CO2 Injection Monitoring: Enhancing Time-Lapse Seismic Inversion for Injected Volume Estimation in the Utsira Formation, Sleipner Field, North Sea

Pelemo-Daniels, Doyin; Nwafor, Basil O.; Stewart, Robert R.

doi:10.3390/jmse11122275

Cited by 2 publications

(1 citation statement)

References 32 publications

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“…The fluid substitution models employed in this study were built using the calibrated rock physics model and Biot-Gassmann theories [30,44,45]. For the partial saturation in brine and supercritical CO 2 , an effective fluid phase with different average techniques, used as "one fluid phase" as an input into the Biot theory for saturated porous media, is used [46,47].…”

Section: Co 2 Fluid Substitutionmentioning

confidence: 99%

Petrophysical Property Prediction from Seismic Inversion Attributes Using Rock Physics and Machine Learning: Volve Field, North Sea

Pelemo-Daniels,

Stewart

2024

Applied Sciences

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An accurate petrophysical model of the subsurface is essential for resource development and CO2 sequestration. We present a new workflow that provides a high-resolution estimate of petrophysical reservoir properties using seismic data with rock physics modeling and machine-learning techniques (i.e., deep learning neural networks). First, we compare the sequential prediction of the following petrophysical attributes: mineralogy, porosity, and fluid saturation, with the simultaneous prediction of all of the properties using the Volve field in the Norwegian North Sea as an example. The workflow shows that the sequential prediction produces a more efficient and accurate classification of petrophysical properties (the RMS error between the predicted and the original seismic trace is 50% smaller for the sequential compared to the simultaneous procedure). Next, the seismic amplitude response of the reservoirs was studied using rock physics modeling and amplitude versus offset (AVO) analysis to distinguish the different lithologies and fluid types. To ascertain the optimal hydrocarbon production areas, we performed Bayesian seismic inversion and applied machine learning to estimate the petrophysical properties. We examined how porosity, Vclay, and fluid variations affect the elastic properties. In Poisson’s ratio versus the P-wave impedance domain, a 10% porosity increase decreases the acoustic impedance (AI) by 30%, while a 20% Vclay decrease increases the AI by 12%. The Utsira Formation in the Volve field (5 km north of the Sleipner Øst field) was evaluated as a potential CO2 geological storage unit using Gassmann fluid substitution and seismic modeling. We look to assess the elastic property variation caused by CO2 saturation changes for monitoring purposes and simulate the effect. In the first 10% CO2 substitution, the P-wave velocity decrease is 12%, a subtle effect is observed for higher CO2 saturation values, and S-wave velocity (Vs) increases with CO2 saturation. Our analysis aspires to assist future reservoir studies and CO2 sequestration in similar fields.

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Section: Co 2 Fluid Substitutionmentioning

confidence: 99%

Petrophysical Property Prediction from Seismic Inversion Attributes Using Rock Physics and Machine Learning: Volve Field, North Sea

Pelemo-Daniels,

Stewart

2024

Applied Sciences

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Carbon Capture and Storage Subsurface Study for a Natural Gas-Burning Power Plant in Oltenia, Romania

Suditu,

Dumitrache,

Brănoiu

et al. 2024

Processes

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The article presents carbon capture and storage, CCS, as a climate change mitigation method. Many industrial processes, such as the manufacture of cement, the metallurgical industry, and the production of electricity from fossil fuels, produce large CO2 quantities. Carbon capture and storage is a method for these industrial areas to become carbon neutral for the environment. To combat climate change, the EU wants to achieve climate neutrality by 2050, and this goal, along with an intermediate goal of reducing emissions by 55% by 2030, is enshrined in the European Climate Law. The EU has launched various initiatives to achieve these goals, one of which is the ‘Fit for 55’ legislation. The first step that countries wanting to apply these technologies must take is the evaluation of the underground CO2 storage potential. The potential for CO2 storage in the depleted hydrocarbon reservoirs in Oltenia, one of the eight regions of Romania, makes it possible to develop safe long-term storage projects for the neighboring power plants currently producing energy from burning coal or hydrocarbons. The results of dynamic simulations of CO2 storage in one of these geological structures, Bradesti, which hosts depleted hydrocarbon reservoirs, using a numerical simulator are successfully presented for the neighboring Isalnita Power Plant. In this case, the impact on the environment and climate will be minimal and in alignment with the European Union’s long-term objectives. Our study also opens the path for future similar analyses.

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CO2 Injection Monitoring: Enhancing Time-Lapse Seismic Inversion for Injected Volume Estimation in the Utsira Formation, Sleipner Field, North Sea

Cited by 2 publications

References 32 publications

Petrophysical Property Prediction from Seismic Inversion Attributes Using Rock Physics and Machine Learning: Volve Field, North Sea

Petrophysical Property Prediction from Seismic Inversion Attributes Using Rock Physics and Machine Learning: Volve Field, North Sea

Carbon Capture and Storage Subsurface Study for a Natural Gas-Burning Power Plant in Oltenia, Romania

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