Aquifer Characterization of a Gas Reservoir Using Ensemble Kalman Filter

Kim, S.; Lee, C.; Lee, K.; Jo, Kichun; Ryu, Min-Cheol; Choe, Jonggeun

doi:10.3997/2214-4609.201412676

Cited by 2 publications

(5 citation statements)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The first one is very low water production because there is no water source. The second one is low uncertainty compared to cases with an aquifer because gas behaviors are very simple compared to water behaviors (Kim et al, 2015). Figure 5 shows gas rates of the initial and updated ensembles.…”

Section: Case 1: Reservoir Without An Aquifermentioning

confidence: 99%

“…In this case, they just assumed the same aquifer strength at each side. Kim et al (2015) characterized conventional gas reservoirs with an aquifer using only production data and considered different aquifer sizes at the four sides.…”

Section: Introductionmentioning

confidence: 99%

“…Therefore, strict reservoir characterization is not needed for those cases. However, an aquifer hugely increases uncertainty of gas reservoirs (Kim et al, 2015). Figure 1 demonstrates effects of an aquifer on uncertainty of gas productions.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Characterization of channelized gas reservoirs using ensemble Kalman filter with application of discrete cosine transformation

Kim

Lee

et al. 2016

Energy Exploration & Exploitation

View full text Add to dashboard Cite

Reservoir characterization is a process to make a model similar to a true reservoir by integrating available data. It is necessary for reliable estimation of future productions and making proper decisions. Ensemble Kalman filter is one of the most powerful reservoir characterization methods. Ensemble Kalman filter can update reservoir models in real time and assess uncertainty by multiple models. Characterization of a channelized reservoir is difficult because of irregular channel pattern and connectivity. In this paper, ensemble Kalman filter with preservation of facies ratio and discrete cosine transformation is proposed for solving overshooting problem and maintaining properties of channel. The proposed method gives us stable characterization performances on gas and water productions, channel pattern and connectivity, and aquifer strengths regardless of the existence of an aquifer and uncertainty of facies ratio. Although facies ratio has AE 10% uncertainty with the reference, updated models maintain channel properties properly. Discrete cosine transformation helps to preserve channel pattern and connectivity and, therefore, overall performances are enhanced. Consequently, the proposed method can provide reliable updated models and credible prediction of future reservoir performances for making a proper decision.

show abstract

Section: Case 1: Reservoir Without An Aquifermentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Characterization of channelized gas reservoirs using ensemble Kalman filter with application of discrete cosine transformation

Kim

Lee

et al. 2016

Energy Exploration & Exploitation

View full text Add to dashboard Cite

show abstract

“…An aquifer has negative influence on gas production and increases uncertainty of production behavior (Kim et al, 2015(Kim et al, , 2016. Therefore, reservoirs with an aquifer should be characterized by a suitable method because conventional EnKF does not work.…”

Section: Introductionmentioning

confidence: 99%

“…Therefore, reservoirs with an aquifer should be characterized by a suitable method because conventional EnKF does not work. Gaussiandistributed or channel gas reservoirs with an aquifer have been studied in several papers due to crucial effects of an aquifer (Glegola et al, 2012a(Glegola et al, , 2012bKim et al, 2015Kim et al, , 2016. Combination of EnKF, DCT, and PFR can preserve channel properties and give enhanced history matching performance in channel gas reservoirs with an aquifer (Kim et al, 2016).…”

Section: Introductionmentioning

confidence: 99%

Characterization of channel oil reservoirs with an aquifer using EnKF, DCT, and PFR

Kim

Lee

et al. 2016

Energy Exploration & Exploitation

View full text Add to dashboard Cite

Reservoir characterization is necessary for making reliable models to have future reservoir performances. Since an aquifer typically has positive influences on oil production, its characterization has rarely been regarded as a critical issue. However, in channel oil reservoirs, an aquifer amplifies uncertainty of permeability estimations and has its own uncertainty due to limited information without any direct measurement. Although there have been some researches on channel oil reservoirs using discrete cosine transformation, we cannot characterize reliably an aquifer using discrete cosine transformation alone. Thus, we need additional schemes to manage increased uncertainty by an aquifer and to estimate the aquifer itself. In this study, ensemble Kalman filter with the combination of preservation of facies ratio and discrete cosine transformation is proposed for channel reservoirs with an aquifer. By the proposed method, we confirm that discrete cosine transformation and preservation of facies ratio contribute to preservation of overall channel properties and fine-tuning of the channel in the ensemble Kalman filter algorithm, respectively. Consequently, the proposed method gives us stable characterization performances on oil and water productions, permeability distribution, and aquifer strengths for a reasonable decision. Keywords Ensemble Kalman filter, discrete cosine transformation, preservation of facies ratio, channel oil reservoirs with an aquifer, aquifer characterization

show abstract

Aquifer Characterization of a Gas Reservoir Using Ensemble Kalman Filter

Cited by 2 publications

References 0 publications

Characterization of channelized gas reservoirs using ensemble Kalman filter with application of discrete cosine transformation

Characterization of channelized gas reservoirs using ensemble Kalman filter with application of discrete cosine transformation

Characterization of channel oil reservoirs with an aquifer using EnKF, DCT, and PFR

Contact Info

Product

Resources

About