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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39

1Present address: CEPSA E&P, Madrid, Spain.

Copyright n2010 by The American Association of Petroleum Geologists.

DOI:10.1306/13181276M923404

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.

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

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

42 Garca-Carballido et al.

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

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.

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

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.

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

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.

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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Garca-Carballido, C., 2002, Borehole image and dipmetertoolsProcedures and guidelines: Shell Internal Li-brary, Shell Internal Publication, Expro Report ER02005,p. 140.

Lofts, J. C., and L. T. Bourke, 1999, The recognition ofartifacts from acoustic and resistivity borehole devices,in M. A. Lovell, G. Williamson, and P. K. Harvey, eds.,Borehole imaging: Applications and case histories: Geo-logical Society Special Publication 159, p. 5976.

Murray, A., and S. G. Buck, 2007, Structural analysis ofLithoTrack and StarTrack images from the Affleck Field:Task Geoscience, p. 131.

Poppelreiter, M., and C. Garca-Carballido, 2003, Best prac-tice for data management, quality control and datatransfer of borehole image and dipmeter logsTowardan integratedworkflow (abs.): AAPG International Con-ference, Barcelona, http://www.searchanddiscovery.net/documents/abstracts/2003barcelona/extend/81865.pdf (accessed January 11, 2010).

Poppelreiter, M., C. Garca-Carballido, J. Boon, F. Withoff,J. Copper, G. Niks, and R. Buist, 2002, Best practice forborehole image and dipmeter log data quality control:Shell Internal Library, Shell Internal Publication, EPNL-2002-7012, p. 3033.

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Data Management and Quality Control of Dipmeter and Borehole Image-Log Data 49