A Machine-Learning Approach Performed on New Technology for Images in Oil-Based Mud for Advanced Electrofacies Analysis – A Case Study From the Norwegian Sea
Authors:   Halliburton, Ahmad Sayyid , Mirza Dler ,   Aker BP, Waage Henrik ,   Aker BP, Gales Robert ,   Halliburton, Aarseth Nils Andre ,   Aker BP, Engelman Bob ,   Halliburton, Barrett Peter ,   Halliburton, Jambunathan Venkat ,   Halliburton
Published: September 2024

Exploration projects often require high-quality image data acquired in oil-based mud-filled boreholes to evaluate thin laminated sandy shale formations. The introduction of a new oil-based electrical imager technology allows detailed facies analysis and open and closed fracture identification. Simple cutoff methods are widely used for lithofacies identification and facies classification, which can be a time-consuming task. However, this work presents an automated machine-learning-based electrofacies classification using a combination of resistivity and permittivity-dominated image log data integrated with other conventional data. An advanced data set was acquired in a near-vertical well drilled in the Norwegian Sea to target Middle Jurassic shallow marine sand complexes with varying depositional environments. Machine-learning workflows are created after loading all images, other conventional log data, surface seismic, and vertical seismic profile (VSP) data. Before running any facies prediction, the advanced workflow encompasses four preconditioning steps for data standardization: 1) data preprocessing for speed correction, button/pad repair and alignment if needed, 2) data reduction by features and instance selection as well as dimensional reduction, 3) parametric testing to see if the available data follow rules of normality, and 4) normalization of data is performed if required. After standardizing the data, different classifiers are tested iteratively for facies prediction. Each step of machine learning, from data conditioning to facies prediction, is constrained by consideration of the sedimentological and depositional environment. After running the models, the resultant electrofacies were compared with lithofacies interpreted manually by using core and conventional log data acquired in the same well. In addition to machine-learning-based facies classification, fractures were classified into open and closed fractures by interpreting the resistivity and permittivity image components. After iterating the classifiers, six electrofacies were created and discussed based on k-means clustering and self-organizing maps. By integrating high-resolution image log texture and conventional well-log patterns, different depositional environments were linked to the electrofacies identified. Facies 1, 2, and 3 were found to be sand-dominated facies, which comprise mainly transgressive to regressive sands followed by the deposition of aggrading sandy packages deposited in mixed-energy, coastal-deltaic settings during the Lower-to-Middle Jurassic. Facies 4 was found to be silt-dominated heterolithics facies that represent a relatively transitional environment of deposition. Facies 5 and 6 were found to be mudstone-dominated facies, which possibly represent local or regional transgressive events during local or regional flooding events. The classified electrofacies work is integrated with surface seismic and VSP and can potentially be used for future input to describe lateral and vertical rock distribution patterns and for improving static and dynamic reservoir models for enhanced reservoir understanding. In addition, fracture interpretation with permittivity-dominated images is demonstrated and can be used to further improve completion design and well productivity.