Dew point pressure model for gas condensate reservoirs based on multi-gene genetic programming approach |
| |
| Affiliation: | 1. Department of Chemical Engineering, Faculty of Engineering, Shahid Bahonar University of Kerman, Kerman, Iran;2. Department of Petroleum Engineering, AmirKabir University of Technology (Tehran Polytechnics), Tehran, Iran;3. Department of Petroleum Engineering, National Iranian South Oilfield Company (NISOC), Ahvaz, Iran;1. Department of Computer Science & Engineering, Cooch Behar Government Engineering College, Cooch Behar, West Bengal, India;2. Department of Information Technology, RCC Institute of Information Technology, Kolkata, West Bengal 700015, India;3. Department of Computer and System Sciences, Visva-Bharati University, Santiniketan 721 325, India;1. Manchester Business School, University of Manchester, Booth Street West, M15 6PB Manchester, UK;2. Department of Management Control and Information Systems, University of Chile, Av. Diagonal Paraguay 257, 8330015 Santiago, Chile;3. Department of Business Organization, Universitat Politècnica de València, Camino Vera s/n, 46022 Valencia, Spain;4. Department of Business Administration and Marketing, University of Sevilla, Av. Ramón y Cajal s/n, 41018 Sevilla, Spain;1. Department of Actuarial Science and Applied Statistics, Faculty of Business & Information Science, UCSI University, Jalan Menara Gading, 56000 Cheras, Kuala Lumpur, Malaysia;2. Department of Mathematics, B.C. College, Asansol, West Bengal, India;3. School of Mathematical Sciences, Faculty of Science and Technology, Universiti Kebangsaan Malaysia, 43600 UKM Bangi, Selangor DE, Malaysia;1. Chair on System Science and the Energetic Challenge, Fondation Électricité de France (EDF), CentraleSupélec, Université Paris-Saclay, Grande Voie des Vignes, 92290 Châtenay-Malabry, France;2. Energy Departement, Politecnico di Milano, Campus Bovisa, Via Lambruschini 4, 20156 Milano, Italy |
| |
| Abstract: | One of the most critical parameters in characterization of gas condensate reservoirs is dew point pressure (DPP), and its accurate determination is a challenging task in development and management of these reservoirs. Experimental measurement of DPP is a costly and time consuming method. Therefore, searching for a quick, reliable, inexpensive, and robust algorithm for determination of DPP is of great importance. In this paper, first, a new approach based on multi-gene genetic programming (MGGP) to determine DPP of gas condensate reservoirs is presented. Then, a correlation for DPP calculation using MGGP has been developed for gas condensate reservoirs. Finally, the efficiency of the proposed DPP model has been validated by comparing its predictions with the results of other conventional models. It is found that the correlation developed in this work is capable of predicting more accurate values of DPP, with the lowest average relative and absolute errors with respect to the experimental results, and also higher correlation coefficient among the results of all the evaluated DPP correlations. Therefore, it is suggested that the proposed model can be applied effectively for DPP prediction for a wide range of gas properties and reservoir temperatures. |
| |
| Keywords: | Gas condensate reservoir Dew point pressure Multi-gene genetic programming PVT data |
| 本文献已被 ScienceDirect 等数据库收录！ |
|
