Oil production forecasting is one of the essential processes for organizations and governments to make necessary economic plans. This paper proposes a novel hybrid intelligence time series model to forecast oil production from two different oil fields in China and Yemen. This model is a modified ANFIS (Adaptive Neuro-Fuzzy Inference System), which is developed by applying a new optimization algorithm called the Aquila Optimizer (AO). The AO is a recently proposed optimization algorithm that was inspired by the behavior of Aquila in nature. The developed model, called AO-ANFIS, was evaluated using real-world datasets provided by local partners. In addition, extensive comparisons to the traditional ANFIS model and several modified ANFIS models using different optimization algorithms. Numeric results and statistics have confirmed the superiority of the AO-ANFIS over traditional ANFIS and several modified models. Additionally, the results reveal that AO is significantly improved ANFIS prediction accuracy. Thus, AO-ANFIS can be considered as an efficient time series tool.

/pdf/optimized-anfis-model-using-aquila-optimizer-for-oil-574zomai9e.pdf

Optimized ANFIS Model Using Aquila Optimizer for Oil Production Forecasting

http://diposit.ub.edu/dspace/bitstream/2445/162486/1/687154.pdf

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Identification and characteristic analysis of carbonate cap rock: A case study from the Lower-Middle Ordovician Yingshan Formation in Tahe oilfield, Tarim Basin, China

A conceptual model to investigate the impact of diagenesis and residual bitumen on the characteristics of Ordovician carbonate cap rock from Tarim Basin, China