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Artificial Intelligence (AI) predictive models work to help
engineers in Upstream E&P gain actionable insights from complex
models and develop better asset strategies. The IHS Markit models
are intuitive and can analyze vast amounts of data rapidly and
provide interpretability capabilities to help engineers better
understand the model results.
With customizable models, engineers can choose which basin
attributes (production, completions, well, etc.) they want to
evaluate for identifying basin performance drivers. Additionally,
they can incorporate proprietary datasets, validate results and
rerun their predictive models as more data and knowledge
accumulates.
Attribute Analysis
Analytics Explorer's predictive modeling techniques make it easy
to visualize and analyze an entire asset's data. Users can
incorporate their own company data and customize the data inputs
based on domain expertise.
Attribute analysis is the first step in creating a predictive
performance model. This function evaluates specified well data and
ranks attributes by importance. Attributes can include items like
well location, target formation, well path, proppant volume,
spacing, relevant geologic or geophysical data, and more. The most
important attributes have the greatest impact on well performance,
while the least important attributes have a negligible impact. For
each attribute, the models output detailed information about how
the inclusion of that attribute affects the predictability of the
model. This enables users to exclude irrelevant or redundant
attributes and open themselves to surprises about overlooked
variables.
Analytics Explorer's error analysis algorithms determine the
optimal number of attributes for each model. Removing these
attributes that do not reduce error streamlines the modeling
process.
What's Controlling the Asset?
In addition to ranking attributes by importance, Analytics
Explorer also allows for users to group like features together when
diving into what is controlling the asset.
For example, clients often want to group attributes driven by
geology, as they are essentially non-controllable. Examples
include:
Well location (latitude and longitude)
Acreage quality
Formation thickness
Depth to formation
Engineering decisions, on the other hand, may include
controllable and conscious decisions made by the company that can
be optimized:
Lateral length
Frac stages
Fluid and proppant volume
Well spacing
The most important attributes have the greatest influence on
well performance, while the least important attributes have a
negligible impact. Here, well location has the most impact on
performance. Attributes in orange are controllable and attributes
in blue are non-controllable.
Detailed insight about performance drivers can help guide asset
development decisions. If an underperforming asset's performance is
largely driven by non-controllable factors, engineers can determine
which controllable factors can be optimized to increase production.
If a high-performing asset is driven by geology, engineers can
model different completions scenarios to further optimize
production or reallocate capital to maximize returns.
Using Predictive Models
The predictive models generated in Analytics Explorer come
together in a matter of minutes, compared to days, weeks or months
using previous methods. These models are customizable, flexible and
built to reduce uncertainty. Users can incorporate data from a
variety of sources and incorporate their own domain expertise each
step of the way.
Predict Well Production Performance
The predictive models generated in Analytics Explorer can help
predict performance of future wells. Users can select specific well
attributes, like location, TVD, lateral length and completions
design and use their asset's predictive model to see what
production will be over a variety of time frames (12-month,
24-month, EUR, etc.).
Understanding Every Attribute that Impacts Well
Performance
For each well in a model, users can see the contributing
attributes and how each one influences production for that specific
well. These results can then be aggregated and combined with other
attributes (Operator, Vintage Date, etc.) to provide valuable
insight into well performance, in both an asset-wise and basin-wide
context.
Basin-Wide Benchmarking and Opportunity
Identification
Users can compare their wells to other wells in the basin. This
may be useful to screen for investment opportunities. For example,
wells drilled in above average locations with below average
completions are excellent candidates for re-fracking. Similarly,
mergers and acquisitions teams can find targets that bring new
skills (better completions impact) or provide new locations on
which to leverage internal technology. In both cases, the proposed
combination can create synergies for deals.
Each well is plotted by the contribution of location on
production versus the contribution that controllable variables had
on production. A crossplot like this enables the identification of
wells that are located in areas that contributed positively to
production but the controllable factors, like engineering,
negatively impacted performance.
Reach Better Outcomes with Analytics
Explorer
IHS Markit is making advanced data analytics technology easier
to adopt and deploy, giving engineers the ability to utilize
leading data science methodologies along with their domain
expertise to solve complex subsurface problems.
Analytics Explorer is an advanced data analytics solution from
IHS Markit that makes data science accessible to everyone.
Developed for Upstream E&P, Analytics Explorer incorporates
advanced data science methodologies in guided and automated
workflows that incorporate interpretability methods to help
engineers better understand their models with confidence.
With customizable workflows like predictive modeling, Analytics
Explorer can be used for a broad range of applications,
including:
Predicting the performance of wells before drilling them
Quantifying the impact of specific parameters on a well's
performance
Understanding optimum well design
Understanding the impact of location and completion
quality