Modified aquila optimizer for forecasting oil production

MAA Al-qaness, AA Ewees, H Fan… - Geo-Spatial …, 2022 - Taylor & Francis
Oil production estimation plays a critical role in economic plans for local governments and
organizations. Therefore, many studies applied different Artificial Intelligence (AI) based …

Optimized ANFIS model using Aquila Optimizer for oil production forecasting

AM AlRassas, MAA Al-qaness, AA Ewees, S Ren… - Processes, 2021 - mdpi.com
Oil production forecasting is one of the essential processes for organizations and
governments to make necessary economic plans. This paper proposes a novel hybrid …

Forecasting oil production using ensemble empirical model decomposition based Long Short-Term Memory neural network

W Liu, WD Liu, J Gu - Journal of Petroleum Science and Engineering, 2020 - Elsevier
Oil production forecasting is an important means of understanding and effectively
developing reservoirs. Reservoir numerical simulation is the most mature and effective …

Attention-based LSTM network-assisted time series forecasting models for petroleum production

I Kumar, BK Tripathi, A Singh - Engineering Applications of Artificial …, 2023 - Elsevier
Petroleum production forecasting is the process of predicting fluid production from the wells
using historical data. In contrast to the traditional methods of analysing surface and …

Advance artificial time series forecasting model for oil production using neuro fuzzy-based slime mould algorithm

AM AlRassas, MAA Al-Qaness, AA Ewees… - Journal of Petroleum …, 2022 - Springer
Oil production forecasting is an important task to manage petroleum reservoirs operations. In
this study, a developed time series forecasting model is proposed for oil production using a …

Reinforcement learning based automated history matching for improved hydrocarbon production forecast

H Li, S Misra - Applied Energy, 2021 - Elsevier
History matching aims to find a numerical reservoir model that can be used to predict the
reservoir performance. An engineer and model calibration (data inversion) method are …

Efficient deep-learning-based history matching for fluvial channel reservoirs

S Jo, H Jeong, B Min, C Park, Y Kim, S Kwon… - Journal of Petroleum …, 2022 - Elsevier
In history matching, the calibration of a prior reservoir model is computationally expensive
because many forward reservoir simulation runs are required. Multiple posterior (or …

Inversion framework of reservoir parameters based on deep autoregressive surrogate and continual learning strategy

K Zhang, W Fu, J Zhang, W Zhou, C Liu, P Liu, L Zhang… - SPE Journal, 2023 - onepetro.org
History matching is a crucial process that enables the calibration of uncertain parameters of
the numerical model to obtain an acceptable match between simulated and observed …

Reservoir simulation using smart proxy in SACROC unit-Case study

Q He, SD Mohaghegh, Z Liu - SPE Eastern Regional Meeting, 2016 - onepetro.org
In oil and gas industry, quick decisions on reservoir management have a huge impact on
business success. Reservoir simulation is used as a typical tool to predict field performance …

Conditioning Model Ensembles to Various Observed Data (Field and Regional Level) by Applying Machine-Learning-Augmented Workflows to a Mature Field with 70 …

G Vanegas, J Nejedlik, P Neff… - SPE Reservoir Evaluation …, 2021 - onepetro.org
Forecasting production from hydrocarbon fields is challenging because of the large number
of uncertain model parameters and the multitude of observed data that are measured. The …