Facies proportion uncertainty in presence of a trend

Hadavand, Mostafa; Deutsch, Clayton V.

doi:10.1016/j.petrol.2017.03.036

Cited by 9 publications

(2 citation statements)

References 19 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The latter being particular important in areas where the data density is low. In a more general context, similar methodologies could be applied using SIS (Hadavand and Deutsch, 2017), however, this methodology would still be limited to the information content that can be carried in the variogram model.…”

Section: Discussionmentioning

confidence: 99%

Combining Clustering Methods With MPS to Estimate Structural Uncertainty for Hydrological Models

et al. 2019

View full text Add to dashboard Cite

Section: Discussionmentioning

confidence: 99%

Combining Clustering Methods With MPS to Estimate Structural Uncertainty for Hydrological Models

et al. 2019

View full text Add to dashboard Cite

“…In contrast to Journel and Bitanov, who used a single prior NTG, they proposed to use a possible NTG prior distribution [27]. Mostafa adopts the method of unconditional sequential indicator simulation combined with spatial statistical resampling to quantify the uncertainty of a priori lithofacies proportion, and uses various realizations of a trend model to represent that uncertainty [28]. This specific approach is to take each realization as the input statistics of geostatistical modeling to obtain uncertainty models of posterior facies proportion uncertainty models.…”

Section: Introductionmentioning

confidence: 99%

Uncertainty Evaluation Based on Bayesian Transformations: Taking Facies Proportion as An Example

Qiao,

Li,

2023

Energies

View full text Add to dashboard Cite

Many input parameters in reservoir modeling cannot be uniquely determined due to the incompleteness of data and the heterogeneity of the reservoir. Sedimentary facies modeling is a crucial part of reservoir modeling. The facies proportion is an important parameter affecting the modeling results, because that proportion directly determines the net gross ratio, reserves and sandbody connectivity. An uncertainty evaluation method based on Bayesian transformation is proposed to reduce the uncertainty of the facies proportion. According to the existing data and geological knowledge, the most probable value of the facies ratio and the prior distribution of uncertainty are estimated. The prior distribution of the facies proportion is divided into several intervals, and the proportions contained in each interval are used in facies modeling. Then, spatial resampling is carried out for each realization to obtain the likelihood estimation of the facies proportion. Finally, the posterior distribution of the facies ratio is achieved based on Bayesian transformation. The case study shows that the uncertainty interval of sandstone proportion in the study area has been reduced from [0.31, 0.59] to [0.35, 0.55], with a range reduction of 29%, indicating that the updated posterior distribution reduces the uncertainty of reservoir lithofacies proportion, thereby reducing the uncertainty of modeling results.

show abstract

Multiple-Point Statistics Simulation Models: Pretty Pictures or Decision-Making Tools?

Strebelle¹

2020

Math Geosci

View full text Add to dashboard Cite

Facies proportion uncertainty in presence of a trend

Cited by 9 publications

References 19 publications

Combining Clustering Methods With MPS to Estimate Structural Uncertainty for Hydrological Models

Combining Clustering Methods With MPS to Estimate Structural Uncertainty for Hydrological Models

Uncertainty Evaluation Based on Bayesian Transformations: Taking Facies Proportion as An Example

Multiple-Point Statistics Simulation Models: Pretty Pictures or Decision-Making Tools?

Contact Info

Product

Resources

About