ArticleJournal of petroleum exploration and production technology2022
Advance artificial time series forecasting model for oil production using neuro fuzzy-based slime mould algorithm.
Article in Journal of petroleum exploration and production technology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- A Sensor-Aware Decoupled Learning Framework for Robust Multi-Scale Time-Series Forecasting in Oil Production Systems.Sensors (Basel, Switzerland) · 2026Article
- Simultaneous multi-well production forecasting and operational strategy awareness in heterogeneous reservoirs: a spatiotemporal attention-enhanced multi-graph convolutional network.Scientific reports · 2025Article
- Advances in Slime Mould Algorithm: A Comprehensive Survey.Biomimetics (Basel, Switzerland) · 2024Review
- Foreign exchange forecasting and portfolio optimization strategy based on hybrid-molecular differential evolution algorithms.Soft computing · 2023Article
- Slime Mould Algorithm: A Comprehensive Survey of Its Variants and Applications.Archives of computational methods in engineering : state of the art reviews · 2023Article
- Evaluating the Applications of Dendritic Neuron Model with Metaheuristic Optimization Algorithms for Crude-Oil-Production Forecasting.Entropy (Basel, Switzerland) · 2022Article
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7 authors.
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Abstract
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 new improved version of the adaptive neuro-fuzzy inference system (ANFIS). This model is improved by using an optimization algorithm, the slime mould algorithm (SMA). The SMA is a new algorithm that is applied for solving different optimization tasks. However, its search mechanism suffers from some limitations, for example, trapping at local optima. Thus, we modify the SMA using an intelligence search technique called opposition-based learning (OLB). The developed model, ANFIS-SMAOLB, is evaluated with different real-world oil production data collected from two oilfields in two different countries, Masila oilfield (Yemen) and Tahe oilfield (China). Furthermore, the evaluation of this model is considered with extensive comparisons to several methods, using several evaluation measures. The outcomes assessed the high ability of the developed ANFIS-SMAOLB as an efficient time series forecasting model that showed significant performance.
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