Water Saturation Prediction in the Middle Bakken Formation Using Machine Learning
Author:
Mellal Ilyas1ORCID, Latrach Abdeljalil1ORCID, Rasouli Vamegh1, Bakelli Omar2ORCID, Dehdouh Abdesselem1, Ouadi Habib2ORCID
Affiliation:
1. Department of Energy & Petroleum Engineering, University of Wyoming, Laramie, WY 82071, USA 2. Department Petroleum Engineering, University of North Dakota, Grand Forks, ND 58202, USA
Abstract
Tight reservoirs around the world contain a significant volume of hydrocarbons; however, the heterogeneity of these reservoirs limits the recovery of the original oil in place to less than 20%. Accurate characterization is therefore needed to understand variations in reservoir properties and their effects on production. Water saturation (Sw) has always been challenging to estimate in ultra-tight reservoirs such as the Bakken Formation due to the inaccuracy of resistivity-based methods. While machine learning (ML) has proven to be a powerful tool for predicting rock properties in many tight formations, few studies have been conducted in reservoirs of similar complexity to the Bakken Formation, which is an ultra-tight, multimineral, low-resistivity reservoir. This study presents a workflow for Sw prediction using well logs, core data, and ML algorithms. Logs and core data were gathered from 29 wells drilled in the Bakken Formation. Due to the inaccuracy and lack of robustness of the tried and tested regression models (e.g., linear regression, random forest regression) in predicting Sw as a continuous variable, the problem was reformulated as a classification task. Instead of exact values, the Sw predictions were made in intervals of 10% increments representing 10 classes from 0% to 100%. Gradient boosting and random forest classifiers scored the best classification accuracy, and these two models were used to construct a voting classifier that achieved the best accuracy of 85.53%. The ML model achieved much better accuracy than conventional resistivity-based methods. By conducting this study, we aim to develop a new workflow to improve the prediction of Sw in reservoirs where conventional methods have poor performance.
Funder
LeNorman Family Excellence Fund
Subject
General Earth and Planetary Sciences
Reference39 articles.
1. Sorenson, J., Hawthorne, S., Jin, L., Bosshart, N., Torres, J., Azzolina, N., Smith, S., Jacobson, L., Doll, T., and Gorecki, C. (2018). Bakken CO2 Storage and Enhanced Recovery Program—Phase II Final Report, U.S. Department of Energy. 2. Shawaf, A., Rasouli, V., and Dehdouh, A. (2023). The Impact of Formation Anisotropy and Stresses on Fractural Geometry—A Case Study in Jafurah’s Tuwaiq Mountain Formation (TMF), Saudi Arabia. Processes, 11. 3. Kurtoglu, B., Sorensen, J.A., and Braunberger, J. (2013, January 12–14). Geologic Characterization of a Bakken Reservoir for Potential CO2 EOR. Proceedings of the SPE/AAPG/SEG Unconventional Resources Technology Conference, Denver, CO, USA. 4. Malki, M.L., Rasouli, V., Saberi, M., Mellal, I., Ozotta, O., Sennaoui, B., and Chellal, H. (2022, January 26–29). Effect of Mineralogy, Pore Geometry, and Fluid Type on the Elastic Properties of the Bakken Formation. Proceedings of the 56th U.S. Rock Mechanics/Geomechanics Symposium, Santa Fe, NM, USA. 5. Malki, M.L., Rasouli, V., Saberi, M.R., Sennaoui, B., Ozotta, O., and Chellal, H. (2022, January 26–29). Effect of CO2 on Mineralogy, Fluid, and Elastic Properties in Middle Bakken Formation using Rock Physics Modeling. Proceedings of the 56th U.S. Rock Mechanics/Geomechanics Symposium, Santa Fe, NM, USA.
|
|