Seismic Reservoir Characterization Laboratory

Summary

Reservoir characterization is a crucial prerequisite to predict the economic potential of a hydrocarbon reservoir or to examine different production scenarios. Unfortunately, it is impossible to determine the exact reservoir properties at the required scale. The most abundant seismic data has a resolution around 100 ft. Wells resolve the reservoir down to the centimeter scale, but only at some points in the vertical direction. Instead, one resorts to using statistical methods to fill in small variations in the reservoir. The Seismic Reservoir Characterization Laboratory (SRCL) is a new research program in the Department of Geological Sciences at Virginia Tech. Its objective is to develop methods to determine the parameters for the statistical reservoir models from seismic data.

Contact

Dr. M. G. Imhof
Derring Hall 4044 (0420)
Dept. of Geological Sciences
Virginia Tech
Blacksburg, VA 24061 - 0420

phone: 540 231 6004
fax: 540 231 3386
email: mailto://mgi@vt.edu

Introduction

Reservoir characterization is an essential step in exploration and development of a new petroleum or natural gas reservoir. Typically, the process starts with a thorough analysis of a potential region with respect to geology, probability for finding hydrocarbons, and economic factors such as risk or investment needed. Once it has been decided to explore for resources in a given region, one acquires a seismic exploratory survey where controlled sound sources set off acoustic sound waves. These waves penetrate the earth, and propagate, reflect, and refract until they reach back to the surface of the earth where they are recorded by geophones or hydrophones. Figure 1 presents a schematic of the seismic method.
  
Figure 1: Schematic of the seismic method. A truck generates a sound wave which penetrates the earth. At boundaries between different layers, a portion of the sound wave is reflected back to the surface where it is recorded with geophones.
\includegraphics[width=6.5in]{modschematic.eps}


  
Figure 2: Seismic datacube with interpretion overlaid. Shown in green is a layer which got displaced by the red and yellow faults. The pink layer exemplifies the 3D nature of geology as demonstrated by the top surface cutting through some topographically high spots.
\includegraphics[width=6.5in]{seiscube.eps}

After a great deal of data processing, an accurate image of the subsurface can be obtained. An example is shown in Figure 2. This subsurface image is carefully examined for potential accumulation points of hydrocarbons. Indications are bright amplitudes or structural high spots. Currently, however, there exists no surefire method to locate the accumulation points. To prove the presence of hydrocarbons, a well needs to be drilled. Only one in three wells encounters hydrocarbons! Finding hydrocarbons does not mean that they can be produced economically. First, the reservoir might not be porous enough to contain large amounts of fluid. Second, although there are enough pores, they are often only partially filled with hydrocarbons. Lastly, if the pores are not connected with each other, the hydrocarbons cannot be produced even if there is a large amount of porespace filled to the brim with oil! These three constraints are known as porosity, hydrocarbon saturation, and permeability. If they were uniform throughout the reservoir, measurements made in the exploratory well would be enough to determine the economic feasibility of the reservoir. Unfortunately, they vary greatly over the reservoir extension. The process of determining these spatially varying parameters is known as reservoir characterization.

Reservoir characterization is a critical aspect of reservoir development and future production management. Knowing the details of the reservoir allows simulation of different scenarios. The problem, however, is to define an accurate and suitable reservoir model including small-scale heterogeneity. Currently, the most abundant data about the reservoir, i.e. the seismic data, do not have enough resolution. The typical resolution of seismic data is on the order of 100 ft or more. Figure 3 illustrates the seismic resolution issue very nicely.

  
Figure 3: Photograph of one of the cliffs exposing the Roda sandstone in Spain. The geologic interpretation overlain by a typical seismic sound pulse is displayed on the right-hand side to illustrate the differences between seismic and geological resolution.
\includegraphics[width=6.5in]{wl.eps}

Boreholes yield an excellent description of the vertical heterogeneity at scales ranging from centimeters to hundreds of meters, albeit only locally and at a very high price. The lateral component, however, can rarely be derived from well data alone because there are seldom enough wells. This scarce information is usually not sufficient for building a deterministic reservoir model including all the small scale variations (Figure 4).
  
Figure 4: Although the wells yield a perfect measurement of the heterogeneity, they only capture the vertical components at a few locations. In between wells, the reservoir model needs to be filled in based on statistical and geological data.
\includegraphics[width=6.5in]{modwell.eps}

A viable alternative is to construct a deterministic framework using seismic data and well data. In a second step, small heterogeneities are filled in using statistical methods. For any statistical model, many different realizations can be generated which all exhibit the same statistical properties. This trait is actually an advantage, since the uncertainties of the different scenarios can be studied by generating a large number of equally possible reservoir models. Obviously, their validity depends highly on the geological realism. Three different models of stochastic small-scale reservoir heterogeneity appear suitable to describe the Zuata field:
1.
variogram-based,
2.
object-based, and
3.
geologic process response.

Variogram Based

For any two locations, the variogram as a function of distance relates to the statistical probability that the facies at these points differ. Any point of the reservoir gets a facies assigned such that the true facies is prescribed at known locations, e.g. along a borehole, while the statistics of the overall reservoir model approximates the chosen variogram. Parameters to be defined are, among others, the average distances between facies changes which are typically different in vertical and horizontal directions.
  
Figure 5: Two possible realizations of a reservoir sharing the same statistics. Warm colors represent clean sands, while colder ones correspond to clays.
\includegraphics[width=6.5in]{pic1.eps} \includegraphics[width=6.5in]{pic2.eps}

Object Based

Using the object-based approach, the reservoir model is generated from objects that have some genetic significance rather than being built up one elementary pixel or voxel at a time as with the variogram approach. For each lithofacies, a geometric object is selected, e.g. a half-ellipsoid representing a river channel. A realization of the reservoir is generated by randomly placing these objects in the model until the prescribed overall proportions for the different lithofacies are attained. Parameters to be defined include geometric factors, proportions, or how different objects are positioned with respect to others.
 
Figure 6: An object oriented reservoir model whith three channels.
\includegraphics[width=6.5in]{channels.eps}

Geological Processes

The geologic process response simulates the sedimentological processes which formed the reservoir in geologic time. Processes simulated include discharge of clastic sediments, transport, deposition, erosion, compaction, or subsidence. Due to their large number, it is often difficult to specify the input parameters. Many parameters are complexly related to each other.
 
Figure 7: Geological process model of a progressing delta.
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Figure 8: Geostatistical distribution of ancient river channels both filled by a geostatistical simulation of small scale heterogeneity. Succession of transgression, regression, and an alluvial channel system.
\includegraphics[width=4in]{sgrids.eps} \includegraphics[width=5in]{cyclevoid.eps}

Clearly, all three approaches can be used in conjuction. As illustrated, all these descriptions of reservoir heterogeneity contain free parameters which need to be determined. In many instances, such quantitative information is provided by measurements performed on outcropping formations considered geologically analogous to the subsurface case study. For example, the distribution of channels within a reservoir and their size may be derived from measurements performed on a deltaic outcrop for which the depositional environment and the sequence stratigraphy context are analogous to the reservoir. Other methods to obtain these parameters are analogous mature reservoirs with a dense well spacing, additional shallow boreholes, pressure and production tests, or horizontal wells. The most abundant data, however, stem from reflection seismic surveys. Ordinarily, seismic data is only used to delineate deterministic features or to constrain the stochastic modelling procedure. Instead, we propose to use seismic data to parametrize the stochastic reservoir model. Obviously, the validity of forecasts derived from such an approach depends highly on the geological realism of the statistical models. The first problem is the choice of the statistical model. The second problem is to choose a set of suitable parameters. Typically, the correct kind of statistical model can be determined by interpreting well and seismic data. Its parameters are often found by studying outcropping formations considered geologically analogous to the reservoir, or analogous but mature reservoirs with a well spacing dense enough. The key observation is that the parameters are usually not determined in situ from the reservoir of interest but from some analogous one. As previously stated, there are large quantities of in situ data available: the seismic data. Although it does not have enough resolution to resolve small features, it can be used to determine stochastic model parameters. The Seismic Reservoir Characterization Laboratory (SRCL) is being established to investigate how these parameters can be inferred from the seismic data, i.e., how can one determine the parameters needed to generate the statistical reservoir models shown in Figure 5 from seismic data as shown in Figure 2.

Infrastructure

The Seismic Reservoir Characterizatioon Laboratory (SRCL) is being established in the Geological Sciences Department at Virginia Tech to examine how seismic data relates to reservoir heterogeneities at scales below the typical resolution of 100 ft. Currently, the laboratory consists of Dr. Imhof and a graduate student. Two additional graduate students are expected to join within a year. The laboratory will be funded from both industry and government organizations in the form of research grants, sponsored research, and in the future, an industry consortium.


  
Figure 9: Schematic of seismic reservoir characterization project: field data or synthetic data is preconditioned by seismic data processing using industry standard software available to SRCL. The development of Heterogeneity Parameters Estimation software and its theory are the objective of this initiative. The wave equation simulators to generate synthetic test and research data are readily available, e.g., developed by our group or contained in the seismic processing software.
\includegraphics[width=4in]{process.eps}

SRCL owns or accesses a range UNIX workstations and other peripherals: SRCL uses the following software packages: Among others, SRCL has the following datasets available for research:

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Seismic Reservoir Characterization Laboratory

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The translation was initiated by Matthias Georg Imhof on 1999-12-10


Matthias Georg Imhof
1999-12-10