Quantitative seismostratigraphic inversion of a prograding delta from seismic data

Imhof, Matthias G.; Sharma, Arvind K.

doi:10.1016/j.marpetgeo.2006.04.004

Cited by 13 publications

(14 citation statements)

References 21 publications

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“…Geological objects, such as channel belts, shale lenses, and sandy lobes are introduced into such models by invoking templates, so called "analogues" taken from outcrops of rocks inferred to have formed under similar conditions (Deutsch, 2002). This "productbased" approach to prediction of reservoir architecture does provides limited opportunities for incorporating knowledge of the physical laws which govern basin filling into the modelling workflow (Karssenberg et al, 2001;Imhof and Sharma, 2006;Charvin et al, 2009Charvin et al, , 2011Weltje et al, 2013). A recently conducted experiment in which a continuous outcrop was sparsely sampled to mimic subsurface data (Deveugle et al, 2014) illustrates the limitations of state-of-the-art geostatistical algorithms for prediction of lithology between wells.…”

Section: Introductionmentioning

confidence: 99%

“…For practical purposes, however, the added value of stratigraphic modelling relies on our capability to condition these highly non-linear models to case-specific observations, such as seismic and well data (Burton et al, 1987;Heller et al, 1993;Lessenger and Cross, 1996;Cross and Lessenger, 1999;Bornholdt et al, 1999;Wijns et al, 2004;Imhof and Sharma, 2006;Falivene et al, 2014). If this can be accomplished, we may narrow down the range of possible scenarios (realisations) in the exploration stage, which should result in more reliable uncertainty estimates associated with reservoir-architecture models.…”

Section: Introductionmentioning

confidence: 99%

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Towards process-based geological reservoir modelling: Obtaining basin-scale constraints from seismic and well data

Sacchi

Weltje

Verga

2015

Marine and Petroleum Geology

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Highlights:• Fluvio-deltaic stratigraphy was simulated with a 2DH process-based model • Goodness of fit functions were used to infer boundary conditions from subsurface data • Information content of seismic and well data was evaluated • Depth of reservoir top across basin is best predictor of reservoir lithology ABSTRACT Forward stratigraphic modelling aims at representing the spatial distribution of lithology as a function of physical processes and environmental conditions at the time of deposition so as to integrate geological knowledge into the reservoir modelling workflow, thus increasing predictive capabilities of reservoir models and efficient exploitation of hydrocarbons. Application of process-based models in inverse mode is not yet well-established due to our limited insight into the information content of common subsurface data and the computational overhead involved.In this paper we examine inverse modelling of stratigraphy by using a typical dataset acquired in the hydrocarbon industry, which consists of seismic data and standard logs from a limited number of wells. The approach is based on the use of a forward model called SimClast, developed at Delft University of Technology, to generate facies distribution and architecture at the regional scale. Three different goodness of fit functions were proposed for model inversion, following an inference approach. A synthetic reservoir unit was used to investigate the impact of the uncertainty affecting the input parameters and the information content of seismic and well data.The case study showed that the model was more sensitive to the initial topography and to the location of the sediment entry point than to sea level. The depth of the seismic reflector corresponding to the top-reservoir surface was the most informative data source; the initial and boundary conditions of the simulation were constrained by evaluating the depth of this reflector across the whole basin area. In the reservoir area, where the seismic-to-well tie was established, the depth of the reservoir top does not give enough information for constraining the model parameters. Our results thus indicate that evaluation of basinscale data permits reduction of uncertainty in (geostatistical) reservoir models relative to the current workflow, in which only local data are used. Effective use of well data to generate reservoir models conditioned to basin-scale scenarios requires post-processing methods to downscale the output of the forward model used in the experiments.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Towards process-based geological reservoir modelling: Obtaining basin-scale constraints from seismic and well data

Sacchi

Weltje

Verga

2015

Marine and Petroleum Geology

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“…In particular, they have been proven to reasonably reproduce basin-margin clinoform systems formed by broad-scale deltas or transitional systems. In these, sediment transport is much more efficient in the subaerial part than in the submarine part of the system (Kenyon and Turcotte, 1985;Jordan and Flemings, 1991;Kaufmann et al, 1991;Rivenaes, 1992Rivenaes, , 1997Rabineau et al, 2005;Imhof and Sharma, 2006;Postma et al, 2008). Moving to fully 3D settings and properly modeling transitional to deep-water deposits is indeed a nontrivial task.…”

Section: Sediment-transport Assumptionsmentioning

confidence: 99%

Automatic calibration of stratigraphic forward models for predicting reservoir presence in exploration

Falivene¹,

Frascati²,

Gesbert³

et al. 2014

Bulletin

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A B S T R A C TUnderstanding and predicting reservoir presence and characteristics at regional to basin scales is important for evaluating risk and uncertainty in hydrocarbon exploration. Simulating reservoir distribution within a basin by a stratigraphic forward model enables the integration of available prior information with fundamental geologic processes embedded in the numerical model. Stratigraphic forward model predictions can be significantly improved by calibrating the models to independent constraints, such as thicknesses from seismic or well data. A three-dimensional basin-scale stratigraphic forward-modeling tool is coupled with an inversion algorithm. The inversion algorithm is a modification of the neighborhood algorithm (a type of genetic algorithm), which is designed to sample complex multimodal objective functions and is parallelized on computer clusters to accelerate convergence. The process generates a set of representative geological models that are consistent with prior ranges for uncertain parameters, calibration constraints, and associated tolerance thresholds. The workflow is first demonstrated on two data sets: a synthetic example based on a clastic passive margin and a real hydrocarbon exploration example for slope and basin-floor stratigraphic traps in the Neocomian (Lower Cretaceous) of the West Siberian Basin. The analysis of calibrated models provides constraints on stratigraphic controls, and allows prediction of locations with higher potential to develop stratigraphic traps. These locations are related to complex interactions between paleobathymetry, subsidence, eustatic fluctuations, characteristics of sediment-input sources, and sediment-transport parameters. Results show the potential of stratigraphic forward modeling O. Falivene joined Shell in 2008, and since then he has worked for research and development (R&D) developing and applying stratigraphic forward models to support worldwide exploration in clastic and carbonate settings. Oriol has a background in stratigraphy, geocellular modeling, and geostatistics (Ph.D. in modeling outcrop analogs at the University of Barcelona, Spain), and a short period working on reservoir modeling for British Petroleum.A. Frascati ∼ Shell Global Solutions International B.V.,

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“…One of the main reasons why this technique has been delayed to dominate in petroleum reservoir modeling is its inability to implement data conditioning. Since late 1990s, similar techniques but under different names were proposed to overcome the inability and initiated a new research front in computational stratigraphy and sedimentology, such as inverse stratigraphic modeling (ISM) (Griffiths et al 1996;Lessenger and Cross 1996;Cross and Lessenger 1999;Duan et al 2001a;Imhof and Sharma 2006;Charvin et al 2009;Griffiths 2009;Charvin et al 2011), adaptive modeling (Duan et al 1998), modeling optimization (Bornholdt and Westphal 1998;Wijns et al 2003Wijns et al , 2004, or model calibration (Falivene et al 2014). However, the progress of these techniques, all of which will be called ISM afterward for simplicity, has been limited, and one of the major hurdles is still the data conditioning.…”

Section: Introductionmentioning

confidence: 99%

Similarity measure of sedimentary successions and its application in inverse stratigraphic modeling

Duan

2017

Pet. Sci.

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This paper presents a unique and formal method of quantifying the similarity or distance between sedimentary facies successions from measured sections in outcrop or drilled wells and demonstrates its first application in inverse stratigraphic modeling. A sedimentary facies succession is represented with a string of symbols, or facies codes in its natural vertical order, in which each symbol brings with it one attribute such as thickness for the facies. These strings are called attributed strings. A similarity measure is defined between the attributed strings based on a syntactic pattern-recognition technique. A dynamic programming algorithm is used to calculate the similarity. Inverse stratigraphic modeling aims to generate quantitative 3D facies models based on forward stratigraphic modeling that honors observed datasets. One of the key techniques in inverse stratigraphic modeling is how to quantify the similarity or distance between simulated and observed sedimentary facies successions at data locations in order for the forward model to condition the simulation results to the observed dataset such as measured sections or drilled wells. This quantification technique comparing sedimentary successions is demonstrated in the form of a cost function based on the defined distance in our inverse stratigraphic modeling implemented with forward modeling optimization.

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Quantitative seismostratigraphic inversion of a prograding delta from seismic data

Cited by 13 publications

References 21 publications

Towards process-based geological reservoir modelling: Obtaining basin-scale constraints from seismic and well data

Towards process-based geological reservoir modelling: Obtaining basin-scale constraints from seismic and well data

Automatic calibration of stratigraphic forward models for predicting reservoir presence in exploration

Similarity measure of sedimentary successions and its application in inverse stratigraphic modeling

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