ET E-POSTERS_V1_test1

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RMS Amplitude 

Dominant Frequency 

Dip Deviation 

Reflection parallelism 

 

 

Subset 

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Initialize 

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Neural Network Training 

RMS Amplitude + Dom. Frequency + Refl. Intensity + Variance 

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Classify 

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-1372 -1422 

Additional interpretive feature 

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Seismic Facies Result 

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Subject to 

endorsement 

by TRLC 

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Fiber optic distributed acoustic sensing (DAS) acquisition 

technology is used to continuously monitor production and seismic 

data along the entire well and obtain better seismic images. DAS 

utilizes fiber optics to permanently place receivers uniformly every 

8m along the entire wellbore without the need for well 

intervention. The typical method to monitor production data 

typically utilizes localized sensors which are placed non-uniformly 

along the borehole. Borehole seismic data or Vertical Seismic 

Profiling (VSP) data is usually acquired through a well intervention 

procedure to allow receivers to be lowered down the borehole at 

larger, 15-20m intervals. Conventional VSP data is also more 

expensive since well intervention means loss of production for the 

entire duration of the acquisition. The typical production 

monitoring method does not provide continuous data across the 

borehole since conventionally used sensors such as Pressure 

Downhole Gauges (PDGs) are only placed at zones of interest. To 

mitigate these issues, DAS is utilized to continuously record 

production and VSP data at shorter intervals across the entire 

wellbore trajectory. 

Left: Conventional VSP method. Localised 

receiver location. Sparser receiver spacing. 

Limited azimuth coverage 2 . 

Right: DAS VSP method. Continuous receiver 

locations. Denser receiver spacing. Better 

azimuth coverage 2 . 

Top: Seismic source vessel. 

Bottom: GPS receivers installed 

on the Bokor platform. It is 

used to time stamp the DAS raw 

field data based on the GPS 

data. 

Left: Seismic data shows the Bokor field distorted by shallow gas cloud (red 

circle) 3 . No useful information is available for interpreters. 

Right: DAS VSP data overlaid on Bokor’s seismic data shows better resolution 

and lateral continuity under the gas cloud region 3 . 

Light signal is sent down the 

wellbore through fiber optics. 

Receivers are placed all along 

the wellbore to obtain 

production and seismic data. 

Results of DAS. 

Top: Production monitoring data. 

Bottom: Seismic data 

Gas lift at particular depth and time is highlighted 3 . 

Light signal is sent down the 

wellbore via an interrogator 

unit (I/U). The I/U consists 

of the laser and detector 

component. It sends pulses 

of light via laser. Seismic 

events represented by the 

blue dialogue bubble is 

recorded and reflected back 

to the I/U via Rayleigh 

backscatter 1 . 

Operations 

Geophone VSP 

Requirement to shut in 

well and lower tools. More 

expensive and less safe. 

DAS VSP 

No interference with well 

operations. Uses existing 

fiber. Cheaper and safer. 

Data quality 3C, good SNR. Directionality, poor SNR 

need production zones. 

Repeatability 

Possible, but cost is 

prohibitive. 

Always available for repeat 

(4D) measurements. 

Cheaper. 

Borehole Coverage Restricted. 100% of borehole. 

The interrogator unit (I/U) shown 

above transmits and processes light 

signal sent down the wellbore. One I/U 

is required per well. For Bokor, data 

were acquired from five wells. Tapes 

record the production monitoring and 

seismic data. The GPS unit stamps the 

raw field data with time stamps. The 

interrogator unit meanwhile consists of 

the laser and detector components. 

Collaboration with Multimedia University and Optasense for R&D into omni directional 

optical fiber sensors. Collaboration with Delphi Consortium for integrated surface and 

borehole seismic high resolution imaging development. 

Location and well trajectory of the wells 

in Bokor that were selected to obtain 

DAS VSP data. From left is well BO-122, 

BO-125 followed by BO-124. 

1. Dria, D. (2012), E&P Applications of Fiber Optic Technologies. In: Distinguished 

Lecture Program. [online] SPE. Available at: 

http://www.spe.org/dl/docs/2012/dria.pdf [Accessed 14th Oct. 2016]. 

2. Hornby, B.E., Yu, J., Sharp, J.A., Ray, A., Quist, Y., and Regone, C., (2006). VSP: 

Beyond time-to-depth. The Leading Edge, pp. 446-452. 

3. Optasense (2016), Production monitoring and VSP at Bokor internal reports for 

PETRONAS. Kuala Lumpur: Optasense 

Subject to 

endorsement 

by TRLC 

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Presentation Title (acronym) ; 

Division – Name/OPU/HCU/BU (acronym) ; 

Name of Presenter 

 

©Petroliam Nasional Berhad (PETRONAS) 2014 8 

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. 

What is HPC? 

“High Performance Computing (HPC) is a computer with a high-level computational 

capacity compared to a general-purpose computer. Performance of a supercomputer is 

measured in floating-point operations per second (FLOPS) instead of million instructions per 

second (MIPS). 

HPC clusters are characterized by many cores and processors, lots of memory, high-speed 

networking, and large data stores – all shared across many rack-mounted servers. User 

programs that run on a cluster are called jobs, and they are typically managed through a 

queueing system for optimal utilization of all available resources. 

NETWORK (56 Gbps) 

CPU CPU CPU CPU CPU CPU CPU CPU CPU CPU CPU CPU CPU CPU CPU CPU CPU CPU 

MEMORY 

MEMORY 

MEMORY 

Group Research & Technology 

Geophysical Processing & Imaging 

Upstream IM & IT 

Geomechanics Group 

Total users : 40 

Server 1 

Server 2 

Server 3 

No Project Name Project Area OPU Quota 

PARALLEL 

SINGLE 

1 SUR2015BLOCK52 SURINAME Area GR&T 290TB 

2 Omega_Test N/A All User 30TB 

PGI-3D code 

In-house Developed Software 

Technology Division 

Omega 

Eclipse 

Visage 

T-Navigator 

Jason 

Intersect 

Beicip Termis 

High Priority Low 

PGI-3D code 

In-house Developed Software 

Technology Division 

MESA 

CMG 

Prosource 

Geoteric 

Hampson Russell 

Norsar 

Petrel 

3 MYS2015SK307 SK307 GR&T 53TB 

4 MYS2015PM318 ENAU / ABU Area GR&T 5TB 

5 MYS2015PM313 SEPAT Area GR&T 5TB 

6 MYS2015PM315 ANGSI Area GR&T 30TB 

7 MYS2016MAPMIGRATION 

8 MYS20162G2J 

Various Projects (Exploration and 

International Exploration) 

Velocity Pilot Study In 

UNGGUL / WAKID Area 

GPI 

Geomechanics 

Group 

5TB 

5TB 

Scenario 

Test Data Angsi I35LWEST_AS_712_BASE Angsi I-68_CEOR 

Base Case 

SRT = 2.83 hours 

SRT = 21.5 hours 

6 core 12 core 

Active Cells 162,546 1,080,848 

SRT SRT Reduction SRT SRT Reduction 

High End W/S 2.31 hours 18.29 % 9.78 hours 54.49 % 

PACE 1.66 hours 41.50 % 8.00 hours 62.79 % 

DELL EPTD 2.35 hours 16.85 % 10.01 hours 53.46 % 

* SRT = Simulation Run Time 

JASON– Hardware Benchmarking Result 

5 realization over 330057 traces with approximately 500 millisecond window interval 

Environment High End Workstation PACE 

Base Case 20 core 48 core 

SRT = 98 hours 

ECLIPSE – Hardware Benchmarking Result 

Test Data Dulang E300 Dulang E100 Sepat D 

SRT = 51 hours SRT = 5.1 hours SRT = 94.5 hours 

Base Case 

8 core 8 core 12 core 

Active Cells 310,699 1,045,195 129,252 

SRT SRT Reduction SRT SRT Reduction SRT SRT Reduction 

High End W/S 30.6 hours 40.00% 2.4 hours 52.94% 49.2 hours 47.94% 

PACE 30.6 hours 40.00% 2.8 hours 45.10% 43.7 hours 53.76% 

DELL EPTD 35.0 hours 31.37% 3.0 hours 41.18% 56.1 hours 40.63% 

CMG– Hardware Benchmarking Result 

SRT = 4 hours 

9 MYS2016SK307_EOR BOKOR Area GR&T 50TB 

10 MYS2016PM331 PM331, 2D to Pseudo-3D GPI 5TB 

11 MYR20162G2J_UNGGUL 

UNGGUL/LUMUTAN 

Block 2G2J 

Geomechanics 

Group 

10TB 

12 MYS2016RnDGASCLOUD Seismic Imaging under Gas Cloud GR&T 10TB 

13 MYS2016NUM_MODEL 

14 MMR2016LAND3D 

Processing And Imaging For Seismic 

Data Produced From A Known 

Velocity Model 

Land 3D Seismic Processing 

Capability Onshore Myanmar 

GPI 

GPI 

10TB 

10TB 

15 MYS2016SK_DW3F 3D Marine Isometrix PSDM-DW3F GPI 60TB 

16 GAB2016BLOCKF14 GABON 3D APSDM (BLOCK14) GPI 30TB 

17 MYS2016SKB11B12 

18 MYS2016SK316 

Sarawak – PSTM for B11/B12 field 

using DMO gathers 

Limbayong Area - To perform FWI & 

RTM 

GR&T 

GR&T 

10TB 

25TB 

As of 30 November 2016, extracted from Resource Database Administration 

* GR&T = Group Research & Technology 

* GPI = Geophysical Processing & Imaging 

Current 

Specification 

150 Tflops 

91.5 Tflops 

(GPU) 

57.6 Tflop 

(CPU) 

PACE Next Phase 

PACE-II (600 Tflop) 

PACE-I (150Tflop) 

Proposed 

Specification 

600 Tflops 

530 Tflops 

(GPU) 

68 Tflop 

(CPU) 

500 Tflop 

PGI-3D 

30 Tflop 

Geoteric 

968 core 

All apps 

Proposed Scale up 

2015 

PACE-I 

150Tflops 

2018 

PACE-II 

600 

Tflops 

>2021 

PACE upgrade 

> 1 Pflop 





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∆ max = 

v min 

2f max 

2D 

 

Stability condition to archive a higher 

sampling rate 

∆t ≤ 

 

∆ min 

2v max 

2D 

Sepat 2DRTM Stack 

 

 





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S(τ, f) = 

S(τ, f) = 

−∞ 

∞ 

−∞ 

∞ 

h (t) 

h (t)ω MD (t − τ, f)e −i2πft dt 

|Af + B| 

2π e t−τ)2 (Af+B) 2 

2 e −i2πft dt 

A = 

ω MD (t − τ, f) = 

|Af + B| 

2π e t−τ)2 (Af+B) 2 

2 

2.355( 1 

ΔT 2 

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ΔT 1 

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f 2 − f 1 

B = 2.355 

ΔT 1 

− Af 1 





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P-Velocity vs. Porosity 

S-Velocity vs. Porosity 

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P-X 

 

S( ) W X W S( ) 

A A B A 

 

 

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Xˆ S( ) Wˆ P-X ( ) Wˆ 

B A A A 

1 2 

1 

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A 

 

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W W W I W 

 

 

P( z0) 

XA( z0, z0) WA ( z0, zA) XB( zA, zA) WA ( zA, z0) 

S( ) 

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• Enhanced spectral decompositions (eSD) are 

interpretive tools that can be used as both: 

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• Qualitative tool: to delineate different stratigraphic 

features (channels geomorphology, barriers and 

compartments), structural features (faults, fractures, 

karst and dendritic patterns), and a reliable tool for 

well projection and geo-steering. 

• Quantitative tool: when integrated with other 

seismic attributes and geological information to 

discriminate specific lithology / fluid type. 

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• Not available in Petrel. Some eSD’s attributes are not 

available in third party software. 

• Spectral decompositions allow us to view seismic 

reflection events in the form of spectral amplitude 

maps of specific frequency contents. 

• Produce sharp, detailed subsurface images 

• Robust tools for Geomorphology analysis 

• Reveal hidden stratigraphic features 

• Spectral decomposition is a trace by trace inversion 

operation that transforms a 1D seismic trace to 2D 

time-frequency image of spectral magnitudes. 

• The spectral inverse problem (Puryear et. al., 2012) 

has been further enhanced and by Exploration 

technology to produce spectrally balanced and sharp 

maps. 

• The new in-house codes produce new spectralrelated 

attributes as shown in the workflow below. 

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eSD provides sharp images of different structural/ stratigraphic features that are 

not delineated from seismic amplitude maps alone. Effective tool for well 

projecting and geo-steering. 

The technology will be deployed as a Petrel plugin that can replace third party 

software currently used by PETRONAS. 

Puryear, C., Portniaguine, O., Cobos, C., Castagna, J., 2012, Constrained least-squares spectral analysis: 

Application to seismic data, Geophysics, 77, 5, 143-167. 


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λ 

λ 

Ψ μ,σ 

S s d 4 6 

ω 1 ω 2 ω 3 ω 4 

2 d d 

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Create new PGIRTM job 

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Job 2 

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Status bar shows parameter help info. 

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x ′ = x cos θ + y sin θ 

y ′ = −x sin θ + y cos θ 

g x, y; λ, θ, ψ, σ, γ = exp − x′2 + γ 2 y ′2 

f 

q 

s 

l 

g 

2σ 2 

g x, y; λ, θ, ψ, σ, γ = exp − x′2 + γ 2 y ′2 

2σ 2 

cos 2π x′ 

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sin 2π x′ 

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I xx I xy I xz 

I yx I yy I yz 

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Conjugate Gradient) 

Parabolic 

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