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Data Management and Quality Control ofDipmeter and Borehole Image Log Data
Carmen Garca-Carballido1
Maersk Oil North Sea UK Ltd., Aberdeen,Scotland, United Kingdom
Jeannette BoonNAM, Shell EP Europe,Assen, Netherlands
Nancy TsoShell International Exploration and Production,
Houston, Texas, U.S.A.
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ABSTRACT
Numerous dipmeter and borehole image log data sets have been acquired over
the years andare beingused to build subsurfacemodels.Dealingwithdipmeter and
image log data remains a niche skill within the petroleum industry, and because
these are not conventional log data sets, they tend to be neglected in the way data
are stored and quality controlled. A variety of wireline and logging-while-drilling
tools exist, and each logging run contains a variety of curves with tool-specific
mnemonics. For a particular data set, there may be several tens of curves from the
rawdata set andhundreds from theprocessed and interpreteddata sets.Data quality
control (QC) is an essential procedure that has to be conducted to assure dipmeter
and image log data integrity in the subsurface models. Data QC should be per-
formed iteratively during data acquisition, data management, processing, and
interpretation. This chapter presents standard and globally applicable corporate
guidelines for datamanagement anddataQCof dipmeter and image log data sets.
INTRODUCTION
Throughout the world, operators have acquired
thousands of dipmeter and image log data from all
types of reservoirs over several decades. These data
sets provide directional sedimentological and struc-
tural information and are used to build reservoir and
geomechanical models.
Chapter 3
Garca-Carballido, C. , J. Boon, and N. Tso,2010, Data management and qualitycontrol of dipmeter and borehole imagelog data, in M. Poppelreiter, C. Garca-Carballido, and M. Kraaijveld, eds., Dip-meter and borehole image log technology:AAPG Memoir 92, p. 3949.
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1Present address: CEPSA E&P, Madrid, Spain.
Copyright n2010 by The American Association of Petroleum Geologists.
DOI:10.1306/13181276M923404
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Dealing with dipmeter and image log data, how-
ever, remains a niche skill. Even major operating
companies might have very few if any borehole
image (BHI) experts. Once dipmeter and image
log data have been acquired, it is commonly the
project geoscientist and/or petrophysicist who
decides what level of interpretation might be re-
quired either immediately after data acquisition or
several years later, i.e., during a field re-evaluation.
A large percentage of dipmeter and image log pro-
cessing and interpretation is conducted by special-
ized service companies instead of petroleum com-
pany specialists.
As dipmeter and image log data are not conven-
tional log data sets, and commonly require specialist
software, they tend to be neglectedwith respect todata
management. This is because of lack of specialists,
sizable number of curves, and the variety of curve
mnemonics, both tool type dependent, that are in-
cluded in a given tool run. In addition,when each tool
run is taken through data processing, which includes
multiple steps and data interpretation, amultitude of
new curves are generated. For all of these reasons,
dipmeter and image log data require a suitable data-
base that can handle a variety ofmultisampled curves,
store data in a range of formats, e.g., Log Information
Standard, Digital Log Interchange Standard, and an
actual image, as well as having a structure capable
of organizing all the curve versions that correspond
to raw, quality-controlled, spliced, processed, and
interpreted curves. Furthermore, the database dic-
tionary of the database should be updated regu-
larly, as new tools and/or new curve mnemonics
are developed.
Data quality control (QC) is an essential procedure
that has to be conducted to assure dipmeter and im-
age log data integrity in the subsurface models. Qual-
ity control should be performed at all stages, includ-
ing data acquisition, data management, processing,
and interpretation.
It is in the interest of each organization storing
suchdata to have suitable datamanagement anddata
QC procedures to enable the prompt availability of
quality-controlled dipmeter and image log data sets
when these are required by the project geoscientist
or petrophysicist. A set of such data management
and data QC procedures (Garca-Carballido, 2002;
Poppelreiter et al., 2002; Poppelreiter and Garca-
Carballido, 2003; Tso, 2004), which are implemented
across many regions of Shell, is discussed in detail
in this chapter.
DATA MANAGEMENT PROCEDURES
Datamanagementprocedures are required to guar-
antee the immediate availability of suitable BHI and
dipmeter data sets to the geoscientist and/or petro-
physicist working in a particular area. The BHI and
dipmeter data sets have commonly been acquired by
operating companies, but toooften, data are not stored
systematically and different media (such as tapes,
CDs, etc.) are used. In addition, it is common that
there is uncertainty as to whether the available data
are raw or processed. To establish some data man-
agement procedures, the following steps are recom-
mended to arrive at a quality-controlled corporate
database (CDB):
Make an inventory: Establish how many datasets there are, where they are physically located,
and on which media. The aim is to have all data
sets digitally available. Verify the status of the data sets in the inventory:
Establish whether data can be read and whether
thedata sets have all the required curves. If curves
are missing, repair is recommended. Quality control: Apply a set of standardized QC
procedures to ensure that data of poor quality
are not used for interpretation. Structure the database: Organize the database
into master, corporate, and project areas. Establish dataworkflows:Define and implement
how the database will be organized considering
data acquisition, QC procedures, and availabil-
ity to the end user. Make the data available: Provide a Web-based
data search tool and set up data transfer proto-
cols to transfer the results of the data search into
the relevant subsurface applications.
Database Inventory
The first step toward a corporate image log data-
base is to make an inventory of the different legacy
data sets. An example of this is given below (Figure 1).
This example shows a snapshot in a point in time of
the data set froma Shell operating unit, revealing that
more than 700 wells had some kind of BHI and/or
dipmeter log data. Less than half of these data sets are
digitally stored in the company database, whereas
others are available as hard copies (field prints) or in
the tape archive.
40 Garca-Carballido et al.
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The data set inventory needs to include the fol-
lowing for each BHI and dipmeter data set:
well name latitude and longitude logged interval logging run tool setup reference distances inclinometry type offsets comments on logging run repeat or main log acquisition tape name processing applied list of curves and interval spacing list of associated logs with curve names
Verify the Status of Data Sets in the Inventory
Once the inventory has beenmade, the second step
is to establish whether data can be read and whether
the data sets have all the required curves; if this is not
the case, data should be sent for repair to a specialist
BHI contractor if required. To get an overview of the
status of the database, a subset of the digitally stored
data should be selected to perform a few QC checks.
This subset could be selected from areas and reser-
voirs where current subsurface studies are planned,
which require BHI, to maximize business impact.
Following the example shown in Figure 1, a subset
of 30 dipmeter and BHI logs from various vintages,
fields, and reservoirs was chosen. Out of the digitally
stored logs, 70% were of very good to medium qual-
ity (i.e., they met the quality requirements discussed
in this chapter); however, some data sets were in-
complete or data were partially damaged. We found
thatmanydata could easily be repaired andupgraded
in a cost-efficient manner using data from original
tapes, digitizing data from field prints, or splicing in
data from repeat sections. The remaining 30% of the
subsetwas found to beunusable,mainly because some
essential curves such as orientation curvesweremissing
from the database and from the tape, and it was impos-
sible to retrieve them from another data source. Less
Figure 1. The borehole image (BHI) and dipmeter database snapshot from a Shell operating unit (data up to 2001).AST = Acoustic Scanning Tool; CBILSM = Circumferential Borehole Imaging Log (Baker Hughes/Baker Atlas);HDIPSM = Hexagonal Diplog (Baker Hughes/Baker Atlas); EMI
TM= Electrical Micro Imaging (Halliburton); FMI
TM=
Fullbore Formation MicroImager (Schlumberger); FMS = Formation MicroScanner (Schlumberger); HALS = High-Resolution Azimuthal Laterolog Sonde (Schlumberger); HDT = High Resolution Dipmeter Tool (Schlumberger);MBD = Multibutton Dipmeter; OBDT
TM= Oil-Base Dipmeter Tool (Schlumberger); PSD = Precision Strata Dipmeter;
SHDT = Stratigraphic High Resolution Dipmeter Tool (Schlumberger); UBITM= Ultrasonic Borehole Imager
(Schlumberger).
Data Management and Quality Control of Dipmeter and Borehole Image-Log Data 41
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often, severe acquisition artifacts (Lofts and Bourke,
1999) made interpretation impossible.
Quality Control
During the data management routine, QC is first
applied to all newly acquired data sets as soon as they
arrive from the logging contractor and to all legacy
data before they are used in subsurface studies. The
QC procedure includes checking the presence of all
required curves, which will have tool-specific mne-
monics, as well as conducting a QC plot and creating
a QC report. The procedures are described in more
detail below.
Structure the Database
To manage the dipmeter and BHI data sets effi-
ciently, three types of data areas within the corporate
database are proposed as follows:
Master database (MDB) contains acquired andpurchased dipmeter and BHI data from external
sources. The original formats of the data are
stored here. Corporate database contains official and quality-
proven processed and interpreted dipmeter and
BHI results. This database will be used to feed the
data for project studies in aproject database (PDB). Project database contains dipmeter andBHIdata,
along with all other relevant data, in the area of
interest for a specific study. This is the temporary
working data store for a project.
This is probably the most crucial data manage-
ment step where data managers and end users, i.e.,
geoscientists and petrophysicists, need to find a tech-
nical and viable solution to organize the database
structure in the context of data acquisition, data man-
agement, and QC and data accessibility.
A data workflow applicable to dipmeter and BHI
data is illustrated in Figure 2. This workflow encom-
passes all stages, starting from designing the logging
program followed by data acquisition, data man-
agement and data QC, database structure, and data
processing and interpretation until data are exported
to the relevant subsurface applications to build geo-
logical and geomechanical models. This particular
workflow contains some assumptions regarding
whether data are processed in-house or externally, a
practice that may vary in different companies.
BHI-Dipmeter Workflow Management
For a particular data set, the steps of the BHI-
dipmeter workflow presented in Figure 2 would be
as follows:
1) A logging program is defined to include the ac-
quisition of a BHI and dipmeter data set in a
particular well. Data are acquired by the logging
contractor, witnessed by and sent to the operator.
A full raw data set for each run must always be
requested from the logging contractor. This should
be done even if the logging contractor is the pro-
vider of the data processing and the data inter-
pretation. The raw data set is necessary to con-
duct an independent QC by the data manager
of the operating company and for further data
analysis (e.g., alternative processing).
Field prints are also a requirement. These are ar-
ticle copies available for each tool run, which con-
tain the unprocessed dipmeter-BHI data plotted
alongside tool configuration and tool settings,
comments on borehole conditions, mud type
used, and other acquisition parameters.
2) The operators datamanager performs an initial
QC to verify whether data adhere to the com-
panys minimum standards. Incomplete or dam-
aged data sets are sent back to the service com-
pany for repair.
3) Data are loaded into the MDB. A variety of data
sets can be loaded into the MDB, typically the
raw data (after acquisition) but also processed
and interpreted data. Therefore, the database ap-
plication usedmust essentially have (1) standard-
ized curve mnemonics per tool type as well as an
up-to-date data dictionary applicable to all the
dipmeter and BHI log tools in the database, as
well as (2) a file-type structure and file-naming
convention so that BHI and dipmeter raw, pro-
cessed, and interpreted curves are kept separately.
If the data are to be processed and interpreted
by the logging or external contractor, then a com-
plete set of all processed dipmeter-BHI curves
should be provided and loaded into the MDB.
For externally processed and interpreted data
sets, an approval process is implemented by the
project geoscientist and petrophysicist to ensure
that all the necessary data are available for load-
ing. This approval process also applies to data
sets acquired via data rooms, asset acquisitions,
or from partners. The data manager ensures that
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curve-naming conventions are maintained even
if data are to be interpreted externally.
4) Selected data sets will then be loaded into the
CDB.
5) If required for a particular study, selected data
sets will be copied from the CDB into PDBs.
Depending on the company assets, PDBs split by
fields, groups of fields, and exploration areas can
be created for geoscientists and petrophysicists.
6) Data QC.
7) Data processing.
8) Data interpretation.
9) Once the study work has been finalized, it is
important that the results are fed back to the
CDB, ensuring that a track record exists for each
curve showing all processing steps applied to
each of the curves.
10) Interpretation results are transferred to specific
subsurface applications to build geological and
geomechanical models.
Figure 2. Database workflow applicable to dipmeter and borehole image (BHI) data. QC = quality control; Hdr = header;R/W = Read/Write.
Data Management and Quality Control of Dipmeter and Borehole Image-Log Data 43
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Make Data Available
To make data easily accessible to the geoscience
community, the inventoried database is to be linked
to customized Web-based search tools. These show
data previews, spreadsheets, and Arc Geographic
Information System (GIS) maps to locate data sets
geographically. This allows geoscientists and petro-
physicists to check for data availability and quality
at the beginning of subsurface studies. Selected im-
age log data then can be loaded from the CDB into a
PDB and from there to the specific subsurface ap-
plication using a dedicated data transfer protocol
(e.g., via OpenSpiritTM).
An example of the front-end of a BHI anddipmeter
Web site is shown in Figure 3. It not only contains
links to the data search engine, but it might also
contain information on those procedures and guide-
lines of interest to the operator, useful links to ex-
ternal contractors that can interpret the data, links to
technical articles, and interpretation workflows.
QUALITY CONTROL PROCEDURES
Data QC is an essential procedure that is required
to ensure dipmeter and image log data integrity in
subsurface models. Quality control should be per-
formed at all stages, from data acquisition to data
management, data processing, and data interpreta-
tion. Each of these stages is described in detail below.
Quality Control During Data Acquisition
During data acquisition, the logging company
acquiring the data is responsible for conducting the
necessary checks to ensure each tool works properly.
In addition, the logging engineer can make changes
to acquisitionparameterswhile running the toolwhen
necessary to ensure that the correct range or spec-
trum of data is acquired to suit the formation char-
acteristics.After the first acquisition run, a quick-look
composite plot is generated to enable data quality
verification in the presence of the operator. It is there-
fore important that the operators representative wit-
nessing the acquisition job should be conversantwith
basic BHI and dipmeter QC procedures such as the
ones described in this chapter. A repeat run over the
main interval of interest is commonly conducted to
ensure data quality and repeatability. Tool modifi-
cations and/or adjustment in the acquisition param-
eters may be conducted using the information pro-
vided from the first logging run.
Quality Control in Data Managementand Processing
Dipmeter and BHI data should be quality con-
trolled by the operator before it is sent for process-
ing and interpretation either internally or by third
parties. This ensures that cost, time, and resources
are deployed only on acquired intervals of suitable
Figure 3. Dipmeterand BHI Web pageused to search andvisualize the datasets. BHI = boreholeimage; QC = qualitycontrol; GEOCAP =Geological Comput-ing ApplicationsPortfolio (developedby Shell).
44 Garca-Carballido et al.
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interpretation quality. A simple QC check method-
ology has been developed and includes the following
three steps:
Verification of curve completeness and curvemnemonics using the data dictionary for each
BHI and dipmeter tool: This is conducted by
the data manager. QC plot: This is a composite plot (example in
Figure 4) compiled by a suitably trained data
manager to analyze each data set. This plot con-
tains key raw curves and the processed image.
Composite templates can be customized by the
operator for the most commonly used BHI and
dipmeter tools. QC report: This is conducted by a dedicated BHI
focal point in the asset or department, typically
a geologist or a petrophysicistworking alongside
the datamanager. This report contains the results
of the QC analysis using the QC plot. The QC
report should be stored alongside the QC plot.
The QC composite plots (1:200 or 1:500 scale) con-
taining relevant curves (Figure 4) are the best way to
quickly assess the quality of BHI and dipmeter data,
a summary of which can be neatly shown as a red-
yellow-green zonation bar to illustrate poor,medium,
and good image quality. This determines which sec-
tions of the BHI and dipmeter log are useful for pro-
cessing and interpretation.
When data sets do not to meet the minimum QC
standards (as described in this chapter), the data man-
ager sends data back to the relevant acquisition con-
tractor for repair. The responsible project geologist or
petrophysicist should review the final deliverable
from the logging company and ensure that only clean
data are stored in the database and subsequently used
to build earth models.
The following are basic quality checks (illustrated
in Figure 5) that should be conducted on raw BHI
and dipmeter data using the QC plot (Figure 4):
ensure that magnetic and gravitational field mag-nitudes are reading correctly
verify that data are oriented to true north (if notcorrected during processing); data might be also
oriented to the high side or low side of the tool
frame QC tool orientation using an independent well
deviation survey (look at deviation and hole az-
imuth curves)
verify that data are on-depth with the well ref-erence gamma ray or resistivity master log
for on-pad devices, identify areas of tool sticking(look at vertical accelerometer and tension curves)
and irregular pad readings, curve character, or
dead buttons verify caliper reading inside casing verify data versus expected lithologies identify sections of excessive tool rotation (look
at relative bearing curve), i.e., excessive tool ro-
tation occurs within a 30-ft (9-m) interval verify data repeatability from different acquisi-
tion runs (main and repeat) on-pad devices, assess whether pad pressure
was satisfactory (look at the pad pressure curve) identify mud cake buildup and sections of poor
hole conditions and assess the effect on data qual-
ity;mud cakebuildup in excess of 0.5 in. (1.2 cm) is
likely to affect the image and raw curve quality
Quality Control in Data Interpretation
This refers to dip interpretation and image artifacts.
Dip interpretation can be done in two ways: com-
puted dips and/or manually interpreted dips. Com-
puted dips are calculated on processed data, and QC
should be conducted to ensure that (1) only dips from
good to medium image-quality sections are used for
interpretation, (2) suitable processing parameters are
used by the interpreter, (3) dips on separate tool runs
are repeated, and (4)mirror and other artifact-derived
dips (Lofts and Bourke, 1999) are avoided. Different
types of nonmanual dip computation depending on
the algorithm chosen exist, and a dip-quality rating
should be assigned to each computation.
Manual dip interpretation requires geological
knowledge and should be conducted by an experi-
enced interpreter or suitable technical coaching should
be provided; when available, core material should be
used for calibration.
As any other petrophysical log, BHI and dipmeter
logs can be affected by logging artifacts, such as hori-
zontal striping (Figure 6), stick and pull zones, saw-
toothed surfaces, and dead buttons, to name just a
few (Lofts and Bourke, 1999).
Artifacts can sometimes completely obliterate the
image and should be recognized to avoid erroneous
interpretation. This requires a more detailed image
QC by looking at processed images on a 1- to 2-m-
scale (36-ft-scale) slidingwindow.Numerous artifacts
Data Management and Quality Control of Dipmeter and Borehole Image-Log Data 45
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Figure 4. Example of a composite plot fordata QC of an Oil-Base MicroImager (OBMI
TM)
data set. Track 1 = tool inclinometry and welldeviation survey data; Track 2 = tool gamma ray(GR) and calipers and reference open-hole GRlogs; Track 3 = open-hole logs (density, neutron,sonic); Track 4 = open-hole logs (resistivity);Track 5 = depth; Track 6 = tool microresistivitycurves; Track 7 = overall image quality; Track 8 =false color static image; Track 9 = false colordynamic image; Track 10 = tool accelerometer,tension, and magnetometer curves; Track 11 =computed dips.
46 Garca-Carballido et al.
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Figure 5. The BHI and dipmeter data quality control (QC) applied to an Oil-Base MicroImager (OBMI, Schlumberger)data set; note that some of the tool mnemonics shown in this diagram will vary depending on the kind of tool. ANOR =acceleration computed norm; FNOR = magnetic field intensity computed norm; DEVI = deviation; HAZI = holeAzimuth; HAZI_ORI = calculated orientation of the hole azimuth; GR = gamma ray; BHI = borehole image; OBDT =Oil-Base Dipmeter Tool Schlumberger.
Data Management and Quality Control of Dipmeter and Borehole Image-Log Data 47
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are documented in the literature (Lofts and Bourke,
1999) and can be classified according to the cause
that originated them:
Acquisition artifacts: These relate to drilling op-erations (e.g., stabilizer grooving or sidetrack win-
dow), whereas others relate to the logging opera-
tions themselves (e.g., mud smear, tool sticking,
or signal loss). Borehole wall artifacts: These are very common
and result from physical irregularities in the bore-
hole wall, such as rugosity, washouts, mud cake,
spiral hole, and even breakouts. Processing artifacts: These are caused during pro-
cessing, but unlike acquisition artifacts, which
permanently impact data sets, these can some-
times be corrected by amore detailed processing.
The causes of these artifacts include choosing the
wrong borehole diameter from which incorrect
dip values would have been calculated (unless
your software uses caliper measurements di-
rectly), incorrect speed correction, mismatch be-
tween pads and flaps, or inappropriate normal-
ization windows.
Geological formation-related artifacts: For exam-ple, halo effects around a highly conductive py-
rite nodule or fracture aureoles, mottling caused
by the presence of gas, or even a strong image
character change if datawere acquired across the
hydrocarbon water contact.
CONCLUSIONSANDRECOMMENDATIONS
A wealth of BHI and dipmeter data sets seem to
have been obtained bymanyoperators over the years.
Nowadays, logging-while-drilling imagedata are com-
monly being acquired. The interpretation of these
data sets provides sedimentological and structural
information and orientation of the subsurface, all of
which are valuable high-resolution data to integrate
into the subsurface models. Data interpretation de-
pends ondata accessibility.Data accessibility is closely
related to the data management structure of each
operating company. A recommendation would be
to have a BHI and dipmeter Web-based page in the
company intranet with links to the available BHI
and dipmeter database as well as additional links to
tool information, data QC, and processing guidelines
and also links to the different logging contractors. In
addition, suitable software is required to enable at
least data visualization.
Experience has shown that having a set of fit-for-
purpose corporate data management and data QC
procedures will ensure that suitable BHI and dip-
meter data sets are available to the project geosci-
entists and petrophysicists in our organizations in a
timely and cost-effective manner. Having a set of
such procedures also ensures that these data are not
underused.
Elements of Shell global data management strat-
egy are as follows:
1) High-quality datawill allow fast standard reports
to be created automatically, which provides both
time savings and enables auditable results.
2) High-quality data in a central store will enable
better integration by combining dipmeter and
BHI data with other data (e.g., composite well
logs, petrophysical summary plots).
Furthermore, having corporate guidelines in place
means that even if expert BHI staff take different
positions, a framework is set to effectively handle dip-
meter and BHI data sets.
Figure 6. Horizontal stripping on a StarTrackTMimage
log (Murray and Buck, 2007). Data from the Affleck field(reprinted courtesy of Maersk Oil North Sea UK Ltd).
48 Garca-Carballido et al.
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ACKNOWLEDGMENTS
The authors of this article are grateful for constructivecomments by Christine McKay (Maersk Oil North Sea UKLtd.), Stuart Buck (Task Geoscience), Heike Delius (TaskGeoscience), and Michael Poppelreiter (Shell).
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Murray, A., and S. G. Buck, 2007, Structural analysis ofLithoTrack and StarTrack images from the Affleck Field:Task Geoscience, p. 131.
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Data Management and Quality Control of Dipmeter and Borehole Image-Log Data 49