Automated Log Data Analytics Workflow – The Value of Data Access and Management to Reduced Turnaround Time for Log Analysis

Abstract

The oil and gas industry of today is undergoing rapid digitalization. This implies a massive effort to transform standard work procedures and workflows into more efficient practices and implementations using machine learning (ML) and automation. This will enable geoscientists to explore and exploit vast amounts of data quickly and efficiently. To address these current industry challenges, we propose a pilot well-log database in HDF5 (Hierarchical Data Format version 5) format that can be continuously extended if new data become available. It also provides versatility for data preparation for further analysis. We show an alternative way to store and use log files in a hierarchical structure that is easy to understand and handle by research institutes, companies, and academia. We also touch upon well-log depth matching, a long-standing industry challenge, to synchronize data from different logging passes to a single depth reference. Having a robust automated solution for depth matching is important to facilitate the use of all available data in a depth interval for analysis by ML. We propose an automatic well-log depth-matching workflow capable of handling multiple log types simultaneously and its integration with the database. The updated depth-matched logs are added to the database with their corresponding metadata, giving the geoscientist full control. We implemented two algorithms—classical cross correlation combined with a scaling factor to simulate stretch-squeeze effects and a constrained dynamic time warping (DTW). Our results indicate that the classical cross correlation outperforms the warping for both robustness and speed when the DTW is constrained to avoid excessive signal distortion and when the number of processed curves increases, respectively. Some limitations of our approach are related to large changes in the log patterns between the runs, as well as the assumption of negligible depth shift between log types within the same run. The cross correlation also allows a consistent application of depth matching to the metadata. This prototype workflow is tested using two wells from the Norwegian North Sea. We see the potential for extending this automatic database-processing workflow to give geoscientists access to all the data to improve interpretation.

Automated well log depth matching: Late fusion multimodal deep learning

“…Motivated by the successful results achieved by 1D CNNs in similar tasks (Brazell et al ., 2019; Wang et al ., 2020), we propose replacing Torres Caceres et al . (2022a)’s analytical depth matching workflow with a fully data‐driven procedure based on deep learning, also extending the work by Torres Caceres et al . (2022b).…”

“…Previously we developed a semi‐automatic depth matching workflow, reported in Torres Caceres et al . (2022a), based on cross‐correlation; so, we have already formulated the depth matching problem as a signal‐processing problem. We focus our workflow on synchronizing log signals between logging while drilling (LWD) and electrical wireline logging (EWL) based on the correlated depth concept.…”

“…This work is an extension of previous research by Torres Caceres et al . (2022a) on the automation of well log depth matching algorithms and procedures. They presented a depth matching workflow that can synchronize several well log measurements to a common reference simultaneously in a couple of minutes.…”

“…Our results are also compared to results from other workflows such as the semi‐automatic cross‐correlation well log depth matching of Torres Caceres et al . (2022a) and manual procedures performed by an experienced petrophysicist.…”

“…First, we give a brief introduction to the classical method of cross‐correlation as well as its implementation within the semi‐automatic workflow of Torres Caceres et al . (2022a). Second, a more extensive introduction to multimodal machine learning is given focusing on fusion techniques.…”