Experimental Design Methodology for Reserves Quantifications Based on Soft Computing Modelling Methods
- Ritu Gupta (Curtin Uni. of Tech) | Jan Folkert Van Elk (RDS Limited) | Dewi Tjia (Curtin University of Technology) | Gregory Charles Smith (Woodside Energy Ltd)
- Document ID
- Society of Petroleum Engineers
- SPE Asia Pacific Oil and Gas Conference and Exhibition, 22-24 October, Perth, Australia
- Publication Date
- Document Type
- Conference Paper
- 2012. Society of Petroleum Engineers
- 5.5 Reservoir Simulation, 1.10.1 Drill string components and drilling tools (tubulars, jars, subs, stabilisers, reamers, etc), 5.1.1 Exploration, Development, Structural Geology, 5.5.8 History Matching, 4.3.4 Scale, 5.6.3 Deterministic Methods, 5.7 Reserves Evaluation, 5.1.5 Geologic Modeling, 1.6.9 Coring, Fishing
- 1 in the last 30 days
- 274 since 2007
- Show more detail
- View rights & permissions
|SPE Member Price:||USD 8.50|
|SPE Non-Member Price:||USD 25.00|
Over the past decade the statistical experimental design and analysis (EDA) methodology has been used widely in multiple deterministic modelling for a range of applications such as the development of surrogate models for estimation of ultimate recovery, history matching, screening of potential development options etc. Typically the first step in the EDA application is to quantify all uncertainties, secondly to generate the appropriate design with a minimal number of scenarios, thirdly create and simulate 3D geological models and finally calculate a surrogate model.
The goal of the EDA methodology is to minimize the number of 3D model scenarios simulation, necessary to accurately estimate hydrocarbon reserves for a given uncertainty profile. The fundamental question here is "How is an optimal design selected with in the EDA methodology???. The answer is simple; first we lock in the method that will be used to develop surrogate model and then search for the best scenarios to simulate that minimize errors in the final surrogate model. For instance if we plan to use response surface regression modelling, then designs like Placket-Burman, factorial designs or D-optimal designs are a good choice. Alternatively if we propose to use multi-dimensional kriging for surrogate modelling then space filling designs are a better choice.
In general we cannot mix-and-match designs with surrogate modelling methods. With increasing computing power there is a trend in the industry to try new soft computing methods such as neural network and decision tree based modelling to develop surrogate models. In this paper we will demonstrate that soft computing methods can be used for surrogate modeling. However the classical designs are in this case not the best choice. In these instances, the space filling designs like LHD perform better. The use of an incorrect design can lead to serious over estimation of the P90, which must be avoided.
Experimental design and analysis (EDA) methods have been extensively used in all kinds of industrial experiments since being developed for phycial agricultural experiments, some 50 years ago. Initially EDA methods were developed for agricultural experiments with inherent natural variation in experimental material. Later on these methods were used for physical experiments (PE) in all endeavours of study. Now, the advent of immense computing power has replaced PE with complex computer model experiments (Morris 2000). Computer experiments (CE) provide an economical and straightforward method of experimentation. EDA methods have evolved to keep pace with the challenges of design and analysis of CE (Koehler and Owen 1996). Some examples of CE are models for predicting damages due to fires (Sahama and Diamond 2001), models in hydrology for predicting runoff (Cullmann et al. 2010) and design of hip prosthetics (Chang et al. 1999). Some of these models are so complex that a single run of these models may take a week to give a response of interest. The 3D reservoir simulation models also fall under the class of CE. These models are used for predicting responses (like STOIIP, Ultimate Recovery) for a given scenario of uncertainty settings (eg. Gross rock volume, facies, oil-water contact, facility uptime).
|File Size||1 MB||Number of Pages||11|