production control and monitoring strategies
TRANSCRIPT
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