1. Understanding the fundamentals in ESA is
Important as the data is to be used in a critical decision-making
framework
Many names have been used to cover this topic, such as GIS Analytics, Multi Criteria Decision Support Systems and so on.
This presentation only covers the preparation phase for the GIS Analytics;
a) Strategy to implement GIS Analytics into Exploration workflow
b) what type of data will be required to create grids of
c) deciding whether to make deterministic grids or geostatistical grids
d) What other type of data can be made available apart from Raster data?
Stig-Arne Kristoffersen, July 2011
(Based on a presentation made in February 2010)
ESA assisting Exploration Performance Analysis
EXPLORATION SPATIAL ANALYSES (ESA)
WITHIN THE EXPLORER ENVIRONMENT

2. GIS Strategy within Exploration
Business needs/drivers/value of Spatial Data
• Drive by business demand (see later slide)
Front Office environment – Decision Support Environment
• First introduce ‘light’ web based GIS technology and later the ‘heavyweight’ desktop version
• Slowly introduce new ways of working with spatial data
Back Office - Information Supply
• Need to develop tools to support specific workflows
• Support and Maintenance network (ECC, AED, ERAD)
Content
• Expose high-value legacy data (e.g Cartography and key Exploration parameter data)
• Use ‘trusted’ data sources (e.g. EPPR)
• Link to Interpretation systems data (e.g. Geoframe, Petrel, Petrosys)
SDE Data management
• Increased data management required to develop and maintain spatial data sets
• Levels of integration with E&P data is still patchy, GIS to be an integrated part of the Information
system
• Establish Procedures and Standards for loading, edit and validate GIS data
Integrated Integration of GIS

3. GIS Strategy within Exploration
GIS part of total Information Management to make it available to
Exploration Applications
High Visibility
Low Visibility
Front Office
Back Office
Standards
Procedures
Physical archive
Physical archive
Project data
Master data
Data Lifecycle Review
EDMS
Web GIS
Desktop GIS
Knowledge
• Integrate
• Edit
• Validate
• Load
• Access
• Transfer
• Usage
(modified from Kjetil Tonstad, Statoil, 2003)

4. Who
We
Are
How
We
Do
Things
Increase
Recovery
Lower
Business
Running
Costs
Improve
Risk Mgmt
Improve
Expl
Forecast
Understand
Subsurface
Shorten
Cycle Time
Get information
Make people
productive
Create &
Support
Values
Required
Maximise
Return On
People
Employed
Deliver
Skills
Required
Productivity
Maximise
NPV
Increase
Exploration
Portfolio
Volumes
Reduce
Exploration
Cost
Maximise
Reserve
Replacement
Optimise
Portfolio
Mgmt
Team working
Extended
Enterprise
Integrated
Real-time
Data
Access
Information
Access
Analytical
Tools &
Processes
Probabilistic
Visualise
Complex
Data
Abstraction
& Drilldown
Use information
Communicate
information
Flat &
Open
Structure
Delegate
& Trust
Encourage
Team
Ethos
Encourage
Creativity
Motivate
To Deliver
Identify
Work
Priorities
Retain
Staff
Recruitment
Reduce
Non-
Productive
Time
Informed
Buyer
Culture
HR Process
Virtual
Working
Learn
before
during &
after
Link
staff with
knowledge
Manage
change
processes
Objectives
Drivers
Enablers
Learning
Organisation
Develop
Appropriate
Skills
(modified from Enterprise Oil, 2005)

5. GIS Functionalities
We want to achieve Spatial Analysis
Spatial Analysis is lies at the core of GIS
All Exploration data has a spatial element to it, so GIS is required in our Analysis
A large range of Geospatial analysis capabilities
GIS Data model
Network Analyst
Cartographic Unit
Modeling interfaces Visualisation
Raster Analysis (Grid)
Geometric Analysis
(modified from Enterprise Oil, 2005)

6. GIS Refinement process
First step for us is to ensure all key data is represented in the GIS database (SDE) in the proper format, verified and maintained according to changes
and available in database.
From collection of spatial data to Analytics and Reporting
ESRI Grid
Esri grid is a raster GIS file format
developed by Esri, which has two
formats:
• A proprietary binary format, also known
as an ARC/INFO GRID, ARC GRID and
many other variations
• A non-proprietary ASCII format, also
known as an ARC/INFO ASCII GRID
Perform analysis and managing
geographic data. Modeling and
analysis capabilities.
Simple buffers and polygon overlays,
complex regression analysis and
image classification. Automate GIS
tasks and develop custom workflows
that can be shared
• Mapping
• Charting
• Reporting
• Visualization
Spotfire
DecisionSite
MapConnect
Point data
• Isolated plots
• Integration of the ESRI map into a
report.
• Tightly integrate the rich spatial
analytic capabilities with reporting
function
• Online GIS reports
• GIS report portal
Collect all types of spatial data that can be
used in GIS analysis
• Points (parameters, wells, etc)
• Cultural (open/restricted regions etc)
• Infrastructure (pipelines, cities etc)
• Geo referenced graphics (paleo
geographic maps, surface maps)
• Ascii Grids (surfaces, topography etc)
GIS report portal/
dashboard
Cognos
• ArcMap built-in
reporting tool
• GIS and SAP
Crystal Report
(modified from ExxonMobil, 2006)

7. GIS Database
First step for us is to ensure all key data is represented in the GIS database (SDE) in the
proper format, verified and maintained according to changes in database.
A large range of Geospatial analysis capabilities
• Perform analysis and managing geographic data. Modeling and analysis capabilities.
• Simple buffers and polygon overlays, complex regression analysis and image classification.
Automate GIS tasks and develop custom workflows that can be shared
(modified from ESRI, 2011)

8. WHAT IS
EXPLORATION SPATIAL ANALYSES (ESA)?
“Exploration Spatial Analysis” (ESA) refers to a group of techniques whose aim is to:
• Analyze the data and to describe the spatial distribution of values,
• Detect forms of spatial instability (non-stationary),
• Identify anomalous locations (spatial outliers),
• Discover unexpected directional components (trends) and spatial dependency among values.
ArcGIS offer tools to perform ESA:
• Histogram and Normal QQ plot to check the distribution of the data and determine how far from a
Gaussian (normal) distribution the input dataset is (spatial instability or non-stationary)
• Trend Analysis for detecting and mapping unexpected long-range variations in the data
• Voronoi map to identify local outliers
• Semivariogram/ covariogram cloud for examining the degree of spatial auto-correlation among the
variable’s values and to identify global outliers.
Combined Geo Statistics with Risk evaluation tools like:
• Tornado diagrams
• Spider diagrams
And it offers a solid decision support in the exploration process
ESA assisted Exploration Performance Analysis

9. The need for Exploration GIS grids
to support Decision making
• Most data available in Exploration databases are point data sets of various spatial sampling
o These data needs to be presented as grids in an appropriate manner (interpolated)
• Deterministic method
– Global interpolators (long spaced variations with Spline function)
– Local interpolators (local variations with dense data using Inverse Distance Weighting)
• Geostatistic method
– mutual correlation by means of variograms and allow assumptions about anisotropy in the spatial structure of
data to be incorporated by the use of Kriging methodology
o Understand their relations to each others, hence need to understand hierarchy of data dependencies
• Assess the quality of the prediction in grids made
o Prediction Standard Error Map (standard errors of interpolated values)
o Probability Map (interpolated values exceeding given thresholds)
o Quantile Map (to identify overestimated or underestimated interpolated values)
• Choice of use of Deterministic over Geostatistical grids has to be made based on the
knowledge of the data points to be used. The less known, the more plausible it is to use
deterministic as a first approach
• The aim of the establishing of grids is to support critical exploration decision processes, and the
flexibility and uncertainty estimation offered by a geostatistical approach is to be preferred
wherever it is possible
Understand data and its Quality is imperative

10. Critical E&P Data and how to use them in
Exploration Spatial Analysis ?
• FOR PLAY TYPES, PROSPECTS AND LEADS
o RESERVOIR
o TRAP (called Closure in PAL)
o SEAL
o CHARGE
• FOR CONTINGENT RESOURCES
o SHOWS
o UNTESTED HYDROCARBON COLUMN
o UNDEVELOPED DISCOVERIES
• FOR DRILLING OPERATIONS DATA
• FOR WELL DATA REQUIREMENTS
Determine data dependencies to understand Probability Of Success (POS) trends

11. Create GIS Grids
Derive a continuous surface from point observations with little understanding.
(from Exprodat, 2010)

12. Create Geostatistical GIS Grids
Derive a continuous surface from point observations were we have some
understanding of
Step 1: Check for Spatial Instability and Non-Stationary
Simple, ordinary and universal kriging, are distribution-free spatial interpolators, they do not depend on any
distributional assumption and work well with both skewed and normal distributions. However, other kriging
interpolators are crucially dependent on the stationarity assumption to provide reliable results.
Step 2: Detect and Map Directional Components/Trends
investigate the existence of a drift affecting the distribution of values in one or more directions, and take them into
account when modeling the variable’s spatial structure.
Step 3: Identifying Global and Local Outliers
If the outliers are interpreted to represent a real feature of the spatial dataset rather than errors in measurements,
they should not be excluded from the interpolation.
Step 4: Examine Spatial Autocorrelation
Autocorrelation is the basic principle of geostatistics: if sampled values are not spatially autocorrelated then a
geostatistical approach is not a suitable choice and the results of the interpolation are likely to be unrealistic.
Two main questions have to be answered
1. Are the values in my dataset spatially dependent?
2.If the observed values are spatially dependent, are there directional influences (anisotropy) which may affect
autocorrelation throughout the study area?

13. Exploration attribute Grids supporting
Probability of Success (POS tot) trend grids
What are their dependencies and how should they be used in ESA to understand
and increase our POS?
POS(tot)
POS (reservoir) POS (Closure) POS (Seal) POS (Charge)
• Target / objective reservoir Fm
• Depth (Formation tops)
• Lithology/Environment
• gross reservoir thickness
• net reservoir thickness
• net pay
• porosity
• permeability
• hydrocarbon saturation (gas)
• hydrocarbon saturation (oil)
• Main risk/Reason for Failure
• porosity
• permeability
• formation damage
• facies change
• pinch out
• eroded
• diagenesis
• faulted out
• Unknown
.
• Formation
• TrapType
• dip closure
• dip closure with strat comp
• fault bounded dip closure
• fault bounded dip closure with strat comp
• stratigraphic with no structural comp
• subcrop (completely stratigraphic
• subcrop (completely structural)
• subcrop (structural and stratigraphic)
• Areal Closure (sq km)
• Vertical Closure (ft)
• Main risk/Reason for Failure
• no closure
• depth conversion
• seismic attribute integrity
• horizon mispick
• fault alias
• interpretation integrity
• unknown
• Formation / Member
• top seal
• lateral seal
• Main risk/Reason for failure
• top seal
• thief zone
• lateral seal
• fault breach
• bottom seal
• sub-seismic fractures
• salt or anhydrite
• Unknown
• base seal
Formations
• primary
• secondary
Type
• oil
• condensate
• gas
• oil / gas
• oil / condensate / gas
• Water
Oil Gravity (oAPI)
Gas Density (g/cc)
TOC (level and type)
GOR
Contaminants
• sulfur
• nitrogen
• carbon dioxide
Main risk/Reason for Failure
• sufficient maturity
• Presence/ richness
• timing versus closure
• limited access, fetch
• migration shadow
• immature source
• over mature source
• unknown

14. Amount of Geostatistical Grids
Make sense of relations between Grids and how to use them
POS(tot)
POS (reservoir) POS (Closure) POS (Seal) POS (Charge)
10 continuous geostatistical
grids
9 discrete grids and/or
continuous deterministic
grids
.
16 discrete and/or deterministic
continuous grids
2 geostatistical continuous grids
11 deterministic continuous
grids
18 deterministic continuous
grids
3 geostatistical continuous
grids
2 sets of each (P/S)
Most maps are geostatistical grids to be maintained in the SDE database, however there will be a set of derivative
grids associate with these, that will be geo-processed using GIS Analyst.
This since most points used to generate Geostatistical grids have a range in values (Minimum, Most Likely and
Maximum values) which should be used in creation of;
• Standard Error Map (standard errors of interpolated values)
• Probability Map (interpolated values exceeding given thresholds)
• Quantile Map (to identify overestimated or underestimated interpolated values)
There are numerous Play types (Objectives), with the prospect of this to increase as use of Play type in resource
assessment matures in the exploration department.
Updating of these grids needs to be done whenever new values are brought into the database (after prospect
review, PDA’s etc). Process for this has to be established to ensure input, QC and maintenance of SDE database.

15. How to create the Geostatistical Grids
What are their dependencies and how should they be used in ESA to understand
and increase our POS?
Which key attribute contributes most to the POS variance within one Objective?
What effect does the various attributes have on the POS total ?
Consistency in the various key attributes and the POS for each risk factor and total?
Do we have all data required to verify the various key attributes identified in our E&P data?
F.Inst Paleogeographic maps to guide the geostatistical grids of porosity, thicknesses, environment
etc?

16. Need GIS grids of critical E&P data
• Most data available in present Exploration databases are point data sets
o Need to be gridded appropriately
• Deterministic method
– Global interpolators
– Local interpolators
• Geostatistic method
– mutual correlation by means of variograms and allow assumptions about anisotropy in the
spatial structure of data to be incorporated by the use of Kriging methodology
o Understand their relations to each others, hence need to understand hierarchy of data dependencies
• Assess the quality of the prediction in grids made
o Make Prediction Standard Error Map (standard errors of interpolated values)
o Probability Map (interpolated values exceeding given thresholds)
o Quantile Map (to identify overestimated or underestimated interpolated values)
Understand data and its Quality is imperative

17. These entities will have the same parameters as any other Prospect in the portfolio would have
(see slides ahead), however in addition these entities would have more emphasis on
parameters stated below.
• SHOWS
o Stratigraphy (name)
o Type (oil/gas/condensate)
o Quality (classification 1-5 based on scale determined)
• UNTESTED HYDROCARBON COLUMN
o stratigraphy (name)
o Interval (ft)
o type of hydrocarbon (oil, gas, condensate)
• UNDEVELOPED DISCOVERIES (where, production test volumes
Not any different from other Exploration data apart from the fact;
there is more data associated with these entities to perform analysis on
Contingent Resources Analysis

18. WELL DATA REQUIREMENTS
These data would be able to analyze at objective level combined with any other type of attribute set from all risk
factors (reservoir, closure, seal and charge) and contingent resources entities.
o Wireline and Mud Logs
o Tests (DSTs, RFTs, etc.)
o Cores, SWCs, Cuttings
o Mud Type
o Pressure Data
• depth
• Formation
Support decisions on well log program and guide interpreters to what data is available for analysis of wells
Overview of available data and assist in optimizing future evaluation programs
Visualize well data

19. • DRILLING OPERATIONS DATA
o Drilling Time / Depth Curve
o Drill time to all objectives (days)
o Coring
o Time used for coring ( hours)
o Logging
o Testing
o Pressure
• WELL RESULTS
o Success
o Failure
o Well Classification
o Technical Drilling Operations Problems
Support interpreters understanding drilling technical aspects of their prospects
Drilling related data in GIS Analytics

20. Quick look Analysis on Play type/ Objective level
Drilling related data presented in GIS Analytics
Determine well was a valid test of the objective(s) and if success, the result indicated pr objective
level or reason for failure pr objective level if no success.
Use of symbols to ease the visualization of these scenarios.
Risk Factor color code Validity color codeExample of combined color code
used at well location
Reservoir
Closure
Seal
Charge
Working
Uncertain
Not working
Valid
Not valid
Optional – Not working elements could have pop up or extra boxes telling user what did not work
(see previous slides for reasons for failure)

21. Examples of analyzing data before creating deterministic
grids
Normal QQ plots for Eocene depth values Normal QQ plots for Eocene thickness values

22. Examples of analyzing data before creating deterministic
grids
Spatial outliers in Eocene thickness values – high value
Spatial outliers in Eocene thickness values – low values

23. Examples of analyzing data before creating deterministic
grids
Spatial local outliers in Eocene thickness values

24. Examples of deterministic grids
Result

25. Develop Formulas
Use of Probability Scale
P10P50P90
Net thickness
P65 P35
20’ 40’ 80’30’ 60’
P10P50P90
Porosity
P65 P35
7% 10% 15%8% 12%
X= Value given from well
sP100= Value of P100
sP90= Value of P90
sP50= Value of P50
sP10= Value of P10
sP1= Value of P1
If X< sP90*0.8; X=P100
If P100>x>P90; X=(sP100-sP90)/10*X +P90
P50<X<P90; X= (sP50-sP90)/40*X+P50
If P10<X<P50; X= (sP10-sP50)/40*x+P50
If x>P10*1.2; X=P1
If P1<X<P10; X=(sP1-sP10)/10*X + P1
P10 P50 P90

26. Risk Factor Ranking
illustrate key messages regarding pre and post drill risk factor ranking
RESERVOIR CLOSURE SEAL CHARGE Comments
90 80 70 60 POS (%)
4 3 2 1 Risk Factor Rank
(1 is highest risk)
4 1 3 2 post drill cause of
failure (1 is primary; 2
secondary)