Spatial business intelligence (SBI) uses location data and analytics to understand "where" beyond traditional business intelligence's "who", "what", and "when". SBI visualizes trends to help scientists and decision-makers understand "why". It provides advanced location analytics and supports multiple BI solutions while enabling bidirectional data flow between maps and dashboards. SBI solutions offer location options for enterprise infrastructure and big data challenges. SBI allows visualizing relationships through techniques like clustering, density maps, and heat maps. Producing prospectivity maps involves processing geoscience data through weighted modeling methods to identify hydrocarbon prospective areas. SBI helps understand a play's prospectivity by identifying optimal parameter combinations in fairways and incorporating valuation models.

1. SPATIAL
BUSINESS
INTELLIGENCE
SBI
Intelligent Maps

2. LOCATION
ANALYTICS
5W
Spatial Business intelligence (SBI) find value beyond “who”, “what”
and “when”, by understanding “where” data is located and the
ability to query location-based information.
This will guide the scientists as well as the decision makers to
understand “why” as trends are visualized.
SBI provides advanced location analytics and supports multiple BI
software solutions
Exploring applications and workflows to enable the bi-directional
flow of data between maps and dashboards for Spatial Business
solutions.
SBI Solutions provide location intelligence options for enterprise IT
infrastructures and big data challenges.
“GIS Enabled SBI Reports.”
Who What When Where Why

3. VISUALIZE
RELATIONSHIPS
Location Analytics
Visualization options within SBI allow for location analytics by
letting SBI include data clustering, surface density, Voronoi
diagrams, heat maps and more.
SBI again allow for bi-directional capabilities for interactively
querying the data to visualize proximity relationships within
the SBI solution.
“Visualize Logic important for the Business.”
Who What When Where Why

4. PROSPECTIVITY MAP
Producing Hydrocarbon Prospectivity Maps
Prepare and process geoscience data in a variety of ways for producing
Prospectivity maps.
Data used to produce these maps could include, but not limited to geologic,
geochemical, geophysical, and remotely sense.
A number of modeling methods could be used and grouped into data-driven (weights
of evidence, logistic regression) and knowledge-driven (index and Boolean overlay)
methods. The Weights of Evidence (WoE) technique could be used to compare the
spatial association of known oil and gas prospects with various indicators (evidence
maps) of hydrocarbon generation, to derive a set of weights used to produce the
final oil and gas Prospectivity map. Logistic regression could derive statistical
information from evidence maps over each known oil and gas prospect and the
coefficients derived from regression analysis could be used to weight each evidence
map. The Prospectivity map produced from the index overlay process could use a
weighting scheme that is derived from input by the geologist, whereas the Boolean
method uses equally weighted binary evidence maps.
“Visualize Logic important for the Business.”
Who What When Where Why

5. METHOD
PROSPECTIVITY MAP
Methodology for the oil and gas Prospectivity mapping
may be divided into a number of steps that are illustrated
in a workflow diagram as seen here.
“Modified from Dr. Martiya Sadeghi, Senior Geologist, the Geological Survey of Sweden”
Who What When Where
Study Area
Geological Knowledge
Base
Literature review
Exploration Database
Data collection
Conceptual Models of
various geological
factors
CRS model
Facies Model
Tectonic model +++
Exploration Criteria’s
Prospects
Exploration criteria's
Predicted
Geological map
Processing
Evidential Maps
One map for each exploration
variable
Unique Conditions Grid
Combined predictor maps for
each factor
Overlay
Mathematical
Modeling
e.g. WoE, EBF
Continuous - scale
Favorability Map
Threshold
Binary Favorability Map
Map on key criteria’s
Validation
Prospect Potential Map
Favorable areas for prospects
in study area
Why

6. SBI HELP UNDERSTAND
PROSPECTIVITY OF A PLAY
Identify Hydrocarbon
Prospective areas
The weighted overlay of the scored
inputs results in the final
prospectivity map. The combination
of the best scores of play depth,
thermal maturity and thickness
determines the fairway of the
Poland Silurian shale play, with
highest prospectivity in the Podlasie
Basin and coastline areas of the
Baltic Basin.
“Visualize Logic.”
Who What When Where Why

7. SBI HELP UNDERSTAND
PROSPECTIVITY OF A PLAY
Non-Geological factors
There are plenty of other geologic and non-
geologic factors, which can determine the
commerciality of unconventional production
from shale plays.
One of such factors is topography, where
hardly accessible mountain areas, or dense
urban areas, may limit development activities
significantly. Figure here shows an example of
Longmaxi shale in Sichuan Basin in China.
Northeastern and western areas with relatively
high prospectivity are located in mountainous
terrain, which prioritizes the prospectivity
trends just east of Chongqing city.
“Visualize Logic.”
Who What When Where Why

8. SBI HELP UNDERSTAND
PROSPECTIVITY OF A PLAY
SPATIAL BUSINESS IINTELLIGENCE
The automated method does not attempt to replace expert geologic exploration research, but brings a simplified
GIS valuation model, which enables relative comparison of shale play acreage.
It observes variation of geological and non-geological parameters across the plays and determines zones, which
are more prospective and commercial than others, using e.g. proved analogies from North America shale plays.
The GIS valuation model looks for optimal combination of key geological parameters, such as play depth, thermal
maturity and thickness, in order to identify play fairways.
Incorporating the GIS valuation model into the Spatial Business valuation model, creates a valuable product for
the decision makers to act just in time for the right opportunity arising.
SPATIAL BUSINESS INTELLIGENCE REPORT
“Visualize Logic.”
Who What When Where Why