Uncertainty in static CO<sub>2</sub> storage capacity estimates: Case study from the North Sea, UK

Hedley, Benjamin J.; Davies, Richard J.; Mathias, Simon A.; Hanstock, David; Gluyas, Jon

doi:10.1002/ghg.1343

Cited by 8 publications

(6 citation statements)

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“…Tipam, Barail and Lk + Th to know the contribution of the uncertainity of the individual input parameters towards the total (5) www.nature.com/scientificreports/ uncertainity. From the above sensitivity analysis for the three formations, it is observed that the reservoir parameters like porosity, gross-thickness and area contributed most to the total uncertainity in the present Monte-Carlo simulation study 24,35 . The higher contribution of these parameters towards the total uncertainty stems from significant difference in their {P10, P90} values; for instance, the {P10, P90} porosity values of the Tipam formation {0.2, 0.45} indicate more than 100% variation.…”

Section: Resultsmentioning

confidence: 74%

“…Previous research works have employed the Monte Carlo simulation approach 24,35 to estimate the theoretical storage capacity 36,37 of a saline aquifer.The analysed petrophysical data was considered as input parameters to www.nature.com/scientificreports/ perform a Monte Carlo simulation to develop a probabilistic model for estimating the CO 2 storage capacity in the depleted oil fields or reservoirs. The simulation considered a triangular statistical distribution of the input parameters by considering the probable (P10), possible (P50), and inferred (P90) petrophysical characteristics to calculate the theoretical storage capacity.…”

Section: Methodsmentioning

confidence: 99%

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Integrated coupled assessment of geostorage and geothermal prospects in the oil fields of Upper Assam Basin

Dutta,

Gogoi,

Hussain

et al. 2024

Sci Rep

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This study proposes an integrated approach of assessing CO2 storage potential and geothermal energy prospect based on the data of seventeen depleted wells of Upper Assam Basin which could assist the global objective of net zero transition. The petrophysical properties of Tipam, Barail and Lakadong + Therria Formations from the seventeen wells have been utilised to perform the Monte Carlo simulation for probabilistic estimation of the CO2 storage in the Upper Assam Basin. This preliminary work showed that the mean storage capacity of 18.8 ± 0.7 MT, 19.8 ± 0.9 MT and 4.5 ± 0.8 MT could potentially be stored in the three geological formations of the basin. The corrected bottom hole temperature values for the studied seventeen wells were determined using the well log data and Waples and Harrison method; these values provided a static geothermal gradient for each well, which varies widely from 0.017 to 0.033 °C/m. In order to enable geothermal prospectivity, static formation temperature maps have been generated for the studied wells. The probabilistic assessment of stored heat-in-place and formation temperature maps delimited five prospective sites for the extraction of geothermal energy in the basin. The study also presented a risk assessment for CO2 storage development in the basin. Further, the study illustrated an economic analysis of the implementation of a CO2 storage project and geothermal operations in the basin.

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Section: Resultsmentioning

confidence: 74%

Section: Methodsmentioning

confidence: 99%

Integrated coupled assessment of geostorage and geothermal prospects in the oil fields of Upper Assam Basin

Dutta,

Gogoi,

Hussain

et al. 2024

Sci Rep

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“…This is based around real geophysical data for the Boulby Mine, in Yorkshire, England, which extends laterally for 7 km beneath the sea at depths of between about 750 m and 1.4 km. While CO 2 storage is not planned for this site, it is chosen because a deep science facility [the Boulby Underground Laboratory (Murphy & Paling 2012)] is already located there, and the geology is very similar to that known to exist at several of the preferred storage sites in the North Sea (Hedley et al 2013). This demonstration is a useful way of testing the equations derived in the previous sections with realistic numbers, rather than just abstract values.…”

Section: A P P L I C At I O N T O G E O L O G I C a L S T O R A G E Omentioning

confidence: 99%

Optimizing geophysical muon radiography using information theory

Benton

Mitchell

Coleman

et al. 2019

Geophysical Journal International

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The full-text may be used and/or reproduced, and given to third parties in any format or medium, without prior permission or charge, for personal research or study, educational, or not-for-prot purposes provided that:• a full bibliographic reference is made to the original source • a link is made to the metadata record in DRO • the full-text is not changed in any way The full-text must not be sold in any format or medium without the formal permission of the copyright holders.Please consult the full DRO policy for further details. S U M M A R YCosmic ray muons are highly penetrating, with some reaching several kilometres into solid rock. Consequently, muon detectors have been used to probe the interiors of large geological structures, by observing how the muon flux varies with direction of arrival. There is an increasing need to discriminate between materials differing only slightly in bulk density. A particularly demanding application is in monitoring underground reservoirs used for CO 2 capture and storage, where bulk density changes of approximately 1 per cent are anticipated. Muon arrival is a random process, and it is the underlying expectation values, not the actual muon counts, which provide information on the physical parameters of the system. It is therefore necessary to distinguish between differences in muon counts due to real geological features, and those arising from random error. This is crucial in the low-contrast case, where the method can reach the information theoretic limit of what a data source can reveal, even in principle. To this end, methods to analyse information availability in low-contrast muon radiography have been developed, as have means to optimally interpret the available data, both for radiography and for tomography. This includes a method for calculating expectation values of muon flux for a given geological model directly, complementing existing Monte Carlo techniques. A case study, using a model of carbon capture is presented. It is shown that the new data analysis techniques have the potential to approximately double the effective sensitivity of the detectors.

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“…into saline aquifers, and many potential sites have a paucity of existing data (see e.g. Hedley et al 2013). A broad review, utilizing exploration and production data from compartmentalized oil and gas fields, therefore has the potential to provide useful insights into the effects of reservoir compartmentalization on the storage of carbon dioxide in saline aquifers.…”

Section: Introductionmentioning

confidence: 99%

Stratigraphic reservoir compartmentalization: causes, recognition, and implications for the geological storage of carbon dioxide

Herringshaw¹,

Gluyas²,

Mathias³

2019

Preprint

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The impact of carbon capture and storage (CCS) in mitigating anthropogenic emissions of greenhouse gases is potentially great, but its success is strongly dependent on identifying suitable geological storage sites. One of the key uncertainties in this regard is the degree of compartmentalization of the target storage horizon. Many studies have examined reservoir compartmentalization in oil and gas fields and its implications to hydrocarbon production, but few have looked at compartmentalization from the perspective of CCS storativity. Using case studies from the oil and gas industry, this paper examines the predictability of reservoir compartmentalization and the techniques used to assess it. It then focusses on stratigraphic compartmentalization in saline aquifers, a major category of potential carbon storage sites, examining in particular the Triassic Bunter Sandstone Formation of the UK North Sea. It is clear that the analysis of stratigraphic, seismic and fluid compositional data can provide early evidence of reservoir compartmentalization. The available data indicate that the Bunter Sandstone has a low risk of stratigraphic and structural compartmentalization. However, the presence of compartments in oil and gas fields is often confirmed only once well production data are available. As such, though the broad likelihood of compartmentalization in a CCS site can be forecasted, understanding its exact nature may be dependent on injection data.

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Uncertainty in static CO₂ storage capacity estimates: Case study from the North Sea, UK

Cited by 8 publications

References 48 publications

Integrated coupled assessment of geostorage and geothermal prospects in the oil fields of Upper Assam Basin

Integrated coupled assessment of geostorage and geothermal prospects in the oil fields of Upper Assam Basin

Optimizing geophysical muon radiography using information theory

Stratigraphic reservoir compartmentalization: causes, recognition, and implications for the geological storage of carbon dioxide

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Uncertainty in static CO2 storage capacity estimates: Case study from the North Sea, UK

Cited by 8 publications

References 48 publications

Integrated coupled assessment of geostorage and geothermal prospects in the oil fields of Upper Assam Basin

Integrated coupled assessment of geostorage and geothermal prospects in the oil fields of Upper Assam Basin

Optimizing geophysical muon radiography using information theory

Stratigraphic reservoir compartmentalization: causes, recognition, and implications for the geological storage of carbon dioxide

Contact Info

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Uncertainty in static CO₂ storage capacity estimates: Case study from the North Sea, UK