Prudhoe Bay reservoir model: Making the link between seismic and borehole data to gravity, electrical, and EM methods

Krahenbuhl, Richard; Li, Yaoguo; Reitz, Anya; Wagner, Sean V.; Konkler, J.

doi:10.1190/segam2016-13681138.1

Cited by 6 publications

(3 citation statements)

References 2 publications

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“…This equation facilitates the calculation of the density change between two time states in the reservoir. Utilizing Equation ( 2) with different combinations of porosity and fluid properties throughout the reservoir over time allows for the construction of a sequence of time-lapse density models (Krahenbuhl et al, 2016).…”

Section: Density-saturation Relationmentioning

confidence: 99%

Three‐axis borehole gravity monitoring for CO₂ storage using machine learning coupled to fluid flow simulator

Alyousuf

Krahenbuhl

et al. 2023

Geophysical Prospecting

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The field of geophysics faces the daunting task of monitoring complex reservoir dynamics and imaging carbon dioxide storage up to several decades into the future. This presents numerous challenges, including sensitivity to parameter changes, resolution of obtained results, and the cost of long‐term deployment. To effectively store CO2 subsurface, it is necessary to monitor and account for the injected CO2. The gravity method provides several advantages for CO2 monitoring, as changes in fluid saturation correspond directly and uniquely to observed density changes. Three‐axis borehole gravity has demonstrated significant promise as a next‐generation tool for reliably monitoring reservoir dynamics across a range of depths and sizes. However, the gravity inverse problem is highly ill‐posed, necessitating regularization that incorporates prior knowledge. To address this issue, we propose using a feed‐forward neural network, a machine learning (ML) method, to invert time‐lapse three‐axis borehole gravity data and monitor CO2 movement within a reservoir. By training the neural network on models that analyze changes in density and corresponding gravity responses resulting from perturbations made to the reservoir model, we can create scenarios that train the algorithm to identify unexpected CO2 migration in addition to the normal movement of CO2. Our method is demonstrated using reservoir models for the Johansen formation in offshore Norway. We convert reservoir saturation models into density changes and generate their corresponding three‐axis gravity data in a set of boreholes. Our results show that the developed ML inversion algorithm has high reliability and resolution for imaging density change associated with CO2 plumes, as demonstrated in the Johansen reservoir models utilized by the simulator. We also investigate ML inversion using regularization parameters and show that it is robust, with a strong tolerance for higher levels of noise. Our study demonstrates that the developed ML algorithm is a powerful tool for inverting three‐axis borehole gravity data and monitoring the migration and long‐term storage of injected CO2.This article is protected by copyright. All rights reserved

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Section: Density-saturation Relationmentioning

confidence: 99%

Three‐axis borehole gravity monitoring for CO₂ storage using machine learning coupled to fluid flow simulator

Alyousuf

Krahenbuhl

et al. 2023

Geophysical Prospecting

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“…I next create a set of models that define the change in density throughout the reservoir over time. As stated previously, the matrix density and porosity are assumed to be independent of time, so the first term in (Krahenbuhl et al, 2016).…”

Section: Dynamic Reservoir Modelsmentioning

confidence: 99%

“…The concept of constructing a sequence of dynamic reservoir models to expand the potential of reservoir simulations to less traditional monitoring techniques, for instance gravity, electrical, and electromagnetic data, is raised and illustrated in Krahenbuhl et al (2016).…”

Section: Dynamic Reservoir Modelsmentioning

confidence: 99%

Time-lapse gravity data at Prudhoe Bay: New understanding through integration with reservoir simulation models

Yin¹,

Krahenbuhl²,

Li³

et al. 2016

SEG Technical Program Expanded Abstracts 2016

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In this thesis, I revisit the time-lapse gravity data collected to monitor gas-cap water injection at Prudhoe Bay to understand how we can best integrate a field's detailed reservoir simulation data into the gravity inversion for an integrated interpretation. For this, I explore two methodologies that can directly incorporate into the inversions a set of time-lapse density models that can be constructed from the reservoir simulation parameters.The first approach is a continuous variable inversion where the reservoir information can be converted into an appropriate reference model to guide both the shape and amplitude of the recovered densities from the water flood. The second is a binary inversion, where the reservoir data are included as background density models to predefine the amplitude of the recovered time-lapse densities, allowing the algorithm to focus on recovering the locations and shape of the expanding water plume.Results for the two methods show that the reservoir data do guide and constrain the gravity inversions to distribution comparable to water movement within the simulation models, and thus provide valuable approaches for integrated monitoring with time-lapse gravity. The results additionally demonstrate deviations from the reservoir simulation models, indicating that either the surface gravity is not sensitive to the subtle changes in saturation in particular regions, or that the reservoir models contain saturation changes that may be too high or too low in areas.

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Borehole vector gravity for waterflood monitoring in vertical and horizontal wells: Feasibility study using numerical conceptual simulation model

Alyousuf,

Colombo,

Turkoglu

et al. 2024

Seventh International Conference on Engineering Geophysics, Al Ain, UAE, 16–19 October 2023

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Prudhoe Bay reservoir model: Making the link between seismic and borehole data to gravity, electrical, and EM methods

Cited by 6 publications

References 2 publications

Three‐axis borehole gravity monitoring for CO₂ storage using machine learning coupled to fluid flow simulator

Three‐axis borehole gravity monitoring for CO₂ storage using machine learning coupled to fluid flow simulator

Time-lapse gravity data at Prudhoe Bay: New understanding through integration with reservoir simulation models

Borehole vector gravity for waterflood monitoring in vertical and horizontal wells: Feasibility study using numerical conceptual simulation model

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Prudhoe Bay reservoir model: Making the link between seismic and borehole data to gravity, electrical, and EM methods

Cited by 6 publications

References 2 publications

Three‐axis borehole gravity monitoring for CO2 storage using machine learning coupled to fluid flow simulator

Three‐axis borehole gravity monitoring for CO2 storage using machine learning coupled to fluid flow simulator

Time-lapse gravity data at Prudhoe Bay: New understanding through integration with reservoir simulation models

Borehole vector gravity for waterflood monitoring in vertical and horizontal wells: Feasibility study using numerical conceptual simulation model

Contact Info

Product

Resources

About

Three‐axis borehole gravity monitoring for CO₂ storage using machine learning coupled to fluid flow simulator

Three‐axis borehole gravity monitoring for CO₂ storage using machine learning coupled to fluid flow simulator