rule, internal functions)
+ software used / implementationCommendable for me in this manuscript is the critical discussion of seismic attributes
as given in the discussion chapter.
Maybe it would be worth to mention that seismic attributes are signal properties, and
not inherent rock physical properties.
As an example, frequency based attributes could be influenced by different factors
including inherent seismic attenuation, complex geological structures
such as thin layers or gradient structures with potential shifts of signal frequencies,
data processing or acquisition footprints.
At least from my perspective the formulation "reservoir control factors
that may affect the physical properties of the seismic signal" (line 550)
is suggesting that the signal properties of the seismic reflection waveform directly
represent subsurface rock physical properties. As descibed above, the causality is
more complicated in my opinion.
But this might be an overcritical comment.The important support for the interpretation of results in this paper is given
by the presented empirical correlations between attributes and borehole data.
The compehensive attribute analysis and their interpretation is a very nice
contribution to improve the characterization of geothermal carbonate reservoirs.Citation: https://doi.org/10.5194/egusphere-2023-275-RC2 -
AC2: 'Reply on RC2', Sonja Wadas, 25 May 2023
Author response to reviewer and editor comments
We would like to thank the two anonymous reviewers for their constructive comments that helped to improve the manuscript. We have tried to answer all comments and questions as best as we could and incorporated them into the manuscript accordingly.
A detailed tabular overview of the comments by the referees, sorted by chapter, and our corresponding answers can be found in the attached ZIP-folder in the PDF file ‘Table of Reviewer Comments & Answers’.
The highlighted changes (tracked changes) can be found in the attached ZIP-folder in the PDF file ‘LatexDiff_ Advanced seismic characterization_tracked changes’.
Please also note the name change of the co-author, Johanna Bauer. She now has the name of Johanna Krumbholz.
With best regards,
Sonja Halina Wadas - on behalf of all co-authors
-
AC2: 'Reply on RC2', Sonja Wadas, 25 May 2023
Peer review completion
Journal article(s) based on this preprint
Sonja H. Wadas et al.
Sonja H. Wadas et al.
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The requested preprint has a corresponding peer-reviewed final revised paper. You are encouraged to refer to the final revised version.
- Preprint
(54388 KB) - Metadata XML