novel digital technology and methodology enables...

JOGMEC TECHNO-FORUM 2018

Novel Digital technology and methodology enables subsurface & surface integration

for the Oilfield Optimization

Toyo Engineering Corporation November 27 , 2018

- 2 -TOYO ENGINEERING CORPORATION ©2018

Market Forecast : How technology unlocks oil and gas resources

⇒ Innovation technology is being welcomed to unlock oil & gas.⇒ Optimization of Operation will become viable option (eg: Middle East)..

Reference : 2018 BP Technology Outlook

Technically Recoverable Oil & Gas Resources Technology advances to 2050 could

increase recoverable oil reserves by around 50%, compared to around 25% for gas.


Market Forecast : Potential volume increases and cost reductions through Technology

Reference : 2018 BP Technology Outlook

Technology can play a major role in improving access to oil & gas and in reducing the costs of production.

The potential reduction in average lifecycle costs will be ~30% for oil and gas resulting from technology advances to 2050.

The lifecycle costs of different oil types and gas resources vary considerably.


Collaboration with Baker Hughes

Upstream Midstream Downstream

Subsurface & Surface Integration for Oilfield Development Explore Digital solutions utilizing GE Predix, a unique cloud-based platform

System Integrator EPC Upstream Business

Technology Provider Subsurface Digital


Digital PlantPlant Owner

User Interface

Actual Plant

Predictive Maintenance

Real Time Mirror Plant

Vertical Integration with ERP

Engineering Digital Twin

Virtual Plant

E/O/M/B Service

Data/Record

Virtual Plant

DX-PLANT Digital Solution Scheme


EPC : Digital Twin Portfolio


Project Management and Field Implementation

Reservoir Characterization and Modeling

Development Planning for Field and Facilities. Mitigate the risks.

Reservoir Management and Optimization

Start of Field Development

Application Study of IOR, or 2ndary

, or 3rdary Recovery Application Flooding Study with Feasibility Check

Model Development with Production Model & Facility

(Study & FEED)Production Mode Monitoring

Abandon of Field

A continuous process to assist oil and gas companies optimizethe economic performance of oil and gas fields

Upstream Business : Total Support during Project Cycle


Big Data Approach for Field Management

Applications

Data Driven Model

Sparse events and no perfect fittings. Correlation with statistics can be

made, but it sometimes shows poor. Forecast can be done without much

causality.

Machine Learning is used to • extract functional relationship• estimate missing data

Blue = Collected data. Red / Green = Estimated data. Poor correlation with statistical models

Data + estimated data + physical model = prediction

Time

Machine learning

Time

Para

met

er

Physics Based Model

Physics-based models capture variation with causality.

Not all data available to build model Time consuming process

Para

met

er

Time

Casing (Pipe) ModelReservoir Model Pump Model Tubing Model Pressure Gradient

@ Surface

Flow Rate, Q

Head

Efficiency

Horse Power

Best Efficiency Point

⇒ Hybrid Model with Machine Learning can speed up prediction with causality.

Reference : BHGE


Production Optimization Injection Rate CO2 Concentration WAG Schedule Draw Down (ESP)

Surface Facility Operation Optimization Daily Production Short Term Production Mid Term Production

Subsurface/Surface Monitoring Pressure & Temperature Oil/Gas/Water/CO2 Rate CO2/Water Breakthrough Formation Stress

Update Control

Calibrate Model

Analytics

Validate Model

Physics-based ModelData Driven Model

Reservoir Simulation with Hybrid Model

Prediction

Probabilistic learning

AI (Deep learning)

Deep domain models

Integrated Dynamic Production Optimization

Background image reference : NETL

novel digital technology and methodology enables...

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