An Automatic Approach for Core-To-Log Depth Matching in Pre-Salt Carbonate Reservoirs

This study introduces an automated approach for aligning core depths with well logs. Core samples can be a very accurate and reliable source of petrophysical measurements. Conversely, well logs present a higher level of uncertainty but offer the advantage of covering a larger portion of the lithological formation when compared to cores. These two sources of information can be combined to improve formation evaluation for a more reliable formation evaluation. Core and logs are acquired using different tools and at different times, requiring core depths to be checked and adjusted to the logs. This process is usually tedious, time consuming, and prone to human bias and errors. Furthermore, the conventional adjustment approach is usually based on gamma ray measurements, which may limit its applicability to carbonate rocks, as gamma ray variations in carbonates are commonly not very representative of formation heterogeneity. This complicates the manual depth-matching process, especially for the presalt carbonate reservoirs in Brazil. Therefore, this study presents a method for automating core-to-log depth matching by comparing petrophysical properties obtained through laboratory analysis of core data with the corresponding well logs. The depth-matching algorithm was developed using statistical methods and consists of three main steps. First, a data preprocessing step normalizes the inputs with the Z-score so that the algorithm can handle different properties, measured in different resolutions, depending on the desired scenario. The second step involves removing outliers to mitigate scale inconsistencies between core and log data based on their standard deviation. The cutoffs retain approximately 95% of the data, assuming they follows a normal distribution. The final and crucial step suggests the depth shift of core samples based on the maximum correlation between the core and log values, quantified by the normalized cross-correlation (NCC) metric. The NCC measures the similarity between two signals by calculating the cross correlation between them, which is then normalized to account for differences in their mean and standard deviation. Using the NCC as a metric for core-to-log correlation simplifies implementation and interpretation and provides a quantitative measure of the correlation between the core and log data, enabling an objective assessment of alignment quality. In addition, for those cases where core plugs are extracted from whole core samples, the algorithm can apply the same rule to each group of plug samples that belong to the same whole core. With this, core plugs derived from the same whole core are shifted together, minimizing depth errors that may arise from core fragmentation, especially in unconsolidated formations or highly porous or fractured carbonate environments. For validation purposes, we applied this innovative method to a challenging Brazilian presalt field, using neutron porosity, nuclear magnetic resonance, and bulk density as reference logs for depth-matching core porosity. Core sample depths from nine different wells were adjusted. In this case study, core porosity values could vary by up to 15% at the same depth, highlighting the value of the data preprocessing and outlier removal steps. Following this approach, wells with a core position delta of less than 2.0 m showed an 80% improvement in correlation compared to the manual matching process. For wells with a core position delta larger than 2.0 m, the improvement in correlation was 150%. Compared to manual matching, the developed solution has been proven to enhance the value of core samples for petrophysical models, improve overall accuracy, especially for carbonate reservoirs, and reduce the time and effort required from hours to minutes. Finally, the method has potential application to other scenarios using the same gamma ray measurements, with the advantage of being an automated approach.