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

Abstract

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.,

Towards a sequence stratigraphic solution set for autogenic processes and allogenic controls: Upper Cretaceous strata, Book Cliffs, Utah, USA

“…Just as importantly, numerical forward and inverse models now provide the means to turn the multiple hypotheses contained within a solution set into a probabilistic range of predictions for lithology distributions in a hydrocarbon exploration or production context (e.g. Burgess et al 2006;Falivene et al 2014).…”

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

“…The capability to predict stratigraphic architecture is relevant to reservoir modelling because highresolution sequence-stratigraphic representations of (local) basin-fill architecture may be used to guide different stages of the reservoir-modelling workflow: from the early phase of stratal pattern reconstruction by well correlation and definition of possible depositional scenarios (Wendebourg and Harbaugh, 1997;Burgess et al, 2006;Falivene et al, 2014) to the final stage of constraining stochastic lithofacies distributions for the assessment of reservoir volumes and connectivity, and the planning of infill wells (Doligez et al, 1999).…”

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

“…The succession simulated by forward modeling very commonly contains time resolution, say, 5000 year (modeling time step), whereas the observed succession contains dated time resolution usually in million years, or at higher resolution hundreds of thousand years. The method published so far to calculate the mismatch of simulated and observed successions is to only compare the thickness of the smallest dated stratigraphic units such as a strata cycle (Cross and Lessenger 1999;Charvin et al 2009), or the unit thickness maps (Falivene et al 2014). Of course, in these methods, the higher resolution of time calibration the observed succession has, the more accurate the comparison of the simulated and observed successions is.…”

“…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.…”