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Top-Down, Full Field Reservoir Simulation & Modeling (TDM) of Shale Assets09122-04Shahab D. MohagheghWest Virginia University
Summary of Results from Completed GTI Marcellus Shale R&D ProjectMay 7, 2013Meeting Location
rpsea.org
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Contacts
PI: Shahab D. MohagheghWest Virginia [email protected] 293 3984
RPSEA PM: Kent [email protected]
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Outline
o Conventional Modeling of Shale Assetso Top-Down Modeling of Shale Assets
• Definition• Technology• Workflow
o Results & Discussions• History Matching• Sensitivity Analysis• Forecasting Production
Existing Wells New Wells (pads)
o Conclusions
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CONVENTIONAL MODELING OF
SHALE ASSETS
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Conventional Modeling of Shale Assets
o Material Balance: ultra-low matrix permeability makes obtaining average reservoir pressure impractical (Strickland-Blasingame-2011)
o Volumetric: Matrix flow properties can vary more widely than it is practical to measure and ultimate recovery also depends heavily on how the rock responds to hydraulic fracturing (Cox-Gilbert-2002)
o Decline Curve Analysis : Hyperbolic Decline behavior commonly observed in the shale wells but using a traditional Arp’s decline curve methodology during either transient flow or the transitional period between the end of transient flow and onset of boundary dominated flow may cause a significant error for reserve evaluation (Rushing and et al-2007)
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Conventional Modeling of Shale Assets
o Rate Transient Analysis: It can better represents the actual physics involved in reservoir dynamics but early applications face steep problems. It is a good tool for production analysis which gives some initial estimation of fracture properties for reservoir simulation (Nobakht, Mattar-2010)
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Conventional Modeling of Shale Assets
o Numerical Simulation : So far, the most flexible tool for shale gas assessments ... It is undergoing further development to include better understandings of the basic physics controlling gas flow as the industry learns more (Lee, Sidle-2010)
Dual Porosity/Single Permeability Models: Industry Standard Dual Porosity/Dual Permeability Models: Important in some shale plays in which
unstimulated fracture permeability may be significant (Cipola 2010) Quad Porosity Models (Micro-Scale Modeling): Consideration of four porosity
systems as Organic, Inorganic, Natural Fractures and Hydraulic Fractures Porosity- (Civan et al-2010, 2012)
Reduced Physics Surrogate Model: It provides a single-porosity model without desorption or grid refinement but with tuned values for permeability and porosity in the stimulated zone. (Wilson- Durlofsky-2012)
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TOP – DOWN MODELING OF
SHALE ASSETS
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Top-Down Modeling - Characteristics
o The only technology currently available for full field reservoir simulation and modeling of shale assets.
o Makes no assumptions, approximations, or simplifications about the state of our understanding of the underlying physics (flow within and across the matrix, natural fracture, and induced fracture).
o Incorporates only “Hard Data”• Type and Amount of Fluid and Proppant, Clean and Slurry Volumes, Proppant
Concentration, Injection Rates, Injection & Breakdown Pressure, ISIP, Well Logs, Well Types, Offset Well Performance, Frac Hit, etc.
o Avoids using “Soft Data”• Fracture Half Length, Fracture Width, Fracture Height, Fracture Conductivity,
Stimulated Reservoir Volume (SRV), etc.
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Top-Down Modeling - Definition
o A formalized and comprehensive, empirical, multi-variant, reservoir simulation model, developed solely based on field measurements (logs, core, well test, seismic, etc.) and historical production /injection data.
o Top-Down Modeling uses the pattern recognition capabilities of Artificial Intelligence & Data Mining in order to deduce physics from the available data, instead of imposing our current understanding of the physics on the modeling process.
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Top-Down Modeling – Technology (Paradigm Shift)
o FIRST PARADIGM: Thousands of year ago:• Science was Empirical (describing the natural phenomena)
o SECOND PARADIGM: Last few hundred years :• Theoretical branch (using models, generalizations)
Kepler’s Law, Newton’s Law of Motion, Maxwell’s Equation
o THIRD PARADIGM: Last few decades :• Computational branch (simulating complex phenomena)
Theoretical models grew too complicated to solve analytically
Jim Gray: (1944-2007) Legendary American computer scientist , received the Turing Award for seminal contributions to computer science.
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Top-Down Modeling – Technology (Paradigm Shift)
o FOURTH PARADIGM: Today:• Data-Intensive, Data Exploration, eScience• Unify theory, experiment, and simulation• Data captured by instruments or generated by simulator• Processed by Software• Information/Knowledge stored in computer• Scientists analyze database/files using data management tools
and statistics
Jim Gray: (1944-2007) Legendary American computer scientist , received the Turing Award for seminal contributions to computer science.
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Artificial Intelligence & Data Mining
o Inspired by functionalities of human brain.o Human brain has remarkable pattern recognition capabilities.o Using Machine Learning, we build computer software that can learn
from experience.o This is called “Data-Driven Solutions”
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Artificial Intelligence & Data Mining
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Artificial Intelligence & Data Mining
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Machine Learning in Action
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Top-Down Modeling - Workflow
Model Validation
Model Validation
Prediction of Reservoir Performance
Forecasting and Sensitivity Analysis
Dynamic InformationDays of
ProductionWellhead Pressures
Water Production
Rich Gas Production
Well Information
Static InformationReservoir
CharacteristicsCompletion
DataStimulation
DataGeomechanical
Reservoir Model Base Model Flow Regimes Effect
Well Types Effect
Well/Pads Distances Effect
Offset Wells Effect
Final Model
History Matching
Key Performance Indicator ‐ KPI
DATA
Integration into aSpatio‐Temporal Database
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RESULTS & DISCUSSION
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Results & Discussions
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Results & Discussions
History matching result of the entire asset.
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Well 10081 located in a two‐wells pad. Start of production: March 2009.
Good Results in History Matched Model
Results & Discussions
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Good Results in History Matched Model
Results & Discussions
SPE 161184• Modeling and History Matching of Hydrocarbon Production from Marcellus Shale using Data Mining and Pattern Recognition Technologies • Soodabeh Esmaili
100061000610062100621012510125
1009610096
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Well 10072 located in a three‐wells pad
Start of production: August 2006.
Poor Results in History Matched Model
Results & Discussions
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Poor Results in History Matched Model
Results & Discussions
1003210032
1002610026
10120101201003410034
SPE 161184• Modeling and History Matching of Hydrocarbon Production from Marcellus Shale using Data Mining and Pattern Recognition Technologies • Soodabeh Esmaili
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The error percentage of monthly gas production rate for all 135 wells was calculated using the following equation:
Results & Discussions
SPE 161184• Modeling and History Matching of Hydrocarbon Production from Marcellus Shale using Data Mining and Pattern Recognition Technologies • Soodabeh Esmaili
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Results & Discussions
o Results from the final history matched model:• Excellent Match (error < 10%):
101 wells (75% of wells)• Good Match (error < 20%):
22 wells (16% of wells)• Average Match (error < 30%):
6 wells (5% of wells)• Poor Match (error > 30%):
6 wells (4% of wells)
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Results & Discussions
Input Parameters used in developing the Top-Down Model
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Results & Discussions – Sensitivity Analysis
Degree of influence for each 8
parameters for the production of a pad as a function of
time.
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Results & Discussions – Sensitivity Analysis
Type curves can be generated for individual wells, group of wells, and for entire field.
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Results & Discussions - Forecast
Blind Ve
rification
Well 10105 located in a pad with 6 other laterals. This well came to production on November 2010, so it has 16 months history. The 20% of data for blind verification includes last 4 months.
Blin
d Ve
rific
atio
n
Well 10054 located in a two‐wells pad. This well came to production on February 2009, so it has 37 months history. The 20% of data for blind verification includes last 8 months.
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Results & Discussions - Forecast
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Results & Discussions - Forecast
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Results & Discussions - Forecast
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Results & Discussions - Forecast
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Conclusions
o Conventional modeling of production from shale (pre-Shale technology), over-simplifies a complex problem.• It uses “Soft Data” and cannot be used as inverse problem to help design new
wells and completions.• It lacks predictive capabilities for new wells/pads in new locations• Computationally expensive• Impractical for full field analysis
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Conclusions
o Top-Down Modeling may be used for full field modeling.o It has small computational foot print.o It demonstrates impressive predictive capabilities.o It can be set up as inverse problem to assist in identifying optimum
drilling locations, and to optimize frac design.o It uses only “Hard Data” and avoids “Soft Data” (interpretations)
