Quantitative stratigraphic inversion: Numerical study

Sharma, Arvind K.; Imhof, Matthias G.

doi:10.1190/1.2792781

Cited by 2 publications

(1 citation statement)

References 11 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Specifically, the original NA was modified to seek multiple models within the calibration constraints and with input parameters that are as different as possible, in order to obtain samples from all the different combinations of uncertain parameters that can yield valid models. The modifications include (1) the use of error thresholds for the identification of calibrated models found during the inversion process ( Figure 5A, 5B), and (2) the ability to shift the search to new areas of the parameter space once low-error-model regions have been oversampled (i.e., local minima, Figure 5A; Sambridge and Mosegaard, 2002;Sharma, 2006). The inversion algorithm is run for several iterations until a stopping criterion (such as a predefined number of iterations or number of calibrated models found) is met ( Figure 5C).…”

Section: Accommodationmentioning

confidence: 99%

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

Falivene¹,

Frascati²,

Gesbert³

et al. 2014

Bulletin

View full text Add to dashboard Cite

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.,

show abstract

Section: Accommodationmentioning

confidence: 99%

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

Falivene¹,

Frascati²,

Gesbert³

et al. 2014

Bulletin

View full text Add to dashboard Cite

show abstract

Reducing the uncertainty of static reservoir models: implementation of basin-scale geological constraints

et al. 2013

View full text Add to dashboard Cite

We propose a new workflow for building static reservoir models of siliciclastic fluvio-deltaic systems. The proposed strategy requires a process-based stratigraphic simulation model which incorporates a reservoir-scale alluvial architecture module nested within a low-resolution basin-scale (sequence-stratigraphic) model. The basin-scale model is run with the intent to approximate large-scale basin-fill properties (based on geological/geophysical background information about palaeotopography, sea level, sediment supply, subsidence, and so forth). Subsequently, the model may be stochastically optimised by dedicated post-processing software to mimic sub-grid (reservoir-scale) properties of selected parts of the basin fill. This approach allows us to narrow down the range of possible scenarios (realisations) from the outset, which results in more reliable uncertainty estimates associated with reservoir models. Pilot studies suggest that the improvement of geological credibility of stochastically simulated fluvial reservoir models may go hand-in-hand with a significant reduction of the computational effort of inverting basin-scale (process-based) stratigraphic forward models. The implementation of geological constraints on object-based models is expected to improve estimation of sand-body connectivity and dynamic reservoir behaviour, and will therefore contribute to reduction of the non-uniqueness in current static reservoir models. Furthermore, the uncertainty associated with each basin-scale parameter can be propagated all the way through to reserve estimation. The partitioning of the overall uncertainty into contributions at the basin and reservoir scales may be quantitatively assessed, and the information content of all available data may be quantified.

show abstract

Quantitative stratigraphic inversion: Numerical study

Cited by 2 publications

References 11 publications

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

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

Reducing the uncertainty of static reservoir models: implementation of basin-scale geological constraints

Contact Info

Product

Resources

About