PHM required a new approach to building an optimal field
development plan based on available data. It needed to evaluate
if machine learning could minimize property modeling time and improve the quality of the property model by integrating multiple
secondary properties, such as seismic geomorphology and other
geometrical properties.
Solution
PHM selected Machine Learning for Property Modeling to achieve
a fast and robust estimate of reservoir properties utilizing its
available data. Machine Learning for Property Modeling utilizes
the Embedded Model Estimator (EMBER) hybrid algorithm,
which leverages machine learning combined with classic
geostatistical estimation and simulation techniques. EMBER
enables a significant reduction in data preparation by removing
the need for stationarity assumptions or preparatory work, such as
removal of trends, data transformation, variogram analysis, zones,
and facies conditioning. EMBER also enables the handling of
multiple secondary variables simultaneously.
The solution was implemented on PHM’s North Handil Field, where
PHM conducted a series of tests to compare the EMBER results
with traditional geostatistical modeling techniques. The success of
the project was measured utilizing pre-defined success criteria and
metrics, such as time reduction and model quality improvement
compared to traditional geostatistical approaches.
During the COVID-19 pandemic, PHM and SLB carried out
the proof-of-value project remotely to ensure the safety of
their employees. Teams from Norway, Indonesia, and the UK
collaborated seamlessly in the DELFI environment, delivering
hands-on workshops and testing sessions.
Benefit
With Machine Learning for Property Modeling, PHM quickly
incorporated all its existing data into a robust reservoir model,
enabling quick identification of reservoir sweet spots and delivery
of a field development plan that took into account technical and
economic constraints.
EMBER passed all blind well test scenarios, delivering a reduction
in average prediction error by 18% compared to traditional
geostatistical techniques. Geomodelers reduced the time they
spent on repetitive and laborious data analysis tasks by more than
75%, enabling themto spend more time analyzing the results and
gain a greater understanding of reservoir uncertainty. Because
of the EMBER algorithm’s agility, PHM can now easily update the
models as new field data is received.
