A Machine Learning Based Approach to Automate Stratigraphic Correlation through Marker Determination

Abstract

Stratigraphic correlation is well-recognized as one of the essential processes, providing information regarding stratigraphic and compartmentalization in a reservoir. It becomes a starting point for subsurface evaluation processes ranging from reservoir characteristics to reserves and resources estimation and economic evaluation. It has always been a focus area in numerous traditional and modern research. Several practices approach stratigraphic correlation, including direct tracing from outcrop, relating geological markers, and comparing the organism characteristics. This work focuses only on one of the traditional work processes, utilizing geological markers to identify stratigraphic correlation. The author primarily studies the potential adoption of data analytics and machine learning in identifying geological markers and connecting them to derive stratigraphic correlation. Well logging information is the primary data source to interpret geological markers. Determining markers was previously done based on the specific well log characteristics that are rare and uniquely identified in the geological area. It usually takes tremendous efforts to find a particular marker from well logging information, especially when many wells scale up the works. Deriving computer-assisted technology through the use of machine learning becomes a key enabler to accelerating and enhancing the business process. The machine learning assisted system has been trained with the entire geoscientists’ marker interpretations. The system consists of two connected machine learning models. The first model, designed as a multi-class classification, identifies the geological markers using well logging information. The first model’s predicted markers are then fed as an input to the second model, designed as a binary classification. It analyzes the relationship between markers in the same wellbore. Subsequently, the predicted markers resulting from two connected models are linked between two or more wells in the same region to create the stratigraphic correlation. Aiming to determine the practicality and potential adoption from one to another, the author implements the same model concept with two different sets of data, two fields in the Gulf of Thailand. The system has been proven successful in model development and deployment and has achieved nearly human performance levels.