Oilfield Analytics: Optimize Exploration and Production ......Title: Oilfield Analytics: Optimize...
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Copyright © 2013, SAS Institute Inc. All rights reserved. ENERGY ANALYTICS SUMMIT 2014 Keith Holdaway Principal O&G Domain Expert, Global Energy Practices, SAS Institute Inc.
Transcript of Oilfield Analytics: Optimize Exploration and Production ......Title: Oilfield Analytics: Optimize...
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ENERGY ANALYTICS SUMMIT 2014
Keith HoldawayPrincipal O&G Domain Expert, Global Energy Practices,
SAS Institute Inc.
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OILFIELD ANALYTICS:
OPTIMIZE EXPLORATION AND PRODUCTION WITH
DATA-DRIVEN MODELS
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AGENDA
• Data Mining Virtuous Cycle
• Data Mining: What is it?
• Data Mining: O&G Input Space
• Deterministic to Probabilistic
• SEMMA Process: Case Studies
• The Witch Methodology
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DATA MINING
VIRTUOUS CYCLE
“Those who do not
learn from the past
are condemned to
repeat it.”
George Santayana
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DATA MINING: WHAT IS IT?
• Data Mining Styles
• Hypothesis Testing
• Directed Data Mining
• Undirected Data Mining
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DATA MINING: O&G INPUT SPACE
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Data
•Historical
•Real-time
Deterministic analysis
•Experience
Outcomes
•Situation A
•Situation B
•Situation C
Data
•Historical
•Real-time
Probabilistic analysis
•Experience
•Variability
•Complex relationships
Predictive Outcomes
•Situation A 95%
•Situation B 22%
•Situation C 36%
Actionable workflows
•Workflow A
•Workflow B
•Workflow C
DETERMINISTIC TO PROBABILISTIC
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SUBJECT MATTER EXPERTS
DATA SCIENTISTS
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CASE STUDIES
ENERGY ANALYTICS SUMMIT 2014
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Isolated the impact of significant variables Reconciled fragmented, unreliable, sparse data Analyzed well information from 211 wells and 2399 stages Developed accurate measure of poor vs. exceptional wells
Identified new models to include pressure depletion Identified 7 potential stages that could be eliminated Showed positive economic impact starting at $2.60 Mscf/d Identified additional opportunities to further economic benefit Identified optimization opportunities in 25% of the wells studied
Declining stimulation success in Pinedale Anticline Field Unable to understand critical factors impacting well performance High degree of uncertainty in recommending completion strategy Unable to understand interaction of multiple variables in complex geology
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Value: 2-3% incremental annual production with lower cost
Problem
Benefits
Approach
PINEDALE: COMPLETION STRATEGY OPTIMIZATION SPE 135523
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USING OUR NEURAL
NETWORKDERIVING VALUE FROM COMPLEXITY
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EXPLANATION OF
PARAMETERSFROM DATA SET MESA_SAS
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Value: 30% cost reduction in proppant with considerable savings
Identified key performance factors Analyzed data from over 11,000 wells Stratified wells by various key characteristics Developed statistical clusters based on significant variables
Reduced cycle time for job planning Increased annual well production over lifetime Developed consistent and repeatable workflows Identified significant opportunities for fracture cost reduction Enabled modeling methodology for application in de-risking new plays
Unable to understand impact of proppant volume on production Unable to isolate significant variables impacting fracking process Unable to understand interaction of multiple variables in complex geology
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Problem
Benefits
Approach
MAJOR BARNETT OPERATOR: HYDRAULIC FRACTURING
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Many work silos causing inefficiencies & slowing analytical efforts Inconsistent data quality, data verification, and analytics Limited toolsets and capabilities to analyze large numbers of wells Deterministic sampling process created uncertainty in forecasts Inconsistent results from well studies Inability to make best decisions based on available data
Background
Solution
Reduction in new well drilling Deferment reduced 25% Improved forecast accuracy from production wells and reservoirs 1/3 faster data collection Standardized and automated production surveillance
Problem
Reservoir maturity impacting performance creating increased need to change business process workflows for faster response to production deferments
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Value: 5% improvement in forecast accuracy; 2% increased recovery factor
AUTOMATING WELL SURVEILLANCE: SPE 141110
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Well Categorization
Production performance
Estimated EUR
Performance
Production forecast
Plan vs. Action
Data Integration
Integration
Standardization
Preparation
Well categorization
Well categorization
Good and Bad Wells
DCA Estimation
Event estimation
Analysis and distribution
TEXT
Well control and monitoring
Seismic data
Well Logs
Well Portfolio
DCA catalog
Production Reports
DCA Actual
Plan vs. Actual
Event quality control
Control analysis
Case Study OPTIMIZE PRODUCTION WITH
INTELLIGENT WELL
MANAGEMENT
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OPTIMIZE PRODUCTION WITH
INTELLIGENT WELL
MANAGEMENT
Case Study
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DATA
MANAGEMENTCase Study
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EXPLORATORY DATA
ANALYSISCase Study
• Surface hidden patterns
• Identify trends and correlations
• Establish relationships among independent and
dependent variables [Factors and Targets]
• Data QC
• Reduce input space: Factor Analysis, PCA
• Identify key parameters for model building
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• Traditional DCA
• Probabilistic methodology
• Well Forecasting Solution• Bootstrapping module
• Clustering module
• Data mining workflow
Case Study DATA
MODIFICATION
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1. Cumulative liquid production
2. Cumulative oil or gas production
3. Water cut (Percentage determined
by water production/liquid
production)
4. B exponent (Decline type curve)
5. Initial rate of decline
6. Initial rate of production
7. Average liquid production
Case Study CLUSTER
ANALYSIS
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METHODOLOGY
ASSESSMENTCase Study
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ANALYTICAL CENTER OF
EXCELLENCE
“The fundamental idea of cross-functional
teams and goals appears to surface about
every 10 years with a new label. Usually,
attempts to implement this concept in the E&P
business ended with utter failure for a variety
of reasons.”
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THE WITCH
METHODOLOGY
Which Witch?
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Q&A
ENERGY ANALYTICS SUMMIT 2014
