How to address high operational demands for drilling and fracking wells. How can improved planning address inefficiencies in unconventional fields. Agile end-to-end service value chains are needed from Edge Computing scenarios to newly formed Data Science teams. Making the Data Science teams efforts operational is an industry challenge. Real-time operations of all assets are needed for improved margins in the industry. Improve margins in shale oil fields with sand frac logistics improvements.

1. Field Development and Operational
Optimization for Unconventionals
John Archer
Senior Energy Solution Architect
joarcher@redhat.com

2. 2
Field Development Challenge

3. 3
Field Development Cycle
Long…Medium….Short loops
Field Development Optimizations

4. Business Optimizer4
Goals
Maximize economics of field
Minimize non productive time
Minimize required assets
Analyze macro level effects
Trucks (capacity, fuel)
Storage (location, capacities)
People (transportation, skills)
Rigs (type, location, performance)
Resources
Water, Sand, Chemicals
Safety and Trained Employees
Lease Restrictions
Weather Conditions
Constraints
Unconventional Asset Planning and Optimization
Improve overall field asset utilization and economics

5. DATA DRIVEN DRILLING AND PRODUCTION OPTIMIZATION @ RED HAT & IPCOS5
AI Workloads For Operators
ARTIFICIAL INTELLIGENCE & MACHINE LEARNING
Artificial intelligence (AI) aims to emulate or exceed human
performance by:
● Understand & adapt to complex content
● Engage in natural dialogs with people
● Enhance human cognitive performance
● Replace people on execution of non-routine tasks
Machine learning (ML) is a type of AI that provides computers
with the ability to learn without being explicitly programmed
● Dependent on the quantity and quality of training data
Intelligent applications learn from data in order to provide more
value with longevity and popularity
● Model, feature-extraction structures
● Feature-extraction, training, and prediction routines

6. Business Optimizer6
Exploring all all possible solutions of a
problem takes years! …Millions of years!
Software optimization engines are
expensive.
Expertise is expensive (mathematicians,
Operations Research specialist).
Planning problems are very hard to solve

7. 7

8. Poincaré Conjecture Solved by Grigori Perelman
8Source http://www.claymath.org/millennium-problems

9. Business Optimizer9
● Exploring all all possible solutions of a problem takes
years! …Millions of years!
● Software optimization engines are expensive
● Expertise is expensive
○ (mathematicians, Operations Research specialist)
Unconventional Planning Scenarios

10. Business Optimizer10
● Reduce CAPEX 5%
● Remove a Rig from the field - how to reallocate assets and crew
● Model additions and changes to Rig, Crew, Storage, Process to a Field
● Financial impact of increased planned BOE
Impact of What IF….

11. 11
Solve Complex Planning Problems
Scheduling Assigning Routing Finance
Optimization
Rig schedule,
conference
presentations
affinity / skill matching,
wage calculation,
engineers, landmen,
frack crews, workover
crews
Sand, water, propants
delivered and
optimized routed
trucks, rigs, pipeline,
personnel
Field investment
portfolio balancing, JV
risk spreading,
Explainable Artificial Intelligence (XAI)

12. 12
Change Velocity: Frac Sand Demand Forecast
● Today most sand from
Chippewa county, WI
● Increasing Demands
● TX based sand mines under
construction
● DUCs have built up
● Frac fleet availability limited
● Permian will be 119B lbs by
2022
● Switch from pneumatic to
belly dump trucks
● Moves to finer grade mesh
Source: IHS Markit

13. Business Optimizer13
Business Value
-15% Driving Time (Fuel and Driver savings)
-3% Reduction in Non Productive Time
Determine optimal sand density lbs/ft
Rules:
Direct to pad when needed
Storage availability 40/70/100
Redirect due to well site issue, weather or traffic
Increase sand while production volumes increase
Sand Delivery and Proppant Optimization
Execute deliveries and changes with more efficiency and NPV maximized

14. Business Optimizer14
Constraints Number of Drill Crews
Number of Rigs
Number of Workover Rigs
Lease Terms
Distance
Storage
Cash Flow Cap
Drill Planning Optimization
Rules
Medium Maintain 6 Drilling Rigs and 2 Workover Rigs - Add
a Cost to Mob to DeMob
Medium Add Cost and Days to Move Rig - Escalate Cost if
Rig Moves Beyond a Circle
Hard Do Not Workover/Drill Wells unless Facility Can
Handle Production after Completion Date
Hard Add Need to Drill 1 Well in Every County Every 6
Months to Maintain Lease, Or In a Certain Lat/Long Box By
a Fixed Date

15. Business Optimizer15
● Exhaustive Search
○ Brute Force
○ Branch And Bound
● Construction Heuristics
○ First Fit (Decreasing)
○ Weakest/Strongest Fit (Decreasing)
○ Cheapest Insertion
● Metaheuristics (Local Search, ...)
○ Hill Climbing
○ Tabu Search
○ Strategic Oscillation
○ Simulated Annealing
○ Late Acceptance
○ Step Counting
Planning Heuristics

16. Business Optimizer16
Cloud-based Shift-Assignment solution
Driver Rostering Example
● OpenShift-based, cloud-ready, Truck logistics solution
● Driven by data: Crew, Rigs, Trucks, Fields, Wells

17. Business Optimizer
Operator Field Level Optimization Workflow
Objective Function
Planning Department Goals
– Increase ROI, Not to
Exceed Capex, Production
Targets by Asset
Optimization
Well Projects
Optimization
Facility Projects
Input Information
Asset Production
Forecast and Facilities
Limitations
Input Information
Rig Availability,
Capability, and
Contractual Terms
Constraints
Legal, Facilities,
Completion and Drilling
Limitations

18. DATA DRIVEN DRILLING AND PRODUCTION OPTIMIZATION @ RED HAT & IPCOS18
Openshift - Platform For Intelligent Applications
INTEGRATED FRONTEND FOR JOB MANAGEMENT, MONITORING, DEBUGGING,
DATA / MODEL / EVALUATION VISUALIZATION
SHARED CONFIGURATION FRAMEWORK & JOB ORCHESTRATION
SHARED UTILITIES FOR GARBAGE COLLECTION, DATA ACCESS CONTROLS
PIPELINE STORAGE
MODEL
EVALUATION
&
VALIDATION
LOGGING
MODEL
SERVING
DATA
INGESTION
DATA
ANALYSIS
DATA
TRANSFORMATION
DATA
VALIDATIO
N
TRAINER

19. F8979-201710
19
Build Intelligent Applications With Red Hat

20. Container
Business
Automation
Container
Integration
Container
Data &
Storage
Container
Web &
Mobile
Openshift: The Platform For Intelligent Apps
OpenShift Application Lifecycle Management
(CI/CD)
Build Automation Deployment Automation
Service Catalog
(Language Runtimes, Middleware, Databases)
Self-Service
Infrastructure Automation & Cockpit
Networking Storage Registry
Logs &
Metrics
Security
Container Orchestration & Cluster Management
(kubernetes)
Container Runtime & Packaging
(Docker)
Enterprise Container Host
Red Hat Enterprise LinuxAtomic Host
RED HAT PRODUCT COMPONENT
JBOSS DATA GRID IN-MEMORY
PIPELINE STORAGE
JBOSS DATA
VIRTUALIZATION
DATA
TRANSFORMATION
JBOSS AMQ DATA
INGESTION
JBOSS BRMS RULES EXECUTION &
EVENT PROCESSING
JBOSS BPM BUSINESS PROCESS
AUTOMATION
JBOSS FUSE DATA TRANSFORMATION,
API INTEGRATIONS
3SCALE API MANAGEMENT
RED HAT STORAGE CONTAINER-NATIVE
PIPELINE STORAGE

21. Business Optimizer
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