Spatio-temporal sequence prediction of CO2 flooding and sequestration potential under geological and engineering uncertainties
CO 2 injection for subsurface hydrocarbon development not only enhances oil and gas
recovery but also enables CO 2 sequestration in the subsurface. It is essential to develop …
recovery but also enables CO 2 sequestration in the subsurface. It is essential to develop …
[HTML][HTML] Well production forecast in Volve field: Application of rigorous machine learning techniques and metaheuristic algorithm
CSW Ng, AJ Ghahfarokhi, MN Amar - Journal of Petroleum Science and …, 2022 - Elsevier
Developing a model that can accurately predict the hydrocarbon production by only
employing the conventional mathematical approaches can be very challenging. This is …
employing the conventional mathematical approaches can be very challenging. This is …
Probabilistic decline curve analysis: state-of-the-art review
The decline curve analysis (DCA) technique is the simplest, fastest, least computationally
demanding, and least data-required reservoir forecasting method. Assuming that the decline …
demanding, and least data-required reservoir forecasting method. Assuming that the decline …
Machine learning based decline curve analysis for short-term oil production forecast
A Tadjer, A Hong, RB Bratvold - Energy Exploration & …, 2021 - journals.sagepub.com
Traditional decline curve analyses (DCAs), both deterministic and probabilistic, use specific
models to fit production data for production forecasting. Various decline curve models have …
models to fit production data for production forecasting. Various decline curve models have …
Removing the outlier from the production data for the decline curve analysis of shale gas reservoirs: a comparative study using machine learning
Decline curve analysis (DCA) is one of the most common tools to estimate hydrocarbon
reserves. Recently, many decline curve models have been developed for unconventional …
reserves. Recently, many decline curve models have been developed for unconventional …
Production forecasting in shale reservoirs through conventional DCA and machine/deep learning methods
Predicting EUR in unconventional tight-shale reservoirs with prolonged transient behavior is
a challenging task. Most methods used in predicting such long-term behavior have shown …
a challenging task. Most methods used in predicting such long-term behavior have shown …
基于贝叶斯推断的产量递减综合预测新模型.
王军磊， 位云生， 齐亚东， 倪佳， 于伟… - Natural Gas …, 2022 - search.ebscohost.com
油气井产量递减分析对于产能建设和后期方案调整优化具有重要意义, 其中单井最终可采储量(
EUR) 的准确计算对非常规油气规模效益开发尤为关键. 为了解决定量评价生产历史拟合和EUR …
EUR) 的准确计算对非常规油气规模效益开发尤为关键. 为了解决定量评价生产历史拟合和EUR …
Bayesian deep decline curve analysis: A new approach for well oil production modeling and forecasting
A Tadjer, A Hong, R Bratvold - SPE Reservoir Evaluation & …, 2022 - onepetro.org
Following the rapid growth of unconventional resources, many models and methods have
been proposed for forecasting the performances of unconventional wells. Several studies …
been proposed for forecasting the performances of unconventional wells. Several studies …
Bayesian model evaluation for multiple scenarios
SI Aanonsen, K Fossum, T Mannseth - Computational Geosciences, 2023 - Springer
Traditional uncertainty analysis for subsurface models is typically based on a single dynamic
model with a number of uncertain parameters. Improved and more robust forecasting can be …
model with a number of uncertain parameters. Improved and more robust forecasting can be …
Simultaneous well spacing and completion optimization using an automated machine learning approach. A case study of the Marcellus Shale reservoir, northeastern …
E Fathi, A Takbiri-Borujeni, F Belyadi… - Petroleum …, 2024 - lyellcollection.org
Optimizing unconventional field development requires simultaneous optimization of well
spacing and completion design. However, the conventional practice of using cross plots and …
spacing and completion design. However, the conventional practice of using cross plots and …