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New Developments in Screening and Scoping Methodologies for
CO2 EOR
Glen MurrellWyoming Enhanced Oil Recovery Institute
Presented at the 14th Annual CO2 Flooding Conference
December 11-12, 2008
Midland, Texas
The objective of the EORI database, screening and scoping program is to:
• Consider prospect identification as an exercise in decision making utilizing a systematic information management
process
• The EORI database, Screening and Scoping programs are components in a system of information management.
• Database: 350 Mb data, multiple tables, in excess of 4000 Field Reservoir Combinations (FRC’s).
• Accurately subdividing database into groups suitable for particular EORI applications is impossible to do with a high degree of certainty (would require a simulation model of each reservoir).
• Screening and scoping reduces uncertainty and dataset size.
Information Management
• Decision Making Under Uncertainty• Cannot define outcome probability with any certainty because
database is limited.
• Level of uncertainty (or risk) depends on number of potential “false positives” in dataset.
• Screening removes as many of these potential “false positives” as possible, thus reducing the uncertainty.
• More parameters are introduced (e.g. economics), adding uncertainty and then Scoping reduces this by again removing potential false positives.
Decision Making
7Risk (Potential for reality to be something
other than what is expected)Precision(Reduction of predictive uncertainty)
EORI Reservoir Database• A dbase of EOR pertinent parameters
• Production related: Cumulative Prod., OOIP, decline rates, water cut
• Petrophysical: Poroperm, Field size, Net pay, Lithology, Depth, Temp., Fracture Pressure.
• Crude Chemistry: API, Viscosity, mwC5+, MMP, Sulfur content.• Produced Water Chemistry: TDS, pH, Calcium, Chloride,
Magnesium.• Field information: locations, shape files, well counts.
• Data sources• External datasets – Wyoming Geological Association (WGA),
Wyoming Oil & Gas Conservation Commission (WOGCC), Department of Energy/National Energy Technology Laboratory (DOE/NETL).
• Internal data acquisition – decline curve analysis, lab studies.
EORI Reservoir Database
• Data Processing
• Data-Cleaning• Sorting, relational queries, random checking
• Data-Infilling• Using correlations (e.g. Depth-Temp, API-Viscosity)
• Data-Mining• Correlations, geo-spatial analysis, multi-component statistics
EORI Reservoir Database
Viscosity (cp) vs. API Correlation
y = 46.283x‐0.1878
R2 = 0.9479
1.E+01
1.E+02
1.E+00 1.E+01 1.E+02 1.E+03 1.E+04
Viscosity
API
• Data Infilling example (Viscosity vs. API)
Figure courtesy of Samiha El-Sayed.
Data from DOE/NETL Crude Oil Database
EORI Reservoir Database
• Data Mining example (Porosity vs. Depth)
• May not identify individual prospects but will reveal trends and relationships that may aid understanding of the play.
Screening• Five major types:
• 1) Database screening - filtering database using certain criteria• e.g. Reservoir crudes with API > 22o
• 2) Process Screening - screen database for all reservoirs amenable to certain EOR method
• e.g. Reservoirs amenable to CO2 miscible flooding
• 3) Project Screening - Assess amenability of various EOR methods in a single reservoir based on criteria
• e.g-1 What is the most appropriate EOR method for reservoir ‘A’.
• e.g-2 Will CO2 flooding be technically (or economically) feasible in reservoir ‘A’
Screening
• Five major types:
• 4) Geospatial screening - screening on proximity to other resources.
• e.g. Reservoirs within ‘x’ miles of CO2 pipeline
• 5) Economic Screening (Scoping) - using some economic function determine economic viability of CO2 flood.
• e.g. Reservoirs profitable with 20% ROR
Screening
• Systematic screening has two requirements:• A set of criteria built on empirical evidence or experience.• A framework within which to compare parameters to the
criteria set.• “Go/no-go” criteria• “Fuzzy” criteria• Neural networks, machine learning, artificial intelligence
• Benchmark example: Taber et. al. 1997 Parts I & II. SPE 35385 & 39234
Screening
• Criteria Selection:• Generally experience
based and acquired from laboratory. experiments and/or field case studies.
• Represent application of global knowledge base.
• May need to be tuned on a regional basis.
Figure courtesy of Samiha El-Sayed.
Data from Oil and Gas Journal 2008 EOR survey
Screening
• Simple ‘go/no-go’ example: CO2Mac• Single value criteria (Means, Medians)
Screening
• Fuzzy logic screening example• Range based criteria (Max and Min)• Qualitative criteria• Commercial Example: SWORD
Image from SWORD, Pre simulation/screening tool for rapid IOR and reservoir evaluation Version 2.1.0.6. Copyright 2000-07 IRISwww.iris.no
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Screening
• Future Work
• Tuning the criteria for Wyoming• Criteria weighting system• Identification of EOR potential groups
• e.g. all those reservoirs amendable to CO2 flooding• Greater GIS utilization
Scoping• “Economic” Screening
• Requirements• New cost and revenue based parameters• Single criteria (ROR)• Some method of estimating production
• Production analogues, Compositional model
• Outputs• Incremental Oil• PV of Profits • Cumulative CO2 use• Average CO2 demand• Operating Period
Scoping
• P = Price of Oil• Qt = the projected incremental amount of oil recovered in
period t• xR = Royalties• xSP = severance and property taxes• pqp
t = cost of purchasing CO2• cr
t qrt = cost of recycling and re-injecting CO2
• cot = other incremental operating costs
• K = upfront investment costs
22
GREEN RIVER
WIND RIVER
POWDER RIVER
BIG HORN
Jackson
Gillette
Pinedale Casper
Laramie
Cheyenne
Rock Springs
Madden/ Lost Cabin
Shute Creek
Medicine Bow Fuel & PowerCoals to Liquids Plant
SaltCreek
Monell
Lost Soldier/Wertz
BeaverCreek
HartzogDraw
BigMuddy
Grieve
CHEVRON
ANARDARKO
EXXONMOBIL
23
GREEN RIVER
WIND RIVER
POWDER RIVER
BIG HORN
Jackson
Gillette
Pinedale Casper
Laramie
Cheyenne
Rock Springs
Madden/ Lost Cabin
Shute Creek
Medicine Bow Fuel & PowerCoals to Liquids Plant
SaltCreek
Monell
Lost Soldier/Wertz
BeaverCreek
HartzogDraw
BigMuddy
Grieve
CHEVRON
ANARDARKO
EXXONMOBIL
BHB PRB
WRB Oil: $70 bbl
CO2 : $2.50 Mcf
ROR: 20%
Spac.: 80 A/W
Analog: LSTP
GRB
Scoping
• Future Work
• Synthetic Dimensionless Curves• Greater GIS utilization• Risk and sensitivity analysis
Screening and Scoping Models are tools for identifying prospects.
They should not be used to assess categorically the viability, technical or economic, of
individual reservoirs.
Summary
• The EORI database, screening and scoping programs are components in a information management system.
• The three components have all been used, both independently and collectively, for identification of prospects.
• Future developments involve greater utilization of GIS, criteria and methodology optimization, risk and sensitivity analysis.
Vladimir AlvaradoBenjamin Cook
Carolyn CoolidgePatrick Gardner
Andrew KauppilaDiem Pham
Owen PhillipsBrian Reyes
Samiha El-SayedSavanna Sharp
Geoff ThyneKlaas van ‘t Veld Schaochang Wo
Peigui YinXiufen Yu
Contributors
