Lowering the Operational Costs through Improving Crew Readiness and - - PowerPoint PPT Presentation

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Lowering the Operational Costs through Improving Crew Readiness and - - PowerPoint PPT Presentation

Feb 07, 2024 600 likes •755 views

Lowering the Operational Costs through Improving Crew Readiness and Automated Real-time Decision support using Digital Twin eDrilling a world leading supplier of AI, machine learning, and predictive analytics solutions to the oil and gas

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Lowering the Operational Costs through Improving Crew Readiness and Automated Real-time Decision support using Digital Twin

eDrilling – a world leading supplier of AI, machine learning, and predictive analytics solutions to the oil and gas industry

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T ech chnolo

  • logy

gy fo founda dation ion Mode del ba base sed d reaso sonin ning g in AI

Thermohydraulic wellbore model: Mass, Momentum and Energy conservation equations for the drilling system

  • Static and dynamic Density
  • Pressure and flow
  • Temperature
  • Rheology of different fluid
  • Frictional pressure loss
  • Cuttings load and transport in the annulus
  • Gains/losses across pits
  • Kick development and kick tolerance
  • Multiphase flow

( ) ( ) ( ) ( ) ( ) ( ) ( ) ( )

                  + −          = =  +     −    + −       

2 2 1 1 2 2 2 2 , 1 2 2 1 2 2 1

1 3 1 cos cos , 2 3 3 sin cos 2 cos cos 1 cos , 2 3

f f n

R E d d E T r w d R E d R d E

Dynamic Model Dynamic Model

  • WOB from hook load or vice versa.
  • TOB from surface torque or vice versa.
  • Axial and rotational friction factors
  • Bit depth corrections due to string elasticity,

buoyancy, pump rates and pressure

  • Well depth
  • Rotational speed and block speed
  • Drag forces
  • Nozzle pulse

Torque and drag with ROP model

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Mode del Base sed d Re Reaso sonin ning g AI

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PLAN

Well construction planning and design

ANALYSE

Lessons learned/ competence transfer

PREPARE

Trial-run of well, procedure/equipment testing, training

OPERATE

Real-time optimization, automated monitoring

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Why pr pre-dr drill ill a well l in si simu mula lato tor? r?

Crew competence Crew readiness T esting new equipment Pre-operati

  • perations

ns chall llen enges ges Advanced nced Simulato ulator for operat ationa

  • nal

preparati aration

  • n and traini

ining ng

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wellSim

  • For engineering and training of all well

engineering disciplines

  • Advanced downhole simulator with:
  • Dynamic ROP model
  • Flow & Torque/Drag model
  • Dynamic effects
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Pre-drill Your Own Well in Simulator

  • 6000+ participants in team training
  • 200+ well specific training scenarios
  • Managed Pressure Drilling, MPD
  • High Pressure - High Temperature, HPHT
  • Extended Reach Drilling, ERD
  • Deep Water Wells
  • Drilling and tripping operations with dynamic surge & swab
  • Multi fluid operations
  • Fingerprinting in Deep Water & HPHT wells
  • Well control (kick and losses)
  • Pressure Mud Cap
  • Nitrogen Mud Cap
  • Through Tubing Rotary Drilling, TTRD
  • Dual Gradient Drilling (DGD)
  • Coil Tubing Drilling (CTD)
  • Pipehandling
  • Standbuilding Man/Auto
  • Machine Control
  • Tripping
  • Stripping operations
  • WOB Auto-drilling
  • Mud handling & control
  • Traditional Well Control
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SLIDE 8

Example: Pre-drill an MPD well

  • Challenges
  • Drilling a well with narrow margins
  • Adding new technology in operation
  • Extra crew members from MPD service company
  • Rig time - training
  • What we did
  • Actual well in the simulator
  • MPD service company MPD control system hooked up

to the simulator

  • Procedures
  • Communication
  • Outcome
  • Awareness of the risks connected to the actual well
  • Understanding of procedures and responsibilities
  • Reduced the offshore training to save rig time
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SLIDE 9

Real-time Decision Support using Digital Twin

  • Well depth
  • Bit depth
  • Block position
  • ROP
  • Hookload
  • WOB
  • Rotation speed
  • Standpipe pressure
  • Mud flow in
  • Mud density in
  • Mud temperature in

ANATOMY OF A PHYSICAL WELL DIGITAL TWIN OF THE WELL

Predictive Analytics Diagnostics Forecasting What-if analysis

ARTIFICIAL INTELLIGENCE MACHINE LEARNING

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Diagnostics (example)

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Deci cisi sion

  • n Supp

pport SW – Depl ployment yment Op Options

  • 1. Installed in RTOC; Assigned superuser, full software
  • wnership
  • 2. Decision support SW and live well support service
  • 3. Decision support SW at the rig for focus on Safety and

Drilling Optimization

  • Return on Investments:
  • Increased Drilling Efficiency by at least 10%
  • Reduced Complex situations and accidents by 30-40%
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100% TRACK RECORD

With 6000 users and counting, on 200+ digital twins, we are proud of no hazardous incidents and no (non- geological) sidetracks, as well as significant performance improvement.

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Summary - Value proposition

Avoidance of NPT Safe

  • peration

Optimized performance Better decision support Better risk visibility Drilling parameters control

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