Oil production estimation plays a critical role in economic plans for local governments and organizations. Therefore, many studies applied different Artificial Intelligence (AI) based meth-ods to estimate oil production in different countries. The Adaptive Neuro-Fuzzy Inference System (ANFIS) is a well-known model that has been successfully employed in various applica-tions, including time-series forecasting. However, the ANFIS model faces critical shortcomings in its parameters during the configuration process. From this point, this paper works to solve the drawbacks of the ANFIS by optimizing ANFIS parameters using a modified Aquila Optimizer (AO) with the Opposition-Based Learning (OBL) technique. The main idea of the developed model, AOOBL-ANFIS, is to enhance the search process of the AO and use the AOOBL to boost the performance of the ANFIS. The proposed model is evaluated using real-world oil produc-tion datasets collected from different oilfields using several performance metrics, including Root Mean Square Error (RMSE), Mean Absolute Error (MAE), coefficient of determination (R2), Standard Deviation (Std), and computational time. Moreover, the AOOBL-ANFIS model is compared to several modified ANFIS models include Particle Swarm Optimization (PSO)- ANFIS, Grey Wolf Optimizer (GWO)-ANFIS, Sine Cosine Algorithm (SCA)-ANFIS, Slime Mold Algorithm (SMA)-ANFIS, and Genetic Algorithm (GA)-ANFIS, respectively. Additionally, it is compared to well-known time series forecasting methods, namely, Autoregressive Integrated Moving Average (ARIMA), Long Short-Term Memory (LSTM), Seasonal Autoregressive Integrated Moving Average (SARIMA), and Neural Network (NN). The outcomes verified the high performance of the AOOBL-ANFIS, which outperformed the classic ANFIS model and the compared models.

Mohammed A.A.Al-qaness;Ahmed A.Ewees;Hong Fan;Ayman Mutahar AlRassas;Mohamed Abd Elaziz

State Key Laboratory for Information Engineering in Surveying,Mapping and Remote Sensing,Wuhan University,Wuhan,China;Systems,University of Bisha Department of e-,Bisha,Kingdom of Saudi Arabia;Department of Computer,Damietta University,Damietta,Egypt;School of Petroleum Engineering,China University of Petroleum(East China),Qingdao,China;Department of Mathematics,Faculty of Science,Zagazig University,Zagazig,Egypt;Artificial Intelligence Research Center(AIRC),Ajman University,Ajman 346,United Arab Emirates;Faculty of Computer Science&Engineering,Galala University,Suze 435611,Egypt

地球空间信息科学学报（英文版）

[1]Mohammed A.A.Al-qaness;Ahmed A.Ewees;Hong Fan;Ayman Mutahar AlRassas;Mohamed Abd Elaziz-.Modified aquila optimizer for forecasting oil production)[J].地球空间信息科学学报（英文版）,2022(04):519-535

Opposition,AOOBL

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