U.S. Department of the Interior

U.S. Geological Survey

Scientific Investigations Report 2008–5184

National Water Availability and Use Pilot Program

Processing, Analysis, and General Evaluation of 

Well-Driller Logs for Estimating Hydrogeologic Parameters 

of the Glacial Sediments in a Ground-Water Flow Model 

of the Lake Michigan Basin

41°

42°

43°

44°

45°

46°

90° 89° 88° 87° 86° 85° 84° 83° 82°

SAGINAW BAY

LAKE

MICHIGAN

GREEN BAY

Base from U.S. Geological Survey digital data 1:100,000 1983. 

Universal Transverse Mercator projection, Zone 16, 

Standard parallel 0° (Equator), Central meridian 87° W,

North American Datum 1983

70 140 MILES

0 70 140 KILOMETERS

0

ILLINOIS

INDIANA

MICHIGAN

WISCONSIN

OHIO

1

25 

50

75 

100

EXPLANATION

Equivalent horizontal hydraulic 

conductivity, in feet per day

Processing, Analysis, and General 

Evaluation of Well-Driller Logs for 

Estimating Hydrogeologic Parameters of 

the Glacial Sediments in a Ground-Water 

Flow Model of the Lake Michigan Basin

By Leslie D. Arihood

National Water Availability and Use Pilot Program 

Scientific Investigations Report 2008–5184

U.S. Department of the Interior

U.S. Geological Survey
U.S. Department of the Interior

DIRK KEMPTHORNE, Secretary

U.S. Geological Survey

Mark D. Myers, Director

U.S. Geological Survey, Reston, Virginia: 2009

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Suggested citation:

Arihood, L.D., 2009, Processing, analysis, and general evaluation of well-driller records for estimating hydrogeologic 

parameters of the glacial sediments in a ground-water flow model of the Lake Michigan Basin: Scientific Investigations Report 2008-5184, 26 p.

ISBN 1-411-32302-5
iii

Contents

Abstract...........................................................................................................................................................1

Introduction.....................................................................................................................................................2

Purpose and Scope ..............................................................................................................................2

Geologic Setting....................................................................................................................................2

Processing of Well-Log Data .......................................................................................................................5

Evaluation of Well-Log Data................................................................................................................5

Calculation of Equivalent Horizontal and Vertical Hydraulic Conductivities for the 

Glacial Layers of the Model...................................................................................................5

Calculation of Aquifer Hydraulic Conductivity from Well-Log Specific-Capacity Data..........10

Compilation of Water-Level Calibration Data from Well Logs.....................................................11

Estimation of Thickness and Hydraulic Conductivity for Glacial Sediments beneath 

Lake Michigan........................................................................................................................14

Analysis of Well-Log Data ..........................................................................................................................15

Comparison of Hydrogeologic Products Derived from Well-Log Data with Other 

Hydrogeologic Products ...............................................................................................................20

Summary and Conclusions.........................................................................................................................24

References Cited..........................................................................................................................................25

Figures

1. Study area of the Lake Michigan ground-water model and boundary of the 

Lake Michigan Basin....................................................................................................................3

2. Thickness of Quaternary Period sediments in the study area of the Lake Michigan 

ground-water model.....................................................................................................................4

3. Distribution of logs from drilled wells used to generate hydraulic conductivity 

and water-level data for the glacial layers of the Lake Michigan Basin groundwater-flow model..........................................................................................................................6

4. Flow chart for processing well-log lithologies into grids of horizontal and vertical 

hydraulic conductivity..................................................................................................................7

5. Relation between lithology-based equivalent hydraulic conductivity and the 

percentage of aquifer material in the model layer..................................................................9

6. Flow chart for the processing of well-log water levels into model-calibration 

data for the Lake Michigan ground-water-flow model. .......................................................12

7. Estimated thickness of glacial sediments beneath Lake Michigan. Estimates 

based on data from Soller and Packard (1998), Colgan and Principato (1998), 

and National Oceanic and Atmospheric Administration (2005)..........................................15

8. Distribution of equivalent horizontal hydraulic conductivity for layer 1 of the 

model based on assumed hydraulic conductivity of 1 foot per day for nonaquifer 

material and 100 feet per day for aquifer material................................................................16

9. Distribution of equivalent vertical hydraulic conductivity for layer 1 of the model 

based on assumed vertical hydraulic conductivity of 0.001 foot per day for 

nonaquifer material and 10 feet per day for aquifer material. ............................................17
iv

Figures–Continued

10. Distribution of horizontal hydraulic conductivity calculated from well-log derived 

specific-capacity data for aquifers in layer 1 of the ground-water-flow model..............19

11. Distribution of equivalent horizontal hydraulic conductivity for layer 1 from this 

study and surface geology from Fullerton and others (2003)..............................................21

12. Distribution of equivalent horizontal hydraulic conductivity for layer 1 from this 

study and generalized ground-water availability for a part of northern Indiana 

(Indiana Department of Natural Resources, 1980)................................................................22

13. Distribution of equivalent horizontal hydraulic conductivity distribution for layer 

1 of the model and flow-duration ratios for a part of northern Indiana.............................23

Tables

1. Amount of surface area of the Lake Michigan ground-water model covered by 

individual sediment textures and sediment texture classifications.....................................4

2. Comparison of median horizontal hydraulic conductivity calculated using specific 

capacity data from Indiana and Michigan .............................................................................11

3. Kriging parameters used to interpolate ground-water-level surfaces in Indiana, 

Michigan, and Wisconsin for the Lake Michigan ground-water-flow model ..................13

4. Median horizontal hydraulic conductivity for different sediment texture classes 

calculated from specific-capacity data from Indiana, Michigan, and 

Wisconsin for model layers 1, 2, and 3 of a ground-water-flow model of the 

Lake Michigan Basin..................................................................................................................18
v

Conversion Factors

Inch/Pound to SI

Multiply By To obtain

Length

inch (in.) 2.54 centimeter (cm)

inch (in.) 25.4 millimeter (mm)

foot (ft) 0.3048 meter (m)

mile (mi) 1.609 kilometer (km)

Area

square mile (mi2

) 259.0 hectare (ha)

square mile (mi2

) 2.590 square kilometer (km2) 

Flow rate

gallon per minute (gal/min) 0.06309 liter per second (L/s)

Hydraulic conductivity

foot per day (ft/d) 0.3048 meter per day (m/d)

Transmissivity*

foot squared per day (ft2

/d) 0.09290 meter squared per day (m2/d) 

SI to Inch/Pound

Multiply By To obtain

Length

centimeter (cm) 0.3937 inch (in.)

millimeter (mm) 0.03937 inch (in.)

meter (m) 3.281 foot (ft) 

kilometer (km) 0.6214 mile (mi)

Area

hectare (ha) 0.003861 square mile (mi2

) 

square kilometer (km2

) 0.3861 square mile (mi2)

Flow rate

liter per second (L/s) 15.85 gallon per minute (gal/min) 

Hydraulic conductivity

meter per day (m/d) 3.281 foot per day (ft/d) 

Transmissivity*

meter squared per day (m2

/d) 10.76 foot squared per day (ft2/d) 

Horizontal coordinate information is referenced to the North American Datum of 1983 (NAD 83) 

*Transmissivity: The standard unit for transmissivity is cubic foot per day per square foot times 

foot of aquifer thickness [(ft3

/d)/ft2]ft. In this report, the mathematically reduced form, foot 

squared per day (ft2/d), is used for convenience.
vi

Abbreviations

AML ARC Macro Language

ASCII American Standard Code for Information Exchange

DEM Digital elevation model

GWSI Ground Water Site Inventory

K Equivalent horizontal hydraulic conductivity

KZ Equivalent vertical hydraulic conductivity

KSC Horizontal hydraulic conductivity derived from water-well log specific-capacity data

T Transmissivity, in foot squared per day 
Processing, Analysis, and General Evaluation of 

Well-Driller Logs for Estimating Hydrogeologic 

Parameters of the Glacial Sediments in a 

Ground-Water Flow Model of the Lake Michigan Basin

Abstract

In 2005, the U.S. Geological Survey began a pilot study 

for the National Water Availability and Use Program to

assess the availability of water and water use in the Great

Lakes Basin. Part of the study involves constructing a

ground-water flow model for the Lake Michigan part of the 

Basin. Most ground-water flow occurs in the glacial sediments 

above the bedrock formations; therefore, adequate representation by the model of the horizontal and vertical hydraulic conductivity of the glacial sediments is important to the accuracy 

of model simulations. This work processed and analyzed well 

records to provide the hydrogeologic parameters of horizontal 

and vertical hydraulic conductivity and ground-water levels for 

the model layers used to simulated ground-water flow in the 

glacial sediments. The methods used to convert (1) lithology 

descriptions into assumed values of horizontal and vertical 

hydraulic conductivity for entire model layers, (2) aquifer-test 

data into point values of horizontal hydraulic conductivity, 

and (3) static water levels into water-level calibration data are 

presented.

A large data set of about 458,000 well driller well logs 

for monitoring, observation, and water wells was available 

from three statewide electronic data bases to characterize 

hydrogeologic parameters. More than 1.8 million records of 

lithology from the well logs were used to create a lithologicbased representation of horizontal and vertical hydraulic 

conductivity of the glacial sediments. Specific-capacity data 

from about 292,000 well logs were converted into horizontal 

hydraulic conductivity values to determine specific values 

of horizontal hydraulic conductivity and its aerial variation. 

About 396,000 well logs contained data on ground-water levels that were assembled into a water-level calibration data set.

A lithology-based distribution of hydraulic conductivity 

was created by use of a computer program to convert well-log 

lithology descriptions into aquifer or nonaquifer categories 

and to calculate equivalent horizontal and vertical hydraulic 

conductivities (K and KZ, respectively) for each of the glacial 

layers of the model. The K was based on an assumed value 

of 100 ft/d (feet per day) for aquifer materials and 1 ft/d for 

nonaquifer materials, whereas the equivalent KZ was based on 

an assumed value of 10 ft/d for aquifer materials and 0.001 

ft/d for nonaquifer materials. These values were assumed for 

convenience to determine a relative contrast between aquifer 

and nonaquifer materials. The point values of K and KZ from 

wells that penetrate at least 50 percent of a model layer were 

interpolated into a grid of values. The K distribution was based 

on an inverse distance weighting equation that used an exponent of 2. The KZ distribution used inverse distance weighting 

with an exponent of 4 to represent the abrupt change in KZ that 

commonly occurs between aquifer and nonaquifer materials.

The values of equivalent hydraulic conductivity for 

aquifer sediments needed to be adjusted to actual values in the 

study area for the ground-water flow modeling. The specificcapacity data (discharge, drawdown, and time data) from the 

well logs were input to a modified version of the Theis equation to calculate specific capacity based horizontal hydraulic 

conductivity values (KSC). The KSC values were used as a guide 

for adjusting the assumed value of 100 ft/d for aquifer deposits 

to actual values used in the model.

Water levels from well logs were processed to improve 

reliability of water levels for comparison to simulated water 

levels in a model layer during model calibration. Water levels 

were interpolated by kriging to determine a composite waterlevel surface. The difference between the kriged surface and 

individual water levels was used to identify outlier water 

levels.

Examination of the well-log lithology data in map form 

revealed that the data were not only useful for model input, but 

also were useful for understanding the glacial hydrogeology 

of a multistate area. The distribution of K and KZ provided a 

three-dimensional view of aquifer and confining systems. The 

distribution of KSC revealed an aerial difference from state to 

state and a relation to glacial sediment texture. Median KSC

was larger for Indiana (264 ft/d) than for Michigan (89 ft/d) 

or Wisconsin (48 ft/d). The difference could be related to past 

glacial processes. A pattern in KSC was observed with sediment texture. Aquifers within sediment textures deposited 

By Leslie D. Arihood
2 Processing, Analysis, and General Evaluation of Well-Driller Logs for Estimating Hydrogeologic Parameters

under a higher energy environment, for example, outwash 

textures, had higher values of KSC than those deposited under a 

lower energy environment.

Introduction

At the request of Congress, the U.S. Geological Survey 

is assessing the availability and use of the Nation’s water 

resources. In 2005, the U.S. Geological Survey established 

a program called the National Water Availability and

Use (U.S. Geological Survey, 2002). The program is

designed to characterize how much surface and ground 

water is currently available, how water availability is changing, and how much water will be available in the future. The 

Great Lakes Basin was chosen as a pilot study area. Methods 

developed during the pilot study are intended to best evaluate 

the resource and deliver accurate and timely information to 

planners at local, regional, and national levels.

Part of the study involves constructing a ground-water 

model for the Lake Michigan part of the Basin. Mandle and 

Kontis (1992, p. 92) found that most ground-water flow for 

the northern Midwest United States, which includes the Lake 

Michigan Basin west of Lake Michigan, occurs in the glacial 

sediments as opposed to the underlying bedrock. Therefore, adequate representation by the model of the horizontal 

and vertical hydraulic conductivity of the glacial sediments 

is important to the accuracy of model flow simulations. 

Adequate representation of the glacial sediments is difficult 

because of their extreme heterogeneity. The occurrence of 

interbedded sand and gravel deposits within the fine grained 

glacial sediments is difficult to predict. Many observation 

points are required to define the extent of intermittent zones of 

aquifer and nonaquifer sediments. Neff and others (2005, p. 7) 

explained the improvement in using the lithology information 

from well logs to better represent geology beneath the surface. 

Abundant data that can provide information on hydraulic 

conductivity, as well as ground-water levels, are available from 

well driller well logs that were provided to state regulatory 

agencies. The monitoring-, observation-, and water-well logs 

have been computerized by the states, stored in statewide data 

bases, and are available for processing and analysis by computer programs to provide data for model construction. 

Purpose and Scope

This report documents the process of converting information from about 458,000 electronic well logs from the state 

data bases of Michigan, Wisconsin, and Indiana into data 

useful for construction of a regional ground-water model for 

the Lake Michigan Basin. The report first establishes the use 

and limitations of well logs in hydrogeologic studies, and then 

explains how lithologic descriptions recorded by drillers onto 

well logs were converted into hydraulic conductivities. The 

procedures are explained for converting specific-capacity data 

(well pumpage rate, time, and water-level drawdown data) into 

values of horizontal hydraulic conductivity and for characterizing the horizontal hydraulic conductivities of different 

hydrogeologic units. A quality-control procedure to eliminate 

outlier water-level data from a well-log data set to be used 

as model-calibration data also is presented. No well-log data 

were available for the glacial sediments beneath Lake Michigan, therefore, the procedure for estimating glacial thickness 

and the horizontal and vertical hydraulic conductivities of the 

sediments beneath the lake is explained.

Results of the well-log processing are illustrated, analyzed, and compared to similar hydrogeologic products as a 

qualitative method to evaluate the accuracy of well-log data. 

Distributions of the well-log derived values of horizontal and 

vertical hydraulic conductivity are illustrated across three 

states for the top layer (layer 1) of the ground-water model. 

Patterns in the distributions are described and analyzed. The 

distributions are compared to surface-geology and groundwater availability maps and a type of flow-duration map as 

a way to qualitatively evaluate the accuracy of the hydraulic 

conductivity distributions. This comparison process revealed 

that well-log based products have potential advantages over 

conventional products like surface geology maps to determine 

hydrologic parameters, and those advantages are presented. 

Geologic Setting

The 181,000 mi2 study area covers nearly all of Michigan and parts of Wisconsin and Indiana (fig. 1). The study 

area corresponds to the area of the Lake Michigan groundwater model currently (2008) being constructed as another 

part of the pilot study on water availability and use. Although 

northeastern Illinois is included in the modeled area, hydraulic conductivity data for that area were not derived from well 

logs. Data for Illinois were derived from another model being 

constructed by the Illinois State Water Survey for northeastern 

Illinois (Lin and others, 2008). No well logs were analyzed 

for Ohio or the Canadian parts of the ground-water model. 

Hydraulic conductivity, to be discussed in the modeling report, 

was instead based on texture classes defined by Fullerton and 

others (2003) or Soller and Packard (1998). 

The study area is overlain by glacial sediments deposited by four glaciations, which created mostly flat to gently 

rolling topography (Weist, 1978, p. J3). Although glacial 

sediments cover the study area, in some areas those sediments are reworked into different deposits, such as alluvium. Glacial sediments can be as much as 1,100 ft thick in 

Michigan (Weist, 1978, p. J3) but usually are less than 200 ft 

thick (fig. 2). The glacial sediments at the surface consist of 

several textures, but fine grained sediments dominate (table 1). 

The distribution of the textures is shown in a surface geology map presented later in the report. A description of the 

regional surficial aquifer system within the glacial sediments 

is provided by Coon and Sheets (2006, p. 7–8). The glacial 

sediments overlie mostly consolidated sedimentary formations 
Introduction 3

Figure 1. Study area of the Lake Michigan ground-water model and boundary of 

the Lake Michigan Basin.

Base from U.S. Geological Survey digital data

1:100,000 1983. Universal Transverse Mercator

projection, Zone 16, Standard parallel 00 (Equator),

EXPLANATION

Boundary of Lake Michigan Basin

WISCONSIN

ILLINOIS

INDIANA

MICHIGAN

OHIO

LAKE 

MICHIGAN

900

890

880

870 860

850 840 830

820

460

450

440

430

420

410

Central meridian 870 W, North American Datum 1983

GREEN BAY

SAGINAW BAY

Study area and model boundary

WISCONSIN

I

ILLINOIS

INDIANA OHIO

PENNSYLVANIA

NEW

YORK

ONTARIO

QUEBEC MINNESOTA

IOWA

EXPLANATION

KILOMETERS

0 100 200 MILES

0 100 200

GREAT LAKES 

BASIN WITHIN 

UNITED STATES

Base from Environmental Systems Research Institute, 

2001; U.S. Army Corps of Engineers, 1998.

CANADA

U.S.A

MI CHI G AN

900

800

500

400

70 140 MILES

0 70 140 KILOMETERS

0
4 Processing, Analysis, and General Evaluation of Well-Driller Logs for Estimating Hydrogeologic Parameters

ranging in age from Jurassic to Precambrian Period (Sheets 

and Simonson, 2006, p. 4). The major bedrock feature is the 

deep sedimentary basin in Michigan (Weist, 1978, p. J3) that 

is reflected in the area of thick Quaternary Period sediments 

shown in figure 2. Additional information on the bedrock formations can be found in Sheets and Simonson (2006).

Figure 2. Thickness of Quaternary Period sediments in the study area of the Lake Michigan ground-water model.

Table 1. Amount of surface area of the Lake Michigan groundwater model covered by individual sediment textures and 

sediment texture classifications.

[Data from Fullerton and others, 2003]

Sediment 

texture

Area covered by 

asediment 

texture 

(percent)

Sediment texture 

classification

Till 55.9 Fine grained

Coarse1 24.6 Coarse grained

Lake sediments 17.5 Fine grained

Peat and muck 1.5 Fine grained

Loess 0.5 Fine grained 1

Outwash, ice contact forms, sand, colluvium, alluvium

70 140 MILES

0 70 140 KILOMETERS

0

Thickness from 0 to

less than 50 ft

Thickness from 50 to

less than 100 ft

Thickness from 100 to

less than 200 ft

Thickness from 200 to

less than 400 ft

Thickness from 400 to

less than 800 ft

Thickness from 800 to

less than 1200 ft

Water body

No data

EXPLANATION

Sediment thickness from Soller and Packard, 1998

460

450

440

430

420

410

900

890 880

870

860

850

840 830 820

Base from U.S. Geological Survey digital data

1:100,000 1983. Universal Transverse Mercator

projection, Zone 16, Standard parallel 00 (Equator),

Central meridian 870 W, North American Datum 1983
Processing of Well-Log Data 5

Processing of Well-Log Data

This section begins by providing a qualitative evaluation of data from all available well driller reports (well logs) 

for use in hydrogeologic interpretations, then describes the 

processing of data from well logs into hydrogeologic information useful for ground-water modeling. The methods used 

to convert (1) lithology descriptions into assumed values of 

horizontal and vertical hydraulic conductivity for entire model 

layers, (2) pump-test data into point values of horizontal 

hydraulic conductivity, and (3) static water levels into waterlevel calibration data are presented. Hydraulic conductivities 

for the glacial sediments beneath Lake Michigan also were 

required for the ground-water model, but well logs do not exist 

in that area. An alternate method for estimating horizontal and 

vertical hydraulic conductivity for Lake Michigan sediments is 

explained.

Evaluation of Well-Log Data

Data recorded on well logs from monitoring, observation, 

and water wells may not be thought as reliable as those collected by geologists or hydrologists who are typically guided 

by training programs or a quality-assurance plan during data 

collection. Still, the data can potentially be useful when other 

data are not available. For example, Fowler and Arihood 

(1998, sheet 1) used well-log water-level data to indicate 

general ground-water flow direction and water-level altitude 

for St. Joseph County in northern Indiana. By comparison, 

the mean absolute error between 46 water levels measured in 

1990–1992 by U.S. Geological Survey staff (Bayless and Arihood, 1996) and a kriged water-level surface created from the 

well-log data was 4 ft. Horizontal hydraulic conductivity from 

a study area in northeastern Illinois calculated from driller-collected specific-capacity data (12 ft/d) compared favorably with 

values calculated from professionally supervised aquifer tests 

(8 ft/d) and ground-water model calibration (17 ft/d) (Kay and 

others, 2006). The following evaluation is a qualitative one 

that discusses the limitations of well-log data and the general 

approach of typical driller practice in well-log data collection.

Well logs can provide aerially extensive hydrogeologic 

information. However, as with all data sets, sources of errors 

need to be considered. Errors in location of the well log can 

result in misplaced lithologies and incorrect altitudes for 

those lithologies. Errors in location also result in incorrect 

altitudes for ground-water levels. The potential for sampling 

bias from water-well logs is present because wells are drilled 

only until they reach aquifer material that can yield sufficient 

water. Aquifers at higher or lower altitudes with possibly different characteristics are not sampled. Interpolation between 

points where aquifers yield sufficient water could result in the 

impression that all aquifers in the area are transmissive, yet the 

area may contain generally low-yielding aquifers. In addition, 

the low-yielding aquifers, if encountered, are not reported. 

These limitations to well-log information need to be considered during the interpretation of the data. The large quantity of 

well-log data may help to reduce the influence of occasional 

errors, but in areas with few well logs, the uncertainty in interpretation increases.

Well-log data are recorded by well drillers who generally are not required by an organization to follow specific 

procedures in data collection. Therefore, questions can arise 

as to the quality of data collected by drillers. During a series 

of ground-water availability studies by the U.S. Geological 

Survey during the 1970’s and 1980’s (for example, Lapham, 

1981), project hydrologists decided to interview about 15 to 

20 well drillers in four central Indiana counties about their 

data-collection practices. Information was acquired about 

how each driller recorded lithologies, static water levels, and 

specific-capacity data. Although some drillers used unacceptable practices, such as completing the well log long after 

drilling the well, the majority of drillers followed timely and 

reasonable procedures, such as waiting until a true static water 

level had developed before recording the static water level 

for the well. The impression from the interviews was that the 

typical driller exhibited a desire to provide accurate information and recorded data in a manner approaching that used by a 

geologist or hydrologist. The driller sometimes demonstrated 

the same desire as a scientist to accurately record his findings. 

In addition, the driller may be driven by economic interests 

in having to justify well placement on the basis of drilling 

records. That is, it may be difficult to sell a deep well when 

shallow wells have been successfully established.

Calculation of Equivalent Horizontal and 

Vertical Hydraulic Conductivities for the Glacial 

Layers of the Model

Well logs provided about 458,000 observation points 

with more than 1.8 million records of lithology (fig. 3). The 

well logs have become computerized and stored in statewide 

computerized data bases. The well logs from Wisconsin (Wisconsin Geological and Natural History Survey, 2004), Michigan (Michigan Department of Environmental Quality, 2003), 

and Indiana (Indiana Department of Natural Resources, 2002) 

were obtained for processing in 2006 and 2007 by computer 

programs into information useful for ground-water modeling.

Well-log processing used ARC Macro Language programming (AML) available in ArcInfo software (Environmental Systems Research Institute, 2003) and consisted of 

several steps that are illustrated in figure 4. The well-log 

data bases were obtained as two files formatted as American 

Standard Code for Information Interchange (ASCII) for each 

state. The first file contained basic well-site information, 

such as owner name, X-Y coordinates of the well, well depth, 

aquifer-test data, and water level. This file was converted into 

a point coverage. The second file was the record of lithologies 

recorded by the well drillers. The second file was converted 

into an INFO data base, which was then read by a second 

AML program that automatically converted driller lithology 
6 Processing, Analysis, and General Evaluation of Well-Driller Logs for Estimating Hydrogeologic Parameters

Figure 3. Distribution of logs from drilled wells used to generate hydraulic conductivity and water-level data for the glacial layers 

of the Lake Michigan Basin ground-water-flow model.
Processing of Well-Log Data 7

Figure 4. Flow chart for processing well-log lithologies into grids of horizontal and vertical hydraulic conductivity.

ASCII file of 

lithologies

ASCII file of well-site 

information

(x-y, depth, water level)

INFO file of

lithologies

INFO file of standardized

lithologies and aquifer

classifications

Point coverage

of well-site 

information

Point coverage 

of all well-log 

information

Lithology

interpretation 

checks

County 

coverage 1

County 

coverage 2

County 

coverage 3

Remaining

county coverages

Calculate equivalent horizontal and vertical hydraulic conductivities for each

layer of the ground-water flow model using assumed values for the 

hydraulic conductivity of aquifer, nonaquifer, and unknown aquifer type

Combine county covers 

to one statewide cover

Interpolate point cover values 

with Inverse Distance Weighting 

to generate a statewide grid

Grid values input 

to model-calibration

process 

Grid maps used for

qualitative analysis

of hydrogeology

Statewide water-well

log database
8 Processing, Analysis, and General Evaluation of Well-Driller Logs for Estimating Hydrogeologic Parameters

descriptions into standard U.S. Geological Survey GroundWater Site Inventory System (GWSI) lithology codes (Mathey, 

1989, p. 2–110). The program searches for key phrases that 

describe lithology, such as sand, clay, or silt, and compares the 

phrases to lithology text strings in an interpretation file that 

converts the driller text into a GWSI lithology. The second 

AML program searches the phrases for as many as three 

lithology descriptors to determine the best match to a GWSI 

code, which can have as many as three lithology descriptors. 

The AML also classified each lithology into either aquifer 

or nonaquifer material. After the interpretation, the lithology 

records were checked for lithology descriptions that could 

not be interpreted because of misspellings or new lithology 

phrases not referenced in the interpretation file. Additional 

lithology text strings were added to the interpretation file so 

that the undefined lithology records could be converted to 

GWSI lithologies. Errors in the lithology record, such as gaps 

in the lithology record or the appearance of glacial lithology 

in a bedrock section of the well log, resulted in eliminating the 

well record from the statewide coverage. On average, about 5 

percent of the well logs were deleted from each original state 

well-log data base. The INFO lithology file was joined to the 

well-site information point coverage, and then subdivided into 

individual county coverages. Further processing is done on 

single county coverages, because data for an individual well 

can be accessed during subsequent programming more rapidly 

if the data are in a county coverage rather than in one set of the 

entire state.

The lithology records in the county coverages were converted by a third AML program into an equivalent, or effective 

horizontal and vertical hydraulic conductivity for each well 

site for specified thicknesses of the glacial sediments. The 

specified thicknesses correspond to the predefined thicknesses 

of glacial-sediment layers used in the ground-water model. 

The glacial sediments are represented in the model by three 

layers: a maximum 100-ft thick layer 1, a maximum 200-ft 

thick layer 2, and a layer 3 that accounts for all remaining 

glacial thickness. These layer thicknesses were used to provide 

a relatively even division of the glacial sediments in the majority of the modeled area and to provide an opportunity to vary 

the hydraulic conductivity with depth. Some areas have thin 

glacial sediments, therefore, a value of horizontal and vertical 

hydraulic conductivity is calculated at a well site for less than 

the 100- or 200-ft thickness and possibly for fewer than three 

layers. 

The program calculates equivalent horizontal and vertical 

hydraulic conductivities (referred to subsequently as K and 

KZ) for the predefined layers on the basis of percentage of 

aquifer and nonaquifer material in each layer. The equivalent 

K is based on an assumed value of 100 ft/day for aquifer material and 1 ft/day for nonaquifer material, whereas the equivalent KZ is based on an assumed value of 10 ft/day for aquifer 

material and 0.001 ft/day for nonaquifer material. These values are assumed for convenience to determine a relative contrast between aquifer and nonaquifer material. The calculation 

of equivalent horizontal and vertical hydraulic conductivity for 

each glacial layer of the model uses the following equations 

(DeWeist, 1965, p. 231–232):

(1)

(2)

where 

K = equivalent horizontal hydraulic conductivity 

for a model layer

KZ = equivalent vertical hydraulic conductivity 

for a model layer

Ki = assumed horizontal hydraulic conductivity 

of the ith lithology record within the layer

Ki

z = assumed vertical hydraulic conductivity of 

the ith lithology record within the model 

layer

t

i = thickness of the ith lithology record within 

the model layer

i = the ith lithology record, 

and

n = total number of lithology records within the 

model layer.

After equivalent hydraulic conductivities have been calculated 

for well sites in each county, the county coverages are rejoined 

into a statewide coverage. Sites without lithology records were 

eliminated from the statewide coverage.

To further explain the relation between the equivalent 

hydraulic conductivities, assumed values of hydraulic conductivity, and quantity of aquifer / nonaquifer material, the relation is illustrated in figure 5. The K value is proportional to the 

thickness of aquifer material present within the model layer. 

For a 100-ft layer that is 50 percent aquifer material (50 ft of 

aquifer), K is 50 percent of the maximum value (100 ft/d), 

or 50 ft/d. The proportional relation can be applied to other 

assumed ranges for horizontal hydraulic conductivity of aquifer material, such as 1 to 1000 ft/d. That is, the distribution of 

horizontal hydraulic conductivity does not have to be recalculated from the well-log record; a simple multiplication can be 

applied to the distribution of values. The KZ is not proportional 

to the percentage of aquifer material, but is strongly influenced 

by the value chosen for vertical hydraulic conductivity of the 

nonaquifer material. The lower the value for vertical hydraulic 

conductivity of nonaquifer material, the more quickly KZ for 

the model layer decreases in value as the percentage of aquifer 

material decreases from 100 percent. If a different value for 

vertical hydraulic conductivity of nonaquifer material is chosen during model calibration, then KZ has to be recalculated. 

According to equation 2, the recalculation requires values for 

∑

∑

=

= = n

i

i

z

i

n

i

i

Z

K

t

t

K

1

1

∑

∑

=

=

×

= n

i

i

n

i

ii

t

Kt

K

1

1

)(
Processing of Well-Log Data 9

thicknesses of aquifer and nonaquifer material (other values in 

equation 2 are known or given), which can be obtained from 

the equivalent horizontal hydraulic conductivity. For example, 

if the model layer is 100 ft thick at a model cell and aquifer 

material has an assumed horizontal hydraulic conductivity of 

100 ft/d, and if K at a model node is 75 ft/d, then the thickness 

of nonaquifer material is 25 ft and aquifer material thickness 

is 75 ft. A value for vertical hydraulic conductivity is assumed, 

and the remaining unknown, KZ, can be calculated.

Several additional details associated with the calculation 

of hydraulic conductivities are provided to help understand the 

processing of well logs:

1. The value of assumed hydraulic conductivity for a lithology record is determined by a file that defines all GWSI 

lithology codes to be aquifer, nonaquifer, or undefined 

lithology, such as drift. For undefined lithologies, the geometric mean of the equivalent horizontal hydraulic conductivity of aquifer and nonaquifer material is assigned 

to the lithology record. Because aquifer material has an 

assumed horizontal hydraulic conductivity of 100 ft/d and 

nonaquifer material has an assumed horizontal hydraulic 

conductivity 1 ft/d, then the assigned horizontal hydraulic 

conductivity for the undefined lithology is approximately 

10 ft/d.

2. The model design uses three layers (1, 2, and 3) to represent the glacial sediments. Where glacial sediments range 

from 1 to 100 ft thick, layer 1 has a thickness equal to the 

glacial thickness. Where glacial sediments are from 101 

to 300 ft thick, layer 1 has a thickness of 100 ft and layer 

2 has a thickness equal to the remainder of the glacial 

thickness. Where glacial sediments are in excess of 300 

ft thick, layer 1 extends from the land surface to 100 ft; 

layer 2 extends from 100 ft to 300 ft below land surface; 

and layer 3 extends from 300 ft below land surface to 

the depth where the glacial sediments contact the top of 

bedrock.

3. A lithology record (for example, clay 85 to 120 ft) may 

begin in one layer and end in the layer below. For that 

case, the lithology record, along with the associated 

hydraulic conductivity, is split proportionally between the 

two layers. 

Figure 5. Relation between lithology-based equivalent hydraulic conductivity and the percentage of aquifer material in the model layer.

0

20

40

60

80

100

0 20 40 60 80 100

0.001

0.01

0.1

1

10

Equivalent horizontal

hydraulic conductivity, K

Equivalent vertical

hydraulic conductivity, Kz

CALCULATED EQUIVALENT HORIZONTALHYDRAULIC CONDUCTIVITY, IN FEET PER DAY

CALCULATED EQUIVALENT VERTICALHYDRAULIC CONDUCTIVITY, IN FEET PER DAY

AQUIFER MATERIAL, IN PERCENT
10 Processing, Analysis, and General Evaluation of Well-Driller Logs for Estimating Hydrogeologic Parameters

4. A penetration depth into each model layer, expressed as 

a percentage, was calculated for each well. Wells that 

penetrate to a depth equal to or greater than 50 percent 

of a layer were selected from the coverage of equivalent 

hydraulic conductivity and used to create the continuous 

distributions of hydraulic conductivity. 

5. Hydraulic conductivities are calculated only for that part 

of the lithology record that is below the driller-measured 

water level. In a few wells, the water level is greater than 

100 ft deep, meaning that layer 1 is dry and calculated 

hydraulic conductivities are 0 ft/d. If no water-level value 

is available for the well, then the equivalent hydraulic 

conductivities are based on all lithology records within a 

layer. This decision should not noticeably alter the distributions because 85 percent of the wells have water-level 

data and the water table is usually close to the surface in 

fine grained sediments, which comprises most of the area. 

The near-surface water table is evidenced by the general 

need for tile drainage and the high surface-water levels in 

borrow pits along interstate highways.

6. In areas where the glacial sediments are thicker than 300 

ft and a third glacial layer is present, the thickness of the 

third glacial layer is defined as the thickness from 300 

ft below ground to the depth of bedrock as defined by 

the well log. If the well is not drilled to bedrock, then an 

estimated thickness of model layer 3 is determined by 

use of an interpolated value of bedrock surface altitude 

from a bedrock-surface map constructed in another one of 

the water-availability set of studies (David Lampe, U.S. 

Geological Survey, written commun., 2006). The percent 

penetration of the well into the third layer is based on the 

actual or estimated layer thickness. 

7. If the well extends through a layer bottom, this process 

also computes an altitude of the layer bottom at each well. 

If a layer has zero thickness, then the altitude of that layer 

is assigned the same value as the bottom altitude of the 

first layer above with a finite thickness. For example, if 

layer 3 is zero thickness, and layer 2 is 50 ft thick with a 

bottom altitude of 650 ft, then the bottom altitude of layer 

3 is also 650 ft. The altitude of the bedrock surface would 

also be 650 ft. During model construction, layer 3 would 

be given a small thickness of 0.2 ft so that the layer is 

continuous.

Calculation of Aquifer Hydraulic Conductivity 

from Well-Log Specific-Capacity Data

Values of horizontal hydraulic conductivity for aquifer 

material also were calculated directly from about 292,000 

well logs by use of the specific-capacity data (well discharge, 

duration of pumping, and water-level drawdown) and are 

referred to as KSC. The calculated KSC provided an estimate of 

horizontal hydraulic conductivity. The modeling process used 

the estimate as a guide for adjusting the assumed K value of 

100 ft/d to actual values used in calculating a lithology-based 

K for a model layer. The process of converting pump-test, or 

specific-capacity data, into KSC is presented.

Well discharge, duration of pumping, and water-level 

drawdown for each well were input to a modified form of the 

Theis equation shown below (Prudic, 1991, p. 11) to determine an aquifer transmissivity, T. Because T appears on both 

sides of equation 3, an iterative process was used to calculate 

T by use of an initial value of 500 ft2

/d. The process stops after 

several iterations when the difference between the old estimate 

and new estimate for T becomes less than 5 ft2/d, and the last 

new estimate is used. The value of T was adjusted for the 

effect of partial penetration by the well screen into the aquifer 

by use of a method described by Butler (1957, p. 160). Values 

of KSC were calculated by dividing T by the thickness of saturated aquifer material penetrated by the well, which provides a 

conservative estimate for KSC. Well loss was not accounted for 

because there were no available data to analyze well loss, and 

the well was assumed 100 percent efficient. It was assumed 

that, in whole, the gravel pack around the well screen compensated for the additional drawdown caused by turbulent flow 

through the well screen and in the well casing. The calculation 

of T from specific-capacity data is often done, and two alternative, computerized methods are referenced for convenience: 

(Bradbury and Rothschild, 1985; McLin, 2005).

 

(3)

where

T = transmissivity, in feet squared per day,

Q = well discharge, in gallons per minute,

s = drawdown, in feet,

r = well radius, in feet,

S = storage coefficient (dimensionless), 

and

t = time, in days.

Equation 3 requires values for storage coefficient, well 

diameter, and screen length. Well logs often do not have all of 

these data (and almost never have storage coefficient), therefore estimates need to be determined. If screen length data 

was missing, then a screen length of 5 ft was assumed when 

pumping rate was less than 200 gal/min, and a screen length 

of 20 ft was assumed if the pumping rate was greater than or 





















 



  −− 

 



 = Tt

Sr

s

Q T e 4 log577.0**32.15

2
Processing of Well-Log Data 11

equal to 200 gal/min. If well diameter data was missing, then 

a well diameter of 4 inches was assumed when pumping rate 

was less than 200 gal/min, and a well diameter of 8 inches was 

assumed if pumping rate was greater than or equal to 200 gal/

min. If the well was confined, then the storage coefficient was 

assumed to be 0.0001; if the well was unconfined, then the 

storage coefficient was assumed to be 0.15. These values are 

well within ranges suggested by Freeze and Cherry 

(1979, p. 60–61), and their difference from actual values 

should not significantly affect the calculation of T (Prudic, 

1991, p. 12). Confined or unconfined conditions were determined by observing if the pumping level for the water surface 

was above or below the top of the aquifer, respectively. 

Calculation of horizontal hydraulic conductivity from 

transmissivity requires a value for well discharge. Well discharge could be generated by a submersible pump, an air line 

(air flow from a compressor), or other methods, like a bailer. 

Because different pumping methods might result in different 

values for KSC, a comparison was made between KSC values 

calculated using data from different methods of pumping. 

Table 2 presents the results of the effect from different pumping methods on median values of KSC for wells in Michigan 

(Michigan Department of Environmental Quality, 2005) and 

Indiana (Indiana Department of Natural Resources, 2002). 

Sample size for each glacial texture was large, always greater 

than 1,000 samples. The values are for sites where the pumping method was known and do not include all of the sites with 

specific-capacity data. The table shows that median KSC for the 

air flow method is greater than that for the submersible pump 

method in Indiana, however, the opposite is true in Michigan. A rank-sum test (Helsel and Hirsch, 1995) was done on 

the individual state data sets and confirmed that the air flow 

method of pumping produces larger values of KSC in the Indiana data and smaller values in Michigan (p > 0.999). Because 

it is not known which method of pumping produces a more 

representative value of KSC, and because the median KSC using 

all methods of pumping is between the median KSC for air flow 

and submersible pumping, the data from all pumping methods 

were used in the analysis of patterns in KSC for the three states.

Table 2. Comparison of median horizontal hydraulic conductivity 

calculated using specific capacity data from Indiana and 

Michigan. 

[ft/d, feet per day; (1,625), number of samples; data from Michigan Department of Environmental Quality (2005) and Indiana Department of Natural 

Resources (2002)]

Method of 

pumping

Median horizontal 

hydraulic 

conductivity

in Indiana

(ft/d)

Median horizontal 

hydraulic 

conductivity

in Michigan

(ft/d)

All 177 (6,425) 18.7 (162,496)

Air flow 226 (2,076) 14.2 (50,455)

Submersible pump 143 (976) 37.0 (12,471)

Compilation of Water-Level Calibration Data 

from Well Logs

About 396,000 water-level values (one value per well log 

collected at the time of well construction) are available from 

the state well-log data bases for the Lake Michigan groundwater model area. In some areas, the well-log water-level data 

are the only source of information on water surfaces of the 

various aquifers. Such a large data set should provide many 

reliable water levels to aid in model calibration. In fact, Fowler 

and Arihood (1998, sheet 1) used well-log water-level data to 

indicate general ground-water flow direction and water-level 

altitude for St. Joseph County in northern Indiana. Water 

levels measured by U.S. Geological Survey (Bayless and Arihood, 1996) were on the average (mean absolute error) 4 ft 

from a kriged water-level surface created from the driller data, 

which is sufficient accuracy for model calibration data. However, appropriate screening techniques were used to eliminate 

inaccurate and inconsistent data. 

All well logs that contained a depth to static water level 

from land surface were processed into a coverage and evaluated for accuracy and consistency. The process of selecting 

water-level data for model calibration is illustrated in figure 6. 

A static water-level altitude was calculated by subtracting the 

depth to the static water level from the land-surface altitude. 

Land-surface altitude typically is recorded on the well log 

along with the depth to static water level. If land-surface 

altitude was not recorded, then a value interpolated from a 

30-meter digital elevation model (DEM) was substituted for 

the missing altitude data. Substituted values of altitude represented less than 1 percent of the data set, except for the data 

from Wisconsin. Land-surface altitude data were not available 

from the well logs; therefore, all altitude data from Wisconsin (86,000 values) were derived from the DEM. The error in 

land-surface altitude will vary depending on the method used 

to determine altitude. Altitudes commonly are estimated from 

topographic maps. The author examined the potential error 

in altitudes estimated from topographic maps by comparing 

estimated altitudes with surveyed values for 35 well sites. The 

mean absolute error for altitudes estimated from a topographic 

map was 1.68 ft. The root mean square error for altitudes 

based on DEM data was estimated to be 8.01 ft (American 

Society for Photogrammetry and Remote Sensing, 2007, 

p. 106).

Although well-log water levels can be helpful, many 

water levels in the data set are expected to contain significant 

measurement and location error and should be eliminated from 

the set of calibration water levels. A method was developed for 

quality assurance of well-log water levels. The method used 

kriging to determine a water-level surface, and then used the 

difference between the kriged surface and water levels from a 

well log to identify and eliminate outlier water levels. 
12 Processing, Analysis, and General Evaluation of Well-Driller Logs for Estimating Hydrogeologic Parameters

A kriging interpolator available in ArcInfo, version 8.3 

(Environmental Systems Research Institute, 2003) was used 

to estimate a water-level surface grid that had grid cells every 

2,000 ft. Kriging was used to improve the estimation of water 

levels by taking advantage of the spatial correlation existing 

Figure 6. Flow chart for the processing of well-log 

water levels into model-calibration data for the Lake 

Michigan ground-water-flow model.

between water-level measurements. Ordinary kriging (McBratney and Webster, 1986) was applied to the individual waterlevel data sets from each state by use of the kriging parameters 

shown in table 3. Included in the table is the mathematical 

function used to fit the semi-variance data. Kriging was 

applied to the individual state data sets, but trends in population distributions between each state were not considered. 

Cover containing all 

well-log information

Reduced cover containing depth to 

water level and land-surface data DEM provides missing 

land-surface values

(water-level altitude calculated)

Water-level altitude

surface interpolated from 

point values of water-level 

altitude by ordinary kriging

Point values of water-level 

altitude subtracted from 

kriged water-level surface

Differences between individual water levels 

and kriged surface sorted by magnitude

Water levels having consistent differences from

kriged water-level surface are kept for model calibration

Remaining water-level values assigned to a model layer 

on the basis of well depth and used to compare to 

simulated values calculated for the same model layer
Processing of Well-Log Data 13

Lack of consideration of the trends probably introduced error 

in the estimates, but the errors were not considered significant relative to the use of the water-level surface, which was 

to eliminate outlier water levels. As explained in the next 

paragraph, only large differences between individual measurements and the kriged water-level surface were used to identify 

outliers. 

Table 3. Kriging parameters used to interpolate ground-waterlevel surfaces in Indiana, Michigan, and Wisconsin for the Lake 

Michigan ground-water-flow model.

State Number of 

data points

Mathematical 

function Sill Range

Indiana 41,407 Gaussian 866 25,754

Michigan 265,399 Spherical 3,057 36,000

Wisconsin 113,886 Gaussian 3,060 36,000

After the water-level surface was generated, the difference between the surface and water level for each well was 

calculated and stored in the coverage. The difference was used 

to identify and eliminate outlier water-level values from each 

state data set. The difference values were sorted from smallest 

to largest, and they were then examined to determine a break 

between large differences that were unique in value and differences that were repetitive. For example, the sorted differences 

might change from -450 ft to -400 ft, then -380 ft. Eventually, 

the sorted differences change by less than 1 ft between two 

values, for example from -39 ft to -38 ft, to -37.5 ft. At that 

point, water levels with similar values for difference exist 

within the data set. Similar difference values may indicate that 

the measured water-level values are confirming each other and 

should remain in the data set. The example difference value 

of -39 ft may seem so large that it should not be included, 

but legitimate reasons may exist for the large difference. For 

example, the water levels might be from a deep or shallow 

layer or near a pumping center. For each state, only the water 

levels with similar values for the differences were kept in the 

coverage. The break in value for acceptable differences varied 

from state to state, but was somewhat above 100 ft.

All water levels in the data set were used together to create a single water-level surface rather than create a different 

water-level surface for each of the three glacial layers in the 

ground-water model. Past experience with dividing waterlevel data into layers and then interpolating several individual 

surfaces has resulted in some areas having an unreasonable 

water-level surface because of insufficient data. The use of all 

water levels together typically results in a reasonable surface, 

but it may create the concern that the resulting water-level 

surface does not represent any specific model layer. If the surface does not represent a specific layer, then the surface may 

not be thought an appropriate indicator of outlier water levels 

for any individual layer. However, in Indiana the water-level 

difference between aquifers is often only 1 to 2 feet (Arihood 

and Cohen, 1998). Water-level differences between layers 

may be greater than a few feet in many parts of the study area 

(for example, see Bayless and Arihood, 1996, p. 17). In those 

areas, the goal is to avoid eliminating water levels that have 

large differences between their values and the interpolated surface, but that still may be representative of the model layer to 

which the water levels belong. The process of retaining waterlevel values that have consistent differences from the average 

surface, as described previously, will help avoid eliminating 

water levels different from the average surface, and that still 

may be useful for calibration. 

Although a single water-level surface was generated, the 

individual water-level values were grouped by model layer. 

The grouping was done by assigning a layer to the water-level 

value on the basis of well depth. During model calibration, the 

water-level values assigned to a specific layer were compared 

to model-simulated values for the same layer. 

The interpolated water-level surface also was created 

using water levels from all periods of the year and from all 

available years, which covers decades of water-level data. 

The vast majority of the data typically span about a 40-year 

period from the 1960’s to about 2005. Water-level data from 

a specific year and time of the year could be reselected to 

create a time-specific surface, but the smaller number of data 

points would lead to areas with few data points and uncertain 

water-level surface. The use of data from all times of the year 

and all years results in a data set that contains the entire range 

of water levels. From such a data set, the kriging interpolator 

would determine the most likely, or average, water-level surface, which may avoid representations of extreme conditions.

Another way in which the single interpolated water-level 

surface can be effectively used to detect outlier water levels 

that should be eliminated from model-calibration data is to 

define areas where wells are located mostly within a specific 

aquifer. In those areas, the interpolated water-level surface 

reflects mostly the water-level surface of that specific aquifer. 

Water levels that vary by several feet from the interpolated 

surface can be eliminated by future users of the data set.

The number of locations where well-log water-level data 

could be used to calibrate the ground-water model for changes 

in water level from one layer to the next is probably limited. 

The sites with water-level data representing both model layers 

must be essentially at the same location. Also, multiple water 

levels that generally confirm each other must be available for 

both layers. 
14 Processing, Analysis, and General Evaluation of Well-Driller Logs for Estimating Hydrogeologic Parameters

Estimation of Thickness and Hydraulic 

Conductivity for Glacial Sediments beneath 

Lake Michigan

Few data sources (and no well logs) were available to 

help provide information on thickness and horizontal and 

vertical hydraulic conductivity of the sediments beneath Lake 

Michigan. Therefore, the values of thickness and hydraulic 

conductivity for sediments beneath Lake Michigan are inferred 

from existing map information and, to a large degree, based 

on what could be considered reasonable by study hydrologists. 

These values may require adjustment during model calibration. 

Different data sources for thickness of sediments were 

used for different parts of the lake bottom. For the southern 

half of Lake Michigan (see fig. 2 for extent), work presented 

by Soller and Packard (1998) was used to estimate the 

thickness of lacustrine clay and till. For the northern half of 

Lake Michigan, a shaded relief map (Colgan and Principato, 

1998) and a discussion of the geomorphology of the lakebed 

(National Oceanic and Atmospheric Administration, 2005) 

were used to estimate the thickness and hydrologic characteristics of the glacial sediments. East of Green Bay, Wis. and for 

two isolated areas of alluvial fan/deltaic deposition, the glacial 

sediments were modeled as a sequence of sand over nonaquifer materials. In areas where Colgan and Principato (1998) 

interpreted smoothly varying lake bottom topography, it was 

assumed that the map indicated deposition of sand and silt. 

In areas where the map showed highly variable lake bottom 

topography, it was assumed that the map reflected bedrock 

surface control of the lake bottom. For areas of highly variable 

lake bottom topography, it was assumed that a 10 ft thick clay 

deposit was the only glacial sediment covering bedrock. For 

the entire near-lakeshore area, the underlying clay and tills 

were assumed to be capped by sand deposits derived from fluvial sources. The resulting thickness map is shown in figure 7.

Equivalent hydraulic conductivity values (K and KZ) were 

assigned based upon the composition of the materials within 

each mapping area. For clay and till, K = 1 ft/d and KZ = 0.1 

ft/d; for sand, K = 100 ft/d and KZ = 10 ft/d. For areas mapped 

as being capped by sand, such as around the lake perimeter, 

the sand deposits were assumed to represent 25 percent of the 

total thickness of the glacial sediments. For example, where 

sediments were mapped as 100 ft thick, sand and clay thickness were assumed to be 25 ft and 75 ft, respectively. The 

assumption of 25 percent sand deposits ensured that there was 

always a sand and clay mixture, regardless of the total thickness of glacial sediments. The resulting horizontal hydraulic 

conductivity distribution is shown in the next section. 

Analysis of Well-Log Data

The processing of well logs was originally intended 

to provide distributions of horizontal and vertical hydraulic 

conductivity for a ground-water model. The values associated 

with the distributions were manipulated by the calibration 

process to obtain final values while maintaining the relative 

differences in the distribution. During the processing, observations of the well-log data in map form indicated additional 

benefits that accrue from this analysis in the understanding of 

the hydrogeology of a multistate area. The distributions of K 

and KZ data provide a three-dimensional view of aquifer and 

confining systems and the distribution of specific-capacitybased hydraulic conductivity reveal aerial differences from 

state to state in horizontal hydraulic conductivity of sand and 

gravel deposits. 

The point values of K and KZ calculated from the welllog lithology records for each layer were used as input to an 

interpolation program within ArcInfo to estimate a grid of 

values for hydraulic conductivity for each state data set. The K 

distribution was based on an inverse distance weighting equation shown below (Isaaks and Srivastava, 1989, p. 258–259). 

According to Isaaks and Srivastava (1989, p. 259), as the value 

of the exponent p becomes greater, the closest input value has 

greater influence on the estimated value. The K distribution 

was calculated with an exponent of 2, which is commonly 

used (Sheppard, 1968). The KZ distribution used an exponent 

of 4 on the log of vertical hydraulic conductivity to better 

represent the abrupt change in KZ that occurs between aquifer 

and nonaquifer materials. The distributions were calculated 

for each state, and then the individual state distributions and 

the distribution beneath Lake Michigan were merged together 

to construct a single distribution of K and KZ for each model 

layer. The K and KZ distributions for layer 1, the most continuous layer, are shown in figures 8 and 9, respectively. The figures show the K and KZ distributions based on assumed values 

for aquifer and nonaquifer materials, but the distributions also 

reflect the total amount of aquifer and nonaquifer material in 

the first layer of the model. The grids are based on well logs 

that penetrate at least 50 percent of the layer thickness. 

 (4)

where 

ν

ˆ = estimated value,

νi = input value,

i = ith input value,

n = last input value,

d = distance between the input value and the 

point of estimation, 

and

p = exponent.

∑

∑

=

= = n

i

p

i

n

i

p i

i

d

ν

d

ν

1

1

1

1

ˆ
Analysis of Well-Log Data 15

The distributions shown in the previous two figures 

provided the pattern of change in hydraulic conductivity for 

the ground-water model. Final values of hydraulic conductivity associated with the distribution were determined during 

model calibration. For calibration, the original values of 

hydraulic conductivity were to be expanded from those in the 

grids (1–100 ft/d) to a wider range that was more effective in 

matching observed values of ground-water levels and flows. In 

addition, the modeled area was divided into zones representing 

different glacial environments. The addition of zones provided 

the flexibility of using different multipliers for hydraulic 

conductivity in areas that may have a consistently higher or 

lower value because of glacial setting. Final details about the 

expansion and zonation will be discussed in the future 

modeling report.

Figure 7. Estimated thickness of glacial 

sediments beneath Lake Michigan. 

Estimates based on data from Soller and 

Packard (1998), Colgan and Principato 

(1998), and National Oceanic and 

Atmospheric Administration (2005).

EXPLANATION

10

100

200

300

440

GREEN BAY

42

43

44

45

46

88 87 86 85 84 0 0 0 0 0

0

0

0

0

0

0 70 140 210 MILES

0 70 140 210 KILOMETERS

Sediment thickness, in feet

LAKE

MICHIGAN

Base from U.S. Geological Survey digital data

1:100,000 1983. Universal Transverse Mercator

projection, Zone 16, Standard parallel 00 (Equator),

Central meridian 870 W, North American Datum 1983
16 Processing, Analysis, and General Evaluation of Well-Driller Logs for Estimating Hydrogeologic Parameters

Figure 8. Distribution of equivalent horizontal hydraulic conductivity for layer 1 of the model based on assumed hydraulic 

conductivity of 1 foot per day for nonaquifer material and 100 feet per day for aquifer material.

ILLINOIS

INDIANA

MICHIGAN

WISCONSIN

OHIO

(no data)

(no data)

1

25 

50

75 

100

41

42

43

44

45

46

90 89 88 87 86 85 84 83 82

EXPLANATION

0 0 0 0 0 0 0 0 0

0

0

0

0

0

0

SAGINAW BAY

Equivalent horizontal hydraulic 

conductivity, in feet per day

LAKE

MICHIGAN

Base from U.S. Geological Survey digital data

1:100,000 1983. Universal Transverse Mercator

projection, Zone 16, Standard parallel 00 (Equator),

Central meridian 870 W, North American Datum 1983

GREEN BAY

70 140 MILES

0 70 140 KILOMETERS

0
Analysis of Well-Log Data 17

Figure 9. Distribution of equivalent vertical hydraulic conductivity for layer 1 of the model based on assumed vertical 

hydraulic conductivity of 0.001 foot per day for nonaquifer material and 10 feet per day for aquifer material.

Vertical hydraulic conductivity, 

in feet per day

Less than or equal to 0.01

Greater than 0.01 and

less than or equal to 0.1

Greater than 0.1 and

less than or equal to 1.0

Greater than 1.0 and

less than or equal to 10.0

EXPLANATION

ILLINOIS

INDIANA

MICHIGAN

WISCONSIN

OHIO

LAKE

MICHIGAN

900 890

880 870 860

850

840

830

820

460

450

440

430

420

410

Base from U.S. Geological Survey digital data

1:100,000 1983. Universal Transverse Mercator

projection, Zone 16, Standard parallel 00 (Equator),

Central meridian 870 W, North American Datum 1983

(no data)

(no data)

70 140 MILES

0 70 140 KILOMETERS

0
18 Processing, Analysis, and General Evaluation of Well-Driller Logs for Estimating Hydrogeologic Parameters

Table 4. Median horizontal hydraulic conductivity for different sediment texture classes calculated from specific-capacity data from 

Indiana, Michigan, and Wisconsin for model layers 1, 2, and 3 of a ground-water-flow model of the Lake Michigan Basin.

[ft/d, feet per day; —, no data]

Glacial texture

at the surface

Horizontal hydraulic 

conductivity in Indiana (ft/d)

Horizontal hydraulic 

conductivity in Michigan (ft/d)

Horizontal hydraulic 

conductivity in Wisconsin (ft/d)

Model layer 1

Coarse 200 35 25

Till 98 28 22

Fine grained stratified 74 12 29

Model layer 2

Coarse 240 18 12

Till 130 16 16

Fine grained stratified 56 6.6 22

Model layer 3

Coarse — 7.7 0.73

Till — 6.3 7.8

Fine grained stratified — 6.3 —

Patterns can be seen in the distributions of hydraulic 

conductivity. Patterns of high and low K over large areas are 

evident within each state, such as the high values in northwestern Indiana and the low values southwest of Saginaw Bay in 

Michigan (fig. 8). Patterns also continue across state boundaries, such as the southwest-to-northeast set of linear features in 

northern Indiana and southeastern Michigan (fig. 8). High values of KZ appear in the same areas in figure 9 as seen in figure 

8, but the extent of high values is more limited in figure 9 than 

in figure 8. The limited extent of high values is related to the 

calculation of KZ. An area can have mostly aquifer material, 

but a thin layer of nonaquifer material within the aquifer can 

significantly lower the overall value of KZ calculated for the 

model layer. 

About 270,000 point values of KSC also were mapped and 

observed for patterns. Using inverse distance weighting with 

an exponent of 2, distributions of KSC were generated from the 

specific-capacity data for individual states, and the state distributions were merged into a single distribution for the threestate area (fig. 10). Figures 8 and 10 both show values for horizontal hydraulic conductivity, but they differ in the volume of 

material that they represent. Figure 8 is the equivalent hydraulic conductivity for model layer 1, which consists of aquifer 

and nonaquifer material. Figure 10 provides an estimate of the 

hydraulic conductivity of aquifer material within layer 1. In 

figure 8, horizontal hydraulic conductivity of aquifer material 

is assumed to be 100 ft/d, whereas in figure 10 the horizontal 

hydraulic conductivity of aquifer material is calculated from 

specific capacity data. Extremely high values of greater than 

10,000 ft/d for KSC were calculated for a few sites, but the 

distribution shown does not exceed 1,000 ft/d because that 

number is considered a realistic maximum value. Median KSC

values decrease from Indiana (264 ft/d) to Michigan (89 ft/d) 

to Wisconsin (48 ft/d). The decline could be related to past 

glacial processes. Kevin Kincare (U.S. Geological Survey, oral 

commun., 2006) described the glaciers as melting in place in 

Michigan. Such melting may have allowed nonaquifer material 

to settle in with the aquifer material and resulted in low horizontal hydraulic conductivities for aquifer material in Michigan. Meltwater may have been more active in Indiana, generating high energy flows and resulting in cleaner aquifer material 

in Indiana. Some correlation can be seen between areas of 

high horizontal hydraulic conductivity and current drainage 

ways in Indiana (fig. 10). More hydrogeologic investigation is 

required to confirm this general observation.

A relation was found between KSC and sediment texture. 

The point data for KSC were grouped by the surface geology 

texture class (defined by Soller and Packard, 1998) within 

which the well resides, and the median value was calculated 

for each texture class, layer, and state (table 4). Median values 

are used to avoid the influence of extremely high values that 

can noticeably influence the computation of average values. Median values also help to eliminate the influence of 

extremely high or low values of horizontal hydraulic conductivity caused by errors in data collection techniques. Again, 

KSC values in Indiana are greater than those in the other two 

states. In addition, the percentage difference between the 

values associated with the different textures in Indiana and 

Michigan are greater than those in Wisconsin for layer 1. For 

example, in model layer 1, the difference between median KSC

from areas of till to areas of coarse grained sediments is 104 

percent for Indiana, 25 percent for Michigan, and 14 percent 

for Wisconsin. Higher values are found in areas where surface 

geology textures consist of coarse grained sediments. Coarse 

grained sediments are associated with a high energy environment present during their deposition. Similar values were 

found for the three textures in Wisconsin. Except for Indiana, 

the values for the three textures decrease with depth, reflecting 

possibly the greater compression and decrease in pore size of 

the sediments with depth.
Analysis of Well-Log Data 19

Figure 10. Distribution of horizontal hydraulic conductivity calculated from well-log derived specific-capacity data for 

aquifers in layer 1 of the ground-water-flow model.

Horizontal hydraulic conductivity, 

in feet per day

EXPLANATION

WISCONSIN

ILLINOIS

INDIANA

MICHIGAN

OHIO

10

250

500

750

1000

460

450

440

430

420

410

900

890 880

870 860

850 840

830 820

LAKE

MICHIGAN

Base from U.S. Geological Survey digital data

1:100,000 1983. Universal Transverse Mercator

projection, Zone 16, Standard parallel 00 (Equator),

Central meridian 870 W, North American Datum 1983

(no data)

(no data)

70 140 MILES

0 70 140 KILOMETERS

0
20 Processing, Analysis, and General Evaluation of Well-Driller Logs for Estimating Hydrogeologic Parameters

Comparison of Hydrogeologic Products 

Derived from Well-Log Data with Other 

Hydrogeologic Products

The map of equivalent horizontal hydraulic conductivity 

of model layer 1, hereinafter termed K map, prepared by this 

study (fig. 8) is compared to three hydrogeologic products: a 

surface geology map of the study area, a ground-water availability map for Indiana, and a map that shows flow-duration 

ratios (the 20-percent flow duration divided by the 90-percent 

flow duration) for northern Indiana. The comparisons indicate 

similarities between the map of K and the three published 

maps, as well as potential advantages that accrue to the K 

map. The similarities indicate additional uses of the map of K 

in interpreting ground- and surface-water hydrology.

The patterns of fine and coarse grained sediments shown 

on the map of K (fig. 8) and the occurrences of those sediments on the surface geology map are similar (fig. 11). For 

example, a large area of coarse grained sediments is present 

in northwestern Indiana on both maps in fig. 11. In addition, 

a fine grained area is shown southwest of Green Bay in both 

parts of the figure. The K map shows additional, three-dimensional information that is not evident on the surface geology 

map. The surface geology map depicts a patchy deposit of 

coarse grained sediments in north-central Wisconsin, but the 

K map indicates that the coarse grained sediments are more 

aerially extensive and continue beneath the patchy loamy till. 

The surface geology map also shows surficial coarse and fine 

grained stratified sediments that rim and extend southwest 

of Saginaw Bay, but the K map suggests that fine grained 

sediments dominate the upper part of glacial sediments in the 

same area.

In figure 12, the K map for northern Indiana is presented 

alongside a ground-water availability map that shows potential 

yields to properly constructed large-diameter wells penetrating 

the full thickness of aquifers (Indiana Department of Natural 

Resources, Division of Water, 1980, p. 33). The ground-water 

availability map is based on maximum well yields reported in 

an area. The blue and green areas along the top of the groundwater availability map correspond to areas of high yield and 

also correspond favorably to the darkest area of red on the 

northern border of the K map (fig. 12). The dark red areas 

represent high K values, but they also represent a high percentage of sand and gravel (fig. 12), which could be the reason for 

the high yields. The correspondence suggests that the K map 

could possibly be used in combination with other indicators of 

ground-water availability. The correspondence between high 

ground-water availability and darker red areas is not exact, 

possibly because well-yield data may not have been available 

to define an area on the ground-water availability map as high 

yield. A correspondence between areas with lower values of K 

and low well yields also can be seen in figure 12. The lowyielding brown and yellow areas in the southwestern corner 

of the ground-water availability map correspond to near-white 

areas of the K map. 

The information in figure 12 or figure 8 (K distribution 

of the entire study area) could be combined with the specific 

capacity-based horizontal hydraulic conductivity information 

in figure 10 for additional ground-water availability information. That is, for two areas of equally high K values (equally 

high percentages of sand and gravel), the area indicated in 

figure 10 as having higher horizontal hydraulic conductivity 

would probably yield greater amounts of water to wells.

The last qualitative comparison between well-log based 

products and published maps will demonstrate a correspondence between the K distribution and low-flow statistics. 

Figure 13 shows the distribution of K overlain by areas of 

equal flow duration ratio for northern Indiana (Arihood and 

Glatfelter, 1991, Plate 1). Flow-duration ratio was defined 

as the 20-percent flow duration at a streamflow-gaging site 

divided by the 90-percent flow duration. Flow duration 

ratio was mapped for Indiana by calculating the ratio for all 

the qualifying streamflow gaging stations in Indiana, then 

transferring the ratios to the watershed areas associated with 

the gages. The flow-duration ratio data is used in a regression equation (Arihood and Glatfelter, 1991, p. 9) to estimate 

the 7-day, 10-year low flow at any point along a stream not 

affected by flow regulation. Low values of flow-duration ratio 

are related to areas predominately containing sand and gravel 

(Arihood and Glatfelter, 1991, p. 8). As can be seen in figure 

13, the red areas of the K map along the northern boundary of 

the state are those with high percentages of sand and gravel, 

and they correspond to areas with low values of flow-duration 

ratio. The large red area in northwestern Indiana is associated 

with the lowest flow-duration ratio value of 3. In addition, the 

near-white areas in the southeastern and southwestern corners 

of the K map correspond to areas on the flow-duration map 

where base flow is so limited that the 7-day 10-year low flow 

is sometimes zero. The near-white areas not only show where 

ground water may not be sufficiently available, but, in contrast, show where surface-water supplies could be developed 

because they have a high percentage of surface runoff and low 

percentage of recharge to ground water.
Comparison of Hydrogeologic Products Derived from Well-Log Data with Other Hydrogeologic Products 21Figure 11. Distribution of equivalent horizontal hydraulic conductivity for layer 1 from this study and surface geology from Fullerton and others (2003). Clayey till Loamy till Fine grained stratified Medium grained stratified Coarse grained stratified Organic EXPLANATION EXPLANATION

GREEN BAY

SAGINAW BAY WISCONSIN

ILLINOIS

INDIANA

MICHIGAN

OHIO

WISCONSIN

ILLINOIS

INDIANA

MICHIGAN

OHIO

1

25 

50

75 

100

Horizontal hydraulic conductivity, in feet per day

460

450

440

430

420

410

890 880 870 860 850 840 830 820 900 900 890 880 870 860 850 840 830 820

460

450

440

430

420

410

Distribution of equivalent horizontal hydraulic conductivity in layer 1 of the ground-water model based on well-log lithologies

Geology of surficial deposits (Fullerton and others, 2003)

Base from U.S. Geological Survey digital data1:100,000 1983. Universal Transverse Mercatorprojection, Zone 16, Standard parallel 00 (Equator),

Central meridian 870 W, North American Datum 1983

Base from U.S. Geological Survey digital data1:100,000 1983. Universal Transverse Mercatorprojection, Zone 16, Standard parallel 00 (Equator),

Central meridian 870 W, North American Datum 1983

Fine grained material

Coarse grained material

70 140 MILES

70 140 KILOMETERS

0

0

70 140 MILES

70 140 KILOMETERS

0

0
22 Processing, Analysis, and General Evaluation of Well-Driller Logs for Estimating Hydrogeologic Parameters Figure 12. Distribution of equivalent horizontal hydraulic conductivity for layer 1 from this study and generalized ground-water availability for a part of northern Indiana (Indiana Department of Natural Resources, 1980). EXPLANATION Maximum well yields, in gallons per minute 10100 200 400 600 50 greater than 1,000 40 52 30 41 15 41 37 30 87 22 30 87 86 37 30 86 15 85 52 30 85 30 85 07 30 10 20 30 40 MILES 10 20 30 40 KILOMETERS

0

0

40 52 30

41 15

41 37 30

87 22 30 87 86 37 30 86 15 85 52 30 85 30 85 07 30

10 20 30 40 MILES

10 20 30 40 KILOMETERS

0

0

EXPLANATION

Generalized ground-water availability 

(from Indiana Department of Natural Resources, 1980, p. 33) Equivalent horizontal hydraulic conductivity for layer 1 of the ground-water model

Horizontal hydraulic conductivity,in feet per day

1

25

50

75

100

Base from U.S. Geological Survey digital data1:100,000 1983. Universal Transverse Mercatorprojection, Zone 16, Standard parallel 00 (Equator),

Central meridian 870 W, North American Datum 1983

low percentage of sand and gravel

high percentage of sand and gravel
Comparison of Hydrogeologic Products Derived from Well-Log Data with Other Hydrogeologic Products 23

Figure 13. Distribution of equivalent horizontal hydraulic conductivity distribution for layer 1 of the model 

and flow-duration ratios for a part of northern Indiana.

10

5

5

15

10 20

3

3

3

10

5

10

25

20

20 10

10

10

10

0

0

0

5

5

15 25

20

5

5 5

Boundary line for

flow duration ratio 

10 20 30 40 MILES

0 10 20 30 40 KILOMETERS

0

EXPLANATION

870 22’ 30’’ 870 860

 37’ 30’’ 860

 15’ 850

 52’ 30’’ 850

 30’ 850

 07’ 30’’

410 37’ 30’’

410

 15’

400

 52’ 30’’

Equivalent horizontal hydraulic 

conductivity, in feet per day

1

25

50

75

100

Base from U.S. Geological Survey digital data

1:100,000 1983. Universal Transverse Mercator

projection, Zone 16, Standard parallel 00 (Equator),

Central meridian 870 W, North American Datum 1983

3 Flow-duration ratio. If

value is 0, then the 7-day 

10-year low flow = 0

Flow duration ratios from Arihood 

and Glatfelter, 1991, plate 1

Low percentage sand and gravel

High percentage of sand and gravel

Low values of flow-duration ratio also are related to 

watersheds with high base flows, which, in turn, are related to 

high rates of ground-water recharge. Because of its correspondence to flow-duration ratio, the K map could also possibly be 

used as an indicator of ground-water recharge. Ground-water 

recharge is often determined by a regression equation that uses 

surface geology as an independent variable because surfacegeologic information is readily available. However, Neff 

and others (2005, p. 7) explained the improvement in using 

the lithology information from well logs to better represent 

geology beneath the surface. The K distribution in figure 13 

represents geologic composition beneath the surface, and its 

use as a basin characteristic may improve the prediction of 

recharge. Other possibilities may exist to improve the results 
24 Processing, Analysis, and General Evaluation of Well-Driller Logs for Estimating Hydrogeologic Parameters

of regression estimation of other hydrologic parameters whenever surface geology is used as an independent variable. For 

example, flow quantity, such as 10-year low flow, or waterquality characteristics could be estimated using K values.

Well logs may provide additional sources of information useful to water availability and use. Coon and Sheets 

(2006) estimated the volume of ground water available in the 

Great Lakes Basin. To estimate the volume of ground water 

in glacial sediments, the characteristics of surficial aquifers 

(thickness, extent, and composition) from the Great Lakes 

study area were used (Coon and Sheets, 2006, p. 12). If a welllog data base were available for a future study area, then the 

K distribution could be constructed for the area. The volume 

of aquifer material within the distribution could be calculated 

by available software, and then the volume of water available 

in the aquifer material could be calculated using an assumed 

storage coefficient. The volume of water calculation would 

be based on more detailed data than was done by Coon and 

Sheets (2006, p. 12). Water use is often recorded on well logs. 

Although water-use data from well logs were not used in this 

study, future studies might benefit from those data. Well-log 

records will not document all water use in a region, but they 

may provide a satisfactory sample of water use, or at least, an 

indication of water use that could supplement other data. The 

usefulness of water-use data from well logs would be based on 

the quality and completeness of data from any given area. 

Summary and Conclusions

In 2005, the U.S. Geological Survey established a program called the National Water Availability and Use.

The Great Lakes Basin was chosen as a pilot study area

where methods were to be developed to best evaluate 

water resources. Part of the pilot study involves constructing a 

ground-water model for the Lake Michigan part of the Basin. 

Most ground-water flow occurs in the glacial sediments; therefore, adequate representation by the model of the horizontal 

and vertical hydraulic conductivity of the glacial sediments 

is important to the accuracy of model simulations. About 

458,000 well driller logs were available in computerized form 

to help characterize hydraulic conductivity of the glacial sediments. Computer programs were written to process well-log 

information into representations of the equivalent horizontal 

and vertical hydraulic conductivity distribution for the glacial 

layers of the model. In addition, the specific-capacity data 

was converted into horizontal hydraulic conductivity data 

that could be used during ground-water modeling as a guide 

for adjustment of equivalent horizontal hydraulic conductivity distributions, and well-log water levels were processed to 

assemble a water-level calibration data set.

A lithology-based distribution of equivalent hydraulic 

conductivity was created by first automatically converting 

well-log lithology descriptions into aquifer or nonaquifer categories, and then calculating equivalent horizontal and vertical 

hydraulic conductivities (K and KZ, respectively) for predetermined thicknesses of glacial sediments. The equivalent K was 

based on an assumed value of 100 ft/d for aquifer material and 

1 ft/d for nonaquifer material, whereas the equivalent KZ was 

based on an assumed value of 10 ft/d for aquifer material and 

0.001 ft/d for nonaquifer material. These values were assumed 

for convenience to determine a relative contrast between aquifer and nonaquifer sediments. The point values of K and KZ

from wells that penetrate at least 50 percent of a model layer 

were interpolated into a continuous distribution. Each K distribution was based on an inverse distance weighting equation 

that used an exponent of 2, which is commonly used. The KZ

distribution used an exponent of 4, which helped to represent 

the abrupt change in KZ that often occurs between aquifer and 

nonaquifer materials.

The assumed values of hydraulic conductivity for aquifer 

sediments needed to be adjusted to actual values that occur in 

the study area. The specific-capacity data (discharge, drawdown, and time data) from driller well logs were used with a 

modified version of the Theis equation that corrects for partial 

penetration of the aquifer to calculate horizontal hydraulic 

conductivities (KSC). The KSC values were used as a guide during ground-water modeling to adjust the assumed value of 100 

ft/d for aquifer deposits to actual values used in the modeling.

A method was developed to obtain potentially more reliable water levels from well logs for comparison with simulated water levels in a model layer during model calibration. 

The method uses kriging of the water-level data to determine 

a water-level surface, and then uses the difference between the 

kriged surface and individual water levels to identify outlier 

water levels.

Examination of the well-log data in map form revealed 

that the data was not only useful for model input, but useful 

in understanding the hydrogeology of a multistate area. The 

distribution of K and KZ provide a three-dimensional view of 

aquifer and confining systems. The distribution of specificcapacity based hydraulic conductivity reveals an aerial difference from state to state and a relation to sediment texture. 

Patterns in high and low hydraulic conductivity over large 

areas are evident within a state, such as the high hydraulic 

conductivity in northwestern Indiana and the low hydraulic 

conductivity southwest of Saginaw Bay in Michigan. Patterns 

also continue across state boundaries, such as the southwestto-northeast set of linear features in northern Indiana and 

southeastern Michigan. Median KSC are larger in Indiana 

(264 ft/d) and relatively smaller in Michigan (89 ft/d) and Wisconsin (48 ft/d). This difference could be related to past glacial 

processes. Another pattern in KSC was observed with sediment 

texture. Aquifers within sediment textures deposited under a 

higher energy environment, for example, outwash textures, 

had higher values of KSC than those deposited under a lower 

energy environment. 
References Cited 25

The products derived from well-log data appear similar 

to published maps and possibly convey more information 

than available in the published maps. The patterns of fine and 

coarse grained sediments shown on the distribution of K for 

model layer 1 (K map) and the surface geology map are similar. In areas where the two maps differ, the difference may be 

related to the content of geologic deposits beneath the surface. 

In an area where the surface geology map shows a mixture 

of fine and coarse grained sediments, the K map shows that 

fine grained sediments dominate the glacial sediments. Areas 

of high and low well yield on a ground-water availability 

map for Indiana correspond to areas of high and low percentages of sand and gravel on the K map for the same area. The 

correspondence suggests that a K map could be a supporting 

indicator of ground-water availability. A flow-duration ratio 

map (20-percent flow duration divided by the 90-percent 

duration) indirectly maps the amount of sand and gravel in 

glacial deposits in Indiana. Areas that have low values of the 

flow-duration ratio and high percentages of sand and gravel 

correspond to areas of high sand and gravel percentage on the 

K map.

The similarities between the K map and hydrogeologic 

maps suggest uses of the map data beyond the generation of 

input data to a ground-water model that are beneficial to the 

understanding of ground-water availability. Areas of high 

K (high percentages of sand and gravel) are related to high 

rates of ground-water recharge and ground-water availability. 

Ground-water recharge is often determined by a regression 

equation that uses surface geology as an independent variable 

because surface-geology information is readily available. But 

the entire thickness of geologic materials beneath the surface 

determine recharge rates, and a K map based on well logs penetrating 100 ft or more of the glacial sediments better represents overall geologic composition. Data from a K map could 

be used as a basin characteristic to improve the prediction of 

recharge or any low or high flow statistic, such as the 7-day, 

10-year low flow or the 10-year flood. 

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