nathan zenero teradata · 2020-04-09 · teradata database connections https https https vendor a...

Nathan ZeneroTeradata

AI IS FULL OF PROMISE

BUT CAN IT DELIVER?

DATA SCIENCE RISK

North Stars Data Quality Walled Gardens Gaps Partners

Agenda

North Stars

©2018 Teradata North Stars

1

SAFE SMOOTH PRECISE EFFICIENT

No one gets

hurt.

Know where

trouble is likely

Deliver wellbores

with

Lower LOE

Cheaper M&R

Hit the geologic

target

…and nothing else

Mitigate

NPT, ILT

Material Waste

2 3 4

Operations Goals (for drilling)

DIGITAL NORTH STAR

Analytics Platform

Data Standards

Interoperability Standards

Sensor Ingest

Micro Service Platform

Integrated Data

Digital Twin

Automated Geosteering

DATA QUALITY

Understand Risk

ValueAnd Cost

Treat DataAs a

Manufactured Good

IADC/SPE 178776-MS • Iron Roughneck Make Up Torque—Its Not What You Think!• Nathan Zenero

-10%

-25%

+25%

+10%

5000

10000

15000

20000

25000

30000

35000

40000

5000 10000 15000 20000 25000 30000 35000 40000

Iro

n R

ou

ghn

eck

Re

po

rte

d T

orq

ue

[ft-

lbs]

IRTT Measured Torque [ft-lbs]

UNDER TORQUED REGION

OVER-TORQUED REGION

When Sensors Lie (or are Missing)

Sensor Attributes

• Range

• Accuracy

• Precision and Sigma Level (Repeatability)

• Sensitivity

• Resolution

• Linearity

• Hysteresis

• Reliability

System Attributes

• Calibration/Validation

• Sampling/Conversion

• Smoothing/Filtering

• Transparency/Manipulation

• Timeliness

• Fidelity

Data Properties

Manufacturing

Instrument Traceability (Calibration/Validation, Verification)

Controls/Data Acquisition (RAW->Measurement) Aggregation, Transmission, Storage Reporting, Operations, Analytics

Engineering Design (Plant, Well, etc.)

Device SpecsOperating LimitsFactory Calibration

Process SpecsInstallation/CommissioningStandards and Practices

SamplingFilteringSmoothing

ScalingNoise ReductionCalculations

CompressionUnit ConversionCalculations

Error Correction/ModelingEnrichment/AugmentationCalculations

Analog to DigitalSpecs

Device

Traceability

Analog Pre-Processing

DSP

Scaling

Intrinsic

Properties

TransformationTime Sync

Error Correction

Co

nte

xt

(or

Err

or) Finance

ERP

Engineering

Master

Beyond Heuristics

0

20

40

60

80

100

120Opportunity Cost ($USD Per Well)

Iterations 1,000

Mean $44,578.64

St Dev $6,932.23

P(10) $35,694.63

Rigs 10

Wells/Year (total) 100

Cost/Rig/Day $199

ROI 391%

Total Savings $2,843,113

Value of Information (www.ogdq.org)

http://www.ogdq.org/

Walled

Gardens

WITSML

API

Vendor

WITSML

Stores

Semantic Layer + Data

Services

Well

MasterERP

Geology

Geoscience

Wellbore

Position

Well

Planning

Sensor

Data

No Walls

16

Job Setup

(MDM+RDM)

Teradata Database Connections

HTTPS

HTTPS

HTTPS

Vendor A

Vendor B

Vendor C

Normalized WITSML

Channel Map, User Permissions

Pu

blic C

lou

d (

AW

S o

r A

zu

re)

Any Application

Any User

Any Partner

Vie

ws

An

aly

tics

VantageData

StoreMachine Learning

Engine

GraphEngine

Hig

h S

pe

ed

Fa

bric

SQLEngine

Open Data Foundations

17

• Sensor Agnostic

• Any sensor, in any location, with any amount of redundancy

• Ordinality is controlled by SMEs, abstracted from data science

• Vendor Agnostic

• Can switch data vendors on-the-fly with no interruption to service or additional complexity

• Business Process Agnostic

• Every rig, business unit, or customer can have their own mnemonics and taxonomy and still be able to talk in a common language

• Time Agnostic

• Data can different clocks with error, drift, and dilation

• All clocks can be tracked, and corrected

Data Foundation Considerations

18

TIMEHOLE

DEPTH

BIT

DEPTH

BLOCK

HEIGHT

DELTA

P

HOOK

LOAD

PUMP

RATE

PUMP

PRESSURERPM TORQUE WOB

SLIP

STATE

RIG

STATE

STATE

CODE

MACRO

STATE

BIT

STATE

01/24/2017 4:30:03 PM 1348.62 1347.04 1.71 -1117 5.296 0 39 42 4.15 14897 IN SLIPSDRILLING_GENERI

C01120110110 0

NEAR

BOTTOM


C01120110110 0

NEAR

BOTTOM


C01120110110 0

NEAR

BOTTOM

01/24/2017 4:30:06 PM 1348.62 1347.04 2.38 -1123 5.649 0 30 51 2.591 14545 IN SLIPSDRILLING_REAMIN

G01120110111 0

NEAR

BOTTOM

01/24/2017 4:30:07 PM 1348.62 1347.04 2.39 -1123 5.883 0 30 51 2.44 14310 IN SLIPSDRILLING_REAMIN

G01120110111 0

NEAR

BOTTOM

01/24/2017 4:30:08 PM 1348.62 1347.04 2.39 -1123 5.883 0 30 2 2.398 14310 IN SLIPS DRILLING_IN SLIPS 01110010111 0NEAR

BOTTOM


BOTTOM


BOTTOM


BOTTOM


C01121010110 0

NEAR

BOTTOM


C01121010110 0

NEAR

BOTTOM


C01121010110 0

NEAR

BOTTOM

01/24/2017 4:30:15 PM 1348.62 1347.04 22 -1124 5.062 0 31 0 2.478 15132 IN SLIPSDRILLING_GENERI

C01121010110 0

NEAR

BOTTOM


C01121010110 0

NEAR

BOTTOM


C01121010110 0

NEAR

BOTTOM


C01121010110 0

NEAR

BOTTOM

01/24/2017 4:30:19 PM 1348.62 1347.04 40.75 -1124 5.179 0 30 0 2.472 15015 IN SLIPS INVALID 01122010110 0NEAR

BOTTOM

No Black

Boxes

WOB: 14897

Slip State: IN SLIPS

Rig State: ROTARY DRILLING

State Code: 01120110110

Marco State: 0

Bit State: ON BOTTOM

Hold Depth: 13148.87

Bit Depth: 13146.27

Block Height: 2.3

Hookload: 5.98

Pump Rate: 114

Pump Pressure: 39

Block Height: 2.37

Delta P: 1117

1/23/2019 4:30:03 pm

Interoperability Requires Modularity and TrustTrust <> unquestioned acceptance; Trust = seamless data flow of known quality

Dis

trib

ute

d C

on

tro

ls

Ab

stra

ctio

n L

aye

rIIo

TD

ata

La

ye

r (w

ith

se

nso

r d

ata

-qu

alit

y m

od

el

ba

sed

on

ISO

Sta

nd

ard

)

ERP

AccountingEngineering / Operations

IIoT Sensor Data

En

terp

rise

Da

ta S

erv

ice

s

(eff

ort

less

in

form

atio

n)

Digital Twin RTM

Virtual Controls

Automation

Data Sources

Sensors

Distributed Controls

Digital Transformation

Open Group

The Kaggle Conundrum

© 2017 Teradata

Limit Feature Extraction

QUANTITY of data

Controlled image from BitBox

Advanced Feature Extraction

QUALITY data

12/16/2019

Inverting Kaggle

AI Suffers without Complete Data

Digital Partners

Can we automatically extract information from technical text fields?

Wellmaster (i.e. Wellview) text logs are verbose, technical and are tied to categorical fields (such as Task and Activity)

Operational Comments, contain multiple sentences, jargon, units, and

One comment may apply to a series of subsequent time-lo entries

Currently, logs are expanded through manual processing

Using Teradata Drilling SME and Data Science tools and skills in Natural Language Processing

Use (NLP) and machine learning techniques to process data and identify records that are a loss event

Algorithmically group loss events to automatically create the Loss Event #

Machine learning to extract Remediation Options Used

Associated Remediation details extracted from log entries with NLP

Automation, Accuracy, Repeatability

Automatic post-event identification is very possible – 93% accuracy achieved

Perfectly reproduced loss events by count and ordinal

Extracted remediation options and numeric details – LCM Volume reproduced with 95% precision

Performance improvements with more data – 10x data volumes; continuous time period (i.e. 2 years); larger geographic area; additional sensor datasets

Loss Event Identification

CHALLENGE SOLUTION BENEFITS

planning

collaboration

automation

operations

Partners Should Understand Eachother

Tota

l In

vest

me

nt

0% Completeness of Solution 100%

What we say we want

(20% POC)

What we are willing to use

(80% MVP)Upscaling is rarely a linear projection of POC investment.

SCALE IS NOT SIMPLE

CO

MM

MER

CIA

LISA

TIO

N G

AP

• Automated Visual Inspection/Forensics • Drillbits (partnering with drillbit manufacturers)• Tubulars (partnering with tubular manufacturers)• BHA components

• Streaming Ingest of All Operations Data• Completion (partnering with sensor package OEMs)• Workover Data (co-developing low-cost workover

sensor kits)

• Industry Leaders• SPE DSATS, OGDQ• OSDU Contributors and Committee Chairs• Energistics Members and Contributors

nathan zenero teradata · 2020-04-09 · teradata database connections https https https vendor a...

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