{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,1]],"date-time":"2026-02-01T06:22:15Z","timestamp":1769926935703,"version":"3.49.0"},"reference-count":31,"publisher":"MDPI AG","issue":"11","license":[{"start":{"date-parts":[[2022,11,17]],"date-time":"2022-11-17T00:00:00Z","timestamp":1668643200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Natural Science Foundation of China","award":["62150410434"],"award-info":[{"award-number":["62150410434"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Entropy"],"abstract":"<jats:p>The forecasting and prediction of crude oil are necessary in enabling governments to compile their economic plans. Artificial neural networks (ANN) have been widely used in different forecasting and prediction applications, including in the oil industry. The dendritic neural regression (DNR) model is an ANNs that has showed promising performance in time-series prediction. The DNR has the capability to deal with the nonlinear characteristics of historical data for time-series forecasting applications. However, it faces certain limitations in training and configuring its parameters. To this end, we utilized the power of metaheuristic optimization algorithms to boost the training process and optimize its parameters. A comprehensive evaluation is presented in this study with six MH optimization algorithms used for this purpose: whale optimization algorithm (WOA), particle swarm optimization algorithm (PSO), genetic algorithm (GA), sine\u2013cosine algorithm (SCA), differential evolution (DE), and harmony search algorithm (HS). We used oil-production datasets for historical records of crude oil production from seven real-world oilfields (from Tahe oilfields, in China), provided by a local partner. Extensive evaluation experiments were carried out using several performance measures to study the validity of the DNR with MH optimization methods in time-series applications. The findings of this study have confirmed the applicability of MH with DNR. The applications of MH methods improved the performance of the original DNR. We also concluded that the PSO and WOA achieved the best performance compared with other methods.<\/jats:p>","DOI":"10.3390\/e24111674","type":"journal-article","created":{"date-parts":[[2022,11,18]],"date-time":"2022-11-18T03:31:10Z","timestamp":1668742270000},"page":"1674","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":27,"title":["Evaluating the Applications of Dendritic Neuron Model with Metaheuristic Optimization Algorithms for Crude-Oil-Production Forecasting"],"prefix":"10.3390","volume":"24","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-6956-7641","authenticated-orcid":false,"given":"Mohammed A. A.","family":"Al-qaness","sequence":"first","affiliation":[{"name":"College of Physics and Electronic Information Engineering, Zhejiang Normal University, Jinhua 321004, China"}]},{"given":"Ahmed A.","family":"Ewees","sequence":"additional","affiliation":[{"name":"Department of Computer, Damietta University, Damietta 34517, Egypt"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2203-4549","authenticated-orcid":false,"given":"Laith","family":"Abualigah","sequence":"additional","affiliation":[{"name":"Faculty of Information Technology, Al-Ahliyya Amman University, Amman 19328, Jordan"},{"name":"Faculty of Information Technology, Middle East University, Amman 11831, Jordan"},{"name":"Faculty of Information Technology, Applied Science Private University, Amman 11931, Jordan"},{"name":"School of Computer Sciences, Universiti Sains Malaysia, Pulau Pinang 11800, Malaysia"}]},{"given":"Ayman Mutahar","family":"AlRassas","sequence":"additional","affiliation":[{"name":"School of Petroleum Engineering, China University of Petroleum (East China), Qingdao 266580, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7094-9380","authenticated-orcid":false,"given":"Hung Vo","family":"Thanh","sequence":"additional","affiliation":[{"name":"Laboratory for Computational Mechanics, Institute for Computational Science and Artificial Intelligence, Van Lang University, Ho Chi Minh City 700000, Vietnam"},{"name":"Faculty of Mechanical-Electrical and Computer Engineering, Van Lang University, Ho Chi Minh City 700000, Vietnam"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7682-6269","authenticated-orcid":false,"given":"Mohamed","family":"Abd Elaziz","sequence":"additional","affiliation":[{"name":"Department of Mathematics, Faculty of Science, Zagazig University, Zagazig 44519, Egypt"},{"name":"Faculty of Computer Science & Engineering, Galala University, Suze 435611, Egypt"},{"name":"Artificial Intelligence Research Center (AIRC), Ajman University, Ajman 346, United Arab Emirates"},{"name":"Department of Electrical and Computer Engineering, Lebanese American University, Byblos 4307, Lebanon"}]}],"member":"1968","published-online":{"date-parts":[[2022,11,17]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"203","DOI":"10.1016\/j.neucom.2018.09.082","article-title":"Time series forecasting of petroleum production using deep LSTM recurrent networks","volume":"323","author":"Sagheer","year":"2019","journal-title":"Neurocomputing"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"107013","DOI":"10.1016\/j.petrol.2020.107013","article-title":"Forecasting oil production using ensemble empirical model decomposition based Long Short-Term Memory neural network","volume":"189","author":"Liu","year":"2020","journal-title":"J. Pet. Sci. Eng."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"109468","DOI":"10.1016\/j.petrol.2021.109468","article-title":"Well production forecast in Volve field: Application of rigorous machine learning techniques and metaheuristic algorithm","volume":"208","author":"Ng","year":"2022","journal-title":"J. Pet. Sci. Eng."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"383","DOI":"10.1007\/s13202-021-01405-w","article-title":"Advance artificial time series forecasting model for oil production using neuro fuzzy-based slime mould algorithm","volume":"12","author":"AlRassas","year":"2022","journal-title":"J. Pet. Explor. Prod. Technol."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"102826","DOI":"10.1016\/j.ijggc.2019.102826","article-title":"Integrated workflow in 3D geological model construction for evaluation of CO2 storage capacity of a fractured basement reservoir in Cuu Long Basin, Vietnam","volume":"90","author":"Thanh","year":"2019","journal-title":"Int. J. Greenh. Gas Control"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"54","DOI":"10.4236\/ojogas.2020.52006","article-title":"Application of 3d reservoir geological model on es1 formation, block nv32, shenvsi oilfield, China","volume":"5","author":"Ren","year":"2020","journal-title":"Open J. Yangtze Oil Gas"},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"AlRassas, A.M., Al-qaness, M.A., Ewees, A.A., Ren, S., Abd Elaziz, M., Dama\u0161evi\u010dius, R., and Krilavi\u010dius, T. (2021). Optimized ANFIS model using Aquila Optimizer for oil production forecasting. Processes, 9.","DOI":"10.3390\/pr9071194"},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Goda, H.M., El-M Shokir, E.M., Fattah, K.A., and Sayyouh, M.H. (2003, January 4\u20136). Prediction of the PVT data using neural network computing theory. Proceedings of the Nigeria Annual International Conference and Exhibition, Abuja, Nigeria.","DOI":"10.2523\/85650-MS"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"106391","DOI":"10.1016\/j.petrol.2019.106391","article-title":"Application of the long short-term memory networks for well-testing data interpretation in tight reservoirs","volume":"183","author":"Wang","year":"2019","journal-title":"J. Pet. Sci. Eng."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"276","DOI":"10.1016\/j.fuel.2015.02.094","article-title":"A LSSVM approach for determining well placement and conning phenomena in horizontal wells","volume":"153","author":"Ahmadi","year":"2015","journal-title":"Fuel"},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Hutahaean, J., Demyanow, V., and Christie, M.A. (2015, January 20\u201322). Impact of model parameterisation and objective choices on assisted history matching and reservoir forecasting. Proceedings of the SPE\/IATMI Asia Pacific Oil & Gas Conference and Exhibition, Nusa Dua, Indonesia.","DOI":"10.2118\/176389-MS"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"402","DOI":"10.1016\/j.jngse.2016.10.010","article-title":"Extended exponential decline curve analysis","volume":"36","author":"Zhang","year":"2016","journal-title":"J. Nat. Gas Sci. Eng."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"106404","DOI":"10.1016\/j.petrol.2019.106404","article-title":"Numerical simulation of the improved gas production from low permeability hydrate reservoirs by using an enlarged highly permeable well wall","volume":"183","author":"Zhang","year":"2019","journal-title":"J. Pet. Sci. Eng."},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Hutahaean, J., Demyanov, V., and Christie, M. (2016, January 6\u20139). Many-objective optimization algorithm applied to history matching. Proceedings of the 2016 IEEE Symposium Series on Computational Intelligence (SSCI), Athens, Greece.","DOI":"10.1109\/SSCI.2016.7850215"},{"key":"ref_15","first-page":"100013","article-title":"A deep gated recurrent neural network for petroleum production forecasting","volume":"3","author":"Jaddoa","year":"2021","journal-title":"Mach. Learn. Appl."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"117637","DOI":"10.1016\/j.eswa.2022.117637","article-title":"Adopting a dendritic neural model for predicting stock price index movement","volume":"205","author":"Tang","year":"2022","journal-title":"Expert Syst. Appl."},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Song, Z., Tang, C., Ji, J., Todo, Y., and Tang, Z. (2021). A simple dendritic neural network model-based approach for daily PM2. 5 concentration prediction. Electronics, 10.","DOI":"10.3390\/electronics10040373"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"214","DOI":"10.1016\/j.knosys.2016.05.031","article-title":"Financial time series prediction using a dendritic neuron model","volume":"105","author":"Zhou","year":"2016","journal-title":"Knowl.-Based Syst."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"107683","DOI":"10.1016\/j.asoc.2021.107683","article-title":"Transmission trend of the COVID-19 pandemic predicted by dendritic neural regression","volume":"111","author":"Dong","year":"2021","journal-title":"Appl. Soft Comput."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"51","DOI":"10.1016\/j.advengsoft.2016.01.008","article-title":"The whale optimization algorithm","volume":"95","author":"Mirjalili","year":"2016","journal-title":"Adv. Eng. Softw."},{"key":"ref_21","unstructured":"Eberhart, R., and Kennedy, J. (April, January 28). Particle swarm optimization. Proceedings of the IEEE International Conference on Neural Networks, San Francisco, CA, USA."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"109787","DOI":"10.1016\/j.petrol.2021.109787","article-title":"Modelling two-phase Z factor of gas condensate reservoirs: Application of Artificial Intelligence (AI)","volume":"208","author":"Faraji","year":"2022","journal-title":"J. Pet. Sci. Eng."},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Ewees, A.A., Al-qaness, M.A., Abualigah, L., Oliva, D., Algamal, Z.Y., Anter, A.M., Ali Ibrahim, R., Ghoniem, R.M., and Abd Elaziz, M. (2021). Boosting arithmetic optimization algorithm with genetic algorithm operators for feature selection: Case study on cox proportional hazards model. Mathematics, 9.","DOI":"10.3390\/math9182321"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"120","DOI":"10.1016\/j.knosys.2015.12.022","article-title":"SCA: A sine cosine algorithm for solving optimization problems","volume":"96","author":"Mirjalili","year":"2016","journal-title":"Knowl.-Based Syst."},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Al-Qaness, M.A., Ewees, A.A., Fan, H., and Abd Elaziz, M. (2020). Optimized forecasting method for weekly influenza confirmed cases. Int. J. Environ. Res. Public Health, 17.","DOI":"10.3390\/ijerph17103510"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"341","DOI":"10.1023\/A:1008202821328","article-title":"Differential evolution\u2013A simple and efficient heuristic for global optimization over continuous spaces","volume":"11","author":"Storn","year":"1997","journal-title":"J. Glob. Optim."},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Yang, X.S. (2009). Harmony search as a metaheuristic algorithm. Music-Inspired Harmony Search Algorithm, Springer.","DOI":"10.1007\/978-3-642-00185-7_1"},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Tuo, S., Geem, Z.W., and Yoon, J.H. (2020). A new method for analyzing the performance of the harmony search algorithm. Mathematics, 8.","DOI":"10.3390\/math8091421"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"3","DOI":"10.2516\/ogst\/2020081","article-title":"Optimized Random Vector Functional Link network to predict oil production from Tahe oilfield in China","volume":"76","author":"Alalimi","year":"2021","journal-title":"Oil Gas Sci. -Technol. -Rev. D\u2019Ifp Energies Nouv."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"T71","DOI":"10.1190\/INT-2017-0043.1","article-title":"Characterization of carbonate microfacies and reservoir pore types based on Formation MicroImager logging: A case study from the Ordovician in the Tahe Oilfield, Tarim Basin, China","volume":"6","author":"Meng","year":"2018","journal-title":"Interpretation"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1007\/s40948-022-00434-x","article-title":"Developing the efficiency-modeling framework to explore the potential of CO2 storage capacity of S3 reservoir, Tahe oilfield, China","volume":"8","author":"Alalimi","year":"2022","journal-title":"Geomech. Geophys.-Geo-Energy -Geo-Resour."}],"container-title":["Entropy"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1099-4300\/24\/11\/1674\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T01:20:05Z","timestamp":1760145605000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1099-4300\/24\/11\/1674"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,11,17]]},"references-count":31,"journal-issue":{"issue":"11","published-online":{"date-parts":[[2022,11]]}},"alternative-id":["e24111674"],"URL":"https:\/\/doi.org\/10.3390\/e24111674","relation":{},"ISSN":["1099-4300"],"issn-type":[{"value":"1099-4300","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,11,17]]}}}