Subsurface Uncertainty Quantification using Machine Learning– Improved Reservoir Management through Data Driven Predictions

• Communicate the uncertainty range associated with subsurface predictions and therefore, convey the risks to peers & management.
• Explain what Conformal Prediction is and how it can be used to quantify subsurface uncertainty.
• Understand that Conformal Prediction can be applied to regression, classification and time series data.
• Apply Conformal Prediction regression techniques to estimate prediction intervals as opposed to just generating single predictions points.
• Classify Conformal Prediction classification to quantify binary and multi-class (facies) uncertainty conveyed by classification being in sets of potential classes rather than a single prediction.
• Develop Conformal Prediction to quantify oil and gas production forecast uncertainty applied to time series (production history) data to provide complementary non-Decline Curve Analysis (DCA) perspectives.
• Learn to use classification and regression metrics to assess the accuracy and effectiveness of predictions.
• Create various plots to assist in the interpretation of Conformal Prediction techniques.

• Petrophysicists involve in analyse and interpret well log data to determine the properties of subsurface formations.
• Reservoir Engineers participate in estimating and optimising the extraction of hydrocarbons from reservoirs.
• Geoscientists such as Geologists and Geophysicists that study the physical aspects of the Earth, particularly the subsurface structures that contain resources like oil and gas.
• Any technical professionals, regardless of their specific roles, that are interest in gaining a deeper understanding of subsurface uncertainty quantification.
• Data Scientists and Machine Learning Specialists in the Energy Sector whose role specialise in data science and machine learning and are looking to apply their skills to the subsurface domain.

• Intermediate

Pre-requisite: Participants are recommended to have a basic understanding of a programming language (preferably Python), particularly of machine learning concepts of classification and regression.

Learning Tools: Participants need to have access to a computer with internet access and a Google account (minimally consisting of Gmail and Google Drive) as most of the course entails the use of the Google Colab platform. Instructions will be provided as Google Colab notebooks and working data will be made available online. Participants can work in pairs or teams if individual participants are not proficient with programming languages. 

Your expert instructor is a Data Scientist from Australia hold a Master of Petroleum Engineering has an extensive and distinguished career in geophysics, marked by his expertise and leadership across various prestigious organisations. His role as a Senior Geophysicist at Baker Hughes in 2020 and as the Asset Geophysicist for the D18 Block offshore Sarawak, Malaysia led the subsurface and surface efforts for a New Field Extension (NFE) opportunity, successfully firming up STOIIP volumes, identifying three sub-prospects, and planned two well proposals for the drilling campaign. His innovative approach to seismic attributes and inversion volumes optimised well trajectories, reduced drilling costs, and enhanced reservoir trend identification.

In 2012, he served as a Senior Production Seismologist at Brunei Shell Petroleum Co., where he was instrumental in well and reservoir management for the Champion Field, one of the largest oil and gas fields in Brunei. His contributions included optimising waterflood well placements, leading 4D seismic interpretations, and designing well trajectories for the B2/B3 waterflood project. He also contributed significantly to the SW Ampa Field, updating full field models, integrating inversion results, and recommending cost-saving measures through well recompletions. Meanwhile at KUFPEC Australia, he focused on exploration and development projects, particularly in the Julimar-Brenello gas fields and Finucane oil field. His critical assessments of gas reserves and seismic interpretations played a key role in advancing the Wheatstone Project.

During his tenure at Premier Oil Indonesia for almost 2 years, he was integral in revising hydrocarbon reserves and de-risking well prognoses for the Natuna Sea ‘Block A’. His work in improving data management and mentoring national staff highlighted his commitment to excellence and knowledge transfer. At Troy Ikoda Australasia in 2002, he managed geoscience projects, including seismic interpretation, depth conversion, and reserves certification for Apache Energy. His experience at Santos Ltd, he involved in interpreting seismic data in the Cooper Basin and assisting in asset performance monitoring. His early career included roles at Enserch Exploration Inc., Hall-Houston Malaysia, Sun Oil Far East, and Western Geophysical Company, where he honed his skills in seismic interpretation, prospect evaluation, and geophysical modeling.

Throughout his career, he has consistently demonstrated a commitment to excellence, leveraging his extensive experience to deliver impactful results across the oil and gas industry. His technical expertise and ability to adapt to new challenges have made him a highly respected professional in the field of geophysics.

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