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Figure 1.2 Wide range of physical scale for different data types associated with different geological and reservoir features.

1.3 SURE Challenge

The ultimate goal is not only to identify and delineate hydrocarbon charged in reservoirs, but also to quantitatively determine the volume and distribution of oil and gas it contains and quantify the associated uncertainty. No single measurement has the required response to achieve this. It is therefore essential to integrate the various types of data to a common earth model. This information includes seismic data, various types of well data, and geologic concepts. The challenge is to integrate measurements that are of different Scale, Uncertainty, Resolution, and Environment or the SURE Challenge as was introduced by Aminzadeh [1] and further elaborated at Aminzadeh and Dasgupta [2] and Aminzadeh [3]. The entire process of exploration for reservoirs to its abandonment involves acquisition and analysis of different types of data. These data types are associated with an enormous range of scale as shown in Figure 1.2. This spans ultra-sonic measurements of pores of the order of 1 millimeter to remote sensing measurements of basins of over 10 Kilometers wide. Examples of many other data measurements for many other objects that lie in between all the features are shown in this figure.

Admittedly not all the data types are integrated at the same time. Nevertheless, Scale and the wide range of differences for different data types is one of the challenges in reservoir characterizations. To make the matter more complicated is the fact that different data types are associated with different levels of uncertainties. For example, the direct measurements of rock properties from the core data may involve little uncertainty. The petrophysical information from well log data may be associated with somewhat more uncertainty. The seismic data used to ascertain reservoir properties, for their indirect nature of measurements involve much more uncertainty. Thus, Uncertainty level and its variations with respect to different data types is poses another challenge in data integration.

Figure 1.3 SURE Challenge: Having to deal with the wide ranges of Scale, Uncertainty, Resolution and Environment of different data types when integrating them, (from Aminzadeh [3]).

In addition, having different data types with vastly different underlying Resolution, also poses a challenge for data fusion. The resolving power of different data types is drastically different. As shown in Figure 1.3, some data types have very high resolving power. For example, while well log data can resolve a reservoir unit of under an inch, seismic data, generally speaking, may not be able to resolve a reservoir under 30 feet. Finally, the effectiveness and usefulness of different data types are impacted by the geological conditions and reservoir “Environment”. This can be associated with different reservoir types (carbonate, clastic, unconventional, heavy oil,) or different reservoir conditions (High Pressure/High Temperature, or reservoir depth (shallow water, Deep, or Ultra Deep Water.)

Figure 1.4 Areal coverage of well data is complemented by the larger areal sampling of the geophysical methods. VSP vertical seismic profile and Crosswell seismic fill a resolution “gap” between sonic log measurements and vertical seismic profiles. Courtesy of SR2020 (now Optasens).

We refer to these four key challenges: Scale, Uncertainty, Resolution and Environment as: the SURE Challenges. Top left side of Figure 1.3 illustrates three key data types: core, well log and seismic data. We will refer to it as a data pyramid. The base of the pyramid is the seismic with very large coverage but with limited resolution and lesser level of certainty. The top of the pyramid is the core data with very little coverage (only at a particular well location involving a fraction of the well) but with high level of certainty and resolution. Effective integration of all the data types, in spite of the SURE challenge is what reservoir characterization is all about. As we will show in the last chapter artificial intelligence and data analytics can play a key role in offering solutions to the SURE challenge.

      The bottom right-hand side in Figure 1.3 shows an upside-down pyramid comprised of a different aspect of integration. That is, vast amount of data needs to be combined with some technical knowledge and experience to perform effective data mining and ultimately reservoir characterization. As an aside, it must be pointed out that borehole geophysical data (e.g. Vertical Seismic Profile and Cross-Well data) fills the gap between core data and well log data on one side of the scale and 3D seismic data on the other side.

Figure 1.5 Vertical and spatial resolution of various geophysical, well logs and laboratory measurements. From www.agilegeoscience.com (left), and Optaense (right).

In general. The resolution of different data types for reservoir characterization and description varies considerably. Figure 1.5 illustrate such a large variability for core to log to borehole geophysics and seismic, gravity magnetics data and control source electromagnetics, among others. This further demonstrates the importance finding a solution to the “SURE Challenge” for reservoir characterization and other E&P problems. Also, see Ma et al. [8] addressing integration of seismic and geologic data for modeling petrophysical properties.

1.4 Reservoir Characterization in the Exploration, Development and Production Phases

      Reservoir characterization has different focus in different phase of the life of a field. In what follows we briefly highlight the main objective of Reservoir Characterization in Exploration, Development, Production (primary recovery) and Production Enhancement (secondary

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