A geostatistical algorithm to reproduce lateral gradual facies transitions: Description and implementation

Falivene, O.; Cabello, Patricia; Arbués, Pau; Muñoz, J. A.; Cabrera, Lluı́s

doi:10.1016/j.cageo.2008.12.003

Cited by 12 publications

(4 citation statements)

References 19 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…GPs are a method for providing a probabilistic model and have been applied to a varied range of geological and geophysical challenges. Some of theses challenges include petrophsyical property prediction (Xu and Torres-Verdín 2013), the investigation of facies transitions (Falivene et al 2009), geostatistical simulation (Armstrong et al 2011; Talebi et al 2013), the interpretation of ground-penetrating-radar (Pasolli et al 2010), and seismic data (Todoeschuck et al 1990).…”

Section: Gaussian Processesmentioning

confidence: 99%

BIF-hosted deposit unit differentiation using multivariate Gaussian processes on measure while drilling data

Silversides

Ball

Melkumyan

2020

Applied Earth Science

View full text Add to dashboard Cite

Measure while drilling (MWD) data collected from production holes can provide information on the location of stratigraphic units in banded iron formation-hosted iron ore deposits. Stratigraphic modelling in these deposits is typically based on data from exploration holes, and adding more densely spaced production data can potentially increase model detail at the bench scale. Previous MWD classification methods struggle to differentiate between neighbouring ore units. In this paper, multivariate Gaussian Processes (GPs) were applied to locate the contact between two iron ore units in the Dales Gorge Member in the Brockman Iron Ore Formation. Production MWD points were then labelled based on the GP output. By altering parameters of the labelling process, 24.4-49.4% of the test data were labelled, with accuracies from 81.4 to 86.8%. Classifications from the same hole were compared to ensure MWD label consistency. The results demonstrate that the proposed method can improve geological unit classification from MWD data.

show abstract

Section: Gaussian Processesmentioning

confidence: 99%

BIF-hosted deposit unit differentiation using multivariate Gaussian processes on measure while drilling data

Silversides

Ball

Melkumyan

2020

Applied Earth Science

View full text Add to dashboard Cite

show abstract

“…The facies tract scale was resolved using a grid-based, deterministic and stochastic geostatistical algorithm based on the truncation of the sum of a deterministic linear expectation trend and a Gaussian random field (MacDonald and Aasen, 1994;Falivene et al, 2009). The linear expectation trend deterministically resolves the stacking of facies belts (first component), whereas the Gaussian random field stochastically resolves interfingering (second component), and in addition ensures conditioning to hard data.…”

Section: Methodsmentioning

confidence: 99%

Modelling facies belt distribution in fan deltas coupling sequence stratigraphy and geostatistics: The Eocene Sant Llorenç del Munt example (Ebro foreland basin, NE Spain)

Cabello¹,

Falivene²,

López‐Blanco³

et al. 2010

Marine and Petroleum Geology

View full text Add to dashboard Cite

“…In order to reduce the influence of the artificial filtering on facies proportion, we only consider those inconsistent objects consisting of at most three cells. More complex postprocessing methods can be found in image processing algorithms, e.g., kernel principal component analysis [ Kim et al , 2005; Mika et al , 1999], or others [ Falivene et al , 2009].…”

Section: Methodsmentioning

confidence: 99%

A pattern‐search‐based inverse method

Zhou¹,

Gómez‐Hernández²,

Li³

2012

Water Resources Research

View full text Add to dashboard Cite

[1] Uncertainty in model predictions is caused to a large extent by the uncertainty in model parameters, while the identification of model parameters is demanding because of the inherent heterogeneity of the aquifer. A variety of inverse methods has been proposed for parameter identification. In this paper we present a novel inverse method to constrain the model parameters (hydraulic conductivities) to the observed state data (hydraulic heads). In the method proposed we build a conditioning pattern consisting of simulated model parameters and observed flow data. The unknown parameter values are simulated by pattern searching through an ensemble of realizations rather than optimizing an objective function. The model parameters do not necessarily follow a multi-Gaussian distribution, and the nonlinear relationship between the parameter and the response is captured by the multipoint pattern matching. The algorithm is evaluated in two synthetic bimodal aquifers. The proposed method is able to reproduce the main structure of the reference fields, and the performance of the updated model in predicting flow and transport is improved compared with that of the prior model.

show abstract

A geostatistical algorithm to reproduce lateral gradual facies transitions: Description and implementation

Cited by 12 publications

References 19 publications

BIF-hosted deposit unit differentiation using multivariate Gaussian processes on measure while drilling data

BIF-hosted deposit unit differentiation using multivariate Gaussian processes on measure while drilling data

Modelling facies belt distribution in fan deltas coupling sequence stratigraphy and geostatistics: The Eocene Sant Llorenç del Munt example (Ebro foreland basin, NE Spain)

A pattern‐search‐based inverse method

Contact Info

Product

Resources

About