Ranking and Upscaling of Geostatistical Reservoir Models Using Streamline Simulation: A Field Case Study
- Harun Ates (Kelkar & Assocs. Inc.) | Asnul Bahar (Kelkar & Assocs. Inc.) | Salem El-Abd Salem (Abu Dhabi Co. for Onshore Oil Operation) | Mohsen Charfeddine (Abu Dhabi Co. for Onshore Oil Operation) | Mohan G. Kelkar (U. of Tulsa)
- Document ID
- Society of Petroleum Engineers
- SPE Reservoir Evaluation & Engineering
- Publication Date
- February 2005
- Document Type
- Journal Paper
- 22 - 32
- 2005. Society of Petroleum Engineers
- 3.3.2 Borehole Imaging and Wellbore Seismic, 5.8.7 Carbonate Reservoir, 5.1.2 Faults and Fracture Characterisation, 5.6.5 Tracers, 5.5.8 History Matching, 5.5.7 Streamline Simulation, 7.2.1 Risk, Uncertainty and Risk Assessment, 3.3.6 Integrated Modeling, 5.1.3 Sedimentology, 5.6.4 Drillstem/Well Testing, 5.8.6 Naturally Fractured Reservoir, 4.3.4 Scale, 5.1.5 Geologic Modeling, 1.6 Drilling Operations, 7.6.2 Data Integration, 5.5 Reservoir Simulation, 1.6.9 Coring, Fishing, 5.6.1 Open hole/cased hole log analysis, 5.7.2 Recovery Factors, 5.5.2 Construction of Static Models, 5.1.7 Seismic Processing and Interpretation, 5.1 Reservoir Characterisation, 1.7.5 Well Control, 5.5.3 Scaling Methods
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In this paper, we present a field example in which multiple reservoirdescriptions were generated to capture uncertainties in reservoir performance;a streamline simulator was used to rank these multimillion-cell geostatisticalrealizations and to determine the optimum level of vertical upscaling.
During geostatistical reservoir characterization, it is a common practice togenerate a large number of realizations of the reservoir model to assess theuncertainty in reservoir descriptions and performance predictions. However,only a small fraction of these models can be considered for comprehensive flowsimulations because of the high computational costs. A viable alternative is torank these multiple "plausible" reservoir models on the basis of an appropriateperformance criterion that adequately reflects the interaction betweenheterogeneity and the reservoir flow mechanisms. One can generate thousands ofgeostatistical realizations with a minimal cost; however, the cost of rankingsuch realizations can be prohibitively expensive, even if fast streamlinesimulators are used. The objective is to generate a manageable number ofrealizations and represent the possible range of uncertainty in reservoirdescriptions. Here, we propose a "hierarchical methodology" in designinguncertainties to be represented in reservoir descriptions.
In this paper, we also show how a streamline simulator can be used to designvertical upscaling of fine-scale reservoir descriptions. The biggest challengeof upscaling is to reduce model size without losing the heterogeneity level ofthe original geological model.
We use streamline time-of-flight connectivity derived from a streamlinesimulator. The time of flight reflects fluid-front propagation at varioustimes, and its connectivity at a given time provides us with a direct measureof volumetric sweep efficiency for arbitrary heterogeneity and wellconfiguration. The volumetric sweep efficiency is the simplest measure thatreflects the interaction between heterogeneity and the flow field. It is adynamic measure that can be updated easily to account for changinginjection/production conditions.
Our field study involves a Middle Eastern carbonate reservoir under amoderate-to-strong aquifer influx. The reservoir is on primary depletion andhas no injectors. In our streamline-simulation exercise, the aquifer pressuresupport is modeled by pseudoinjectors, and pressure updates are used to reflectchanging field conditions.
With the widespread use of geostatistics, it has now become a commonpractice to generate a large number of realizations of the reservoir model toassess the uncertainty in reservoir descriptions and performance predictions.Most commonly, these multiple realizations account for spatial variations inpetrophysical properties within the reservoir as well as the random order inwhich unsampled locations are visited and, thus, represent a very limitedaspect of uncertainty. For reliable risk assessment, we need to generaterealizations that capture a much wider domain of uncertainty, such asstructural, stratigraphic, and petrophysical variations. From a practical pointof view, we want to quantify the uncertainty while keeping the number ofrealizations manageable. In this study, we will adopt an approach that is basedon hierarchical principles. Thus, the uncertainty having the greatest potentialimpact is identified first. For example, with limited well control, thestructural uncertainty derived from the seismic interpretations can have themost impact on the flow performance, or (for faulted reservoirs) theuncertainty with respect to fault locations can have the most impact. Then, thenext level of uncertainty is identified, and so on. The last level ofuncertainty is the multiple geostatistical realizations of reservoir propertiesfor a given set of input parameters. The petrophysical uncertainties generallytend to have a much lower impact on the reservoir performance compared tofactors affecting large-scale fluid movements.
There is, of course, a variety of other sources of uncertainties.Uncertainties may exist related to fault representation or log- vs.core-porosity representation or inclusion of seismic data to modify porosities.For practical applications, we must keep the number of realizations to amanageable level. One way to accomplish this objective is to use, for eachlevel of uncertainty, discrete distributions that can bound the uncertainties.For example, to represent structural uncertainties, we can define low,most-likely, and high surfaces as a discrete way to capture theuncertainties.
One criticism often leveled at geostatistically generated realizations isthat only a select few are ultimately used in the history-matching process. Thequestion is often raised about the purpose of multiple realizations ifultimately only one or very few will be used for history-matching purposes. Thesecond criticism is the upscaling of geostatistical realizations. Geocellularmodels tend to have millions of gridblocks. It is practically infeasible to usethese models directly in the conventional flow simulators. We need to upscalethese realizations before we can include them in a simulator. A relevantquestion here is, what is the appropriate level of upscaling so that criticalheterogeneity details are still captured?
Streamline simulation, which has developed rapidly over the past 10years,1-4 helps to address these criticisms effectively. To address the firstcriticism, we need to conduct history matching of more than one realization sothat we can capture the uncertainties represented by these realizations. Acrucial issue here is to select representative realizations that willadequately represent the uncertainties in the reservoir performancepredictions. We will resort to a streamline-based ranking criterion for thispurpose.5-7 Currently, several ways exist to rank multiple realizations.Realizations can be ranked on the basis of the highest pore volume, highestaverage permeability, closest reproduction of input statistics, and so on. Sometype of permeability threshold connectivity can be used to calculate connectedpore volume and rank the realizations based on such connectivity.8 The drawbackof these simple techniques is that they do not account for dynamic flowbehavior.
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