effect of chemical and environmental parameters on the

Effect of chemical and environmental parameters on the quantification of oil in produced water using EPA Method 1664 and confocal laser fluorescence microscopy (CLFM):

Accuracy as a function of stack number and optical sections

E. N. Sappington1, C. N. Wickramaratne1, H. S. Rifai1 1 University of Houston

Friday, January 27, 2017 1

Outline

• Background

• Part I • Representative sampling technique • Identify patterns among parameters

• Part II • Salinity • Particulates • Chemical additives • Various API gravities

• Conclusion and future work


Background: EPA 1664 and oil and grease (O&G)

Produced water (PW) constituents:

TDS, HCs, particulates, chemical additives, microbes, scales, organics, metals, NORM

Test to ensure compliance with discharge standards for O&G

• Governed by the Clean Water Act

• Daily max: 42 mg/L; monthly avg: 29 mg/L

• O&G defined as “hexane extractable material “ (HEM) • “materials extractable by n-hexane and not evaporated at 70˚C”

3 Friday, January 27, 2017

TDS, HCs, particulates, chemical additives, microbes, scales, organics, metals, NORM

Background: EPA 1664 and O&G

In offshore production, PW is:

1. Treated at surface

2. Tested using EPA 1664

3. Discharged to ocean

EPA 1664:

• Substantial handling

• Laboratory based method

• Not applicable for subsea


Goal: Identify alternative methods to test for compliance and quantify O&G in PW at the seabed

Background: CLFM

CLFM:

spatial filtering to render higher quality images

CLFM diagram: fluorescence mode

Optical sectioning and history of use


Background: transition from previous study

Proof of concept comparison study • Recovery: 87-89% EPA 1664; 85-89% CLFM • CLFM large standard deviation • Lack of a systematic method in retrieving CLFM

data resulted in inability to interpret relationship among parameters

Current objectives:

1) Establish representative sampling technique

2) Assess effect of various parameters on CLFM performance


0

20

40

60

80

100

120

clean medium dirty

Me

asu

red

C

on

cen

trat

ion

(m

g/L)

EPA 1664

Confocal

0

10

20

30

40

50

0 10 20 30 40 50

Oil

and

Gre

ase

R

eco

vere

d (

mg/

L)

Oil and Grease Added to Sample (mg/L)

EPA 1664

Confocal

Sample preparation: oil concentration

Analytical technique: “weight by difference”

1) weigh vial of crude oil + pipet

2) drop crude oil into bottle

3) weigh vial of crude oil + pipet

4) difference = mass of crude oil in bottle

Pasteur pipet

Drop crude oil into sample

bottle

Sample bottle Balance

Vial


Sample preparation: flow cell

Disperse 1 hour @ 12,000 RPM (IKA T 18 digital Ultra-Turrax) Pipet into 100 L flow cell (Ibidi -Slide 0.4 Luer ibiTreat #1.5 polymer coverslip, tissue culture treated, sterilized)


Representative sampling technique: grid cells, random sampling

Obtain stacks on CLFM (Leica DM2500B SPE confocal) Process stacks in MATLAB (Mathworks R2016a)


Methodology: confocal settings


Methodology: MATLAB


image

• Import image

• Convert grayscale to binary using threshold

• Calculate concentration

threshold

• Repeat for all threshold values (0.05 – 0.95)

• Repeat for each grid cell

• Determine optimum threshold based on lowest error

volume

• Randomly select grid cells (3, 6, 9, 12)

• Calculate concentration

grayscale binary threshold = 0.3

Part I: patterns among parameters


3

6

9

12

0

20

40

60

80

100

120

2

4

6

8

10

Number stacks

% R

eco

ery

Z-step

25 ppm 100-120

80-100

60-80

40-60

20-40

0-20

3

6

9

12

0

10

20

30

40

50

60

70

80

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100

2 4

6

8

10

Number stacks

%R

eco

very

Z-step

50ppm 90-100 80-90 70-80 60-70 50-60 40-50 30-40 20-30 10-20 0-10

Part I: crude oil


0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

110%

120%

130% P

erc

en

t R

eco

very

(%

)

Crude Oil Concentration (mg/L)

25 ppm 50 ppm

threshold = 0.3

EPA 1664 recoveries 87 – 88%; standard deviations 2 – 5%

CLFM recoveries 94 – 99%; standard deviations 19 – 31% heterogeneous flow channel

Density: 0.908 g/mL API gravity : 24.3˚

Chemical and environmental parameters

1) Salinity

2) Particles

3) Chemical additives

4) API gravities

Dissolve 35, 100, 250 g NaCl in

1 L nanopure water


MW: 54.88 g/mol

*Benko and Drewes (2008) found TDS concentration in western American basins ranged from 1,000 – 400,000 mg/L with median 32,300 mg/L

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

Pe

rce

nt

Re

cove

ry (

%)

NaCl Concentration (mg/L)

25 ppm 50 ppm

Part II: salinity

Friday, January 27, 2017

16

35,000 100,000 250,000 35,000 100,000 250,000


CLFM recoveries 24 – 79%; standard deviations 5 – 40% small droplets, threshold too large? % recovery decreased with increasing salt concentration


1) Salinity

2) Particles


4) API gravities

Add 10 and 50 mg SiO2 to each 1 L sample


MW: 60.08 g/mol

Part II: particles


0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100% P

erc

en

t R

eco

very

(%

)

SiO2 Concentration (mg/L)

25 ppm 50 ppm

10 50 10 50


CLFM recoveries 11 – 26%; 5 – 64% electrical and physical properties should be further investigated


1) Salinity

2) Particles


4) API gravities


Chemical Deoiler Scale Inhibitor Hydrate Inhibitor

Corrosion Inhibitor

Density (g/mL)

1.1668 1.1542 0.8558 0.9195

Concentration (mg/L)

20 40 100 30

Use in field Neutralize

emulsifying agents

Prevent/ remove scale

build up

Interfere with hydrate crystal

growth

Control corrosion rates

Main ingredient(s) (provided in

MSDS)

NaCl Na2S

n/a methanol

Methanol NR4

+ organic sulfonic acid amine salt

Corrosion inhibitor interference with EPA 1664


Conventional emulsion breaking techniques unsuccessful physical means = loss of oil?

HEM contaminated redissolve in hexane re-distill crystal formation in HEM

Hydrate inhibitor interference with EPA 1664


Hydrate inhibitor interference with CLFM


Deoiler effect on droplets

• Large (up to ~20-30 m)

• Non-spherical

• Expected to coalesce


0%

20%

40%

60%

80%

100%

120%

140%

Pe

rce

nt

Re

cove

ry (

%)

Chemical Additive

25 ppm 50 ppm

deoiler hydrate scale corrosion deoiler hydrate scale corrosion

Part II: chemical additives


EPA 1664 recoveries just under acceptable for deoiler and scale inhibitor

CLFM recoveries range 40 – 140%, large standard deviations

Chemical additives create regulatory issues?

Chemical analysis to identify (1) emulsion type

(2) emulsion breaking techniques (3) interaction between chemical, water, and oil to ensure compliance


1) Salinity

2) Particles


4) API gravities


API 30 residue


0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

Pe

rce

nt

Re

cove

ry (

%)

API Gravity (˚API) 40 30 20 40 30 20

25 ppm 50 ppm

Part II: API gravities


API Gravity (˚API)

EPA 1664 recoveries 60 – 77% with one acceptable: heaviest oil, highest conc. 85% EPA 1664 standard deviations 0.9 – 4% with one acceptable: lightest oil, lowest conc. 13%

CLFM recoveries 28 – 58%

CLFM standard deviations 21-35% with two acceptable: API 40 25ppm and API 30 50ppm

May contain hydrocarbons not extractable by hexane, non-aromatic (paraffins?), or sediment particles

Conclusions

• Large standard deviations indicate non-homogeneity throughout flow channel

• Randomized sampling not ideal until homogenous channel concentration achieved

• Little effect on % recovery from varying Z-step

• CLFM % recovery decreased with increasing NaCl,

• EPA 1664 and CLFM % recovery increased with increasing oil density

• Chemical/oil analysis needed to further understand interactions and behaviors


Future Work

• Design and develop: • Flow loop system to control homogeneity within flow channel

• Grid structure containing more cells (stacks)

• Identify minimum number stacks and z-step needed to save time and $

• Identify appropriate threshold value based on oil type and concentration

• Chemical analyses to understand interaction between oil – water – chemicals


Acknowledgements

Clearview Subsea, LLC and RPSEA (Project No. 12121-6301-03) are acknowledged for funding this research. The NSF GK-12 program, TCEQ, and US EPA are acknowledged for financial support. Jeff Zhang, Debora Rodrigues, and Jingjing Fan are acknowledged for their guidance and helpful comments.


Thank you

Emily Sappington

[email protected]


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