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Top-Down, Full Field, SubSurface ModelsTM,
KEY ADVANTAGES:
Data Requirement:
Top-Down Reservoir Modeling requires only production rate data to
start the analysis. The model is fine tuned as more data (logs,
cores, pressure, seismic …) is incorporated in the analysis.
Short Development Time:
Development of top-down models is measured in weeks and not months.
Analysis Complexity:
Unlike conventional reservoir simulation, no specialized skills are
required for the development and analysis of top-down reservoir model.
Any petroleum engineer can build, understand and fully analyze a top-down
model.
Applicability:
Top-down models are ideal for fields with at least 50 wells and
5 years of production history. More wells and more historical data enhance
the results.
Usage:
Can serve as a compliment to existing reservoir simulation models for an
independent analysis or serve as an alternative to conventional reservoir
simulation when such models are time and cost prohibitive.
Technology:
A completely new and innovative approach that integrates solid reservoir
engineering techniques with state-of-the-art in Artificial intelligence and Data
Mining (AI&DM).
Deliverables:
Field Development Strategies, Remaining Reserves, Optimum Infill Locations
and a List of Underperformer Wells as prime candidates for potential workovers
are among the deliverables of Top-Down modeling technology.
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Conventional reservoir simulation and modeling is a bottom-up approach. It starts with
building a geological model of the reservoir that is populated with the best available
petrophysical and geophysical information at the time of development. Engineering fluid
flow principles are then added and solved numerically so as to arrive at a dynamic
reservoir model. The dynamic reservoir model is calibrated using the production history
of multiple wells and the history matched model is used to strategize field development
in order to improve recovery. .
Top-Down full field simulation and modeling approaches the reservoir simulation and
modeling from an opposite angle by attempting to build a realization of the reservoir
starting with well production behavior (history). The production history is augmented
by core, log, well test and seismic data in order to increase the accuracy of the
Top-Down modeling technique. Although not intended as a substitute for the conventional
reservoir simulation of large, complex fields, this innovative and novel approach to
reservoir modeling can be used as an alternative (at a fraction of the cost) to
conventional reservoir simulation and modeling in cases where performing conventional
modeling is cost (and man-power) prohibitive. In cases where a conventional model of a
reservoir already exists, Top-Down modeling should be considered as a compliment to,
rather than a competition for the conventional technique, to provide an independent look
at the data coming from the reservoir/wells for optimum development strategy and recovery
enhancement.
Top-Down full field reservoir modeling starts with well-known reservoir engineering
techniques such as Decline Curve Analysis, Type Curve Matching, History Matching using
single well numerical reservoir simulation, Volumetric Reserve Estimation and calculation
of Recovery Factors for all the wells (individually) in the field. Using statistical
techniques multiple Production Indicators (3, 6, and 9 months cum. production as well as
1, 3, 5, and 10 year cum. oil, gas and water production and Gas Oil Ratio and Water Cut)
are calculated. These analyses and statistics generate a large volume of data and information
that are snap shots of reservoir behavior. This large volume of data is processed using ISI's
proprietary implementation of state-of-the-art in artificial intelligence and data mining
(neural modeling, genetic optimization and fuzzy pattern recognition), first using a set of
discrete modeling techniques to generate production related predictive models of well behavior.
The set of discrete, intelligent models are then integrated using a continuous fuzzy pattern
recognition algorithm in order to arrive at a cohesive picture and model of the reservoir as a whole.
The Top-Down, full field reservoir model is then calibrated using the most recent set of wells
that have been drilled. The calibrated model is then used for field development strategies
to improve and enhance hydrocarbon recovery.
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COMPONENTS OF TOP-DOWN
, FULL
FIELD RESERVOIR
MODEL
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Top-Down, Full Field Reservoir Modeling is an elegant integration of state-of-the-art
in Artificial Intelligence & Data Mining (AI&DM) with solid reservoir engineering
techniques and principles. It provides a unique perspective of the field and the
reservoir using actual measurements. It provides qualitatively accurate reservoir
characteristics that can play a key role in making important and strategic field
development decisions.
Following is a brief summary of several components of this
innovative approach to reservoir management. The key to the top-down modeling is
ISI's innovative integration of the following components using state-of-the-art
Artificial Intelligence & Data Mining (AI&DM).
Decline Curve Analysis
Conventional hyperbolic decline curve analysis is performed on oil, gas and water
production data of all the wells. ISI's proprietary Intelligent Decline Curve
Analysis is used to model some production data such as GOR and Water Cut that
does not usually exhibit a positive but rather a negative decline.
Type Curve Matching
Using the appropriate type curves from the literature, and other that have
been developed internally, production data from all wells are analyzed. Special
techniques are used to remove the inherent subjectivity associated with type
curve matching process.
History Matching
Segment-based history matching is performed using a numerical simulation model.
Production Statistics
General statistics are generated based on the available production data such
as 3, 6, 9 months cumulative production as well as one, three, five and ten
years cumulative productions. Similar data is generated for Gas Oil Ratio
and water cut as well.
Volumetric Reserve Estimation
Using Voronoi graph theory in conjunction with well logs volumetric reserves
are estimated for each well, individually. Estimated Ultimate Drainage Area
(EUDA) is a byproduct of this procedure. The EUDA is a dynamic property that
is related to the production scheme employed in the field and may change as
new wells are introduced.
Recovery Factor Calculation
Using the results of Decline Curve analysis and Volumetric Reserve Estimation,
a well-based Recovery Factor is calculated for all wells, individually.
A field-wide Recovery Factor is also calculated. This would be an item
that will be optimized in the consequent steps of the analysis.
Discrete Predictive Modeling
Results of the abovementioned analyses are a wealth of data and information
that are generated based on individual wells. Using ISI's unique implementation
of the state-of-the-art AI&DM techniques discrete, intelligent, predictive models
are developed based on the large amount of data and information that has been
generated. The predictive models represent all aspects of reservoir characteristics
that have been analyzed.
Continuous Predictive Modeling
Using ISI's innovative, two-dimensional Fuzzy Pattern Recognition (FPR) technology,
discrete predictive models are fused into a cohesive full-field reservoir model that
is capable of providing a tool for integrated reservoir management. This full field
model can identify the distribution of the remaining reserves, sweet spots for
infill locations as well as under-performer wells. Furthermore, the full field model,
upon calibration, is used as an effective tool for field development strategies.
Model Calibration
The full field model is calibrated based on classifying the reservoir into most to
least prolific areas prior to be used in the predictive mode. This is done using the
latest drilled wells in the field. This practice is an analogy of history matching of
the conventional reservoir simulation models. The calibrated model can then be used in
predictive mode for field development strategies.
Field Development Strategies
Performing economic analysis, while taking into account the uncertainties associated
with decision making, multiple field development strategies are examined in order to
identify the optimum set of operations that would result in recovery enhancement.
This process includes identification of remaining reserves, sweet spots for infill
drilling as well as under-performer wells.
REFERENCES
Intelligent Production Data Analysis; IPDATM
Top-Down Modeling project;
Paper in SPE Journal of Reservoir Engineering & Evaluation
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