Intelligent Energy: The Synergy between Computers … · 2/28/2018 · (variable-speed) Blow-out...

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21 – 22 February 2018 Houston, TX

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Intelligent Energy: The Synergy between Computers and Oil & Gas

Michael Nikolaou

Intelligent Energy?

Need to Spend Energy to Get EnergyUS

Domestic Oil

US Domestic

Oil 1970

US Domestic

Oil 2005

US Imported

Oil 1970

US Imported Oil

Natural Gas

Nuclear

Firewood

PV-solar

Biodiesel

Tar Sands

10 20 30 100

Total Energy(Quad Btu)

Source: C.A.S. Hall, http://www.theoildrum.com/node/3810US

Domestic Oil

US Domestic

Oil 1970

US Domestic

Oil 2005

Pivotal Technologies for Oil & Gas

• Key technologies:– 3D seismic– Horizontal drilling– Hydraulic fracturing

• A key supporting enabler:– Computers

Intelligent Energy

• Upstream– Drilling:

• Managed pressure drilling (MPD)

– Cementing: • Reverse-circulation cementing• Avoid natural gas leaks (Zonal isolation)

– Fracturing: • Optimal geometry and placement

– Production:• Abnormal situation detection, diagnosis, and correction

• Midstream– Transportation of natural gas as CNG

Intelligent Energy

Managed Pressure Drilling (MPD)

Annulus

Maintain Downhole Pressure When Drilling

What Could Go Wrong?

MPD and the Need for Automation

Extraction pump

(variable-speed)

Trip tank

(variable-

opening)

Kill tank

Kill pump

(variable-speed) Blow-out

preventer

On/Off

Variable

opening

Well control line

Main pump

(variable-speed)

On/Off

Stand pipe

On/Off

Back-pressure

pump (variable-

speed)

On/Off

Return pump

(variable-speed)

Supply

Injection pump

(variable-speed)

Isolate pump

(variable-speed)

On/Off

Additives

Model Predictive Control (MPC)

Target

History Matching (?)

Target

MPD Case Study:Dual-Gradient Drilling

Model-Predictive Control for Managed Pressure Drilling: MPC for MPD

• Manipulated inputs:

𝐮 =

𝑢1𝑢2𝑢3

𝑞pump

𝑞sub𝑣ds

𝑞pump

𝑞sub𝑣ds

• Controlled outputs – Keep at setpoint:

𝐲 =𝑦1𝑦2

Hook position

• Additional objective – Keep at setpoint:𝑞pump = 𝑞pump

• Constraints:𝑦1,min ≤ 𝑦1 ≤ 𝑦1,max

𝑦2,min ≤ 𝑦2 ≤ 𝑦2,max

Δ𝑦2,min ≤ Δ𝑦2 ≤ Δ𝑦2,max

Δ𝐮min ≤ Δ𝐮 ≤ Δ𝐮max

Operating Scenarios

Symbol Mode Observed maximum

drill string velocity

No MPC

1 pump 1000 / minˆu q l

ds 12 [m/min]

ds 19 [m/min]

ds 24 [m/min]

ds 28 [m/min]

set set

1 pump 1000 / minˆu q l

ds 12 [m/min]

ds 19 [m/min]

ds 24 [m/min]

ds 28 [m/min]

Drill String Velocity(Tripping into the well)

Hook Position

Main Mud Pump Flow Rate

Subsea Pump Flow Rate

Bottomhole Pressure (BHP)

Insight

• Computers and automation can improve long entrenched practices

• …as long as 𝑠𝑖𝑡𝑢𝑎𝑡𝑖𝑜𝑛𝑎𝑙 𝑎𝑤𝑎𝑟𝑒𝑛𝑒𝑠𝑠 is maintained

Intelligent Energy

Reverse-Circulation Primary Cementing (RCPC)

Drilling Offshore Wells

Annulus

Offshore RCPC

RCPC Simulator Development

• Visualize T, P

• Develop simulator in COMSOL

RCPC Simulator Development

Insight

Intelligent Energy

Natural Gas: A “Bridge” Fuel

Avoiding Natural Gas Leaks

Data Source: Howarth RW, R Santoro, and A Ingraffea, Climatic Change (2011) 106:679–690

Ingredients for Recipes to Cement a Well Well

Casing design

• Internal casing diameter

• Casing weight/length

• Centralization

• Casing and hole diameters ratio

Cement design

• Slurry density

• Free water content

• Spacer

• Cement flow rates

• Cement additives: i.e. accelerator, retarder, fluid loss, gas migration etc.

Rheological Properties

• Gel strength

• Plastic viscosity

• Yield stress

Other Factors

• Bottom hole circulation temperature (BHCT)

• Bottom hole static temperature (BHST)

How to Model Outcomes of Recipes?

• Cement job evaluation (45 wells)

– Sustained casing pressure (SCP)

• Categorical data: Leak/No-Leak (19/26 wells)

• Classification Model: PLS/DA

• Cross-validated correct classification: 81%

– Estimates of conclusive or inconclusive cases

– Ranking of variables in order of importance

Ranking of Variables

Similar and Different Wells

Insight: How to Avoid Leaks

Insight

• Recipes and Rules-of-Thumb:– Cold water leads to poor mixing

– High gel-strength development in short time span desirable for good a cementing job

– High volume of mixing water not good for a cementing job

– Use of extenders in case of high water to cement ratio

– Use of fluid loss cement additive to reduce fluid losses from cement slurry

– Low (but not too low) mud-weight to cement density ratio for better mud displacement

Intelligent Energy

Fracture Design for Horizontal Wells in Low-Permeability Reservoirs

The Unified Fracture Design Method

• Given– Proppant mass, 𝑀𝑝

– Formation permeability, 𝑘– Pay-zone thickness, ℎ– Fracture height, ℎ𝑓– Fracture volume, 2𝑉𝑓

• Define

– Dimensionless fracture conductivity: 𝐶𝑓𝐷 ≜𝑘𝑓𝑤𝑓

𝑘𝑥𝑓

– Proppant number: 𝑁𝑝 ≜4𝑘𝑓𝑉𝑓

𝑘ℎ𝑓𝑥𝑒𝑦𝑒

– Drainage area aspect ratio: 𝑦𝑒𝐷 ≜𝑦𝑒

𝑥𝑒

– Penetration ratio: 𝐼𝑥 ≜2𝑥𝑓

𝑥𝑒

Optimal fracture:

𝑥𝑓,opt =𝑘𝑓𝑉𝑓

𝐶𝑓𝐷,opt𝑘ℎ𝑓

𝑤𝑓,opt =𝐶𝑓𝐷,opt𝑘𝑉𝑓

𝑘𝑓ℎ𝑓

𝐶𝑓𝐷,opt = 𝜙(𝑦𝑒𝐷 , 𝑁𝑝)

Optimal Fracture Designs

Insight

Drained

region

Undrained

region

Low pN

Fracture

High pN

Low eDy

High eDy

Extended to Economic Optimization

Production Estimation

Module

( ), ( )q t Q t

Objective Function

Input Variables:

Reservoir/ Well Properties

Fluid properties

Proppant type

(Economic)

Optimization

Optimal Design opt

wn , opt

fn , opt

pM , opt opt opt

, ,f f fh x w

Fracture Design Module

UFD + PKN + P3D

, , ,f f f treath x w p

Design Variables:

No. of wells, w

No. of fractures, fn

Mass of Proppant, pM

Bounds on

wn , fn , pM

Design

Constraints

(Physical)

Optimization

GLOBAL

Intelligent Energy

Electric Submersible Pumps (ESP): Key for Artificial Lift

• ESP– Second most common artificial-lift

technology– Biggest market share in cost

• Most common ESP concerns:– High workover costs – Low system run life

Image Sources: Spears & Associates, 2009; http://www.chemtech-online.com/O&G/Priyanka_april_may12.html

What Could Go Wrong?

• Causes

• Failures

Design related

Equipment capacity

Material selection

System configuration

Leaking Failed pressure test Stuck Burst Bent Broken Disconnected

Mechanical

Burn Corroded Worn Melted Overheated

Material

Plugged with solids Contaminated fluid

Others

Short circuit Open circuit Faulty power

Electrical

Fabrication

Manufacturing problem

Improper quality control

Reservoir

Reservoir fluids Reservoir

performance

Operational

Normal wear and tear

Inadequate training

Installation

Assembly procedure Installation

procedure

Storage/ Transport

Improper storage Improper

transportation

Source: GE

Standard ESP Monitoring vs. Data-Driven

• Limited to Δpower due to

– Fluid density

– Flow rate

Real-Time Decision Variables

Flowline Pressure

Wellhead Pressure

Wellhead Temp

Motor Current A

Motor Current B

Motor Current C

Discharge Pressure

Intake Pressure

Intake Temperature

Leakage Current

Motor Temperature

Vibration

Water Cut

P Pump

P Choke

P ESP to Wellhead

Free Gas Intake

Overall System Efficiency

Pump Fluid Density

Pump Head

Total Liquid Flowrate

Total Pump Head

ESP Analytics Workflow

Prescription of Preventive action

Determine stable operation range

Reset variables based on ranking

Compute statistics and match patterns

Diagnosis of Potential Cause

Ranking of Variables Plot Contribution Charts

Prediction of Failures

Determine Decision Variables Robust PCA Modeling Pattern Recognition

Case Study

• Major oil-field in Middle-East

• Data from downhole and surface gauges

• Analysis for five events

• Data over 2 years

– Data frequency: 1 minute

• Analysis performed on Matlab

Development of Monitoring System

ESP Health Detection

Principal Component 1 Principal Component 1

ESP Health Prediction Patterns

ESP Health Diagnosis

Decision variables

Trip 1

ESP Health Correction

Decision Variables

Parameter behavior identification

Stable Zone Trip 1 Trip 2

ESP Health Correction

ESP Analytics in a Nutshell

Intelligent Energy

Monetizing Natural Gas:Conversion Strategies

• Gas to wire (GTW)

• Gas to liquids (GTL)– Chemical reactions– 10 bcf/d ~ 1MMb/d

• Liquefied natural gas (LNG)– Freezing (-162ºC, -260ºF)

• Compressed natural gas (CNG) – Compression (150-250 atm)– Possibly chilling

LNG or CNG?

• Cost structure difference– LNG

– CNG

Fleet Terminals

0% 100%40%

Term.Fleet

0% 100%80%

Conclusions

• Marine LNG or CNG?

– Key factor: Distance, L• Short distance: CNG

• Long distance: LNG

– Secondary factor: Amount of gas transported, q

– Gas price not important (for choice between the two)

– Composite CNG containers have economic merits

0 2 4 6 8 100

Distance , L kmiles

Lines of NPVCNG NPVLNG

Liquefied Natural Gas (LNG): Established International Trade

Source: BP Statistical Review (2011)

Potential Marine CNG Markets

Insight

• CNG distribution patterns:

– Hub and spoke

– Milk run

CNG for the Caribbean: The Big Four

CNG for the Caribbean: The Small Ones

Insight

• Marine CNG: "𝑆𝑚𝑎𝑙𝑙 𝑖𝑠 𝑏𝑒𝑎𝑢𝑡𝑖𝑓𝑢𝑙"

Insight

• LNG vs. CNG

– Primary differentiator: Distance, L

CNG Metal LNGCNG Metal LNG CNG Composite LNG

Quick Look Under the Hood

At the End of the Day…

Closing Thoughts

• Computers for both number crunching and insight

• Data is key, but no longer the bottleneck

– “Without data you’re just a person with an opinion.”W. Edwards Deming, perennial

– “Without An Opinion, You're Just Another Person With Data”Milo Jones and Philippe Silberzahn, Forbes, 2016

• Cross-fertilization from related industries

Acknowledgements

• Funding– IRIS, Statoil– RPSEA– Anadarko, Weatherford, Chesapeake– Halliburton– XGas

• Collaborators– MPC for MPD

• Drs. Gerhard Nygaard, Oeyvind Breyholtz, Jan-Einar Gravdal(IRIS/University of Stavanger)

• Dr. John-Morten Godhavn (Statoil)

– Cementing Well(s)• Kyle Macfarlan (UH, Sim2TheMax)• Shyam Panjwani, Shobhit Misra (UH)• Jessica McDaniel (CSI)• Crystal Wreden, Matt Schinnell (Weatherford)

– Hydraulic Fracturing Design• Dr. Srimoyee Bhattacharya (UH, Shell)

– ESP Monitoring• Supriya Gupta (UH)• Dr. Luigi Saputelli (UH, Frontender)• Dr. Cesar Bravo (Halliburton)

– Marine CNG• Dr. Xiuli Wang (UH, XGas)

• Prof. Michael Economides

Intelligent Energy: The Synergy between Computers … · 2/28/2018 · (variable-speed) Blow-out...

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