Plurigaussian Simulations in Geosciences

Armstrong, Margaret; Galli, Alain; Beucher, Hélène; Loc’h, Gaëlle Le; Renard, Didier; Doligez, Brigitte; Eschard, R.; Geffroy, François

doi:10.1007/978-3-662-12718-6

Cited by 118 publications

(117 citation statements)

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“…TGS technique is best suitable for reservoirs where the lithotypes occur in a sequential order and it helps in defining the geometry and internal architecture of the reservoir rocks (Armstrong et al 2003). PGS is a natural extension of TGS and capable of handling complex geological situations when the lithotypes occur in complicated relationship rather than simple sequence.…”

Section: Integrated Modeling Approachmentioning

confidence: 99%

“…In case of PGS simulation, generally two Gaussian random fields are used to define the lithofacies structure. Armstrong et al (2003) distinguishes four steps in a PGS approach as: Step 2 In this step, a basin modeling study is initiated covering the reservoir area. The basin model reconstructs the geologic history (i.e., burial history) by back-stripping the reservoir to its original depositional condition.…”

Section: Integrated Modeling Approachmentioning

confidence: 99%

“…The spatial position of well locations produced a large spatial correlation distance for each of the variables. This correlation allows to progress Plurigaussian simulation (PGS) algorithm (Armstrong et al 2003) was employed with two variogram models for creating 3D lithofacies distribution model. In PGS simulation, the controlling component is lithotype rule, which defines the depositional environment (Armstrong et al 2003).…”

Section: D Geocellular Modelingmentioning

confidence: 99%

“…This correlation allows to progress Plurigaussian simulation (PGS) algorithm (Armstrong et al 2003) was employed with two variogram models for creating 3D lithofacies distribution model. In PGS simulation, the controlling component is lithotype rule, which defines the depositional environment (Armstrong et al 2003). In addition to lithotype rules, the lithotype proportion maps resulted from lithotype curves provided a background guideline for cell-wise facies distribution.…”

Section: D Geocellular Modelingmentioning

confidence: 99%

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An integrated approach to discretized 3D modeling of geomechanical properties for unconventional mature field appraisal in the western Canadian sedimentary basin

Saikia

Ikuku

Sarkar

2017

J Petrol Explor Prod Technol

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In mature field appraisal and development, discretized geomechanical property models play a vital role in providing information on in situ stress regime as a guide for placement of directional wells. Laboratory methods of measuring these properties, in most cases, take only small samples from consolidated rocks. These isolated samples may not be representative of the entire elastic regime existing in the reservoir owing to sample size. In general, geomechanical studies are performed on a well-by-well basis and then these measurements are used as calibration points to convert 3D seismic data (if available) to geomechanical models. However, elastic properties measured this way are restricted to the well location and interpolation across the reservoir may not be always appropriate. To overcome these challenges, this paper describes an integrated approach for deriving 3D geomechanical models of the reservoir by combining results of 3D geocellular models and basin models. The basin model reconstructs the geologic history (i.e., burial history) of the reservoir by back-stripping it to the original depositional thickness. Through this reconstruction, the mechanical compaction, pore pressures, effective stress, and porosity versus depth relationships are established. Next, these mechanical properties are discretized into 3D geocellular grid using empirical formulas via lithofacies model even if no 3D seismic data are available for the reservoir. The discretization of elastic properties into 3D grids results in a better understanding of the prevailing stress regimes and helping in design of hydraulic fracturing operations with minimal risks and costs. This approach provides an innovative way of determining effective horizontal stress for the entire reservoir through 3D distribution of elastic properties.

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Section: Integrated Modeling Approachmentioning

confidence: 99%

Section: Integrated Modeling Approachmentioning

confidence: 99%

Section: D Geocellular Modelingmentioning

confidence: 99%

Section: D Geocellular Modelingmentioning

confidence: 99%

See 2 more Smart Citations

An integrated approach to discretized 3D modeling of geomechanical properties for unconventional mature field appraisal in the western Canadian sedimentary basin

Saikia

Ikuku

Sarkar

2017

J Petrol Explor Prod Technol

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“…In the context of mining and petroleum engineering, a Gibbs sampler applied with a truncated Gaussian distribution has been applied for generating individual simulations of sampled constrained processes [Freulon and de Fouquet, 1993;Freulon, 1994], and this approach has also been described in Lantuéjoul [2002] and Chilès and Delfiner [1999]. More recently, this approach has been used to delineate geologic facies [Armstrong et al, 2003]. In these applications, the emphasis is on generating a single representative map of the sampled process, and not on assessing the uncertainty associated with the estimated process, or exploring the impact of constraints in inverse modeling applications.…”

Section: Use Of Truncated Normal Distribution For Statistical Estimationmentioning

confidence: 99%

A Gibbs sampler for inequality‐constrained geostatistical interpolation and inverse modeling

Michalak

2008

Water Resources Research

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[1] Interpolation and inverse modeling problems are ubiquitous in environmental sciences. In many applications, the parameters being estimated or mapped have physical constraints, such as nonnegativity (e.g. concentration, hydraulic conductivity), solubility limits, censored data (e.g. due to dry wells or detection limits), and other physical boundaries or missing data. Geostatistical interpolation and inverse modeling techniques have often been applied for estimating such parameters, but these methods typically cannot enforce physical constraints. This paper describes a statistically rigorous and computationally efficient Gibbs sampler, a Markov chain Monte Carlo technique, based on an a priori truncated Gaussian distribution model, which allows for multiple and variable physical constraints to be enforced within a geostatistical framework. Sample interpolation and inverse modeling applications confirm that estimates, uncertainty bounds and conditional simulations reflect the specified constraints, leading to conclusions that are more consistent with the underlying conceptual model, and provide a more accurate measure of the posterior uncertainty of the parameters being estimated. In addition, especially in inverse modeling applications, a posteriori confidence bounds are narrower even in areas where constraints are not imposed. The method is applicable in multiple dimensions, for data with or without measurement error, and with any variogram model.

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Advances in understanding river‐groundwater interactions

Brunner

Therrien

Renard

et al. 2017

Reviews of Geophysics

196

126

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River‐groundwater interactions are at the core of a wide range of major contemporary challenges, including the provision of high‐quality drinking water in sufficient quantities, the loss of biodiversity in river ecosystems, or the management of environmental flow regimes. This paper reviews state of the art approaches in characterizing and modeling river and groundwater interactions. Our review covers a wide range of approaches, including remote sensing to characterize the streambed, emerging methods to measure exchange fluxes between rivers and groundwater, and developments in several disciplines relevant to the river‐groundwater interface. We discuss approaches for automated calibration, and real‐time modeling, which improve the simulation and understanding of river‐groundwater interactions. Although the integration of these various approaches and disciplines is advancing, major research gaps remain to be filled to allow more complete and quantitative integration across disciplines. New possibilities for generating realistic distributions of streambed properties, in combination with more data and novel data types, have great potential to improve our understanding and predictive capabilities for river‐groundwater systems, especially in combination with the integrated simulation of the river and groundwater flow as well as calibration methods. Understanding the implications of different data types and resolution, the development of highly instrumented field sites, ongoing model development, and the ultimate integration of models and data are important future research areas. These developments are required to expand our current understanding to do justice to the complexity of natural systems.

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Plurigaussian Simulations in Geosciences

Cited by 118 publications

References 0 publications

An integrated approach to discretized 3D modeling of geomechanical properties for unconventional mature field appraisal in the western Canadian sedimentary basin

An integrated approach to discretized 3D modeling of geomechanical properties for unconventional mature field appraisal in the western Canadian sedimentary basin

A Gibbs sampler for inequality‐constrained geostatistical interpolation and inverse modeling

Advances in understanding river‐groundwater interactions

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