Reservoir Characterization. Группа авторов
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Название: Reservoir Characterization
Автор: Группа авторов
Издательство: John Wiley & Sons Limited
Жанр: Физика
isbn: 9781119556244
isbn:
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 and tertiary recovery) phase. For the very reason, the notion of reservoir characterization often times means different thing to geologists, geophysicists and reservoir engineers. This is primarily due to the fact that their primary focus is different phases of life of the field.
1.4.1 Exploration Stage/Development Stage
The pre-development (exploration) phase requires delineation of the reservoir limits and assessment of its economic feasibility. Development stage requires somewhat more accurate assessment of the reservoir extent, better appraisal of the economic viability of the reservoir and placement of new wells for further delineation of the reservoir. More detailed depth imaging using advanced geophysical methods, borehole geophysics applications as well as fault seal analysis, and better understanding of reservoir compartmentalization are some of the objectives of reservoir characterization at this stage. At this stage it is necessary to determine the reservoir drive mechanism and the size and strength of the aquifer. At the development phase, reservoir continuity rather than reservoir delineation becomes the focus. The focus in this phase is to identify the structural extension or truncation beyond well control in order to minimize the drilling of dry holes. While the pre-development and development phase require considerable attention to reservoir characterization. Geophysicists construct an initial model by correlating lithology, porosity, net pay thickness and other properties at the well location using seismic attributes from surface 3D seismic, VSP and synthetic seismograms. The initial model is then updated by computing seismic attributes between the wells in order to predict reservoir properties in 3D. Seismic attributes like acoustic impedance, variations in amplitudes, frequencies, interval velocities and instantaneous phase are applied in the computation.
1.4.2 Primary Production Stage
As the primary production of the reservoir begins, the goal is to position wells at optimal locations that would maximize hydrocarbon recovery. During secondary recovery and then enhanced recovery process, the engineer’s objective is to maximize the volume of hydrocarbon contacted by injected fluids. This is to achieve maximum volumetric sweep efficiency for fluid production. To minimize cost and risk, engineers attempt to predict reservoir performance—for both planning and evaluation of hydrocarbon recovery projects. Reservoir description in terms of reservoir architecture, flow paths, and fluid-flow parameters are the key to reservoir engineering. Accurate prediction of reservoir production performance is predicated primarily on how well the reservoir heterogeneities are understood and have been modeled and applied for fluid-flow simulation. This stage requires integration of reservoir characterization models with reservoir simulation, history matching for production optimization. Reservoir management process conducts reservoir related studies and applies the results from fluid flow modeling in defining, updating and optimizing a development plan for producing the reservoir and forecast the production profile. This phase also involves optimization and management of reservoir performance СКАЧАТЬ