Interpretation of Groundwater Flow into Fractured Aquifer

doi:10.4236/ijg.2012.32039

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International Journal of Geosciences, 2012, 3, 357-364

http://dx.doi.org/10.4236/ijg.2012.32039 Published Online May 2012 (http://www.SciRP.org/journal/ijg)

Interpretation of Groundwater Flow into

Fractured Aquifer

Sameh W. Al-Muqdadi, Broder J. Merkel

Geology Department, Technische Universität Bergakademie Freiberg, Freiberg, Germany

Email: sameh.wisa[email protected], [email protected]

Received February 11, 2012; revised March 21, 2012; accepted April 16, 2012

ABSTRACT

The region of investigation is part of the western desert of Iraq covering an area of about 12,400 km2, this region in-

cludes several large wadis discharging to the Euphrates River. Since the Tectonic features in particular fault zones play

a significant role with respect to groundwater flow in hard rock terrains. The present research is focus on investigate

lineaments that have been classified as suspected faults by means of remote sensing techniques and digital terrain

evaluation in combination with interpolating groundwater heads and MLU pumping tests model in a fractured rock aq-

uifer, Lineaments extraction approach is illustrated a fare matching with suspected faults, moreover these lineaments

conducted an elevated permeability zone.

Keywords: Fault Interpretation; Lineaments Extraction; Remote Sensing; Digital Terrain Model; Analytical Pumping

Test Evaluation

1. Introduction

Tectonic features and in particular faults, fault zones, and

fracture zones play a significant role with respect to

groundwater flow in hard rock terrains and show sub-

stantial impact at multiple scales. Faults and fracture

zones may be areas of preferential flow but, may act as

barriers as well. Whether a fault zone or fractures are

water bearing or not is mainly controlled by the tectonic

stress and strain and secondary fracture fillings. The

permeability of fractures is assumed to be greater in the

direction parallel to the principal stress field. Stress-relief

fracturing might be as well a reason for increased per-

meability.

Numerous studies have been performed in order to de-

termine fractured zones in aquifers and its impact onto

groundwater flow and heads [1] examined the hydroge-

ology of regional flow system in carbonate terrain by

large-scale pumping for irrigation. Water-level decline in

corresponding wells was used as a proof to identify hy-

draulic connection between several wells. The study

showed that the orientation of water bearing fractures or

conduits inferred is consistent with the major orientation

of local and regional structural features.

A conceptual model of groundwater flow was imple-

mented by Mayo and Koontz, 2000 [2] for fractured

zones associated with faulting in sedimentary rocks. The

model is based on the results of field and laboratory in-

vestigations, groundwater and methane gas inflows from

fault-fracture systems, showing that groundwater stored

in fractured sandstone is confined above and below by

clayey layers.

Electromagnetic surveys by VLF-WADI resistivity

sounding was used by Sharma and Baranwal, 2005 [3] to

interpret the geological structures and groundwater move-

ment through fractures rock. Laboratory experiments

were carried out by Qian et al., 2005 [4] to study ground-

water flow in a single fracture with different surface

roughness and apertures. Results show that the gradient

of the Reynolds number versus the average velocity in a

single fracture was almost independent of the change of

fracture surface roughness, and it decreased when the

aperture decreased under the same surface roughness.

Studying lineaments from remote sensed data is an al-

ternative because high production areas in fractured aqui-

fers are often associated with visible lineaments at the

ground surface. An effective technique for delineation of

fracture zones is based on lineament indices extracted

from air-photos and from satellite imagery. In combina-

tion with structural and tectonic analysis information for

understanding groundwater flow and occurrence in hard-

rock aquifers may be provided. Several techniques has

been developed and applied to extract lineaments from

air-photos, satellite images, and digital terrain models. A

comprehensive toolkit was created by Raghavan et al.,

1995 [5] for extracting lineaments from digital images

using segment tracing and rotation transformation

(START). This algorithm was coded in FORTRAN 77

S. W. Al-MUQDADI, B. J. MERKEL

358

and implemented on a UNIX-based workstation and is

executed in two stages: at first a binary line element im-

age is generated using the segment tracing algorithm;

then in a second step the rotation transformation algo-

rithm is used to extract lineaments from the line element

image. This algorithm may work with different data set

such as LANDSAT MSS, LANDSAT thematic mapper

(TM), DEM’s as well as shaded aeromagnetic images.

Kim et al., 2004 [6] Developed for ArcView SHP files

Avenue scripts to extract lineaments and lineament den-

sity from satellite images. The scripts may facilitate to-

gether with borehole date the analysis of the relationship

between groundwater characteristics and lineament dis-

tribution. Accuracy of extracted lineaments depends str-

ongly on the spatial resolution of images: the higher the

resolution the better will be the quality of lineament map

[7].

2. Motivations

The aim of this paper is to investigate lineaments that

have been classified as suspected faults by means of re-

mote sensing techniques and find out the impact of these

lineaments into the groundwater flow.

3. Regional Geological Setting

The western desert of Iraq (south-west of Euphrates river)

covers nearly 32% from the whole Iraq (437.072 km²)

and habits a population of about 1.3 million [8], UNEP

has adopted an index of aridity, defined as: I = P/PET

where PET is the potential evapotranspiration and P is

the average annual precipitation [9] Hype-arid: <0.05,

arid: 0.05 - 0.2, semi-arid: 0.2 - 0.5, dry sub-humid: 0.5 -

0.65. According to this definition the region has an I-

value of 0.01 and is thus classifies as Hype-arid.

The rather flat terrain is sloping gently towards the

Euphrates River. Rainfall is not sufficient to maintain a

continuous plant cover. Ground water is found in several

horizons in different depths. The majority of the ground

water is nonrenewable flowing in confined aquifers. Re-

charge occurs locally by limited flood events only which

happens immediately after rapid and intense rain events.

The region of investigation is part of the western de-

sert of Iraq (41.14˚E - 32.59˚N and 42.78˚E - 31.86˚N)

covering an area of about 12,400 km² including several

large wadis, such as Ubaiydh, Amij, Ghadaf, Tubal, and

Hauran discharging to the Euphrates River [10]. The re-

gion (Figure 1) was chosen for two reasons: on the one

hand it was classified by Consortium-Yugoslavia, 1977

[11] as a promising groundwater abstraction zone and on

the other hand offering a sufficient number of wells for

carrying out a thorough ground water study. However,

recent political events have made investigations in this

area of Iraq rather difficult after 2008.

An elevation contour map was created by using the 90

m SRTM data. With respect to the ROI the highest value

(609 m) is in the west while the lowest one (233 m) is in

the east.

Figure 1. Western desert-Iraq.

S. W. Al-MUQDADI, B. J. MERKEL 359

The region of interest is considered as a part from sta-

ble shelf zone/Rutba-Jazira sub zone [12]. The Rutba-

Jazira zone is an inverted Paleozoic basin. The inversion

started in the late Permian. Its basement was relatively

stable during Mesozoic-Neogene time and more mobile

during Infracambrian and Paleozoic times. The basement

depth ranges from 5 km in Jazira area up to 11 km south

of Rutba. Jazira area was part of the Rutba uplift domain

in late Permian to early Cretaceous time, following the

Cretaceous Jazira area subsided while Rutba remained

uplifted these two areas are thus differentiated as sepa-

rate sub zones.

Stratigraphic cross section for the Western Desert re-

gion has shown respectively the following formations

Euphrates-L. Miocene, Dammam-M. Eocene, Umm Er-

Rad-huma-M./U. Palaeocene, Tayarat-U. Cretaceous [13]

where Limestone and dolomitic limestone are the most

dominated units for these formations.

Based on (Buday, 1984) three local faults can be dis-

tinguished in the middle and eastern part of the ROI. The

main directions for these faults are NS & NE-SW; these

directions are related to the Najd-Hejaz origin move-

ment which belongs to the Precambrian-Palaeozoic [14].

Fault 1 has been described as a normal fault while

faults 2 and 3 are classified as suspected faults. The Geo-

logical cross sections were generated by four boreholes

[11] (Figure 2): showing the main formation, faults and

moreover a thin layer ~20 m. E recedes to ~6 m. W from

marl located in between the two formations Umm Er-

Radhuma and Tayarat working as an aquiclude while

lenses from marl are imbedded between Dammam and

Umm Er-Radhuma formations. The general tectonic set-

ting indicates that the maximum horizontal stress is ori-

entated SW-NE.

Groundwater flow is suspected to be from W to E to-

wards the Euphrates River with 102 pumping wells scat-

tered in the area tapping the three main aquifers.

4. Methodology

A lineament is usually defined as a straight or curve lin-

ear feature to be seen on the ground surface. Lineaments

can be manmade structures such as roads and canals or

geological structures such as faults/fractures, folds, and

unconformities, differences in vegetation and soil mois-

ture, or drainage networks (rivers). Lineaments can be

mapped during a field survey, or by using air photos and

remote sensing data either manually or by means of pat-

tern recognition algorithms [6]. On contrary lineaments

can be traced from remote sensed data or digital terrain

models by mathematical algorithms.

Sharpening tools are used for automatic merging a low-

resolution color, multi-, or hyper-spectral image with a

Figure 2. Cross sections.

S. W. Al-MUQDADI, B. J. MERKEL

360

high-resolution grey scale image and resampling to the

high-resolution pixel size. The Gram-Schmidt Spectral

Sharpening algorithm [15] may sharpen multispectral

data using high spatial resolution data and simulating a

panchromatic image for the corresponding sensor ETM.

Gram-Schmidt transformation is performed on the simu-

lated panchromatic band and the spectral bands, using the

simulated panchromatic as the first band by swapping the

high spatial resolution panchromatic band with the first

Gram-Schmidt band.

A directional algorithm implemented by ENVI is a

first derivative edge enhancement filter that selectively

enhances image features having specific direction com-

ponents (gradients). The sum of the directional filter

kernel elements is zero. The result is that areas with uni-

form pixel values are zeroed in the output image, while

those that are variable are presented as bright edges.

The lineament extraction algorithm implemented by

Geomatica is based on three fundamental steps to extract

linear features from an image: at first an edge detection

operator is used to produce a gradient image from the

original image. Then a threshold value is applied on the

gradient image to create a binary edge image and finally

linear features are extracted from the binary edge image.

The last step contains several sub steps such as edge

thinning, curve pruning, recursive curve segmentation,

and proximity curve linking.

If lineaments are faults and deep reaching fracture

zones they may impact the groundwater flow either by

being a zone of preferential flow or acting as barriers.

Such zones can be recognized from groundwater contour

lines for both unconfined and confined aquifers. Several

spatial interpolation algorithms may be used for plotting

groundwater contour lines.

Furthermore pumping test might provide additional

information. A great variety of equations and procedures

are known for pumping tests with observations wells.

However, if as in the case of this study no observation

wells are available the evaluation of such pumping test

data is awkward. Analytical and numerical models may

offer a viable approach. In this study the analytical model

MLU was used for drawdown calculations and inverse

modeling of transient well flow. MLU estimates selected

aquifer parameters based on a best fit analytical solution

to measured time-distance-drawdown data. The software

includes an automatic curve-fitting algorithm computing

optimized aquifer parameters and fitted drawdown re-

sults [16].

5. Result and Discussion

5.1. Lineaments Interpretation

SRTM data with 30 m resolution and 15 m Landsat ETM

have been used to determine lineaments in the region of

interest based on both extraction lineaments algorithm by

Geomatics, 2001 [17] and directional filter algorithm by

Haralick et al., 1987 [18]. Figure 3 shows that the

lineaments F1 and F2 based on field survey [14] match-

ing rather well with the automatically extracted linea-

ments with only a small difference between field data

and remote sensed data. On contrary no evidence was

found by lineament interpretation for the suspected F3

element.

5.2. Groundwater Flow Direction

Static water levels from 102 wells were used to deter-

mine the groundwater flow direction for these aquifers

Figure 3. Extracted lineaments.

S. W. Al-MUQDADI, B. J. MERKEL 361

by using different spatial interpolation algorithms. (Fig-

ure 4) shows the groundwater flow net for the three aq-

uifers using minimum curvature algorithm [19]. Kriging

[20] and radial basis function [21] did not result in sig-

nificant different contour lines. The main flow direction

is W-E with the highest value in the NW (~487.5 masl./

well 5) while the lowest was in the NE (~189.8 masl./

well 31).

Between the fault lines F1 and F2 the groundwater

gradient is less steep than in the total area and the flow

direction is diverted slightly from the general W-E direc-

tion to ESE. This can be explained by a better permeabi-

lity in the area between F1 and F2. The main stress direc-

tion is ~40˚ and thus almost parallel to F1 and F2; per-

pendicular to this an anticline is assumed [14]. Thus it

can be speculated that the entire area between F1 and F2

is showing a denser fracturing than the rest of the region

of interest. Evidence for any impact on the groundwater

hydraulics by the assumed F3 lineament was not found.

Therefore the existence of this lineament F3 has to be

negated.

5.3. Pumping Test by Means of MLU Model

Two pumping test were performed close to the lineament

F2 in the unconfined aquifer Dammam using the wells 9

and 17, the location for both wells is shown in the

groundwater flow net (Figure 4). Table 1 shows that

there is a slight difference in depth to the groundwater

between databank and field records. No adjustment of the

pumping rate was possible and observation wells were

Figure 4. Groundwater flow net.

S. W. Al-MUQDADI, B. J. MERKEL

362

not available. Pumping rate was determined by means of

a stop watch filling a gradient container. Drawdown was

monitored with a depth sounder in the pumping wells.

Pumping test in well no 9 was performed for 420 min-

utes to reach steady state, and then pump was switched

off to monitor recovery reaching the former static level

after 72 min (Table 2). Pumping test in well no 17 was

performed for 360 min to reach steady state and then

pump was switched off to monitor recovery which was

reached after 300 minutes (Table 3). Results of pumping

test and recovery evaluated with the analytical model

MLU for Windows are presented in Figures 5 and 6,

Results shown that well 17 has given a higher Transiti-

vity value 0.1048 m2/min in compare with well 9 where

T = 0.0832 m2/min, this results support the assumption of

unique elevated permeability zone occurred in between

of the F1 and F2 because of the tectonic stress and the

anticline structure.

6. Conclusion

The lineaments F1 and F2 recognized by field survey

could be confirmed by automated lineament extraction

from 30 m SRTM data and 15 m Landsat ETM Interpo-

lated groundwater heads of the uppermost Dammam aq-

uifer display a different flow pattern between F1 and F2

which correlates with the assumption of an elevated per-

meability between these two lineaments due to tectonic

stress and the anticline structure. Furthermore the results

of the pumping tests support the hypothesis that the area

between F1 and F2 is characterized by increased perme-

ability.

7. Acknowledgements

The authors wish to acknowledge to University of Frei-

berg/Hydrogeology institute—Germany for providing the

appropriate field instruments. A special thanks goes for

Table 1. Pumping test comparison wells.

Well no. Coordination Elevation Depth to the GW. Data Bank Depth to the GW. Field Difference %

9 42.28 / 31.95 296.7 61.3 59.1 2.2 2

17 42.32 / 32.09 297.9 49.7 46.3 3.4 3.5

Average 2.8 2.75

Table 2. Recovery test data well 9. MLU aquifer test analysis—for unsteady-state flow.

------------------ Drawdown (m) ----------------

Observation well Aquifer Time (day) Calculated Observed Cal-Obs

Observation well 9 1 0.29167 14.581 14.170 0.411

Observation well 9 1 0.29236 4.165 4.530 −0.365

Observation well 9 1 0.29306 2.251 3.150 −0.899

Observation well 9 1 0.29375 1.767 2.350 −0.583

Observation well 9 1 0.29444 1.581 1.700 −0.119

Observation well 9 1 0.29514 1.477 1.550 −0.073

Observation well 9 1 0.29861 1.217 1.190 0.027

Observation well 9 1 0.30208 1.079 1.080 −0.001

Observation well 9 1 0.30556 0.985 1.070 −0.085

Observation well 9 1 0.31250 0.859 0.910 −0.051

Observation well 9 1 0.32292 0.738 0.800 −0.062

Observation well 9 1 0.33958 0.617 0.690 −0.073

Observation well 9 1 0.34375 0.595 0.580 0.015

Sum of squares: 1.4896E+00 m2; K = 0.00208 m/min; T = 0.0832 m2/min.

S. W. Al-MUQDADI, B. J. MERKEL 363

Table 3. Recovery test data well 17. MLU aquifer test analysis—for unsteady-state flow.

------------------ Drawdown (m) ----------------

Observation well Aquifer Time (day) Calculated Observed Cal-Obs

Observation well 17 1 0.25000 12.497 13.370 −0.873

Observation well 17 1 0.25069 12.498 11.000 1.498

Observation well 17 1 0.25139 12.499 9.920 2.579

Observation well 17 1 0.25208 12.500 8.840 3.660

Observation well 17 1 0.25278 12.501 7.960 4.541

Observation well 17 1 0.25347 12.502 7.070 5.432

Observation well 17 1 0.26042 12.511 5.450 7.061

Observation well 17 1 0.27083 12.524 3.660 8.864

Observation well 17 1 0.28472 12.541 2.680 9.861

Observation well 17 1 0.30556 1.059 1.790 −0.731

Observation well 17 1 0.33681 0.682 0.790 −0.108

Observation well 17 1 0.37847 0.498 0.600 −0.102

Observation well 17 1 0.43056 0.383 0.490 −0.107

Observation well 17 1 0.49306 0.303 0.380 −0.077

Observation well 17 1 0.57639 0.238 0.230 0.008

Observation well 17 1 0.70139 0.181 0.100 0.081

Observation well 17 1 0.86806 0.138 0.040 0.098

Observation well 17 1 1.07639 0.107 0.020 0.087

Sum of squares: 2.9944E+02 m2; K = 0.00262 m/min; T = 0.1048 m2/min.

Figure 5. Recovery test well 9.

Figure 6. Recovery test well 17.

logistic support during the fieldwork to the following

colleagues (Khalid Hoshi, Amar Falah, Yasser Kassim

and the guide Abo Duraid).

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