Numerous uncertainties exist in estimating reserves and remaining recoverable
resources of conventional oil held by countries. These uncertainties include: geologic, production performance, product market and uncertainties in oil price forecast,
the use of ambiguous definitions and inclusion of different subcategories of conventional oil by reporting sources, the inclusion of politics in reserves estimation, the
inconsistent and unclear effects of aggregation of reserve data to country and
regional estimation, the anticipated volume of undiscovered oil, and the nature and
extent of reserve growth and its allocation to individual countries.
2.5.1 Uncertainty in Geologic data
Uncertainties arising from geological data include errors in getting the exact locations of the geologic structure, the field size, pay thickness, porosity and permeability variation, reservoir and aquifer sizes, reservoir continuity, fault position,
petrofacies determination, and insufficient knowledge of the depositional environment. A number of techniques are available for the quantification of geologic
uncertainties. One of the widely used techniques is to quantify the uncertainty in
the geological model with a geostatistical tool. Geostatistics involves synthesizing
geological data using statistical properties such as a variogram (Bennett and Graf
2002). This process enables the geologists to generate multiple realizations of the
geological models (Stochastic) which allows quantification and minimization of
uncertainties associated with the geological information.
Uncertainty in Seismic Predictions
• The quality of the seismic data (bandwidth, frequency content, signal-to-noise
ratio, acquisition and processing parameters, overburden effects, etc.)
• The uncertainty in the rock and fluid properties and the quality of the reservoir
model used to tie subsurface control to the 3D seismic volume
2.5.3 Uncertainty in Volumetric Estimate
The uncertainties in reservoir volume estimate will arise from several properties and
characteristics of the reservoir.
2.5.3.1 Gross Rock Volume (GRV) of a Trap
• The incorrect positioning of structural elements during the processing of the
seismic and lack of definition of reservoir limits from seismic data
• Incorrect interpretation
• Errors in the time to depth conversion
• Dips of the top of the formation
• Existence and position of faults
• Whether the faults are sealing to prevent further lateral migration of the
hydrocarbon
2.5.3.2 Rock Properties: Net-to-Gross and Porosity
The uncertainty associated with the properties of the reservoir rock originates from
the variability in the rock. It is determined through petrophysical evaluation, core
measurements, seismic response, and their interpretation. Most times, the core
samples are not properly handled carefully in the process of transporting it from
the field to the laboratory for analysis. Also in the laboratory, artificial properties are
induced during the core preparation and analysis. While petrophysical logs and
measurements in the laboratory may not be quite accurate, the samples collected
may be representative only for limited portions of the formations under analysis.
Thus, there are some risks associated with the petrophysical parameters estimation
such as depth matching, operational risks, log interpretation and reservoir
heterogeneities.
Fluid Properties
For fluid properties, a few well-chosen samples may provide a representative
selection of the fluids. The processes of convection and diffusion over geologic
times have generally ensured a measure of chemical equilibrium and homogeneity
within the reservoir, although sometimes gradients in the fluid composition are
observed. Sampling and analysis may be a significant source of uncertainty. PVT
or fluid properties vary with pressure, temperature, and chemical composition from
one region to another. As a result of this regional trend, correlations developed from
regional samples that are predominantly paraffinic in nature may not provide
acceptable results when applied to other regional crude oil systems that are dominant
in naphthenic or aromatic compounds.
The effective use of PVT correlations depends on the knowledge of their developments and limitations. In addition, samplings of these properties are not always
readily available due to cost and time. Thus, the engineers in view of achieving their
goals resort to the use of empirically derived correlations in estimating these
properties. However, a significant error is usually associated with the estimation of
these fluid properties which in turn propagates additional errors in all petroleum
engineering calculations.
2.5.3.4 Fluid Contacts
One of the parameters required for the estimation of hydrocarbon reserve is the gross
rock volume (bulk volume of the rock) whose accuracy is dependent on the fluid
contacts (gas-oil and/or water-oil contact). Therefore, if the contacts are not adequately determined, it will lead to either over or under estimation of the bulk volume.
Thus, affecting the overall value of the estimated reserve.
2.5.3.5 Recovery Factor (RF)
Recovery is based on the execution of a project and it is affected by the shape and the
internal geology of the reservoir, its properties and fluid contents, and the development strategy. If a reservoir is poorly defined, material balance calculations or analog
methods may be used to arrive at an estimate of the range of RFs. Uncertainty ranges
in the RF can often be based on a sensitivity analysis. Besides, the reservoir drive
mechanism and the problem of reservoir monitoring or management of some level of
uncertainties
Economic Significant of Reservoir Uncertainty
Quantification
During the life of a reservoir, the pre-reservoir and post-reservoir performance
evaluations are generally not equal. This is due to inadequate quantification of
uncertainties associated with the reservoir model input parameters and the resulting
composite uncertainty associated with the pre-reservoir performance prediction. The
decision to develop a reservoir is based on the prediction of production performance
following history-matching process. Likewise, in some instances, the decision to
obtain additional reservoir measurement data is taken when the uncertainty of the
forecast is great.
Hence, acquisition of further data is the reason for accurate quantification of
uncertainty associated with reservoir performance forecast so that projected recovery
will be accurately estimated for economic decisions. These vital reasons underline
the economic importance of increasing interest to properly quantify the uncertainties
associated with reservoir performance simulation.
2.6 Reservoir Characterization
An accurate description of reservoir rock, fluid contents, rock-fluid systems, fluid
description and flow performance are required to provide a sound basis for reservoir
engineering studies. Hence, proper reservoir characterization is important to analyze
the effects of heterogeneity on reserve estimation and reservoir performance due to
primary, secondary, and/or enhanced oil recovery operations. Porosity and permeability are important flow properties; an accurate reservoir characterization requires
accurate porosity and permeability description as a function of space.
Reservoir characterization is a process carried out to reduce geological uncertainties by quantitatively predicting the properties of a reservoir and define reservoir
structural changeability or variability. It is a process ranging from the discovery
phase to the management phase of a reservoir. Prior to performing a reservoir
simulation, accurate characterization is the first key step to undertake which helps
to identify uncertainty range inherent in reservoirs. Here we try to assess the range of
reservoir performance from an understanding of the subsurface uncertainties. This
concept is a limitation and it is not considered in the material balance method
presented in Chap. 5 of this book.
At this point, we need not border ourselves with a thorough review of literature in
reservoir rock characterization which would not be practically possible because of
the wide nature of this discipline and it is not incorporated in this present book.
However, the process combines the technical disciplines of geology, geophysics,
reservoir engineering, production engineering, petrophysics, economics, and data
management with key objectives on modeling each reservoir unit, understanding and
predicting well behavior, understanding past reservoir performance, and forecasting
future reservoir performance. Hence, it is used to assert a strong impact on plans for
the development and performance of a field.
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