Geostatistical Reservoir Modeling Pdf
Obviously, no company can afford the time or expense to history-match all the realizations generated in a stochastic-modeling study, nor is it necessary to do so. All papers were presented at the congress and have been peer-reviewed. Constructing a stochastic model at too coarse a resolution often has proved inaccurate. Consider another example of the volume-support effect when estimating porosity in a typical grid cell using a common computer gridding algorithm.
Use this section to list papers in OnePetro that a reader who wants to learn more should definitely read. Part I presents a conceptual comparison between traditional random function theory and stochastic modelling based on training images, where random function theory is not always used.
Any a priori decision to freeze the uncalibrated data must be made by all members of the reservoir modeling team. The geological data presents novel concepts with a coverage that is both broad in area and in discipline. Reservoir modeling is the final step in the reservoir-characterization process, and consists of building an upscaled geologic model for input to the fluid-flow numerical simulator.
In the earlier example regarding core-plug measurements of porosity, the core porosity is the hard datum and the log porosity is the soft datum. Petro-Canada Placer Dome Inc. There was a broad spectrum of students and seasoned geostatisticians who shared their knowledge in many areas of study including mining, petroleum, and environmental applications. They are grouped by the different sessions that were held in Banff and are in the order of presentation. The practical applications in these proceedings provide nuggets of wisdom to those struggling to apply geostatistics in the best possible way.
Specifically, geostatistical reservoir characterization allows for valid construction of a pdf of hydrocarbon volumes and other key reservoir properties. Part of the reservoir-modeling process can use geostatistical methods that consider the spatial nature of geologic data. Geostatistical conditional simulation. Geologists want the highest-resolution geologic model possible, much to the dismay, though, understanding building and using baluns and ununs pdf of the reservoir engineer tasked with creating it.
The support of the soft data is assumed the same as in the traditional linear or nonlinear regression method. Additionally, fast streamline simulators capable of handling million-node models or more are becoming very popular. The primary reason for creating all these models is to quantify uncertainty in the geologic model to make better reservoir-management decisions. An excellent example of this is the traditional calibration of core porosity to log-derived porosity. Likewise, stochastic-modeling methods provide many plausible images of the reservoir, thus generating multiple realizations and scenarios, an operation generally performed by the geoscientist.
Navigation Main page Recent changes Random page Help. This problem is closely related to modeling since it is based on the inversion of the mathematical steps and often uses modeling for verification and updating. Thus, the modeling team would be wise to select a limited appropriate set of models for this effort. Navigation menu Personal tools Log in. Geology in reservoir models.
Core-plug measurements of porosity often are aligned with log data over a common interval by using a mathematical adjustment, such as some form of linear or nonlinear regression. Stochastic-modeling methods provide many plausible images of the reservoir. Unfortunately, the high-resolution stochastic-modeling approach usually will increase the cycle time of a reservoir study because there is more work to be done. Using geostatistical reservoir-modeling technologies to integrate all static and dynamic data in a consistent framework ensures a better model.
Students and practitioners will be digging through these papers for many years to come. Furthermore, the upscaling process is fraught with assumptions, and because not all upscaling techniques are equal, they can bias the results to the selected method. The high-resolution model typically must be upscaled before importing it to the fluid-flow simulator.
Geostatistical reservoir modeling - PetroWiki
Namespaces Read Discussion. The degree to which the soft data are honored depends partially on the strength of their correlation to the hard data. An important aspect of the project was the inversion of seismic data, that is the determination of the parameters of the medium from observations. The most advantageous workflow uses an appropriate fine-scale model as a guide when defining the flow units and constructing the flow-simulation grid. The fast streamline simulators offer a means to screen and rank realizations relatively quickly on the basis of some agreed-upon criteria.
It is unrealistic to think that such a volume of heterogeneous rock could be represented adequately by a single value of porosity and one permeability value in each of the x, y, and z domains. Concepts such as non-stationary and multi-variate modeling, consistency between data and model, the construction of training images and inverse modelling are treated.
Thus, each scenario can have multiple realizations, with the possibility of generating hundreds of models that honor the available data. In this case, the mathematical calibration procedure is tantamount to shifting, stretching, and squeezing the data to achieve a better fit. Mira Geoscience Nexen Inc. Data in the petroleum industry comes from a variety of sources, measured across many different scales, e.
Basic elements of a reservoir characterization study. In practice, such data often are integrated without regard to the vast differences in their measurement scales, which is problematic. It can imply a blind assumption that the geologic detail in a higher-resolution model is unnecessary. For example, the modeling scale traditionally is thought of as a geologic element, but it affects the amount of upscaling required, and so becomes an engineering element, as well.
Dynamic reservoir simulation is used to forecast ultimate hydrocarbon recovery on the basis of a given production scheme, or to compare the economics of different recovery methods. It is a continuous process that begins with the field discovery and all the way through to the last phases of production and abandonment. The high-resolution models may contain tens of millions of grid cells and require upscaling before flow simulation. The key is to strike a balance that keeps the project objectives clearly in mind. The book provides an overview of this new field in three parts.
Although this is not an especially large stochastic model, it is larger than most reservoir engineers are willing to flow-simulate. Conducting a dynamic flow simulation requires several input data types. Geostatistics attempts to combine appropriately data that have been measured at different scales, using a calibration method that categorizes covariables as hard data and soft data. You will find papers in this two volume set.
Reservoir characterization encompasses all techniques and methods that improve our understanding of the geologic, geochemical, and petrophysical controls of fluid flow. Wave-propagation modeling was carried out either mathematically or physically with the most modern tools.
Using these tools, we can assess the uncertainty in the models, the unknown that inevitably results from never having enough data. Well data, too, are considered hard data, whereas seismic data are soft data. These papers provide a permanent record of different theoretical perspectives from the last four years.
Use this section to provide links to relevant material on websites other than PetroWiki and OnePetro. This book will be an invaluable reference for students, researchers and practitioners of all areas of the Earth Sciences where forecasting based on spatio-temporal data is performed. The beauty of Banff and the many offerings of the Rocky Mountains was the perfect background for a week of interesting and innovative discussions on the past, present and future of geostatistics. The results of these two approaches are not equivalent, and the volume support issue at least partly can explain the concerns about performing a conditional simulation at too coarse a scale.
The scale estimated or simulated through the calibration process is that of the hard data. In the petroleum industry, though, the change of support typically is not addressed.
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These terms often are used informally, their difference generally being relative, tied to the degree of interpretation required to derive the data values and their scale of measurement. Geostatistical reservoir-modeling technologies depart from traditional deterministic modeling methods through consideration of spatial statistics and uncertainties.
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