SEPTEMBER 26, 2017, ST. PETERSBURG, HOTEL ASTORIA

Production Control and Monitoring Strategies

Dr. Arthur Aslanyan, Nafta College

2

PRODUCTION CONTROL AND MONITORING WORKFLOW

Reservoir and Well Diagnostic

3D Modeling

EOR Planning

Control and Monitoring

3

PRODUCTION ANALYSIS

Diagnostic issues – Dual-well analysis or non-physical correlations

Technology challenge – Multi-well production & pressure history auto-matching

W2

W3

P1P2

Multi-well Shut-in Immanent production loss Cross-well Interference

-8

-6

-4

-2

0

2

4

6

8

-40

-30

-20

-10

0

10

20

30

40

0 1000 2000 3000 4000 5000 6000 7000 8000

Давление,а

тм

Время,ч

W2

W3

P2

Multi-well Deconvolution

4

With production losses (10%) Without production losses (0%) FIELD-WIDE FORMATION PRESSURE

Diagnostic issues – Poor coverage and production loss

Technology challenge – Multi-well production & pressure history auto-matching

5

WELL TEST ANALYSIS – TRUE RESERVOIR PROPERTIES AND BOUNDARIES

Diagnostic issues – True reservoir properties and boundaries are affected by offset production

Technology challenge – Using the multi-well deconvolution

0,1

1

10

100

0,001 0,01 0,1 1 10 100 1000

Pre

ssu

re, b

ar

Time, hr P P’ P (Deconvolution) P’ (Deconvolution)

Single-well Deconvolution Multi-well Deconvolution

WI2

Dynamic boundary

Geometric boundary

Influence from dynamic boundaries

WI2

Dynamic boundary

Geometric boundary

NO influence of dynamic boundaries

250 m

400 m

Parameter Truth Single-well Deconvolution

S 0 - 3

σ, mD·m/cP 49 32

re, m 400 (No flow) 250 (Const Pi)

Multi-well Deconvolution

0.2

45

400 (No flow)

6

PRODUCTION LOGGING – NEAR-WELLBORE INFLOW PROFILE

A2

A3

A4

A1

Diagnostic issues – PLT and Temperature is missing down world thief production

Technology challenge – Spectral Noise Logging

7

PRODUCTION LOGGING – NEAR-WELLBORE INJECTION PROFILE

A1

B1

B2

25 %

28 %

38 %

8 %

1 %

80 % 20 %

Diagnostic issues – Injection loss is not quantifiable

Technology challenge – Numerical modeling of multi-rate temperature logging

8

3D MODEL CALIBRATION – TRUE SWEEP PROFILE

Producer

ESV = 100%

ESV = 100%

History Model

Injector

A1

A2

Wat

er

cut,

%

Injector

Producer

ESV = 100%

ESV = 40%

Injector ESV = 100%

ESV = 100%

History

Model

History

Model

Producer

A1

A2

A1

A2

Bypass oil

Modeling issues – Inaccurate sweep profiles

Technology challenge – Calibrate production /injection profiles and shale breaks

Wat

er

cut,

%

Wat

er

cut,

%

9

Oil Reserve Density Map Oil Reserve Mobility Map

P1

P2 P2

Modeling issues – Non-optimal sequence of production optimization activities

Technology challenge – Automatic selection and grading of candidates

PRODUCTION OPTIMIZATION – PLANNING TOOLS AND METHODOLOGY

0

1500

3000

4500

6000

bp

d

date

P1

0

1500

3000

4500

6000

bp

d

date

OP-4

0

2000

4000

6000

8000

10000

bp

d

date

OP-5

10000

17500

25000

32500

40000

bp

d

date

FIELD

0

1000

2000

3000

4000

5000

bp

d

date

P2

0

1000

2000

3000

4000

5000

bp

d

date

OP-2

0

1000

2000

3000

4000

5000

bp

d

date

OP-3

0

7500

15000

22500

30000

37500

45000

bp

d

date

FIELD

не ворует

ворует

12000

14000

16000

18000

20000

bp

d

date

0

10000

20000

30000

40000

50000

bp

d

date

FIELD не ворует ворует

not stealing

stealing

not stealing stealing

10

CONTROL & MONITORING SCALABILITY

FLC W1

QYPT

FLC W2

QYPT

FLC WN

QYPT

3D Model

Data and Instructions Server

Data

Auto-Analysis

Multi-well Test Decoder

Multi-sensors telemetry and remote well control

W1 ↑ 5% W2 ↓ 2% W3 ↓ 10%

QYPT +

σ χ s

QYPT +

σ χ s

QYPT +

σ χ s

QYPT +

σ χ s

APPROVING

WELLS REGIME

HYPOTHESIS CHECKING

INST

RU

CTI

ON

S

…

QYPT

Control and Monitoring issues – Subjective, man-based, under-resourced process

Technology challenge – Automated pro-active monitoring and data analysis

11

THANK YOU!

12

Diagnostic issues – Identifying of the zones with water and gas invasion

Technology challenge – Developing the memory PNL tool

WELL TEST ANALYSIS – PULSE NEUTRON LOGGING – HORIZONTAL WELLS

13

WELL TEST ANALYSIS – PULSE NEUTRON LOGGING – LOW SALINITY

Diagnostic issues – Identifying of the zones with fresh water invasion

Technology challenge – Developing the PNN tool with long counting of neutrons

Near Detector

Far Detector

Formation response

oil

water

14

WELL TEST ANALYSIS – PULSE NEUTRON LOGGING – CALIBRATION

Units Sigma water

Sigma oil

Sigma sand

Sigma shale

A 1 - A 4 35 c.u. 22 c.u. 8 c.u. 33 c.u.

Oil +

formation water

Formation

water

Oil +

formation water

Formation water salinity = 35 g/l

Σform.water = 22+0.35*Salinity = 35 c.u.

Σoil ~ 22 c.u. Peripheral Calibration

Well (PNN)

Displacement Calibration

Diagnostic issues – Estimation of displacement

Technology challenge – Conducting the PNN survey in the calibration wells

15

WELL TEST ANALYSIS – MULTI-PHASE INTERPRETATION

Diagnostic issues – True air permeability

Technology challenge – Multiphase well test modeling

kWT = 50 mD

kOH = 400 mD

• Single-phase PTA

• Multi-phase PTA

kWT = 350 mD Log derivative Log derivative Model

Black Oil PVT

SCAL

Time, hr

Pre

ssu

re, k

Pa

16

3D MODEL CALIBRATION – SHALE BREAKS

W-01

W-02

W-03

Esv = = hPCT

hOH

0.8 3D Survey

0.4 Field Survey

Survey: Pressure Pulse Code Test (PCT)

A1

A1.1

A1.2

W-01

W-02

W-03

0.4 3D Survey

17

3D MODEL CALIBRATION – TRUE AIR PERMEABILITY

Kaver = 127 mD

Kaver = 12 mD

Modeling issues – Inaccurate air permeability calibration

Technology challenge – Multi-well statistics on air permeability from PTA-SNL and cross-well interference

WT @ h_PLT

WT @ h_SNL

WT @ h_perf

WT @ h_OH

KD

yn,

mD

KOH, mD K3D, mD

A1

A2

hperf

hperf

OB-1

<kh> = 30 mD·m

h = ?

A1: KDyn= 1.2 K1.1

OH

A2: KDyn = 0.4 K0.8

OH

h = hOH ? h = hperf ?

Producer Injector

A1

A2

Producer Injector. Perforations

A1

A2

Before calibration After calibration