FIELD

Embodiments disclosed herein generally relate to processes and systems for correlating well log data sets from well logging passes within a well bore.

BACKGROUND

In the upstream oil and gas industry, logging is widely used in hydrocarbon exploration, field development planning, and well completion, to name a few. Log interpretation is utilized for formation evaluation and reservoir characterization. Depth matching well logs acquired from multiple logging passes in a single well has been a long-standing challenge for the industry. It is a part of providing a reliable integrated log interpretation, without which mismatches between well logs leads to erroneous results, especially for thinly laminated formations. Depth matching is also used for other wellbore-related operations, such as perforation, completion, core sampling, formation testing, and drill stem testing, each requiring precise depth positions to ensure successful operations.

Typically, gamma-ray logs from two different logging passes performed in a single well are correlated. The correlation can be performed through depth matching. From the gamma-ray synchronization, other logs generated during each pass can also be synchronized. The existing depth matching approaches employed in commercial platforms are basically variant optimization algorithms based on the cross-correlation and covariance measures of two signals or the dynamic and customized correlations. Examples of optimization algorithms can be found in Kerzner, M. G., 1984, A solution to the problem of automatic depth matching, Paper SPWLA-1984-VV, Transactions, SPWLA 25th Annual Logging Symposium, New Orleans, La., 10-13 June. and Fang, J., Chen, H., Shultz, A., and Mahmoud, W., 1992, Computer-aided well log correlation, AAPG Bulletin, 76, pp. 307-317. In many cases, these algorithms do not provide accurate depth matching results and need additional and inefficient manual adjustments. These solutions do not satisfy the rising demand to minimize user intervention to proceed towards automated data processing and interpretation.

A machine learning-based solution, as described in Le, T., Liang, L., Zimmermann, T., Zeroug, S. and Heliot, D., 2019, A Machine-Learning Framework for Automating Well-Log Depth Matching, Petrophysics, 60(05), pp. 585-595, was developed to address this issue. The proposed solutions included a supervised machine learning approach through a fully connected neural network. The training dataset was obtained by manually labeling a limited set of field data. As it is unrealistic to expect a perfect model from the initial training with limited manually labeled data, this work developed a continuously self-evolving depth matching framework. During the use of depth matching service, this framework allows taking the user input and feedback to further train and improve the depth matching engines. This is facilitated by an automatic quality control (QC) module. This QC module can also be used to assess the quality of the returned results from the depth matching engine. The users review the results and do manual adjustments if some intervals are not ideally depth-matched by the engine. Those manual adjustments are sent back to the database and the framework re-trains the machine learning model based on the updated database. The newly trained model can be validated and compared against existing models using the developed metrics system. Enhanced models are committed to the model pool that is part of the service. As the users keep using the service, the depth matching framework is anticipated to automatically and continuously improve in performance which eventually leads to an optimal and fully automated depth matching service. The limitation of this machine learning-based solution is that it relies on a fully self-evolving mechanism or trained engine requiring significant resources and human intervention before actual well log depth matching can be utilized. Without the trained engine, it would be difficult for the machine learning tool to reach its maturity for acceptable performance.

There is a need, therefore, for processes and systems for robust, automatic, non-machine learning based algorithms and workflows for depth matching gamma-ray logs and/or other well logs measured in multiple logging passes within the same well. Additionally, there is a need for these processes and systems to provide quality control checks for machine learning-based solutions as well as for providing labeled data for the machine-learning algorithm solutions.

SUMMARY

Processes and systems for matching well log depths between two or more well log data sets are provided. In some embodiments, the process can include normalizing a first well log from a first logging pass obtained within a well bore and a second well log from a second logging pass obtained within the well bore. The process can further include performing a pre-shift by calculating a series of correlation coefficients between the first well log and the second well log and utilizing an offset distance corresponding to a highest correlation coefficient as a constant shift value for the second well log. The process can further include performing feature picking to identify one or more features along the second well log. Normalized cross-correlation based optimization can be performed between the first well log and the second well log to match the one or more features along the second well log to the same one or more features of the first well log. A shift table can be generated for depth shifting the one or more features of the second well log to match the depths of the corresponding one or more features in the to first well log. The process can further include applying the generated shift table to the second well log to depth match the second well log to the first well log. The process can further include analyzing geological related data in the depth matched first and second well logs to identify one or more geological structures located along the length of the well bore.

In some embodiments, the system can include a depth matching controller, and instructions that when executed by the depth matching controller cause the depth matching controller to normalize a first well log from a first logging pass obtained within a well bore and a second well log from a second logging pass obtained within the well bore. The instruction can further cause the depth matching controller to perform a pre-shift by calculating a series of correlation coefficients between the first well log and the second well log and utilizing an offset distance corresponding to a highest correlation coefficient as a constant shift value for the second well log. The instruction can further cause the depth matching controller to perform feature picking to identify one or more features along the second well log. The instruction can further cause the depth matching controller to perform a normalized cross-correlation based optimization between the first well log and the second well log to match the one or more features along the second well log to the same one or more features of the first well log, generate a shift table based on the matched one or more features, apply the generated shift table to the second well log to depth match the second well log to the first well log, and analyze geological related data in the depth matched first and second well logs to identify one or more geological structures located along the length of the well bore.

In some embodiments, the process can include normalizing a first well log from a first logging pass obtained within a well bore and a second well log from a second logging pass obtained within the well bore. The process can further include performing a pre-shift by calculating a series of correlation coefficients between the first well log and the second well log and utilizing an offset distance corresponding to a highest correlation coefficient as a constant shift value for the second well log. The process can further include performing feature picking to identify one or more features along the first or second well log. Normalized cross-correlation based optimization can be performed between the first well log and the second well log to match the one or more features along the second well log to the same one or more features of the first well log. A shift table can be generated for depth shifting the one or more features of the second well log to match the depths of the corresponding one or more features in the first well log. The process can further include applying the generated shift table to the second well log to depth match the second well log to the first well log. The process can further include transiting a tool string to a location within the well bore based on geological related data in the depth matched first and second well logs.

