Identification of Uncertainty in Reserves Estimation

Numerous uncertainties exist in estimating reserves and remaining recoverable

resources of conventional oil held by countries. These uncertainties include: geo￾logic, production performance, product market and uncertainties in oil price forecast,

the use of ambiguous definitions and inclusion of different subcategories of conven￾tional 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 loca￾tions of the geologic structure, the field size, pay thickness, porosity and permeabil￾ity variation, reservoir and aquifer sizes, reservoir continuity, fault position,

petrofacies determination, and insufficient knowledge of the depositional environ￾ment. 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 devel￾opments 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 ade￾quately 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 develop￾ment 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 perme￾ability 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 uncer￾tainties 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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