| Título: | Forecasting Oil Formation Volume Factor for API Gravity Ranges Using Artificial Neural Network |
| Autores: |
I. Irene, Azubuike; Department of Petroleum & Gas Engineering, University of Port Harcourt, Port Harcourt S. Sunday, Ikiensikimama; Department of Petroleum & Gas Engineering, University of Port Harcourt, Port Harcourt |
| Fecha: | 2013-03-31 |
| Publicador: | Advances in Petroleum Exploration and Development |
| Fuente: |
 |
| Tipo: |
info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion Peer-reviewed Article |
| Tema: |
Oil formation volume factor; Artificial neural network; Back propagation; Statistical analysis; API gravity ranges |
| Descripción: | The Oil Formation Volume Factor (FVF) parameter is a very important fluid property in reservoir engineering computations. Ideally, this property should be obtained from actual measurements. Quite often, this measurement is either not available, or very costly to obtain. In such cases, empirically derived correlations are used in the prediction of this property. This work centers on building an artificial neural network (ANN) model to predict oil formation volume factor for the different API gravity ranges. The new models were developed using combination of 448 published data from the Middle East, Malaysia, Africa, North Sea, Mediterranean basin, Gulf of Persian fields and 1389 data set collected from the Niger Delta Region of Nigeria. The data have been divided into the following four different API gravity classes: heavy oils for API≤21, medium oils for 21<API≤26, blend oils for 26<API≤35 and light oils for API>35. The data set was randomly divided into three parts of which, 60% was used for training, 20% for validation, and 20% for testing for each particular API grade. Both quantitative and qualitative assessments were employed to evaluate the accuracy of the models to the existing empirical correlations. The ANN models outperformed the existing empirical correlations by the statistical parameters used with the best rank and better performance plots.Key words: Oil formation volume factor; Artificial neural network; Back propagation; Statistical analysis; API gravity ranges |
| Idioma: | Inglés |
1 Economic Analysis of Liquefied Natural Gas Floating Production Storage and Offloading Plant (LNG FPSO) Using Probabilistic Approach por N. Edwin, Lazson; Department of Petroleum and Gas Engineering, University of Port Harcourt,S. Sunday, Ikiensikimama; Department of Petroleum and Gas Engineering, University of Port Harcourt | 6 Design and Operational Procedures for a Locally Made Steam Distillation Apparatus por Hosein, Raffie; Chemical Engineering Department, The University of the West Indies, St Augustine,Lewis-Hosein, Rhonda; Chemical Engineering Department, The University of the West Indies, St Augustine |
2 The Effects of Pore Pressure and Temperature Difference Variation on Borehole Stability por Chi, AI; Key Laboratory of Education Ministry for Enhanced Oil Recovery, Northeast Petroleum University, Daqing, Hei Longjiang Province, China.,Yuwei, LI; Key Laboratory of Education Ministry for Enhanced Oil Recovery, Northeast Petroleum University, Daqing, Hei Longjiang Province, China.,Yu, LIU; Key Laboratory of Education Ministry for Enhanced Oil Recovery, Northeast Petroleum University, Daqing, Hei Longjiang Province, China. | 7 Contents por Chao, Jamie |
3 Study on Matching Ability Between Cement Particle Size and Permeability in the Process of Oil Reservoir Plugging por ZHANG, Qingjie,WEI, Jianguang,SUN, Guojun,JIN, Liyang,GUAN, Pengjun | 8 Finite Volume Method for Solving a Modified 3-D 3-Phase Black-Oil Hydrocarbon Secondary Migration Model, and Its Application to the Kuqa Depression of the Tarim Basin in Western China por Guangren, SHI; Research Institute of Petroleum Exploration and Development, PetroChina,Jinshan, MA; Research Institute of Petroleum Exploration and Development, PetroChina,Xinshe, YANG; Mathematics and Scientiic Computing, National Physical Laboratory,Junhua, CHANG; Research Institute of Petroleum Exploration and Development, PetroChina,Jun, WAN; Research Institute of Petroleum Exploration and Development, PetroChina |
4 Determination of Non-Darcy Porous Flow Boundary Value in Formation Type III por Yikun, LIU; EOR key lab in the Ministry Of Education in Northeast Petroleum University.,Shuang, LIANG; EOR key lab in the Ministry Of Education in Northeast Petroleum University.,Lingyun, CHEN; EOR key lab in the Ministry Of Education in Northeast Petroleum University.,Fengjiao, WANG; EOR key lab in the Ministry Of Education in Northeast Petroleum University.,Yang, XUAN; Production Engineering Research Institute in Daqing Oil Field Co.,Qian, LIU; The second factory in Daqing Oil Field Co. | 9 SUBMISSION OF MANUSCRIPTS por Chao, Jamie |
5 Cotton Oil and Sunflower Oil Fuel Mixtures por Arapatsakos, Charalampos; Department of Production and Management Engineering, Democritus University of Thrace, V. Sofias Street, 67100, Xanthi , GREECE,Karkanis, Anastasios; Department of Production and Management Engineering, Democritus University of Thrace, V. Sofias Street, 67100, Xanthi , GREECE,Christoforidis, Dimitrios; Department of Production and Management Engineering, Democritus University of Thrace, V. Sofias Street, 67100, Xanthi , GREECE,Moschou, Marianthi; Department of Production and Management Engineering, Democritus University of Thrace, V. Sofias Street, 67100, Xanthi , GREECE,Pantokratoras, Ioannis; Department of Production and Management Engineering, Democritus University of Thrace, V. Sofias Street, 67100, Xanthi , GREECE | 10 Principal Points in Cementing Geothermal Wells por Salim, Prisca; Texas A&M University at Qatar,Amani, Mahmood; Texas A&M University at Qatar |