scholarly journals Integration of reservoir simulation, history matching, and 4D seismic for CO2-EOR and storage at Cranfield, Mississippi, USA

Fuel ◽  
2016 ◽  
Vol 175 ◽  
pp. 116-128 ◽  
Author(s):  
Masoud Alfi ◽  
Seyyed A. Hosseini
Keyword(s):  
Reservoir Simulation ◽  
History Matching ◽  
Co2 Eor ◽  
4D Seismic ◽  
And Storage

scholarly journals Effective pressure prediction from 4D seismic AVO data during CO2-EOR and storage

2022 ◽  
Vol 113 ◽  
pp. 103525
Author(s):  
Haofan Wang ◽  
Zhe Wang ◽  
Jinfeng Ma ◽  
Lin Li ◽  
Yadong Wang ◽  
...  
Keyword(s):  
Effective Pressure ◽  
Co2 Eor ◽  
4D Seismic ◽  
And Storage

Semiquantitative 4D seismic interpretation integrated with reservoir simulation: Application to the Norne field

2018 ◽  
Vol 6 (3) ◽  
pp. T601-T611
Author(s):  
Juliana Maia Carvalho dos Santos ◽  
Alessandra Davolio ◽  
Denis Jose Schiozer ◽  
Colin MacBeth
Keyword(s):  
Simulation Model ◽  
Reservoir Simulation ◽  
History Matching ◽  
Time Lapse ◽  
Seismic Signal ◽  
Seismic Interpretation ◽  
Production Forecast ◽  
Seismic Acquisition ◽  
Reservoir Simulation Model ◽  
4D Seismic

Time-lapse (or 4D) seismic attributes are extensively used as inputs to history matching workflows. However, this integration can potentially bring problems if performed incorrectly. Some of the uncertainties regarding seismic acquisition, processing, and interpretation can be inadvertently incorporated into the reservoir simulation model yielding an erroneous production forecast. Very often, the information provided by 4D seismic can be noisy or ambiguous. For this reason, it is necessary to estimate the level of confidence on the data prior to its transfer to the simulation model process. The methodology presented in this paper aims to diagnose which information from 4D seismic that we are confident enough to include in the model. Two passes of seismic interpretation are proposed: the first, intended to understand the character and quality of the seismic data and, the second, to compare the simulation-to-seismic synthetic response with the observed seismic signal. The methodology is applied to the Norne field benchmark case in which we find several examples of inconsistencies between the synthetic and real responses and we evaluate whether these are caused by a simulation model inaccuracy or by uncertainties in the actual observed seismic. After a careful qualitative and semiquantitative analysis, the confidence level of the interpretation is determined. Simulation model updates can be suggested according to the outcome from this analysis. The main contribution of this work is to introduce a diagnostic step that classifies the seismic interpretation reliability considering the uncertainties inherent in these data. The results indicate that a medium to high interpretation confidence can be achieved even for poorly repeated data.

Using 4D Seismic Surveys and History Matching to Estimate Critical and Maximum Gas Saturation

2014 ◽  
Author(s):  
Dennis Chinedu Obidegwu ◽  
Romain Louis Chassagne ◽  
Colin Macbeth
Keyword(s):  
History Matching ◽  
Seismic Surveys ◽  
Gas Saturation ◽  
4D Seismic

scholarly journals Managing Uncertainty in Geological CO2 Storage Using Bayesian Evidential Learning

Energies ◽  
2021 ◽  
Vol 14 (6) ◽  
pp. 1557
Author(s):  
Amine Tadjer ◽  
Reidar B. Bratvold
Keyword(s):  
Uncertainty Quantification ◽  
History Matching ◽  
Co2 Storage ◽  
Carbon Capture And Storage ◽  
Monitoring Program ◽  
Co2 Injection ◽  
Drinking Water Contamination ◽  
Model Inversion ◽  
Geological Co2 Storage ◽  
And Storage

Carbon capture and storage (CCS) has been increasingly looking like a promising strategy to reduce CO2 emissions and meet the Paris agreement’s climate target. To ensure that CCS is safe and successful, an efficient monitoring program that will prevent storage reservoir leakage and drinking water contamination in groundwater aquifers must be implemented. However, geologic CO2 sequestration (GCS) sites are not completely certain about the geological properties, which makes it difficult to predict the behavior of the injected gases, CO2 brine leakage rates through wellbores, and CO2 plume migration. Significant effort is required to observe how CO2 behaves in reservoirs. A key question is: Will the CO2 injection and storage behave as expected, and can we anticipate leakages? History matching of reservoir models can mitigate uncertainty towards a predictive strategy. It could prove challenging to develop a set of history matching models that preserve geological realism. A new Bayesian evidential learning (BEL) protocol for uncertainty quantification was released through literature, as an alternative to the model-space inversion in the history-matching approach. Consequently, an ensemble of previous geological models was developed using a prior distribution’s Monte Carlo simulation, followed by direct forecasting (DF) for joint uncertainty quantification. The goal of this work is to use prior models to identify a statistical relationship between data prediction, ensemble models, and data variables, without any explicit model inversion. The paper also introduces a new DF implementation using an ensemble smoother and shows that the new implementation can make the computation more robust than the standard method. The Utsira saline aquifer west of Norway is used to exemplify BEL’s ability to predict the CO2 mass and leakages and improve decision support regarding CO2 storage projects.

Sign in / Sign up

Export Citation Format

Share Document