Eagle Ford Shale Case Study（美国Eagle Ford页岩成功案例研究）

Eagle

Ford

Shale

Case

StudyDry

GasOilEagle

Ford

LocationEagle

Ford

Paleo

HistoryGeologicAgeEagleFordLithologyCalcareousshaleTotalAreaSize(sqmi)1350TotalGas(tcf)84GIP(bcf/sqmi)200ProducableGas(tcf)9(3BBOE)Depth(feet)11500Thickness(feet)250HorWellCost($M)4.8AverageEUR2-6Pressure(psi)5200Temperature(F)335Ro1.5TOC(%)4.5Porosity(%)4-10MatrixPermeability(nD)100-1500PressureGradient(psi/ft)0.65ClayContent(%)8AdsorbedGas(%)8Eagle

Ford

Geologic

Details75’Source:

Matador/EIA/Harts/USGSEdwardsReefTrendSligo

Shelf

MarginEagle

Ford

Oil/Gas/CondensateWell

ProductionWellIP(BOE)–Nota“factory〞5-fold

range

in

production1270

BOE

MedianTransform

proprietaryinformationProduction

versusWell

LengthTransform

proprietaryinformationProduction

versusWellboreAzimuthTransform

proprietaryinformationProduction

versus

Choke

SizeTransform

proprietaryinformationProduction

versusTotal

FluidVolumeTransform

proprietaryinformationProduction

versusTotal

ProppantTransform

proprietaryinformationProduction

versus

fluid/footTransform

proprietaryinformationProduction

versus

Proppant/footTransform

proprietaryinformationPrediction

versus

DepthTransform

proprietaryinformationProduction

versus

OperatorActual

ProductionPredictedProduction0.681

correlation

highlightsimportance

of

a

non-linear,

multi-variate

statistical

approachNon-linear

Production

Prediction

Model~6000’

optimal?

Wellbore

Length

Well

Vertical

Depth>11,000

feetProduction

Prediction

>

30/64?

Choke

SizeInfluence

ofWell

Engineering

on

ProductionCompletions

drive

Production!IP,

EURPressureBut

much

more

than

completions…Eagle

Ford

DepthLower

Eagle

FordThicknessProductionVariation

with

Depth

andThickness4200’

Well

Thickness

4200’

Well

Depth

Depth

and

Thickness

map

data,

extracted

at

well

locations,

provides

key

spatial

informationfor

production

predictionProduction

and

Extracted

Depth

andThicknessTransform

proprietaryinformationActual

Production

Engineering

and

Geology

Production

Prediction

Predicted

Production

Improved

correlation

(0.804)

and

betterprediction

of

high

producers

highlights

the

importance

of

combining

engineering

and

geologic

data

for

production

prediction.

Opportunity

for

further

increasing

prediction

accuracy

with

detailed

spatial

information,

using

seismic

data5500’

optimal?Larger

chokes350’

optimal?

>

11000

‘

gas

oilTransform

proprietaryinformation

Thicker>150’

optimal?Engineering

and

Geology

Parameter

OptimizationTransform

proprietaryinformationProduction

Prediction

–

Eng/Geology

Using

natural

gas,

oiland

condensate

pricingEagle

Ford

Economic

Sweetspot

Map

Eagle

Ford

Seismic5.0

miles400

msSource:

Global

Geophysical

Services400

msSource:

Global

Geophysical

Services

Eagle

Ford

Seismic5.0

miles

5000

ft

Austin

Chalk

Buda

Pearsall400

ms

Eagle

Ford

Seismic5.0

miles

Austin

Chalk

Buda

PearsallFaults

and

FracturesSource:

Global

Geophysical

ServicesEagle

Ford

Fault/FractureAttributesBudaFaultsFracturesSource:

Global

Geophysical

ServicesFault

HazardsSource:

Global

Geophysical

ServicesEagle

Ford

Fault/FractureAttributesSource:

Global

Geophysical

ServicesTransform

proprietaryinformationActual

ProductionPredictedProduction

Improved

correlation

(0.966)

highlights

the

influence

of

local

faulting/fracturing

androck

property

variations

on

well

productionWell

statistics

determined

by

extent

ofseismic

data

used

in

the

study,

tocomplement

regional

geologic

analysisProduction:

Engineering/Geology(+Seismic)Engineering

+

Geology

+

SeismicLonger

Wells

Larger

Choke

Deeper

Wells

Thicker

ReservoirTransform

proprietaryinformationAvoidFaults

TargetFracturesTransform

proprietaryinformationSligo

Shelf

Margin

Eagle

Ford

Economic

Sweetspot

MapEdwards

Reef

TrendSource:

Global

Geophysical

ServicesEagle

Ford

Summary

Using

crossplots

to

optimize

engineering

parameters

is

not

feasible

due

to

the

number

and

subtle

interactions

We

propose

using

non-linear,multi-variate

statistics

to

create

sweetspot

maps

andestimates

of

best

engineering

parameters

Better

production

in

the

Eagle

Ford

correlates

with

(using

public

and

Global