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

Abstract

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.

Data assimilation for a geological process model using the ensemble Kalman filter

“…(), who condition a surface‐based model with iterative matching of sub‐problems for a turbidite application, and Sacchi et al . (), who use a mismatch criterion for well log and seismic data from simulations. By assimilating data gradually, the approach taken in the current paper exploits the way that the simulated sedimentation process forms layers in sequence.…”

Base level changes based on Basin Filling Modelling: a case study from the Paleocene Lishui Sag, East China Sea Basin

“…Although we cannot completely eliminate the non-uniqueness of the solutions, we can still narrow the range of the possible scenarios through the BFM. The models for siliciclastic stratigraphy can generally be classified into 3 types, i.e., geometric models, hydraulic models and diffusion-based models (Paola 2000;Burgess et al 2006;Sacchi et al 2015). The first type is relatively simple and can directly show the geometric patterns of the objects, while it lacks the ability to reveal the physical processes (Strobel et al 1989;Kendall et al 1991;Li et al 2018).…”

Base Level Changes based on Basin Filling Modelling: a Case Study from the Paleocene Lishui Sag, East China Sea Basin

“…The models for siliciclastic stratigraphy can generally be classified into 3 types, i.e. geometric models, hydraulic models and diffusion-based models (Paola, 2000;Burgess et al, 2012;Sacchi et al, 2015). The first type is relatively simple and can directly show the geometric patterns of the objects, while it lacks the ability to reveal the physical processes (Strobe et al, 1989;Kendall et al, 1991;Li et al, 2018).…”