The document discusses establishing geospatial analysis capabilities within an exploration environment. It outlines collecting all relevant exploration data, representing it properly in a GIS database, and generating continuous geospatial grids using deterministic and geostatistical methods. These grids would support spatial analysis and decision making by allowing visualization and queries of parameters like probability of success. Maintaining data quality and understanding relationships between different data types is important for effective analysis.

1. Workflow from raw data to Geospatial Data and what its going to
be used for
Why we do this?
• all data we deal with is geospatial
• need more robust key data query and decision support tools for our interpreters in Exploration
• exploration works hard to increase GIS competence within their departments
• still lack of Geospatial analysts within Exploration in general
Stig-Arne Kristoffersen, July 25th 2011
Geospatial Exploration Performance Analysis
EXPLORATION SPATIAL ANALYSES (ESA)
WITHIN
EXPLORATION ENVIRONMENT

2. All exploration related attributes that can be plotted and illustrated
on a map, in any combination and complexity, here are some few
examples for illustration purposes only.
1. Where do I find all Brent Fm with larger than 20% porosity?
2. Where are all Heather Fm penetrations with shows in them?
3. Give me all Dunlin Fm well penetrations with porosity trends plotted, both in graph and map,
please…..
4. Plot mean risked volumes for Heimdal Fm traps, both in graph and on map, visualize the range
(P10 – P90) in bubble plots and on map.
5. Plot all prospects belonging to Tarbert with their main risk factor and value combined with the
Total POS trend map
6. Show me all wells where we have cores/tests within the Brent Fm……
Geospatial Exploration Performance Analysis
What will be usual questions asked in Geospatial
environment?

3. GIS Strategy within Exploration
Business needs/drivers/value of Spatial Data
• geo spatial query and search of data to be used in identifying new exploration opportunities
Front Office environment – Decision Support Environment
• web based GIS technology and later the ‘heavyweight’ desktop version
• introduce ways of working with spatial data combined with textual data
Back Office - Information Supply
• develop tools to support specific workflows and geoprocessing
• support and maintenance to Exploration environment
Content
• expose high-value legacy data (e.g Cartography and key Exploration data)
• use ‘trusted’ data sources (e.g. official QC’ed E&P repositories)
• link to Interpretation systems data (e.g. Geoframe, Petrel, Petrosys and others)
SDE Data management
• increased data management required to develop and maintain spatial data sets
• GIS to be an integrated part of the Information data management system
• establish Procedures and Standards for loading, edit and validate Exploration related GIS data
Integrated Exploration related GIS

4. 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)

5. Increase
Recovery
Lower
Business
Running
Costs
Improve
Risk Mgmt
Improve
Expl
Forecast
Understand
Subsurface
Shorten
Cycle TimeMake people
productive
Create &
Support
Values
Required
Maximise
Return On
People
Employed
Deliver
Skills
Required
Maximise
NPV
Increase
Exploration
Portfolio
Volumes
Reduce
Exploration
Cost
Maximise
Reserve
Replacement
Optimise
Portfolio
Mgmt
Extended Enterprise
Integrated
Real-time Data Access
Information Access
Analytical Tools & Processes
Probabilistic
Visualise Complex Data
Abstraction & Drilldown
Encourage Creativity
Identify Work Priorities
Reduce Non- Productive Time
Objectives
Drivers
Enablers
Learning
Organisation
Develop Appropriate Skills
(modified from Enterprise Oil, 2005)

6. All Exploration related data is Geospatial in nature
We therefore need Geo Spatial Analysis to guide our future exploration efforts
Spatial Analysis lies at the core of GIS
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)

7. GIS data processing and analytics
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
Continuous ESRI Grids
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.
Automate GIS tasks and
develop custom workflows
that can be shared
• Mapping
• Charting
• Reporting
• Visualization
Point data
(f.inst. well 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) (modified from ExxonMobil, 2006)
Polygon data (f.inst
Paleo geographic maps)
Discrete raster or vector ESRI Grids

8. Point data in Oracle Database which supports
Probability of Success (POS tot) trend data
Dependencies and how to use in Geo Spatial Analysis to understand and increase 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

9. 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 over 74 Play types (Objectives) at the moment, 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.

10. First things first…….
Then the first step for us is to ensure all key data is present in the GIS database and
what is represented in the GIS database (SDE) is in the proper format, verified and
maintained according to changes in database.
Providing a large range of Geospatial analysis capabilities
(modified from ESRI, 2011)
To be able to perform analysis, managing geographic data, modeling and analysis
capabilities by polygon overlays, complex regression analysis and image classification.
And being able to automate GIS tasks and develop custom workflows that can be
shared

11. 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.
ESA assisted Exploration Performance Analysis

12. The need for Exploration GIS grids
to support Decision making
• Most data available in present AED and ERAD 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

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

14. 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/meters)
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

15. • 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

16. 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
Well data requirements

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

18. 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