Combining Gaussian Fields and Fibre Processes for Modelling of Sequence Stratigraphic Bounding Surfaces

Hektoen, Anne-Lise; Holden, Lars; Skare, Øivind; MacDonald, A.C.

doi:10.3997/2214-4609.201411146

Cited by 6 publications

(4 citation statements)

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“…Of course, we should note that the problem is not only ill-posed with regard to geological variables. Other parameters, such as relative permeability, fluid of the two domains, for example through direct stochastic manipulation of flooding surfaces and sequence boundaries in incised valley modelling (Pulham et al, 1997;Hektoen and Holden, 1997) or through use of accommodation curves derived at wells and extrapolated through a grid to produce 3D trends for stochastic facies models (Massonat, 1999). So, we arrive at a first point where substantial research would be welcome, to better integrate modelling and stratigraphic interpretation.…”

Section: Conditioning To Production Datamentioning

confidence: 97%

Multi-point geostatistics – an introductory overview

Daly¹,

Caers²

2010

First Break

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Multi-point geostatistics is an algorithm which complements the traditional variogram driven cell-based and object modelling approaches to facies modelling. It is situated somewhere between the two, being a cell-based approach, but because it uses estimates of the multi-variate distribution instead of the simple bi-variate distributions that are available to variogramdriven algorithms, it is capable of capturing geometries that are more similar to those available with object modelling. This paper introduces and reviews the merits and weaknesses of this new method, particularly with regard to its ability to provide additional control in conditioning to secondary data such as may be provided from seismic data. Figure 1 A typical indicator simulation with its characteristic 'blob' geometry.www.firstbreak.org © 2010 EAGE

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Section: Conditioning To Production Datamentioning

confidence: 97%

Multi-point geostatistics – an introductory overview

Daly¹,

Caers²

2010

First Break

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“…Since the pioneering work of Hektoen and Holden (1999), aiming to better honor the geological concepts, considerable efforts have been focused on the process-mimicking modeling methods (Pyrcz and Deustch 2014). Conventional surface-based or object-based modeling is constructed to reproduce the final end-result of the reservoir architecture and properties, while process-mimicking modeling tends to integrate rules or geological processes at the very beginning.…”

Section: Other Modeling Techniquesmentioning

confidence: 99%

3D modeling of deepwater turbidite lobes: a review of the research status and progress

Zhang

Mao

2020

Pet. Sci.

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Deepwater turbidite lobe reservoirs have massive hydrocarbon potential and represent one of the most promising exploration targets for hydrocarbon industry. Key elements of turbidite lobes internal heterogeneity include the architectural hierarchy and complex amalgamations at each hierarchical level leading to the complex distribution of shale drapes. Due to limitation of data, to build models realistically honoring the reservoir architecture provides an effective way to reduce risk and improve hydrocarbon recovery. A variety of modeling techniques on turbidite lobes exist and can be broadly grouped into pixel-based, process-based, process-oriented, surface-based, object-based and a hybrid approach of two or more of these methods. The rationale and working process of methods is reviewed, along with their pros and cons. In terms of geological realism, object-based models can capture the most realistic architectures, including the multiple hierarchy and the amalgamations at different hierarchical levels. In terms of data conditioning, pixel-based and multiple-point statistics methods could honor the input data to the best degree. In practical, different methods should be adopted depending on the goal of the project. Such a review could improve the understanding of existing modeling methods on turbidite lobes and could benefit the hydrocarbon exploration activities of such reservoirs in offshore China.

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“…50 realizations of the stochastic model were generated with a program for modeling of sequence stratigraphic bounding surfaces. 9 This model has many parameters, only the most important for this example are described here. The main direction of channels are given by an angle a, which has a prior normal distribution with expected value, ao and standard deviation of 30 degrees (denoted a I'V N(ao,30 2 )) with ao I 'V N(O, 60 2 ).…”

Section: Examplesmentioning

confidence: 99%

Optimizing Reservoir Performance Under Uncertainty with Application to Well Location

Aanonsen

Eide

Holden

et al. 1995

Proceedings of SPE Annual Technical Conference and Exhibition

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Stochastic parameters representing geological uncertainties in reservoir modeling may be classified in 2 types: 1) Continuous stochastic variables (e.g., degree of communication through a fault); and 2) Discrete stochastic variables representing different geological interpretations (e.g., same/different channel observed in different wells) each with a given probability.A method for optimizing reservoir performance is presented, which may take into account both these types of uncertainties in a consistent and simple way by finding values of reservoir management variables that optimize the expected performance over the population of possible reservoirs. The method is based on response surfaces and experimental design and is implemented in a user-friendly computer program which may be used together with any reservoir simulator or analytical flow model.

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Combining Gaussian Fields and Fibre Processes for Modelling of Sequence Stratigraphic Bounding Surfaces

Cited by 6 publications

References 0 publications

Multi-point geostatistics – an introductory overview

Multi-point geostatistics – an introductory overview

3D modeling of deepwater turbidite lobes: a review of the research status and progress

Optimizing Reservoir Performance Under Uncertainty with Application to Well Location

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