Using Non-Stationary Training Images from Process-Based Models for Multiple Point Geostatistics Stochastic Generation of Fluvial-Dominated Delta Reservoir Model

Using Non-Stationary Training Images from Process-Based Models for Multiple Point Geostatistics Stochastic Generation of Fluvial-Dominated Delta Reservoir Model

Adhi Wibowo Prakoso, Adhi (TU Delft Civil Engineering and Geosciences)

Storms, J.E.A. (mentor)
Abels, H.A. (graduation committee)
Vossepoel, F.C. (graduation committee)
Papatrecha, Natalia (graduation committee)

Delft University of Technology

Petroleum Engineering and Geo-sciences

2018-11-29

Process-based method forward stratigraphic modelling provides advantages in reservoir modelling by simulating the geological process mathematically, and the genesis of geologic formations over time (Michael et al., 2010). In spite of its advantages, Miller et al. (2008) have recognised significant challenges in process-based simulation models, one of them is the incapability to condition to subsurface data. The problem in conditioning the data can be addressed by using an alternative method named multiple-point geostatistics (MPS) in modelling the subsurface since its introduction in 1993 by Guardiano and Srivastava. MPS considers the relationship between multiple data points that is different from the conventional geostatistical methods that are commonly limited to using a linear relationship between data (Guardiano and Srivastava, 1993). By using the process-based simulation model as training image (TI) for MPS simulation, MPS should be able to address the conditioning issue in process-based simulation model.

Over the past decade, most research in MPS has emphasized on new algorithms for improving efficiency of MPS (Mariethoz and Caers, 2014; Mariethoz and Lefebvre, 2014), but there are still issues remain for the workflow to be widely used in geosciences. Furthermore, using nonstationary TI such as process-based simulation model in MPS are still an issue because the workflow is always different for specific cases. Until recently, little published works are available in applicating nonstationary TI in MPS.

The principal objective of this project was to determine an optimised methodology that allows the use of nonstationary process-based simulation model for TI input with MPS simulation in the fluvial-dominated delta. The process-based simulation model used in the study is a post-processed data from numerical model done in process-based modelling software Delft3D (Lesser et al., 2004) which is the PhD work of van der Vegt in 2018. There were two different cases utilised in this study that represents the whole delta development: Case A with high repetition in the patterns and Case B with low repetition in the patterns.

In order to achieve the desired outcome, this study links process-based simulation model with MPS using unconditional and conditional 2D MPS simulation with two different approaches: zonation approach and control map approach. The realisations from the unconditional simulation have to be validated until successful unconditional MPS simulation. The conditional MPS simulation were carried out when the unconditional realisations have been acknowledged as the approved results in mimicking the patterns of the Delft3D model. Lastly, the results were evaluated with four methods: connectivity function, E-type models, conditional variance models, and analysis of distance (ANODI).

In all of the MPS simulation results, the use of control map approach with unilateral simulation path proved to deliver better realisations for unconditional and conditional MPS simulations. Also, this study has presented an optimised workflow of 2D MPS simulation on using process-based simulation model in fluvial-dominated delta environment as TI based on different conditions of patterns’ repetition and hard data distribution.

Multiple-point Geostatistics
Process-based modelling
nonstationary
Delta
Delft3D
Jewelsuite

http://resolver.tudelft.nl/uuid:99517b86-048f-4c0d-9240-665e4036d0b0

Student theses

master thesis

© 2018 Adhi Adhi Wibowo Prakoso