A Decision Support System for Evaluating Aquifer Storage and Recovery Feasibility during Regional Water Planning

A Decision Support System for Evaluating Aquifer Storage and Recovery Feasibility

during Regional Water PlanningA Dissertation Defense by

Josue De Lara Bashulto

2

1. Introductiona) Backgroundb) Research objectives

2. Deterministic framework3. Incorporation of supply side uncertainty4. Hydro-geochemistry coupling5. Water quality inference engine6. Summary and Conclusions

OUTLINE

“Not that the story need be long, but it will take a long while to make it short” –H.D. Thoreau

3

INTRODUCTION

“following the order of nature let us begin with the principles which come first”

–Aristotle, Poetics

4

Arid and semi-arid regions Climatic variability Increased population

Water demand Water storage

Traditional: storage behind dams Issues

Natural flows Ecosystem stability Town displacement Large losses (EV)

No new dams Dam decommission

Introduction

0

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Source: Seattle Times, Sept. 17, 2011

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ASR Technology Overview

Aquifers: traditional source of water Recently recognized as natural water

storage systems ASR: Dual purpose well

Extraction and well redevelopment Cost advantages

Injection of water into an aquifer Unconfined, confined, and semi-

confined formations Factors affecting ASR well operation

Quality of stored water (pre-treatment), quality of native groundwater, quality changes from mixing, hydraulic soil properties, radius of well influence, operating cycle

Page(s): 1-7,10

• Growing interest in MUS systems -> ASR• Inclusion into regional water resources

portfolio• Proved technology, however…

• Current systems are designed and operated in an ad-hoc manner

• Need for a better planning tool

6

Operational policy Optimal injection & extraction Based on medium to long-term objectives

Supply and demand Supply: highly variable, dependent on precipitation Demand: less uncertain, estimated from municipal water usage

trends Water quality

Difficult incorporation of water quality models into optimization routines due to high non-linearity and large computational requirements

Decision Support System

Need of a tool for water managers for selecting appropriate operational policies that includes the stochastic nature of precipitation in addition to water quality constraints which are often difficult to incorporate in traditional planning tools.

Page(s): 1-7

7

What fundamental concepts are needed to model and incorporate ASR systems into a DSS framework? What role does supply play on the

DSS and how can it be incorporated into water planning endeavors?

What changes does water undergo while stored on the subsurface, what is their impact on ASR operations?

How can risks related to hydro-geochemical reactions be accounted for and minimized?

Dissertation Organization

Deterministic DSS Framework

Stochastic DSS Framework

Hydro-geochemical Risks

WQ Inference Engine

Page(s): 8-9

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DETERMINISTIC FRAMEWORK




WQ Inference Engine?

What fundamental concepts, factors, and policies are needed to incorporate ASR technology into a regional DSS

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Introduction: Decision Support System

Pillars Conservation principles

Simulation Operations research

Optimization General framework

Regional needs Local operational policies Scientific rigor Unduly complex Poor data availability

10

Decision Support System (DSS) Inter-dependent modules Keystone: simulation-optimization

DSS Structure

Conceptual Model

Ground Water Flow Model

Optimization Model

Operating Policy

Policy Regulations

Post-optimalityAnalysis

Page(s): 15-16

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Conceptual Model

Page(s): 12

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Potentiometric water level changes Injection: increased potentiometric levels (recovery) Extraction: drawdown

Confined aquifer Drawdowns respond linearly to injection/extraction operations January: In general, for a one year injection and extraction operation at

kth month:

Response weights are obtained from MODFLOW simulation with constant injection/extraction profiles. ASR1yr.FOR

Groundwater Flow Model

𝐷𝐷 𝑗𝑎𝑛=𝜀 𝑗𝑎𝑛 𝐼𝑛𝑗𝑒𝑐𝑡𝑖𝑜𝑛 𝑗𝑎𝑛+𝛿 𝑗𝑎𝑛𝐸𝑥𝑡𝑟𝑎𝑐𝑡𝑖𝑜𝑛 𝑗𝑎𝑛

𝐷𝐷𝑘,𝑛=∑𝑖=1

𝑘

(𝜀𝑖 ,𝑘,𝑛𝑄𝑖𝑛𝑗 ,𝑖+𝛿𝑖 ,𝑘 ,𝑛𝑄𝑒𝑥𝑡 , 𝑖 )

Page(s): 16-18

13

Simulation-Optimization Simulation Optimization

Maximize net injection

Subject to the following constraints

Maximize net injection (t=1 to 12)𝑀𝑎𝑥∑𝑡=1

𝑇

( 𝐼 𝑡−𝐸𝑡 )

Constraint Description

Operations constraint subject to supply

Maximum drawdown limit

Maximum recovery limit

Maximum drawdown limit (of overlying formation)

Maximum recovery limit (of overlying formation)

Extraction constraint subject to comprehensive storage

Pump capacity constraint

Non-negative constraints

Page(s): 1-7

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Case Study: CCASRCD• Corpus Christi ASR Conservation District• Seasonal & long term

storage• Water supply

• Choke Canyon Reservoir

• Lake Corpus Christi• Lake Texana

• Hydro-geologic formation• Gulf Coast Aquifer• Chicot• Evangeline

Page(s): 20-22

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Deterministic supplies Optimized operation subject to

operational constraints Drawdowns (≤ 5ft) measured at

observation well (10,000 ft. from ASR well)

Results: Base Case Scenario

Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec0

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Supply

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INJECTION

STORAGE

EXTRACTION

Supply and Demand

Injection & Extraction Drawdowns

Page(s): 22-25

Deterministic framework Base structure of the Decision Support System Underlying assumptions

Isotropic hydraulic conductivity Known supplies and demand Natural gradient

What about water quality? Sensitivity analysis…

16

Sensitivity: hydraulic conductivity


-3

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Random hydraulic conductivity fields

B

D

Drawdown: Case B

Drawdown: Case D

Variability in hydraulic conductivity may result in preferential flow paths. Accounting for this variability is essential to prevent loss of stored water.

Page(s): 25-27

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20% water supply increase from base case

Year long operation Net recovery at the end of

year

Sensitivity: supply increase


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Page(s): 29-31

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20% water supply decrease from base case

Two months of operation Simulation-optimization

bounded by supplies No net storage at the end of

one year cycle

Sensitivity: supply decrease


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Supply and Demand


Page(s): 27-29

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Chloride concentration Native: 1000 mg/l chloride Source: 155 mg/ l chloride

Water quality modeling MT3D

Water quality affected by Mixing Supplies

Sensitivity: water quality


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Base Case

20% Supply Reduction20% Supply Increase

Page(s): 33-36

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Deterministic approach to introduce fundamental concepts of ASR operations

Simulation-optimization One year cycle of operations

Sensitivity Hydraulic conductivity:

preferential paths and land requirements Variability in supply

ASR feasibility, project sizing, water quality considerations Small changes have large implications in terms of water

quality and ASR operations

Summary: Deterministic Framework

Page(s): 37-38

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INCORPORATION OF SUPPLY SIDE

UNCERTAINTYDeterministic DSS

Framework



WQ Inference Engine?How can variability in supplies

be accounted for and included into a decision support system

22

Supplies Precipitation erratic

Simulation-modeling Traditional: Monte Carlo Inter-annual variability

Month to month correlation ~ 0.5 Intra-annual variability (2001-2009)

Markov Chain Able to capture sequential month-to-month variability Introduction of Markov Chain – Monte Carlo method

Supply Variability

Month Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov DecCorrel -0.040 -0.232 0.087 -0.259 -0.327 -0.270 -0.158 -0.469 0.577 0.697 0.146 -0.070

Page(s): 39-43

MAY JUNE

23

DSS Flow-chart

Conceptual Model


Optimization Model

Operating Policy

Policy Regulations

Supply

Call MC-MCI

i=i+1

i=0

i≤n Yes

No

STOP

Page(s): 43-46

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Building the MC-MCI Gather historic supplies: probabilistic analysis

Characterization

Transition probabilities

Applying the MC-MCI

WATER SUPPLY

Page(s): 43-46

Historic Supplies JanuaryMin Max

LOW MED HIGHJANUARY

LOW MED HIGHFEBRUARY

α β γ Low Med HighLow 0.63 0.38 0.00Med 0.00 1.00 0.00High 0.00 0.00 1.00

TRANSITION: Jan-Feb

25

10 year water supplies Choke Canyon Reservoir Lake Corpus Christi Lake Texana

Statistic foundation MC-MCI FRAMEWORK

Case Study: CCASRCD

Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov DecAverage 5994 5605 6464 7231 7206 7689 8267 8259 6690 6664 6282 6090Max 6903 6437 7495 10290 7926 9490 13294 9693 7111 7296 6983 7709Min 5646 5144 5887 5796 5796 6640 6818 7338 6101 6176 5967 4657Range 1257 1293 1608 4494 2130 2850 6476 2355 1010 1120 1016 3052StdDev 381.8 376.9 572.3 1289.3 673.7 873.7 2086.2 773.1 374.0 348.0 325.9 799.9COV 0.064 0.067 0.089 0.178 0.093 0.114 0.252 0.094 0.056 0.052 0.052 0.131

Page(s): 47-49

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Stochastic water supply 10 year diversion data (2001 to 2010) at Nueces River and Mary Rhodes

pipeline monthly supply characterization 100 realizations of annual water supply sequences

Monte-Carlo & Markov Chain Iteration (MC-MCI) Ground water flow (MODFLOW) & quality (MT3D) model

100 x 100 squared cells (500 ft. in length) Two layered system

Unconfined (Chicot formation) Confined (Evangeline formation)

Modeled after the Gulf Coast Aquifer

VBA subroutines MC-MCI Simulation-Optimization

Modeling Parameters

Page(s): 47-49

Water table965 ft

Potentiometricsurface800 ft

Confining layer500 ft

Unconfinedformation

Confinedformation

TOP VIEW SIDE VIEW

ASR

10000 ft

50000 ft

5000

0 ft

500

ft50

0 ft

27

Results: long-term ASR operations


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Supply and Demand

Injection Profile Extraction Profile

Page(s): 50-54

Supply and demand Long-term mean operational profile

Injections January - May, September

Extractions April - December

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Drawdowns Constraint limits

Exceedance Water managers Operations estimate

Water quality Long term improvement


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Water Quality

Page(s): 50-54

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0.00%10.00%20.00%30.00%40.00%50.00%60.00%70.00%80.00%90.00%

100.00%

Acre-ft/month

Exceedance

Exceedance February

29

Challenge: Addressing the temporal disconnect

Solution: Store water at times when it is available Use during periods of high demand

Variability in supply Use stochastic supply distribution long term mean supply MC-MCI approach

Capture inter-annual variability Long-term ASR operational policy

Reliability: 35%

Summary: Supply Side Uncertainty

Page(s): 55

Recap: supply played a huge role in operations and water quality (mixing)

How to incorporate water quality into the DSS? Next chapter…

30

HYDRO-GEOCHEMISTRY COUPLING




WQ Inference Engine?What changes does water

undergo while in storage and what are their impacts to ASR operations

31

Long-term operational policy Water supply availability, storage, and extraction

Water available for extraction Injected water and native groundwater

Thermodynamic equilibrium Dissolution and precipitation

Health and operational concerns DBPs, radioactive, carcinogens (i.e. As, U)

Including water quality into the DSS Water quality is not a single parameter Multimedia reactions (liquid-liquid, liquid-solid, liquid-gas)

Introduction

Understand potential implications of water quality alterations

How much mixing occurs? What are the risks of toxic mineral dissolution? Facilitate the assessment of ASR systems: water quality

parameters characterization

ASR PROJECT OBJECTIVES

IDENTIFY WATER QUALITY (WQ) NEEDS(E.G. INTENDED USE, REGULATIONS)

IS WQ DATA AVAILABLE

GATHER RELEVANT DATA FROM SURROUNDING REGION

AND/OR LITERATURE STUDIES

WQ DATA INTERPOLATION(E.G. KRIGING)GEOCHEMICAL ANALYSIS

MIXING QUANTIFICATION EQUILIBRIUM REACTION MODELING (E.G. PHREEQC)

IDENTIFY POTENTIAL DISSOLUTION/PRECIPITATION USING SATURATION INDICES

IDENTIFY CHANGES IN CONCENTRATION OF

CONSTITUENTS OF INTEREST

IS ASR PROJECT VIABLE CONSIDERING ADDITIONAL COSTS AND INCREASED RISKS CONCERNING WATER

QUALITY?

SOLUTE TRANSPORT MODEL (ADVECTION & DISPERSION)

MIXING FRACTION, EQ(6)

NO

YES

32Page(s): 72-73

33

Parameters of concern for ASR projects Water supply source: reclaimed water vs. surplus fresh water

Regulations & operations research experience Compiled list: parameters of interest

Identification of WQ parameters

General WQ ParametersAlkalinity, DO, pH, redox potential, specific conductance, TDS, TSS, temperature, TC, turbidity, and total hardness

Major ionsCalcium, potassium, sodium, magnesium, chloride, bicarbonate, sulfate, and nitrate

Minor, trace, and otherInorganics (As), radionuclides (U), DBPs (THMs and HAAs)

Page(s): 58-64 Full list and description on Tables 7 and 8, pages 60 & 64, and Appendix A

34

Law of mass-action

Solubility of mineral AB

,

Solubility due to mixing

SI < -0.05 Mineral is undersaturated, indicating dissolution-0.05 ≤ SI ≤0.05 Mineral is in equilibrium with solution

SI > 0.05 Mineral is oversaturated, indicating possible precipitation

Page(s): 65-67

Recovery Efficiency

35

Thermodynamic Equilibrium Packages (Table 10, pg. 70)

Data availability Lack of consistent data in many regions (e.g. CCASRCD)

Water quality data interpolation Use available data at nearby locations Robust geo-spatial interpolation Kriging

Geochemical Modeling & Data Gaps

WATEQ4F MINTEQA2 EQ3NR/EQ6 PHREEQC

Determine data quality and availabilityFirst• Preliminary data analysis, descriptive statistics, data gaps

Variance modelingSecond• Semivariogram fitting and control parameters

Map generationThird• Extraction of water quality at points of interest

Page(s): 70-80

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Case Study: CCASRCD

Page(s): 73-77

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Mixing quantification Chloride: conservative tracer

Native water: ~ 1,000 mg/L Injected water: 155 mg/l Long-term ASR mean operational policy

Results: mixing fraction (Xi)

Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%NativeInjected

Water quality Mixing fraction

Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov DecInjection 65.31 130.43 64.46 1.94 32.44 0.04 0.00 0.00 3.07 0.00 0.00 0.00Extraction 0.00 0.00 0.00 22.57 0.00 18.66 139.73 60.24 1.48 10.87 8.35 13.35

𝐗𝐢=𝐕 𝐢

𝐕 𝐫=𝐂𝐧−𝐂𝐫

𝐂𝐧−𝐂𝐢

Mixing fraction

Page(s): 73-77

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Major ions in extracted water 50% mixing fractions (Fig.42, pg. 82)

Source + native Changes (mmeq/L)

Results: Major Ions

Comparison (mmeq / L)

Mixing fraction Xi = 50%

Page(s): 82-84

Injected Native Mix 50%Ca2+ 2.465 2.300 2.216Mg2+ 0.667 1.135 0.907Na+ 4.828 33.033 23.940K+ 0.003 0.144 0.058CO3

2- 2.418 0.087 0.042HCO3

- 2.426 4.949 2.749Cl- 4.936 26.179 20.290SO4

2- 1.978 5.076 3.390

Graph

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Saturation Index Higher potential for dissolution Uranium species Mineralogy study

Saturation Indices

U3As4

U3S5

U2C3

U2S3

Orpiment

-1000 -500 0

Element Xi=20 Xi=30 Xi=40 Xi=50

Al 0.99 1.48 1.97 2.47 As 0.08 0.07 0.06 0.05 Ba 0.13 0.20 0.26 0.33 C 4937.00 4320.00 3703.00 3086.00 Ca 1220.00 1222.00 1223.00 1225.00 Cl 8451.00 8012.00 7573.00 7134.00 Cu 0.00 0.00 0.01 0.01 F 51.21 51.13 51.05 50.96 K 193.50 169.30 145.20 121.00 Mg 643.20 604.40 565.70 527.00 N 22.89 22.53 22.17 21.80 Na 11940.00 11050.00 10160.00 9272.00 Pb 0.00 0.01 0.01 0.01 S 818.00 839.50 860.90 882.30 Si 403.20 352.80 302.40 252.00 Sr 8.23 7.20 6.17 5.14 U 0.00 0.00 0.00 0.00

Saturation Indices Selected concentrations (mmolal)

Page(s): 84

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ASR systems: water supply quality Differing water quality: native and supply sources

Subsurface interactions Thermodynamic equilibrium disruption Multimedia and multivariate reactions

Reduction-oxidation reactions, adsorption, ion-exchange, and dissolution-precipitation of species.

Concern of increased health and operational risks Framework for assessing changes in water quality

1. Define constituents of interest2. Evaluation metrics (mixing fraction, and saturation index)3. Methodology for addressing data poor conditions4. Hydro-geochemical analysis (loosely coupled)

Summary

Page(s): 86

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WATER QUALITY INFERENCE ENGINE




WQ Inference Engine?How can risks related to

hydro-geochemical reactions be accounted for and minimized?

42

Assessing water quality alterations Loosely coupled

Tightly integrated water quality evaluation framework Water quality decisions fully integrated into the DSS Integral part of the simulation-optimization routine

Challenges Numerical solutions to advection-dispersion PDE Water quality of extracted water non-linear response

Introduction

ASR operational framework (quantity)

Hydro-geochemistry coupling (quality)

Page(s): 87-88

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Water Quality Model

Optimization Model

Operating Policy

Policy Regulations Supply

Call MC-MCI

i=i+1

i=0

i≤N?

STOP

Yes

No

METHODOLOGY

ASR Long-term operational policy

Page(s): 89-90

Conceptual Model


44

Simulation-Optimization Simulation Optimization

Maximize net injection

Subject to the following constraints

Maximize net injection (t=1 to 12)𝑀𝑎𝑥∑𝑡=1

𝑇

( 𝐼 𝑡−𝐸𝑡 )

Constraint Description

Operations constraint subject to supply

Water quality constraint

Maximum drawdown limit

Maximum recovery limit

Extraction constraint subject to comprehensive storage

Pump capacity constraint

Maximum drawdown limit (of overlying formation)

Maximum recovery limit (of overlying formation)

Non-negative constraintsPage(s): 89-90

45

Water Quality: Artificial Neural Network

Generate random one year inj./ext. profile

Injection

Extraction

Water flow model: MODFLOW

Water quality model: MT3D Concentration

j=0

j=j+i

j ≤K

STOP

NO

Yes

ArtificialNeural

Network

Training:Minimize residual

errorANN Weights

VALIDATION

1

2

3

Bias

A

B

C

O

Input Layer Hidden Layer Output Layer

Weights

Weights

Bias

Page(s): 91-97

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CCASRCD Hypothetical well

Based on Kriging maps (Appendix B, pg. 126-142)

Chloride TWDB database MCL 300 mg/l

Case study

47

860 880 900 920 940 960 980 1000800

850

900

950

1000f(x) = 0.999998869997668 xR² = 0.999999999484492

Observed Concentration (mg/L)

Pre

dict

ed C

once

ntra

tion

(m

g/L

)

Month Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov DecInput 2 4 6 8 10 12 14 16 18 20 22 24Hidden nodes 3 3 3 5 5 5 5 5 5 5 5 5Output 1 1 1 1 1 1 1 1 1 1 1 1

ANN structure Training & Validation Mixing Fraction (30-40%)

High mixing in single cycle – operational alternative

Results: ANN PERFORMANCEGOODNESS OF FIT

Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov DecSSD 0.21 0.17 1.11 0.95 3.77 1.81 3.10 3.44 5.42 4.92 8.02 10.22R >0.99 >0.99 >0.99 >0.99 >0.99 >0.99 >0.99 >0.99 >0.99 >0.99 >0.99 >0.99

Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov DecSSD 0.10 0.06 0.64 0.64 1.99 2.01 1.80 2.13 4.47 4.03 6.24 10.01R >0.99 >0.99 >0.99 >0.99 >0.99 >0.99 >0.99 >0.99 >0.99 >0.99 >0.99 >0.99

ANN STRUCTURE

Validation (200 yrs.)

Page(s): 100-104

Training (400 yrs.)

48

Investment Continuous WQ

improvement Build up of buffer zone

One-time investment Economically sound

Results: Water Investment


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Investment Scenarios

Water qualityPost 2 yr. Investment

BUFFER

Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov DecInvestment 65 130 64 0 32 0 0 0 1 0 0 0Injection(+), Extraction(-) 65 130 64 -21 32 -19 -140 -61 1 -11 -9 -14

Page(s): 104-108

ASR feasibility Hydrogeochemical reactions

Increased risks: health and operational Water quality DSS

Computational load System non-linearities

Water quality inference engine Artificial Neural network

Abstract non-linear responses Tight integration simulation-optimization Quick evaluation of water availability of a given quality Maintain recovery efficiencies Meet federal and state water quality regulations

49

Summary

WQ inference

engine

Native &

source WQ

Supply and

demand

Page(s): 104-108

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SUMMARY AND CONCLUSIONS




WQ Inference Engine

51

Water scarcity in arid and semi-arid regions Need to store water when available to use at times of

increased demand Water storage alternatives

Surface reservoirs and managed underground storage (MUS) Traditional vs. evolving perspective

Aquifers = potential water storage infrastructure MUS technologies: SAT & ASR ASR systems: dual purpose well system

Lack of a tool to assist water managers: project feasibility Development of a decision support system (DSS)

Summary and Conclusions

52


Deterministic frameworkChapter 2

•Introduction of fundamental concepts of ASR operations•Development of DSS structure powered by a simulation-optimization•Case study: Corpus Christi ASR Conservation District (CCASRCD)•Obtained operational supply based on known water demand and supply•Sensitivity analysis: supply, hydraulic conductivity, hydraulic gradient, water quality

Incorporation of supply uncertaintyChapter 3

•Importance of characterizing water supply variability project feasibility•Enhanced DSS to capture regional water supply variability•Introduction of Markov Chain – Monte Carlo iteration (MC-MCI) method•Case study: Obtained long-term mean ASR operational policy•Project feasibility and long term reliability (~35%)

53


Water quality considerations of Aquifer Storage and Recovery operations

Chapter 4

• Importance of identifying water quality changes during storage• Loosely coupled hydro-geochemical evaluation framework for

DSS• Parameter categorization, addressing data gaps, hydro-

geochemical reactions• Water quality evaluation metrics: saturation index (SI), and

mixing fraction (Xi)• Case study: identification of potential locations for ASR project

A long-term ASR operational policy with WQ constraints based on an ANN inference engine

Chapter 5

• Capitulating differences of coupling water quality evaluation methodologies

• Challenges of a tightly coupled water quality decision support system

• Introduction of a water quality inference engine based on ANN technology

• Case study: ANN evaluation and the importance of water investing• Tool for WQ alterations, maintain recovery efficiencies and meet

regulations

54

Limitations of the presented work

Future Work

Costs1• Phase one approach: is the system able to enhance water

supplies?• Capital and operational cost estimates of current ASR projects

• Twin Oaks ASR in San Antonio (~1/6th of surface storage alternatives*) Multi-layered ASR2

• Minimal foot print of current ASR systems• Additional advantages of placing multiple wells at a single site• Storing water at different formations

Horizontal directional drilling (HDD)3

• HDD used to mine oil and natural gas• Similar approach to store water in shallow aquifers• Address horizontal hydraulic variability, greater surface area

*Pyne, R.D.G., 2010. Stacking of ASR Wells in Multiple Aquifers, National Ground Water Association 2010 Summit, Denver, CO.Page(s): 114-115

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Horizontal Directional Drilling ASR

ConfinedAquifer

UnconfinedAquiferConfining layer

Confining layer

Pump

Horizontal well

Water table965 ft

Potentiometricsurface800 ft

Confining layer500 ft

Unconfinedformation

Confinedformation

TOP VIEW SIDE VIEW

HDDASR

50000 ft

5000

0 ft

500

ft50

0 ft

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“A simulation-optimization model for ASR operational planning subject to probabilistic supply and water quality constraints” – NGWA Conference, 2011

“Water chemistry effects of Aquifer Storage and Recovery (ASR) operations” – NGWA Conference, 2011

“Técnicas de recarga artificial en zonas áridas y semiáridas” – Universidad Autonoma de Zacatecas Conference, 2010

“A Stochastic Dynamic Programming Model for Planning ASR Operations Under Uncertainty” –NGWA Conference, 2010

“Simulation-Optimization of Soil-Aquifer Treatment System Release Patterns” – Javelina Research Symposium, 2010

“Decision Support Systems (DSS) for Managed Underground Storage Technologies of Recoverable Water (MUS)” - TAMUK- EVEN Seminar Series, 2010

“A probabilistic analysis of wet and dry regimes: Bi-national study along the Rio Grande River” - TAMUK- EVEN Seminar Series, 2008

Presentations

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ACKNOWLEDGMENTS

This material is based upon work supported by the Center of Research Excellence in Science and Technology – Research on Environmental Sustainability of Semi-Arid Coastal Areas (CREST-RESSACA) at Texas A&M University– Kingsville (TAMUK) through a Cooperative Agreement (No. HRD-0734850) from the National Science Foundation (NSF). Any opinions, findings and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of the National Science Foundation.

Consejo Nacional de Ciencia y Tecnología

Department of Environmental Engineering &College of Graduate Studies

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TH NKSQUESTIONS

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Documents

A Decision Support System for Evaluating Aquifer Storage and Recovery Feasibility during Regional Water Planning