Direct Hydrocarbon Indicators

Dr. Ivan Marroquin discusses the use of deep learning, machine learning, and big data to advance seismic interpretation processes for oil & gas exploration.

Geophysicists, Rocky Roden & Patricia Santogrossi, discuss machine learning applications enabling refined assessment of thin beds and DHI characteristics.

The accurate interpretation of DHI characteristics has proven to significantly improve the success rates of drilling commercial wells. In this webinar, Rocky Roden looks at seismic multi-attribute analysis using Self-Organizing Maps (SOMs), a machine learning approach that distills information from numerous attributes to provide an accurate assessment of DHI characteristics.

Applying Self-Organizing Maps (SOM) and Principal Component Analysis (PCA) in sub-seismic resolution to reveal facies and shale.

Seismic interpretation of thin beds below tuning has always been a challenge in the oil and gas industry. A multi-attribute interpretation utilizes SOM to analyze numerous seismic attributes all at once to identify natural patterns in the data.

Utilizing machine learning in Paradise to define and reveal features not seen in conventional interpretation in an offshore Gulf of Mexico oil/gas field. The SOM analyses using DHI characteristics and seismic attributes to reveal hydrocarbon contacts, amplify attenuation features and define ampliltude conformance in a Class 3 AVO.

Exploring shallow Yegua formation as an independent method to accurately identify anomalies and exposing direct hydrocarbon indicators using Self-Organizing Map (SOM) analysis to enhance conventional seismic interpretation to reveal anomalies.

Analyzing seismic data through geologic pattern recognition methods like Self-Organizing Maps (SOM) and Principal Component Analysis (PCA) in Paradise machine learning software.

Seismic attributes identify many geologic features in seismic data where PCA helps identify optimal attributes and help determine which attributes to use in a multi-attribute analysis using SOM. The process in Paradise reveals natural clustering by pattern recognition in the data helping define aspects like stratigraphy, seismic facies, DHI features and sweet spots for shale.

Top 5 class 3 direct hydrocarbon indicator characteristics, top five class 2 DHI characteristics, reasons for failure, implications for resource calculations in exploration and implications for reserve calculations.

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