FastTrack Subsurface Evaluation Method

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Fast-Track Subsurface Evaluation Method

• Steve Sills
• Mark Williams
• Martin Wolff

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Fast-Track SS Evaluation Method

• Presentation Outline
• What is the Fast-Track SS Evaluation Method?
• Why use the FTSSEM?
• FTSSEM Steps
• Summary of Value Added
• Conclusions

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What is the Fast-Track SS Evaluation Method?

• A method to improve the rigor and efficiency of
  reservoir performance evaluations on Exploration
  prospects

Single-Well rates from all parameter combinations
Low-BTE-High Single-Well rates
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What is the Fast-Track SS Evaluation Method?

• Key Elements
• Conceptual simulation modeling to create physical
  representations of reservoir well geometries
• Integrated discipline approach to assigning
  parameter ranges (yes ...we still need maps and
  OHIP estimates!)
• Structured analysis of reservoir uncertainty
• Design of Experiments approach
• EnABLE (front-end for Eclipse)
• Produces unbiased ranges of OHIP, rate,
  recovery
• Low-BTE-High results extracted at any confidence
  level

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What is the Fast-Track SS Evaluation Method?

• Key Elements
• Computations done in hours on Linux cluster
• Spend more time testing, quantifying, and
  validating critical uncertainties their impacts
• Spend less time trying to defend back-of-envelope
  calculations

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Why Use the Fast-Track SS Evaluation Method?
MYTH 1

• We dont need to run simulation models! We
  havent even drilled the first exploration well
  yet!

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Why Use the Fast-Track SS Evaluation Method?
MYTH 2

• You must have lots of data before you can build
  simulation models!

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Why Use the Fast-Track SS Evaluation Method?

• Challenges
• Applicable analogues are not always available
• Performance ranges can be wide due to many
  particulars
• Usually one or more parameters are much different
• Most assessments require guesstimates of
• IPR
• Plateau length (if any)
• Declines
• Water or gas breakthrough
• Recovery process
• Forecasts are not from physical models
• Doing it the easy way can be harder and take
  longer
• Analogue estimates can be biased

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Why Use the Fast-Track SS Evaluation Method?

• Handles reservoir and/or well geometries that are
  too complex for simple analogues or 0-D
  analytical methods
• Provides a more rigorous technical basis for our
  development decisions economic estimates
• Considers multiple SS uncertainties ranges
• Results are consistent, auditable, easily
  updated
• Evaluates short-fuse opportunities
• Exploration prospects
• Merger and acquisitions
• Value Navigator Stage 1 framing calculations

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Fast-Track SS Evaluation Method Steps
Select Model Design
Identify SS Uncertainties
Estimate SS Uncertainty Ranges
Set Up Enable Project or DoE Table
Select Outcomes to be Analyzed
Build Run Simulation Models
Validate Simulation Results
Select Low-BTE-High Cases
Scale-Up to Full-Field Level
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Fast-Track SS Evaluation EnABLE Workflow
Prepare import base simulation deck
Make varied set simulation runs
Set up uncertainties
Make 25 scoping runs
Update proxy model
Select predictions
Monte-Carlo 10,000 trials with proxy model to
calculate s-curves (P10/P50/P90)
Initialize proxy model
Sufficient runs to select from and proxy model
robust?
No
Yes
Select Low-BTE-High Profiles
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Step 1 Select Model Design

• Pattern element model built of single lateral
  that drains 2 km2
• Test runs showed horizontal well too risky
• High-angle well completed in all zones gave
  better results
• Recovery processes tested
• Primary depletion
• Water injection

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Step 2 Identify Subsurface Uncertainties

• Net thickness
• Rsi
• Permeability
• Porosity
• Swir
• Dykstra-Parsons Coefficient
• Kv/Kh ratio
• Krw
• Krg
• Sorw
• Skin Factor

Sw Phi calculated from permeability
Complex relationships such as power-law cloud
transforms can be implemented with EnABLE User
Functions
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Step 3 Estimate Subsurface Uncertainty Ranges
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Step 4 Set Up Enable Project
EnABLE uses a Latin hypercube approach and linear
Bayes techniques to select multiple values within
each of the specified ranges. This allows a more
complete investigation of the uncertainty space,
making it more robust than the manual DoE
approach.
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Step 5 Select Outcomes to be Analyzed

• Cumulative recovery is the most frequently used
  response
• Discounted recovery is becoming more popular
  since it takes rates into account
• Other outcomes include
• OHIP
• Recovery Efficiency
• Initial Production Rate
• Plateau Length
• WOR
• GOR

Oil rates from all Enable runs
Recovery efficiencies from all Enable runs
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Step 6 Build and Run Simulation Models

• Define producing constraints
• Min Flowing BHP
• Max Drawdown
• Max WCT
• Max GOR
• Min Oil Rate
• Min Flowing WHP
• Use flow tables or interfaced tubing/facility
  models if you want
• Run models beyond economic limit to capture full
  range of reservoir performance

Oil rates from all Enable runs
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Step 7 Validate Simulation Results

• Do recovery factors look reasonable?
• Does sweep efficiency look realistic?
• Do we add some parameters?
• Do we revisit the uncertainty ranges?
• Are the initial rates plateaus attainable?
• Are the WOR GOR trends reasonable?
• Are the well geometries optimum?
• Are the operating assumptions valid?

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Step 7 Validate Proxy Model Results

• Do estimator statistics (quality of proxy model)
  show low uncertainty?
• Have sufficient runs been made to select
  representative Low-BTE-High cases?

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Step 7 Validate Proxy Model Results
Estimator (Proxy) Statistics Plot
Low Uncertainty Robust Proxy
Sufficient Runs for Case Selection
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Step 8 Select Low-BTE-High Cases
Statistics (s-curves with P10/P50/P90) Determined
using Proxy Monte-Carlo
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Step 8 Select Low-BTE-High Cases

• Select Low, BTE, High cases from ECLIPSE
  simulation results
• Choose cases honoring the Low-BTE-High values
  for OOIP ultimate recovery (or discounted
  recovery)
• Can be guided by statistics (P10/P50/P90) from
  s-curves
• Reflects uncertainty ranges quantified with
  EnABLE using deterministic simulation results
• Can select cases reflecting ranges of some
  (generally not all) parameters

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Step 8 Select Low-BTE-High Cases

• Select Low-BTE-High discounted oil recovery
• Select individual simulation runs that provide
  discounted recoveries close to those values
• Use actual rate profiles from those 3 cases
• WOR and GOR curves from 3 EnABLE runs can be used

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Step 8 Select Low-BTE-High Cases

• EnABLE uses proxy model to build tornado charts
• Show which parameters have the largest impact on
  a specific outcome
• Key uncertainties differ for static (OOIP) vs.
  dynamic (recovery) outcomes
• Low-BTE-High cases should reflect a logical
  progression of big-hitter parameters

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Step 8 Select Low-BTE-High Cases
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Step 9 Scale-Up to Full-Field Level

• Rate profiles are usually for wells or pattern
  elements
• Low-BTE-High profiles are placed into spreadsheet
  tool to take into account
• Varying well counts
• Drilling schedule
• Facility limits
• Multiple cases can be run without having to rerun
  full-field simulation models

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Summary

• How does the Fast-Track SS Evaluation Method add
  value?
• Conceptual reservoir simulation models can be
  quickly built and run that capture complex
  geometries uncertainty, particularly when it is
  not evident which analogs, if any, are
  appropriate
• EnABLE approach allows engineer to spend more
  time analyzing results less time manipulating
  data by automating simulation deck construction,
  job submission, post processing
• Understanding which uncertainties have the
  greatest impact early in the evaluation will
  facilitate appraisal decisions, economic
  evaluation, and data acquisition plans
• Results will have a physical basis that capture
  the dependencies between parameters and outcomes
• Provides unbiased Low-BTE-High profiles at known
  confidence levels, thus enhancing decision quality